diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 4f5a2068a1..f87ba92bce 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-02T14:36:27","documenter_version":"1.6.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-03T01:04:35","documenter_version":"1.6.0"}} \ No newline at end of file diff --git a/dev/APIs/Bucket/index.html b/dev/APIs/Bucket/index.html index 45b694ac4a..66c49323d3 100644 --- a/dev/APIs/Bucket/index.html +++ b/dev/APIs/Bucket/index.html @@ -2,15 +2,15 @@ Bucket Model · ClimaLand.jl
GitHub

Bucket

Types

ClimaLand.Bucket.BucketModelParameters — Type
struct BucketModelParameters{
     FT <: AbstractFloat,
     PSE,
-}

Container for holding the parameters of the bucket model.

  • κ_soil: Conductivity of the soil (W/K/m); constant

  • ρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant

  • albedo: Albedo Model

  • σS_c: Critical σSWE amount (m) where surface transitions from to snow-covered

  • f_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)

  • W_f: Capacity of the land bucket (m)

  • f_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)

  • p: Exponent used in β decay (unitless)

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • τc: τc timescale on which snow melts

  • earth_param_set: Earth Parameter set; physical constants, etc

source
ClimaLand.Bucket.PrescribedBaregroundAlbedo — Type
PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel

An albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).

source
ClimaLand.Bucket.PrescribedSurfaceAlbedo — Type
PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}
-                   <: AbstractBucketAlbedoModel

An albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.

Note that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.

source
ClimaLand.Bucket.BucketModel — Type
struct BucketModel{
+}

Container for holding the parameters of the bucket model.

  • κ_soil: Conductivity of the soil (W/K/m); constant

  • ρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant

  • albedo: Albedo Model

  • σS_c: Critical σSWE amount (m) where surface transitions from to snow-covered

  • f_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)

  • W_f: Capacity of the land bucket (m)

  • f_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)

  • p: Exponent used in β decay (unitless)

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • τc: τc timescale on which snow melts

  • earth_param_set: Earth Parameter set; physical constants, etc

source
ClimaLand.Bucket.PrescribedBaregroundAlbedo — Type
PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel

An albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).

source
ClimaLand.Bucket.PrescribedSurfaceAlbedo — Type
PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}
+                   <: AbstractBucketAlbedoModel

An albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.

Note that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.

source
ClimaLand.Bucket.BucketModel — Type
struct BucketModel{
      FT,
      PS <: BucketModelParameters{FT},
      ATM <: AbstractAtmosphericDrivers{FT},
      RAD <: AbstractRadiativeDrivers{FT},
      D,
- } <: AbstractBucketModel{FT}

Concrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.

  • parameters: Parameters required by the bucket model

  • atmos: The atmospheric drivers: Prescribed or Coupled

  • radiation: The radiation drivers: Prescribed or Coupled

  • domain: The domain of the model

source

Misc Functions

ClimaLand.surface_albedo — Function
ClimaLand.surface_albedo(
+ } <: AbstractBucketModel{FT}

Concrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.

  • parameters: Parameters required by the bucket model

  • atmos: The atmospheric drivers: Prescribed or Coupled

  • radiation: The radiation drivers: Prescribed or Coupled

  • domain: The domain of the model

source

Misc Functions

ClimaLand.surface_albedo — Function
ClimaLand.surface_albedo(
     model::EnergyHydrology{FT},
     Y,
     p,
-) where {FT}

Returns the surface albedo field of the EnergyHydrology soil model.

source
surface_albedo(model::SnowModel, Y, p)

A helper function which computes and returns the snow albedo.

source
surface_albedo(model::BucketModel, Y, p)

Returns the bulk surface albedo, which gets updated in update_aux via next_albedo.

source
surface_albedo(model::AbstractModel, Y, p)

A helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.Bucket.beta_factor — Function
beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}

Computes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:

β = (x/xc)^p x < xc 1 otherwise

where x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.

source
+) where {FT}

Returns the surface albedo field of the EnergyHydrology soil model.

source
surface_albedo(model::SnowModel, Y, p)

A helper function which computes and returns the snow albedo.

source
surface_albedo(model::BucketModel, Y, p)

Returns the bulk surface albedo, which gets updated in update_aux via next_albedo.

source
surface_albedo(model::AbstractModel, Y, p)

A helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.Bucket.beta_factor — Function
beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}

Computes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:

β = (x/xc)^p x < xc 1 otherwise

where x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.

source
diff --git a/dev/APIs/ClimaLand/index.html b/dev/APIs/ClimaLand/index.html index b43d9b30e0..a825b428bc 100644 --- a/dev/APIs/ClimaLand/index.html +++ b/dev/APIs/ClimaLand/index.html @@ -3,11 +3,11 @@ FT, SEH <: Soil.EnergyHydrology{FT}, SB <: Soil.Biogeochemistry.SoilCO2Model{FT}, -} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.

source
ClimaLand.LandHydrology — Type
struct LandHydrology{
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.

  • soil: The soil model

  • soilco2: The biochemistry model

source
ClimaLand.LandHydrology — Type
struct LandHydrology{
     FT,
     SM <: Soil.AbstractSoilModel{FT},
     SW <: Pond.AbstractSurfaceWaterModel{FT},
-} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil and surface water component.

  • soil: The soil model

  • surface_water: The surface water model

source
Missing docstring.

Missing docstring for ClimaLand.make_interactions_update_aux. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.initialize_interactions. Check Documenter's build log for details.

ClimaLand.land_components — Function
land_components(land::AbstractLandModel)

Returns the component names of the land model, by calling propertynames(land).

source
ClimaLand.lsm_aux_vars — Function

lsmauxvars(m::AbstractLandModel)

Returns the additional aux variable symbols for the model in the form of a tuple.

source
lsm_aux_vars(m::SoilCanopyModel)

The names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_types — Function

lsmauxtypes(m::AbstractLandModel)

Returns the shared additional aux variable types for the model in the form of a tuple.

source
lsm_aux_types(m::SoilCanopyModel)

The types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_domain_names — Function

lsmauxdomain_names(m::AbstractLandModel)

Returns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.

This is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.

source
lsm_aux_domain_names(m::SoilCanopyModel)

The domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
Missing docstring.

Missing docstring for ClimaLand.domain_name. Check Documenter's build log for details.

Land Hydrology

ClimaLand.infiltration_capacity — Function
function infiltration_capacity(
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil and surface water component.

  • soil: The soil model

  • surface_water: The surface water model

source
Missing docstring.

Missing docstring for ClimaLand.make_interactions_update_aux. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.initialize_interactions. Check Documenter's build log for details.

ClimaLand.land_components — Function
land_components(land::AbstractLandModel)

Returns the component names of the land model, by calling propertynames(land).

source
ClimaLand.lsm_aux_vars — Function

lsmauxvars(m::AbstractLandModel)

Returns the additional aux variable symbols for the model in the form of a tuple.

source
lsm_aux_vars(m::SoilCanopyModel)

The names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_types — Function

lsmauxtypes(m::AbstractLandModel)

Returns the shared additional aux variable types for the model in the form of a tuple.

source
lsm_aux_types(m::SoilCanopyModel)

The types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_domain_names — Function

lsmauxdomain_names(m::AbstractLandModel)

Returns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.

This is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.

source
lsm_aux_domain_names(m::SoilCanopyModel)

The domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
Missing docstring.

Missing docstring for ClimaLand.domain_name. Check Documenter's build log for details.

Land Hydrology

ClimaLand.infiltration_capacity — Function
function infiltration_capacity(
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
-)

Function which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.

Defined such that positive means into soil.

source
ClimaLand.infiltration_at_point — Function
infiltration_at_point(η::FT, i_c::FT, P::FT)

Returns the infiltration given pond height η, infiltration capacity, and precipitation.

This is defined such that positive means into soil.

source
ClimaLand.PrognosticRunoff — Type
PrognosticRunoff <: Pond.AbstractSurfaceRunoff

Concrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.

This is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source
ClimaLand.RunoffBC — Type
RunoffBC <: Soil.AbstractSoilBC

Concrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.

This is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source

SoilCanopyModel

ClimaLand.PrognosticSoil — Type
 PrognosticSoil{FT} <: AbstractSoilDriver

Concrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

source
ClimaLand.RootExtraction — Type
RootExtraction{FT} <: Soil.AbstractSoilSource{FT}

Concrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.

This is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.

source

LandSoilBiogeochemistry

Missing docstring.

Missing docstring for ClimaLand.PrognosticMet. Check Documenter's build log for details.

+)

Function which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.

Defined such that positive means into soil.

source
ClimaLand.infiltration_at_point — Function
infiltration_at_point(η::FT, i_c::FT, P::FT)

Returns the infiltration given pond height η, infiltration capacity, and precipitation.

This is defined such that positive means into soil.

source
ClimaLand.PrognosticRunoff — Type
PrognosticRunoff <: Pond.AbstractSurfaceRunoff

Concrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.

This is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source
ClimaLand.RunoffBC — Type
RunoffBC <: Soil.AbstractSoilBC

Concrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.

This is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source

SoilCanopyModel

ClimaLand.PrognosticSoil — Type
 PrognosticSoil{FT} <: AbstractSoilDriver

Concrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

source
ClimaLand.RootExtraction — Type
RootExtraction{FT} <: Soil.AbstractSoilSource{FT}

Concrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.

This is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.

source

LandSoilBiogeochemistry

Missing docstring.

Missing docstring for ClimaLand.PrognosticMet. Check Documenter's build log for details.

diff --git a/dev/APIs/Regridder/index.html b/dev/APIs/Regridder/index.html index 19be8bdb1c..f3bc66e02a 100644 --- a/dev/APIs/Regridder/index.html +++ b/dev/APIs/Regridder/index.html @@ -1,2 +1,2 @@ -Parameter Dataset Tools · ClimaLand.jl
GitHub

Bucket

Functions

Missing docstring.

Missing docstring for ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.Regridder.swap_space. Check Documenter's build log for details.

+Parameter Dataset Tools · ClimaLand.jl
GitHub

Bucket

Functions

Missing docstring.

Missing docstring for ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.Regridder.swap_space. Check Documenter's build log for details.

diff --git a/dev/APIs/Snow/index.html b/dev/APIs/Snow/index.html index 6f958e8a8c..d32cc09f6a 100644 --- a/dev/APIs/Snow/index.html +++ b/dev/APIs/Snow/index.html @@ -1,2 +1,2 @@ -Snow Model · ClimaLand.jl
GitHub

Snow Model

Snow Parameters

ClimaLand.Snow.SnowParameters — Type
SnowParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SnowModel.

Note that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.

  • ρ_snow: Density of snow (kg/m^3)

  • z_0m: Roughness length over snow for momentum (m)

  • z_0b: Roughness length over snow for scalars (m)

  • α_snow: Albedo of snow (unitless)

  • ϵ_snow: Emissivity of snow (unitless)

  • θ_r: Volumetric holding capacity of water in snow (unitless)

  • Ksat: Hydraulic conductivity of wet snow (m/s)

  • κ_ice: Thermal conductivity of ice (W/m/K)

  • Δt: Timestep of the model (s)

  • ρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)

  • earth_param_set: Clima-wide parameters

source

Snow Functions of State

```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe

+Snow Model · ClimaLand.jl
GitHub

Snow Model

Snow Parameters

ClimaLand.Snow.SnowParameters — Type
SnowParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SnowModel.

Note that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.

  • ρ_snow: Density of snow (kg/m^3)

  • z_0m: Roughness length over snow for momentum (m)

  • z_0b: Roughness length over snow for scalars (m)

  • α_snow: Albedo of snow (unitless)

  • ϵ_snow: Emissivity of snow (unitless)

  • θ_r: Volumetric holding capacity of water in snow (unitless)

  • Ksat: Hydraulic conductivity of wet snow (m/s)

  • κ_ice: Thermal conductivity of ice (W/m/K)

  • Δt: Timestep of the model (s)

  • ρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)

  • earth_param_set: Clima-wide parameters

source

Snow Functions of State

```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe

diff --git a/dev/APIs/Soil/index.html b/dev/APIs/Soil/index.html index d6dae20ded..586035635d 100644 --- a/dev/APIs/Soil/index.html +++ b/dev/APIs/Soil/index.html @@ -1,56 +1,56 @@ -Soil Energy and Hydrology · ClimaLand.jl
GitHub

Soil Models

Soil Models

ClimaLand.Soil.AbstractSoilModel — Type
AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}

The abstract type for all soil models.

Currently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.

source
ClimaLand.Soil.RichardsModel — Type
RichardsModel

A model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.

A variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).

If you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water

source
ClimaLand.Soil.EnergyHydrology — Type
EnergyHydrology <: AbstractSoilModel

A model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.

A variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.

  • parameters: The parameter sets

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat

source

Soil Parameter Structs

ClimaLand.Soil.RichardsParameters — Type
RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}

A struct for storing parameters of the RichardsModel.

  • ν: The porosity of the soil (m^3/m^3)

  • hydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

source
ClimaLand.Soil.EnergyHydrologyParameters — Type
EnergyHydrologyParameters{
+Soil Energy and Hydrology · ClimaLand.jl
GitHub
Soil Models
Soil Models
ClimaLand.Soil.AbstractSoilModel — Type
AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}
The abstract type for all soil models.
Currently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.
source
ClimaLand.Soil.RichardsModel — Type
RichardsModel
A model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.
A variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).
If you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.
  • parameters: the parameter set
  • domain: the soil domain, using ClimaCore.Domains
  • boundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions
  • sources: A tuple of sources, each of type AbstractSoilSource
  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water
source
ClimaLand.Soil.EnergyHydrology — Type
EnergyHydrology <: AbstractSoilModel
A model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.
A variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.
  • parameters: The parameter sets
  • domain: the soil domain, using ClimaCore.Domains
  • boundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions
  • sources: A tuple of sources, each of type AbstractSoilSource
  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat
source
Soil Parameter Structs
ClimaLand.Soil.RichardsParameters — Type
RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}
A struct for storing parameters of the RichardsModel.
  • ν: The porosity of the soil (m^3/m^3)
  • hydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey
  • K_sat: The saturated hydraulic conductivity (m/s)
  • S_s: The specific storativity (1/m)
  • θ_r: The residual water fraction (m^3/m^3
source
ClimaLand.Soil.EnergyHydrologyParameters — Type
EnergyHydrologyParameters{
         FT <: AbstractFloat,
         F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
         SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
         C,
         PSE,
-    }
A parameter structure for the integrated soil water and energy  equation system.
Note that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:
  • Surface parameters: albedo in each wavelength band (SF)
  • Scalar parameters: emissivity, α, β, γ, γT_ref, Ω,
roughness lengths z0, dds ) (FT)
  • Parameters defined in the interior: all else (F)
  • κ_dry: The dry soil thermal conductivity, W/m/K
  • κ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K
  • κ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K
  • ρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)
  • ν: The porosity of the soil (m^3/m^3)
  • ν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)
  • ν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)
  • ν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)
  • α: The parameter α used in computing Kersten number, unitless
  • β: The parameter β used in computing Kersten number, unitless
  • hydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey
  • K_sat: The saturated hydraulic conductivity (m/s)
  • S_s: The specific storativity (1/m)
  • θ_r: The residual water fraction (m^3/m^3
  • Ω: Ice impedance factor for the hydraulic conductivity
  • γ: Coefficient of viscosity factor for the hydraulic conductivity
  • γT_ref: Reference temperature for the viscosity factor
  • PAR_albedo: Soil PAR Albedo
  • NIR_albedo: Soil NIR Albedo
  • emissivity: Soil Emissivity
  • z_0m: Roughness length for momentum
  • z_0b: Roughness length for scalars
  • d_ds: Maximum dry soil layer thickness under evaporation (m)
  • earth_param_set: Physical constants and clima-wide parameters
source
Soil Hydrology Parameterizations
ClimaLand.Soil.volumetric_liquid_fraction — Function
volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}
A pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.
source
ClimaLand.Soil.pressure_head — Function
pressure_head(
+    }
A parameter structure for the integrated soil water and energy  equation system.
Note that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:
  • Surface parameters: albedo in each wavelength band (SF)
  • Scalar parameters: emissivity, α, β, γ, γT_ref, Ω,
roughness lengths z0, dds ) (FT)
  • Parameters defined in the interior: all else (F)
  • κ_dry: The dry soil thermal conductivity, W/m/K
  • κ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K
  • κ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K
  • ρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)
  • ν: The porosity of the soil (m^3/m^3)
  • ν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)
  • ν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)
  • ν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)
  • α: The parameter α used in computing Kersten number, unitless
  • β: The parameter β used in computing Kersten number, unitless
  • hydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey
  • K_sat: The saturated hydraulic conductivity (m/s)
  • S_s: The specific storativity (1/m)
  • θ_r: The residual water fraction (m^3/m^3
  • Ω: Ice impedance factor for the hydraulic conductivity
  • γ: Coefficient of viscosity factor for the hydraulic conductivity
  • γT_ref: Reference temperature for the viscosity factor
  • PAR_albedo: Soil PAR Albedo
  • NIR_albedo: Soil NIR Albedo
  • emissivity: Soil Emissivity
  • z_0m: Roughness length for momentum
  • z_0b: Roughness length for scalars
  • d_ds: Maximum dry soil layer thickness under evaporation (m)
  • earth_param_set: Physical constants and clima-wide parameters
source
Soil Hydrology Parameterizations
ClimaLand.Soil.volumetric_liquid_fraction — Function
volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}
A pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.
source
ClimaLand.Soil.pressure_head — Function
pressure_head(
     cm::vanGenuchten{FT},
     θ_r::FT,
     ϑ_l::FT,
     ν_eff::FT,
     S_s::FT,
-) where {FT}
A point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential  if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.
source
pressure_head(
+) where {FT}
A point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential  if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.
source
pressure_head(
     cm::BrooksCorey{FT},
     θ_r::FT,
     ϑ_l::FT,
     ν_eff::FT,
     S_s::FT,
-) where {FT}
A point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential  if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.
source
ClimaLand.Soil.hydraulic_conductivity — Function
 hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}
A point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.
source
 hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}
A point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.
source
ClimaLand.Soil.impedance_factor — Function
impedance_factor(
+) where {FT}
A point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential  if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.
source
ClimaLand.Soil.hydraulic_conductivity — Function
 hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}
A point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.
source
 hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}
A point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.
source
ClimaLand.Soil.impedance_factor — Function
impedance_factor(
     f_i::FT,
     Ω::FT
-) where {FT}
Returns the multiplicative factor reducing conductivity when  a fraction of ice f_i is present.
Only for use with the EnergyHydrology model.
source
ClimaLand.Soil.viscosity_factor — Function
viscosity_factor(
+) where {FT}
Returns the multiplicative factor reducing conductivity when  a fraction of ice f_i is present.
Only for use with the EnergyHydrology model.
source
ClimaLand.Soil.viscosity_factor — Function
viscosity_factor(
     T::FT,
     γ::FT,
     γT_ref::FT,
-) where {FT}
Returns the multiplicative factor which accounts for the temperature dependence of the conductivity.
Only for use with the EnergyHydrology model.
source
ClimaLand.Soil.matric_potential — Function
 matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}
A point-wise function returning the matric potential, using the van Genuchten formulation.
source
 matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}
A point-wise function returning the matric potential, using the Brooks and Corey formulation.
source
ClimaLand.Soil.dψdϑ — Function
dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)
Computes and returns the derivative of the pressure head  with respect to ϑ for the van Genuchten formulation.
source
dψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)
Computes and returns the derivative of the pressure head  with respect to ϑ for the Brooks and Corey formulation.
source
ClimaLand.Soil.inverse_matric_potential — Function
 inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}
A point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.
source
 inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}
A point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.
source
ClimaLand.Soil.AbstractSoilHydrologyClosure — Type
AbstractSoilHydrologyClosure{FT <: AbstractFloat}
The abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported  concrete types.
To add a new parameterization, methods are required for:
  • matric_potential,
  • inversematricpotential,
  • pressure_head,
  • dψdϑ,
  • hydraulic_conductivity.
source
ClimaLand.Soil.vanGenuchten — Type
vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}
The van Genuchten soil hydrology closure, chosen when the  hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).
  • α: The inverse of the air entry potential (1/m)
  • n: The van Genuchten pore-size distribution index (unitless)
  • m: The van Genuchten parameter m = 1 - 1/n (unitless)
  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface
source
ClimaLand.Soil.BrooksCorey — Type
BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}
The Brooks and Corey soil hydrology closure, chosen when the  hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).
  • c: The pore-size distribution index (unitless)
  • ψb: The air entry matric potential, when S=1 (m)
  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface
source
Soil Heat Parameterizations
ClimaLand.Soil.volumetric_heat_capacity — Function
volumetric_heat_capacity(
+) where {FT}
Returns the multiplicative factor which accounts for the temperature dependence of the conductivity.
Only for use with the EnergyHydrology model.
source
ClimaLand.Soil.matric_potential — Function
 matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}
A point-wise function returning the matric potential, using the van Genuchten formulation.
source
 matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}
A point-wise function returning the matric potential, using the Brooks and Corey formulation.
source
ClimaLand.Soil.dψdϑ — Function
dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)
Computes and returns the derivative of the pressure head  with respect to ϑ for the van Genuchten formulation.
source
dψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)
Computes and returns the derivative of the pressure head  with respect to ϑ for the Brooks and Corey formulation.
source
ClimaLand.Soil.inverse_matric_potential — Function
 inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}
A point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.
source
 inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}
A point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.
source
ClimaLand.Soil.AbstractSoilHydrologyClosure — Type
AbstractSoilHydrologyClosure{FT <: AbstractFloat}
The abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported  concrete types.
To add a new parameterization, methods are required for:
  • matric_potential,
  • inversematricpotential,
  • pressure_head,
  • dψdϑ,
  • hydraulic_conductivity.
source
ClimaLand.Soil.vanGenuchten — Type
vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}
The van Genuchten soil hydrology closure, chosen when the  hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).
  • α: The inverse of the air entry potential (1/m)
  • n: The van Genuchten pore-size distribution index (unitless)
  • m: The van Genuchten parameter m = 1 - 1/n (unitless)
  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface
source
ClimaLand.Soil.BrooksCorey — Type
BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}
The Brooks and Corey soil hydrology closure, chosen when the  hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).
  • c: The pore-size distribution index (unitless)
  • ψb: The air entry matric potential, when S=1 (m)
  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface
source
Soil Heat Parameterizations
ClimaLand.Soil.κ_solid — Function
κ_solid(ν_ss_om::FT,
         ν_ss_quartz::FT,
         κ_om::FT,
         κ_quartz::FT,
-        κ_minerals::FT) where {FT}
Computes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.
source
ClimaLand.Soil.κ_sat_frozen — Function
function κ_sat_frozen(
+        κ_minerals::FT) where {FT}
Computes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.
source
ClimaLand.Soil.κ_sat_frozen — Function
function κ_sat_frozen(
     κ_solid::FT,
     ν::FT,
     κ_ice::FT
-) where {FT}
Computes the thermal conductivity for saturated frozen soil.
source
ClimaLand.Soil.κ_sat_unfrozen — Function
function κ_sat_unfrozen(
     κ_solid::FT,
     ν::FT,
     κ_l::FT
-) where {FT}
Computes the thermal conductivity for saturated unfrozen soil.
source
ClimaLand.Soil.κ_sat — Function
κ_sat(
     θ_l::FT,
     θ_i::FT,
     κ_sat_unfrozen::FT,
     κ_sat_frozen::FT
-) where {FT}
Compute the expression for saturated thermal conductivity of soil matrix.
source
ClimaLand.Soil.κ_dry — Function
function κ_dry(ρp::FT,
+) where {FT}
Compute the expression for saturated thermal conductivity of soil matrix.
source
ClimaLand.Soil.κ_dry — Function
function κ_dry(ρp::FT,
                ν::FT,
                κ_solid::FT,
                κ_air::FT;
-               a = FT(0.053)) where {FT}
Computes the thermal conductivity of dry soil according to the model of Balland and Arp.
source
ClimaLand.Soil.kersten_number — Function
kersten_number(
+               a = FT(0.053)) where {FT}
Computes the thermal conductivity of dry soil according to the model of Balland and Arp.
source
ClimaLand.Soil.kersten_number — Function
kersten_number(
     θ_i::FT,
     S_r::FT,
     α::FT,
@@ -58,17 +58,17 @@
     ν_ss_om::FT,
     ν_ss_quartz::FT,
     ν_ss_gravel::FT,
-    ) where {FT}
Compute the expression for the Kersten number, using the Balland and Arp model.
source
ClimaLand.Soil.relative_saturation — Function
relative_saturation(
         θ_l::FT,
         θ_i::FT,
         ν::FT
-) where {FT}
Compute the expression for relative saturation.  This is referred to as θ_sat in Balland and Arp's paper.
source
ClimaLand.Soil.volumetric_internal_energy — Function
volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,
-                             earth_param_set::EP) where {FT, EP}
A pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.
source
ClimaLand.Soil.volumetric_internal_energy_liq — Function
volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}
A pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.
source
ClimaLand.Soil.temperature_from_ρe_int — Function
temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT
-                        earth_param_set::EP) where {FT, EP}
A pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.
source
ClimaLand.Soil.volumetric_internal_energy — Function
volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,
+                             earth_param_set::EP) where {FT, EP}
A pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.
source
ClimaLand.Soil.volumetric_internal_energy_liq — Function
volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}
A pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.
source
ClimaLand.Soil.temperature_from_ρe_int — Function
temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT
+                        earth_param_set::EP) where {FT, EP}
A pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.
source
ClimaLand.Soil.phase_change_source — Function
phase_change_source(
     θ_l::FT,
     θ_i::FT,
     T::FT,
@@ -77,17 +77,17 @@
     θ_r::FT,
     hydrology_cm::C,
     earth_param_set::EP,
-) where {FT, EP, C}
Returns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the  equations.
Note that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.
source
ClimaLand.Soil.thermal_time — Function
thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}
Returns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.
source
Soil Surface Parameterizations
Missing docstring.
Missing docstring for ClimaLand.soil.soil_resistance. Check Documenter's build log for details.
ClimaLand.Soil.dry_soil_layer_thickness — Function
dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}
Returns the maximum dry soil layer thickness that can develop under vapor flux;  this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).
source
ClimaLand.Soil.soil_tortuosity — Function
soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}
Computes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.
See Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.
source
Soil Runoff Types and Methods
ClimaLand.Soil.Runoff.SurfaceRunoff — Type
SurfaceRunoff <: AbstractRunoffModel
A simple model for runoff appropriate for single column runs.
Only surface runoff is computed, using a combination of Dunne  and Hortonian runoff.
source
ClimaLand.Soil.Runoff.TOPMODELRunoff — Type
TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel
The TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.
The runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".
  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)
  • f_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.
  • subsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.
source
ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff — Type
TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}
The TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.
The runoff flux is given by Equation 12 of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".
  • R_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated
  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)
source
ClimaLand.Soil.Runoff.update_runoff! — Function
update_runoff!(p, runoff::NoRunoff, _...)
Updates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.
source
update_runoff!(
+) where {FT, EP, C}
Returns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the  equations.
Note that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.
source
ClimaLand.Soil.thermal_time — Function
thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}
Returns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.
source
Soil Surface Parameterizations
Missing docstring.
Missing docstring for ClimaLand.soil.soil_resistance. Check Documenter's build log for details.
ClimaLand.Soil.dry_soil_layer_thickness — Function
dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}
Returns the maximum dry soil layer thickness that can develop under vapor flux;  this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).
source
ClimaLand.Soil.soil_tortuosity — Function
soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}
Computes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.
See Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.
source
Soil Runoff Types and Methods
ClimaLand.Soil.Runoff.SurfaceRunoff — Type
SurfaceRunoff <: AbstractRunoffModel
A simple model for runoff appropriate for single column runs.
Only surface runoff is computed, using a combination of Dunne  and Hortonian runoff.
source
ClimaLand.Soil.Runoff.TOPMODELRunoff — Type
TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel
The TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.
The runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".
  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)
  • f_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.
  • subsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.
source
ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff — Type
TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}
The TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.
The runoff flux is given by Equation 12 of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".
  • R_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated
  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)
source
ClimaLand.Soil.Runoff.update_runoff! — Function
update_runoff!(p, runoff::NoRunoff, _...)
Updates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.
source
update_runoff!(
     p,
     runoff::SurfaceRunoff,
     Y,
     t,
-    model::AbstractSoilModel,
)
The update_runoff! function for the SurfaceRunoff model.
Updates the runoff model variables in place in p.soil for the SurfaceRunoff  parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration
source
update_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)
Updates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration
source
Soil BC Methods and Types
ClimaLand.Soil.MoistureStateBC — Type
MoistureStateBC <: AbstractWaterBC
A simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.HeatFluxBC — Type
HeatFluxBC <: AbstractHeatBC
A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.WaterFluxBC — Type
WaterFluxBC <: AbstractWaterBC
A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.TemperatureStateBC — Type
TemperatureStateBC <: AbstractHeatBC
A simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.FreeDrainage — Type
FreeDrainage <: AbstractWaterBC
A concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.
source
ClimaLand.Soil.RichardsAtmosDrivenFluxBC — Type
RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC
A concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.
A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.
If you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.
  • precip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.
  • runoff: The runoff model. The default is no runoff.
source
ClimaLand.Soil.AtmosDrivenFluxBC — Type
AtmosDrivenFluxBC{
+    model::AbstractSoilModel,
)
The update_runoff! function for the SurfaceRunoff model.
Updates the runoff model variables in place in p.soil for the SurfaceRunoff  parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration
source
update_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)
Updates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration
source
Soil BC Methods and Types
ClimaLand.Soil.MoistureStateBC — Type
MoistureStateBC <: AbstractWaterBC
A simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.HeatFluxBC — Type
HeatFluxBC <: AbstractHeatBC
A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.WaterFluxBC — Type
WaterFluxBC <: AbstractWaterBC
A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.TemperatureStateBC — Type
TemperatureStateBC <: AbstractHeatBC
A simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.
source
ClimaLand.Soil.FreeDrainage — Type
FreeDrainage <: AbstractWaterBC
A concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.
source
ClimaLand.Soil.RichardsAtmosDrivenFluxBC — Type
RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC
A concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.
A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.
If you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.
  • precip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.
  • runoff: The runoff model. The default is no runoff.
source
ClimaLand.Soil.AtmosDrivenFluxBC — Type
AtmosDrivenFluxBC{
     A <: AbstractAtmosphericDrivers,
     B <: AbstractRadiativeDrivers,
     R <: AbstractRunoffModel
-} <: AbstractEnergyHydrologyBC
A concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.
This choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation,  and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.
A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.
  • atmos: The atmospheric conditions driving the model
  • radiation: The radiative fluxes driving the model
  • runoff: The runoff model. The default is no runoff.
source
ClimaLand.Soil.WaterHeatBC — Type
WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:
-   AbstractEnergyHydrologyBC
A general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.
source
ClimaLand.Soil.soil_boundary_fluxes! — Function
soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)
updates the boundary fluxes for ϑl and ρeint.
source
soil_boundary_fluxes!(
+} <: AbstractEnergyHydrologyBC
A concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.
This choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation,  and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.
A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.
  • atmos: The atmospheric conditions driving the model
  • radiation: The radiative fluxes driving the model
  • runoff: The runoff model. The default is no runoff.
source
ClimaLand.Soil.WaterHeatBC — Type
WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:
+   AbstractEnergyHydrologyBC
A general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.
source
ClimaLand.Soil.soil_boundary_fluxes! — Function
soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)
updates the boundary fluxes for ϑl and ρeint.
source
soil_boundary_fluxes!(
     bc::AtmosDrivenFluxBC{
         <:PrescribedAtmosphere,
         <:PrescribedRadiativeFluxes,
@@ -98,7 +98,7 @@
     Y,
     p,
     t,
-)
Returns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.
If you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.
This function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.
source
soil_boundary_fluxes!(
+)
Returns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.
If you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.
This function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.
source
soil_boundary_fluxes!(
     bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},
     boundary::ClimaLand.TopBoundary,
     soil::EnergyHydrology{FT},
@@ -106,4 +106,4 @@
     Y,
     p,
     t,
-) where {FT}
A method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.
source
Soil Source Types
ClimaLand.Soil.AbstractSoilSource — Type
AbstractSoilSource{FT} <:  ClimaLand.AbstractSource{FT}
An abstract type for types of source terms for the soil equations.
In standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).
source
Missing docstring.
Missing docstring for ClimaLand.Soil.RootExtraction. Check Documenter's build log for details.
Soil Jacobian Structures
ClimaLand.ImplicitEquationJacobian — Type
ImplicitEquationJacobian{M, S}
A struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.
matrix is a block matrix containing one block on the diagonal for each     variable in the model. solver is a diagonal solver because our matrix is block diagonal.
Note that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.
source

+) where {FT}

A method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.

source

Soil Source Types

ClimaLand.Soil.AbstractSoilSource — Type
AbstractSoilSource{FT} <:  ClimaLand.AbstractSource{FT}

An abstract type for types of source terms for the soil equations.

In standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).

source
Missing docstring.

Missing docstring for ClimaLand.Soil.RootExtraction. Check Documenter's build log for details.

Soil Jacobian Structures

ClimaLand.ImplicitEquationJacobian — Type
ImplicitEquationJacobian{M, S}

A struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.

matrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.

Note that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.

source
diff --git a/dev/APIs/SoilBiogeochemistry/index.html b/dev/APIs/SoilBiogeochemistry/index.html index 6b88eab63f..0d4212bfe9 100644 --- a/dev/APIs/SoilBiogeochemistry/index.html +++ b/dev/APIs/SoilBiogeochemistry/index.html @@ -1,7 +1,7 @@ -Soil Biogeochemistry · ClimaLand.jl
GitHub

Soil Biogeochemistry

Model Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2Model — Type
SoilCO2Model

A model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type NamedTuple

  • sources: A tuple of sources, each of type AbstractSource

  • drivers: Drivers

source

Parameter Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters — Type
SoilCO2ModelParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SoilCO2Model.

All of these parameters are currently treated as global constants.

  • D_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)

  • D_liq: Diffusivity of soil C substrate in liquid (unitless)

  • α_sx: Pre-exponential factor (kg C m-3 s-1)

  • Ea_sx: Activation energy (J mol-1)

  • kM_sx: Michaelis constant (kg C m-3)

  • kM_o2: Michaelis constant for O2 (m3 m-3)

  • O2_a: Volumetric fraction of O₂ in the soil air, dimensionless

  • D_oa: Diffusion coefficient of oxygen in air, dimensionless

  • p_sx: Fraction of soil carbon that is considered soluble, dimensionless

  • earth_param_set: Physical constants used Clima-wide

source

Model-specific Types

ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver — Type
AbstractSoilDriver

An abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).

source
ClimaLand.Soil.Biogeochemistry.SoilDrivers — Type
SoilDrivers

A container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).

  • met: Soil temperature and moisture drivers - Prescribed or Prognostic

  • soc: Soil SOM driver - Prescribed only

  • atmos: Prescribed atmospheric variables

source
ClimaLand.Soil.Biogeochemistry.PrescribedMet — Type
PrescribedMet <: AbstractSoilDriver

A container which holds the prescribed functions for soil temperature and moisture.

This is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.

  • temperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat

  • volumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat

  • ν: Soil porosity (m³ m⁻³)

  • θ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)

  • b: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)

source

Functions of State

ClimaLand.Soil.Biogeochemistry.volumetric_air_content — Function
volumetric_air_content(θ_w::FT,
+Soil Biogeochemistry · ClimaLand.jl
GitHub
Soil Biogeochemistry
Model Structure
ClimaLand.Soil.Biogeochemistry.SoilCO2Model — Type
SoilCO2Model
A model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.
  • parameters: the parameter set
  • domain: the soil domain, using ClimaCore.Domains
  • boundary_conditions: the boundary conditions, of type NamedTuple
  • sources: A tuple of sources, each of type AbstractSource
  • drivers: Drivers
source
Parameter Structure
ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters — Type
SoilCO2ModelParameters{FT <: AbstractFloat, PSE}
A struct for storing parameters of the SoilCO2Model.
All of these parameters are currently treated as global constants.
  • D_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)
  • D_liq: Diffusivity of soil C substrate in liquid (unitless)
  • α_sx: Pre-exponential factor (kg C m-3 s-1)
  • Ea_sx: Activation energy (J mol-1)
  • kM_sx: Michaelis constant (kg C m-3)
  • kM_o2: Michaelis constant for O2 (m3 m-3)
  • O2_a: Volumetric fraction of O₂ in the soil air, dimensionless
  • D_oa: Diffusion coefficient of oxygen in air, dimensionless
  • p_sx: Fraction of soil carbon that is considered soluble, dimensionless
  • earth_param_set: Physical constants used Clima-wide
source
Model-specific Types
ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver — Type
AbstractSoilDriver
An abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).
source
ClimaLand.Soil.Biogeochemistry.SoilDrivers — Type
SoilDrivers
A container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).
  • met: Soil temperature and moisture drivers - Prescribed or Prognostic
  • soc: Soil SOM driver - Prescribed only
  • atmos: Prescribed atmospheric variables
source
ClimaLand.Soil.Biogeochemistry.PrescribedMet — Type
PrescribedMet <: AbstractSoilDriver
A container which holds the prescribed functions for soil temperature and moisture.
This is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.
  • temperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat
  • volumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat
  • ν: Soil porosity (m³ m⁻³)
  • θ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)
  • b: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)
source
Functions of State
ClimaLand.Soil.Biogeochemistry.co2_diffusivity — Function
co2_diffusivity(
                 T_soil::FT,
                 θ_w::FT,
                 P_sfc::FT,
@@ -9,9 +9,9 @@
                 b::FT,
                 ν::FT,
                 params::SoilCO2ModelParameters{FT},
-                ) where {FT}
Computes the diffusivity of CO₂ within the soil (D).
First, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.
This parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.
source
ClimaLand.Soil.Biogeochemistry.microbe_source — Function
microbe_source(T_soil::FT,
+                ) where {FT}
Computes the diffusivity of CO₂ within the soil (D).
First, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.
This parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.
source
ClimaLand.Soil.Biogeochemistry.microbe_source — Function
microbe_source(T_soil::FT,
                θ_l::FT,
                Csom::FT,
                ν::FT,
                params::SoilCO2ModelParameters{FT}
-               ) where {FT}
Computes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).
source
Extendible Functions
ClimaLand.Soil.Biogeochemistry.soil_moisture — Function
soil_moisture(driver::PrognosticSoil, p, Y, t, z)
Returns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.
source
soil_moisture(driver::PrescribedMet, p, Y, t, z)
Returns the soil moisture at location (z) and time (t) for the prescribed soil case.
source
ClimaLand.Soil.Biogeochemistry.soil_temperature — Function
soil_temperature(driver::PrognosticSoil, p, Y, t, z)
Returns the prognostic soil temperature.
source
soil_temperature(driver::PrescribedMet, p, Y, t, z)
Returns the soil temperature at location (z) and time (t) for the prescribed soil case.
source
Missing docstring.
Missing docstring for ClimaLand.Soil.Biogeochemistry.soil_SOM_C. Check Documenter's build log for details.

+               ) where {FT}

Computes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).

source

Extendible Functions

ClimaLand.Soil.Biogeochemistry.soil_moisture — Function
soil_moisture(driver::PrognosticSoil, p, Y, t, z)

Returns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.

source
soil_moisture(driver::PrescribedMet, p, Y, t, z)

Returns the soil moisture at location (z) and time (t) for the prescribed soil case.

source
ClimaLand.Soil.Biogeochemistry.soil_temperature — Function
soil_temperature(driver::PrognosticSoil, p, Y, t, z)

Returns the prognostic soil temperature.

source
soil_temperature(driver::PrescribedMet, p, Y, t, z)

Returns the soil temperature at location (z) and time (t) for the prescribed soil case.

source
Missing docstring.

Missing docstring for ClimaLand.Soil.Biogeochemistry.soil_SOM_C. Check Documenter's build log for details.

diff --git a/dev/APIs/SurfaceWater/index.html b/dev/APIs/SurfaceWater/index.html index f71d5fd077..d7b708cdfe 100644 --- a/dev/APIs/SurfaceWater/index.html +++ b/dev/APIs/SurfaceWater/index.html @@ -1,7 +1,7 @@ -Surface Water Models · ClimaLand.jl
GitHub

SurfaceWater

Models

ClimaLand.Pond.PondModel — Type
PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}

A stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.

  • domain: The domain for the pond model

  • runoff: The runoff model for the pond model

source

Methods and Types

ClimaLand.Pond.PrescribedRunoff — Type
PrescribedRunoff{F1 <: Function, F2 <: Function} <:  AbstractSurfaceRunoff

The required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.

source
ClimaLand.Pond.surface_runoff — Function
function Pond.surface_runoff(
+Surface Water Models · ClimaLand.jl
GitHub
SurfaceWater
Models
ClimaLand.Pond.PondModel — Type
PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}
A stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate  and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.
  • domain: The domain for the pond model
  • runoff: The runoff model for the pond model
source
Methods and Types
ClimaLand.Pond.PrescribedRunoff — Type
PrescribedRunoff{F1 <: Function, F2 <: Function} <:  AbstractSurfaceRunoff
The required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.
source
ClimaLand.Pond.surface_runoff — Function
function Pond.surface_runoff(
     runoff::PrognosticRunoff,
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-)
Extension of the Pond.surface_runoff function, which computes  the surface runoff, for use in an LSM when the runoff is determined prognostically.
source

+)

Extension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.

source
diff --git a/dev/APIs/canopy/AutotrophicRespiration/index.html b/dev/APIs/canopy/AutotrophicRespiration/index.html index 14d352a8ce..f4adb9b258 100644 --- a/dev/APIs/canopy/AutotrophicRespiration/index.html +++ b/dev/APIs/canopy/AutotrophicRespiration/index.html @@ -1,5 +1,5 @@ -Canopy Autotrophic Respiration · ClimaLand.jl
GitHub

Autotrophic Respiration

Parameters

ClimaLand.Canopy.AutotrophicRespirationParameters — Type
AutotrophicRespirationParameters{FT<:AbstractFloat}

The required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. "The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics." Geoscientific Model Development 4.3 (2011): 701-722.

  • ne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)

  • ηsl: Live stem wood coefficient (kg C m-3)

  • σl: Specific leaf density (kg C m-2 [leaf])

  • μr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0

  • μs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1

  • Rel: Relative contribution or Rgrowth (-)

source

Methods

ClimaLand.Canopy.nitrogen_content — Function
nitrogen_content(
+Canopy Autotrophic Respiration · ClimaLand.jl
GitHub
Autotrophic Respiration
Parameters
ClimaLand.Canopy.AutotrophicRespirationParameters — Type
AutotrophicRespirationParameters{FT<:AbstractFloat}
The required parameters for the autrophic respiration model, which is based  off of the JULES model. Clark, D. B., et al. "The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics." Geoscientific Model Development 4.3 (2011): 701-722.
  • ne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)
  • ηsl: Live stem wood coefficient (kg C m-3)
  • σl: Specific leaf density (kg C m-2 [leaf])
  • μr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0
  • μs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1
  • Rel: Relative contribution or Rgrowth (-)
source
Methods
ClimaLand.Canopy.nitrogen_content — Function
nitrogen_content(
                  ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)
                  Vcmax25::FT, #
                  LAI::FT, # Leaf area index
@@ -10,14 +10,14 @@
                  σl::FT # Specific leaf density (kg C m-2 [leaf])
                  μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0
                  μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 
-                ) where {FT}
Computes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).
source
ClimaLand.Canopy.plant_respiration_maintenance — Function
plant_respiration_maintenance(
     Rd::FT, # Dark respiration
     β::FT, # Soil moisture factor
     Nl::FT, # Nitrogen content of leafs
     Nr::FT, # Nitrogen content of roots
     Ns::FT, # Nitrogen content of stems
-    ) where {FT}
Computes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns),  and the soil moisture factor (β).
source
ClimaLand.Canopy.plant_respiration_growth — Function
plant_respiration_growth(
+    ) where {FT}
Computes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns),  and the soil moisture factor (β).
source
ClimaLand.Canopy.plant_respiration_growth — Function
plant_respiration_growth(
     Rel::FT, # Factor of relative contribution
     An::FT, # Net photosynthesis
     Rpm::FT # Plant maintenance respiration
-    ) where {FT}
Computes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.
source

+    ) where {FT}

Computes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.

source
diff --git a/dev/APIs/canopy/Canopy/index.html b/dev/APIs/canopy/Canopy/index.html index 9641a5a241..9602e9d45c 100644 --- a/dev/APIs/canopy/Canopy/index.html +++ b/dev/APIs/canopy/Canopy/index.html @@ -1,2 +1,2 @@ -Canopy Models · ClimaLand.jl
GitHub

Canopy

Canopy Model Structs

ClimaLand.Canopy.CanopyModel — Type
 CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}

The model struct for the canopy, which contains

  • the canopy model domain (a point for site-level simulations, or

an extended surface (plane/spherical surface) for regional or global simulations.

  • subcomponent model type for radiative transfer. This is of type

AbstractRadiationModel.

  • subcomponent model type for photosynthesis. This is of type

AbstractPhotosynthesisModel, and currently only the FarquharModel is supported.

  • subcomponent model type for stomatal conductance. This is of type

AbstractStomatalConductanceModel and currently only the MedlynModel is supported

  • subcomponent model type for plant hydraulics. This is of type

AbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.

  • subcomponent model type for canopy energy. This is of type

AbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.

  • subcomponent model type for canopy SIF. prognostically.
  • canopy model parameters, which include parameters that are shared

between canopy model components or those needed to compute boundary fluxes.

  • The atmospheric conditions, which are either prescribed

(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).

  • The radiative flux conditions, which are either prescribed

(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).

  • The soil conditions, which are either prescribed (of type PrecribedSoil, for

running the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)

Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.

  • autotrophic_respiration: Autotrophic respiration model, a canopy component model

  • radiative_transfer: Radiative transfer model, a canopy component model

  • photosynthesis: Photosynthesis model, a canopy component model

  • conductance: Stomatal conductance model, a canopy component model

  • hydraulics: Plant hydraulics model, a canopy component model

  • energy: Energy balance model, a canopy component model

  • sif: SIF model, a canopy component model

  • atmos: Atmospheric forcing: prescribed or coupled

  • radiation: Radiative forcing: prescribed or coupled

  • soil_driver: Soil pressure: prescribed or prognostic

  • parameters: Shared canopy parameters between component models

  • domain: Canopy model domain

source
ClimaLand.Canopy.SharedCanopyParameters — Type
SharedCanopyParameters{FT <: AbstractFloat, PSE}

A place to store shared parameters that are required by multiple canopy components.

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • earth_param_set: Earth param set

source

Canopy Model Fluxes

ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration — Type
DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}

A concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.

source

Canopy Model Soil Drivers

ClimaLand.Canopy.PrescribedSoil — Type
 PrescribedSoil <: AbstractSoilDriver

A container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.

  • root_depths: The depth of the root tips, in meters

  • ψ: Prescribed soil potential (m) in the root zone a function of time

  • T: Prescribed soil surface temperature (K) as a function of time

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

  • ϵ: Soil emissivity

source
+Canopy Models · ClimaLand.jl
GitHub

Canopy

Canopy Model Structs

ClimaLand.Canopy.CanopyModel — Type
 CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}

The model struct for the canopy, which contains

  • the canopy model domain (a point for site-level simulations, or

an extended surface (plane/spherical surface) for regional or global simulations.

  • subcomponent model type for radiative transfer. This is of type

AbstractRadiationModel.

  • subcomponent model type for photosynthesis. This is of type

AbstractPhotosynthesisModel, and currently only the FarquharModel is supported.

  • subcomponent model type for stomatal conductance. This is of type

AbstractStomatalConductanceModel and currently only the MedlynModel is supported

  • subcomponent model type for plant hydraulics. This is of type

AbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.

  • subcomponent model type for canopy energy. This is of type

AbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.

  • subcomponent model type for canopy SIF. prognostically.
  • canopy model parameters, which include parameters that are shared

between canopy model components or those needed to compute boundary fluxes.

  • The atmospheric conditions, which are either prescribed

(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).

  • The radiative flux conditions, which are either prescribed

(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).

  • The soil conditions, which are either prescribed (of type PrecribedSoil, for

running the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)

Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.

  • autotrophic_respiration: Autotrophic respiration model, a canopy component model

  • radiative_transfer: Radiative transfer model, a canopy component model

  • photosynthesis: Photosynthesis model, a canopy component model

  • conductance: Stomatal conductance model, a canopy component model

  • hydraulics: Plant hydraulics model, a canopy component model

  • energy: Energy balance model, a canopy component model

  • sif: SIF model, a canopy component model

  • atmos: Atmospheric forcing: prescribed or coupled

  • radiation: Radiative forcing: prescribed or coupled

  • soil_driver: Soil pressure: prescribed or prognostic

  • parameters: Shared canopy parameters between component models

  • domain: Canopy model domain

source
ClimaLand.Canopy.SharedCanopyParameters — Type
SharedCanopyParameters{FT <: AbstractFloat, PSE}

A place to store shared parameters that are required by multiple canopy components.

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • earth_param_set: Earth param set

source

Canopy Model Fluxes

ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration — Type
DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}

A concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.

source

Canopy Model Soil Drivers

ClimaLand.Canopy.PrescribedSoil — Type
 PrescribedSoil <: AbstractSoilDriver

A container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.

  • root_depths: The depth of the root tips, in meters

  • ψ: Prescribed soil potential (m) in the root zone a function of time

  • T: Prescribed soil surface temperature (K) as a function of time

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

  • ϵ: Soil emissivity

source
diff --git a/dev/APIs/canopy/CanopyEnergy/index.html b/dev/APIs/canopy/CanopyEnergy/index.html index 7d6f49daa1..7e8008778d 100644 --- a/dev/APIs/canopy/CanopyEnergy/index.html +++ b/dev/APIs/canopy/CanopyEnergy/index.html @@ -1,16 +1,16 @@ -Canopy Energy · ClimaLand.jl
GitHub

Canopy Energy Model

Methods

ClimaLand.Canopy.canopy_temperature — Function
canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)

Returns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.

source
canopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)

Returns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.

source
ClimaLand.Canopy.root_energy_flux_per_ground_area! — Function
root_energy_flux_per_ground_area!(
+Canopy Energy · ClimaLand.jl
GitHub
Canopy Energy Model
Methods
ClimaLand.Canopy.canopy_temperature — Function
canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)
Returns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.
source
canopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)
Returns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.
source
ClimaLand.Canopy.root_energy_flux_per_ground_area! — Function
root_energy_flux_per_ground_area!(
     fa_energy::ClimaCore.Fields.Field,
     s::PrognosticSoil{F},
     model::Canopy.AbstractCanopyEnergyModel{FT},
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-) where {FT, F}
A method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).
Note that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.
source
root_energy_flux_per_ground_area!(
+) where {FT, F}
A method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).
Note that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.
source
root_energy_flux_per_ground_area!(
     fa_energy::ClimaCore.Fields.Field,
     s::PrescribedSoil,
     model::AbstractCanopyEnergyModel{FT},
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-) where {FT}
A method which updates the ClimaCore.Fields.Field fa_energy in place with  the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.
Background information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.
source
Types
Missing docstring.
Missing docstring for ClimaLand.Canopy.AbstractCanopyEnergyModel. Check Documenter's build log for details.
ClimaLand.Canopy.PrescribedCanopyTempModel — Type
PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}
A model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.
No equation for the energy of the canopy is solved.
source

+) where {FT}

A method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.

Background information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source

Types

Missing docstring.

Missing docstring for ClimaLand.Canopy.AbstractCanopyEnergyModel. Check Documenter's build log for details.

ClimaLand.Canopy.PrescribedCanopyTempModel — Type
PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}

A model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.

No equation for the energy of the canopy is solved.

source
diff --git a/dev/APIs/canopy/Photosynthesis/index.html b/dev/APIs/canopy/Photosynthesis/index.html index f1aa634971..501a460e5e 100644 --- a/dev/APIs/canopy/Photosynthesis/index.html +++ b/dev/APIs/canopy/Photosynthesis/index.html @@ -1,44 +1,44 @@ -Canopy Photosynthesis · ClimaLand.jl
GitHub

Photosynthesis

Parameters

ClimaLand.Canopy.SIFParameters — Type
SIFParameters{FT<:AbstractFloat}

The required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.

  • kf: The rate coefficient for florescence, unitless

  • kd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • min_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kp: Rate coefficient for photochemical quenching

  • kappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

  • kappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

source
ClimaLand.Canopy.FarquharParameters — Type
FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}

The required parameters for the Farquhar photosynthesis model.

  • Vcmax25: Vcmax at 25 °C (mol CO2/m^2/s)

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercelluar O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]

  • pc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]

  • mechanism: Photosynthesis mechanism: C3 or C4

source
ClimaLand.Canopy.OptimalityFarquharParameters — Type
OptimalityFarquharParameters{FT<:AbstractFloat}

The required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.

  • mechanism: Photosynthesis mechanism: C3 only

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercellular O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Fitting constant to compute the moisture stress factor (Pa^{-1})

  • pc: Fitting constant to compute the moisture stress factor (Pa)

  • c: Constant describing cost of maintaining electron transport (unitless)

source

Methods

ClimaLand.Canopy.arrhenius_function — Function
arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)

Computes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.intercellular_co2 — Function
intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}

Computes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).

source
ClimaLand.Canopy.co2_compensation — Function
co2_compensation(Γstar25::FT,
+Canopy Photosynthesis · ClimaLand.jl
GitHub
Photosynthesis
Parameters
ClimaLand.Canopy.SIFParameters — Type
SIFParameters{FT<:AbstractFloat}
The required parameters for the SIF parameterisation  Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.
  • kf: The rate coefficient for florescence, unitless
  • kd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless
  • kd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless
  • min_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless
  • kn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)
  • kn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)
  • kp: Rate coefficient for photochemical quenching
  • kappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.
  • kappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25.  Lee et al 2015.
source
ClimaLand.Canopy.FarquharParameters — Type
FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}
The required parameters for the Farquhar photosynthesis model.
  • Vcmax25: Vcmax at 25 °C (mol CO2/m^2/s)
  • Γstar25: Γstar at 25 °C (mol/mol)
  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)
  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)
  • ΔHkc: Energy of activation for CO2 (J/mol)
  • ΔHko: Energy of activation for oxygen (J/mol)
  • ΔHVcmax: Energy of activation for Vcmax (J/mol)
  • ΔHΓstar: Energy of activation for Γstar (J/mol)
  • ΔHJmax: Energy of activation for Jmax (J/mol)
  • ΔHRd: Energy of activation for Rd (J/mol)
  • To: Reference temperature equal to 25 degrees Celsius (K)
  • oi: Intercelluar O2 concentration (mol/mol); taken to be constant
  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)
  • θj: Curvature parameter, a fitting constant to compute J, unitless
  • f: Constant factor appearing the dark respiration term, equal to 0.015.
  • sc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]
  • pc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]
  • mechanism: Photosynthesis mechanism: C3 or C4
source
ClimaLand.Canopy.OptimalityFarquharParameters — Type
OptimalityFarquharParameters{FT<:AbstractFloat}
The required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.
  • mechanism: Photosynthesis mechanism: C3 only
  • Γstar25: Γstar at 25 °C (mol/mol)
  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)
  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)
  • ΔHkc: Energy of activation for CO2 (J/mol)
  • ΔHko: Energy of activation for oxygen (J/mol)
  • ΔHVcmax: Energy of activation for Vcmax (J/mol)
  • ΔHΓstar: Energy of activation for Γstar (J/mol)
  • ΔHJmax: Energy of activation for Jmax (J/mol)
  • ΔHRd: Energy of activation for Rd (J/mol)
  • To: Reference temperature equal to 25 degrees Celsius (K)
  • oi: Intercellular O2 concentration (mol/mol); taken to be constant
  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)
  • θj: Curvature parameter, a fitting constant to compute J, unitless
  • f: Constant factor appearing the dark respiration term, equal to 0.015.
  • sc: Fitting constant to compute the moisture stress factor (Pa^{-1})
  • pc: Fitting constant to compute the moisture stress factor (Pa)
  • c: Constant describing cost of maintaining electron transport (unitless)
source
Methods
ClimaLand.Canopy.arrhenius_function — Function
arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)
Computes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal  gas constant R, and the energy activation ΔH.
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.intercellular_co2 — Function
intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}
Computes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar,   mol/mol), and the Medlyn factor (unitless).
source
ClimaLand.Canopy.co2_compensation — Function
co2_compensation(Γstar25::FT,
                  ΔHΓstar::FT,
                  T::FT,
                  To::FT,
-                 R::FT) where {FT}
Computes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.rubisco_assimilation — Function
rubisco_assimilation(::C3,
+                 R::FT) where {FT}
Computes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.rubisco_assimilation — Function
rubisco_assimilation(::C3,
                      Vcmax::FT,
                      ci::FT,
                      Γstar::FT,
                      Kc::FT,
                      Ko::FT,
-                     oi::FT) where {FT}
Computes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax),  the leaf internal carbon dioxide partial pressure (ci),  the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).
The empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
rubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}
Computes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).
source
ClimaLand.Canopy.light_assimilation — Function
light_assimilation(::C3,
+                     oi::FT) where {FT}
Computes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax),  the leaf internal carbon dioxide partial pressure (ci),  the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).
The empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
rubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}
Computes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).
source
ClimaLand.Canopy.light_assimilation — Function
light_assimilation(::C3,
                    J::FT,
                    ci::FT,
-                   Γstar::FT) where {FT}
Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
light_assimilation(::C4, J::FT, _...) where {FT}
Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).
source
ClimaLand.Canopy.C3 — Type
C3 <: AbstractPhotosynthesisMechanism
Helper struct for dispatching between C3 and C4 photosynthesis.
source
ClimaLand.Canopy.C4 — Type
C4 <: AbstractPhotosynthesisMechanism
Helper struct for dispatching between C3 and C4 photosynthesis.
source
ClimaLand.Canopy.max_electron_transport — Function
max_electron_transport(Vcmax::FT) where {FT}
Computes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s,  as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.electron_transport — Function
electron_transport(APAR::FT,
+                   Γstar::FT) where {FT}
Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
light_assimilation(::C4, J::FT, _...) where {FT}
Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).
source
ClimaLand.Canopy.C3 — Type
C3 <: AbstractPhotosynthesisMechanism
Helper struct for dispatching between C3 and C4 photosynthesis.
source
ClimaLand.Canopy.C4 — Type
C4 <: AbstractPhotosynthesisMechanism
Helper struct for dispatching between C3 and C4 photosynthesis.
source
ClimaLand.Canopy.max_electron_transport — Function
max_electron_transport(Vcmax::FT) where {FT}
Computes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s,  as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.electron_transport — Function
electron_transport(APAR::FT,
                    Jmax::FT,
                    θj::FT,
-                   ϕ::FT) where {FT}
Computes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). 
See Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.net_photosynthesis — Function
net_photosynthesis(Ac::FT,
+                   ϕ::FT) where {FT}
Computes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). 
See Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.net_photosynthesis — Function
net_photosynthesis(Ac::FT,
                    Aj::FT,
                    Rd::FT,
-                   β::FT) where {FT}
Computes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of  the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). 
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.optimality_max_photosynthetic_rates — Function
optimalitymaxphotosynthetic_rates(APAR::FT,  θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)
Computes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21)  the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.
See Smith et al. 2019.
source
ClimaLand.Canopy.moisture_stress — Function
moisture_stress(pl::FT,
+                   β::FT) where {FT}
Computes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of  the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). 
See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.optimality_max_photosynthetic_rates — Function
optimalitymaxphotosynthetic_rates(APAR::FT,  θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)
Computes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21)  the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.
See Smith et al. 2019.
source
ClimaLand.Canopy.moisture_stress — Function
moisture_stress(pl::FT,
                 sc::FT,
-                pc::FT) where {FT}
Computes the moisture stress factor (β), which is unitless,  as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and  the leaf water pressure (pl, Pa) . 
See Eqn 12.57 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.dark_respiration — Function
dark_respiration(Vcmax25::FT,
+                pc::FT) where {FT}
Computes the moisture stress factor (β), which is unitless,  as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and  the leaf water pressure (pl, Pa) . 
See Eqn 12.57 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.dark_respiration — Function
dark_respiration(Vcmax25::FT,
                  β::FT,
                  f::FT,
                  ΔHkc::FT,
                  T::FT,
                  To::FT,
-                 R::FT) where {FT}
Computes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.compute_GPP — Function
compute_GPP(An::FT,
+                 R::FT) where {FT}
Computes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.compute_GPP — Function
compute_GPP(An::FT,
          K::FT,
          LAI::FT,
-         Ω::FT) where {FT}
Computes the total canopy photosynthesis (GPP) as a function of  the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).
source
ClimaLand.Canopy.MM_Kc — Function
MM_Kc(Kc25::FT,
+         Ω::FT) where {FT}
Computes the total canopy photosynthesis (GPP) as a function of  the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).
source
ClimaLand.Canopy.MM_Kc — Function
MM_Kc(Kc25::FT,
       ΔHkc::FT,
       T::FT,
       To::FT,
-      R::FT) where {FT}
Computes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.MM_Ko — Function
MM_Ko(Ko25::FT,
+      R::FT) where {FT}
Computes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.MM_Ko — Function
MM_Ko(Ko25::FT,
       ΔHko::FT,
       T::FT,
       To::FT,
-      R::FT) where {FT}
Computes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.compute_Vcmax — Function
compute_Vcmax(Vcmax25::FT,
+      R::FT) where {FT}
Computes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source
ClimaLand.Canopy.compute_Vcmax — Function
compute_Vcmax(Vcmax25::FT,
        T::FT,
        To::FT,
        R::FT,
-       ep5::FT) where {FT}
Computes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s,  as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).
See Table 11.5 of G. Bonan's textbook,  Climate Change and Terrestrial Ecosystem Modeling (2019).
source

+       ep5::FT) where {FT}

Computes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
diff --git a/dev/APIs/canopy/PlantHydraulics/index.html b/dev/APIs/canopy/PlantHydraulics/index.html index 5f1482912a..f5a50d88d6 100644 --- a/dev/APIs/canopy/PlantHydraulics/index.html +++ b/dev/APIs/canopy/PlantHydraulics/index.html @@ -1,35 +1,35 @@ -Plant Hydraulics · ClimaLand.jl
GitHub

PlantHydraulics

Models

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel — Type
PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}

Defines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.

This model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can "turn off" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:

  • n_leaf = 1
  • n_stem = 0
  • LAI = SAI = RAI = 0.

A plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.

Finally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.

  • n_stem: The number of stem compartments for the plant; can be zero

  • n_leaf: The number of leaf compartments for the plant; must be >=1

  • compartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters

  • compartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.

  • compartment_labels: The label (:stem or :leaf) of each compartment

  • parameters: Parameters required by the Plant Hydraulics model

  • transpiration: The transpiration model, of type AbstractTranspiration

source

Plant Hydraulics Diagnostic Variables

ClimaLand.Canopy.PlantHydraulics.effective_saturation — Function
effective_saturation(
+Plant Hydraulics · ClimaLand.jl
GitHub
PlantHydraulics
Models
ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel — Type
PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}
Defines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.
This model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In  global run with patches of bare soil, you can "turn off" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:
  • n_leaf = 1
  • n_stem = 0
  • LAI = SAI = RAI = 0.
A plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.
Finally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.
  • n_stem: The number of stem compartments for the plant; can be zero
  • n_leaf: The number of leaf compartments for the plant; must be >=1
  • compartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters
  • compartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.
  • compartment_labels: The label (:stem or :leaf) of each compartment
  • parameters: Parameters required by the Plant Hydraulics model
  • transpiration: The transpiration model, of type AbstractTranspiration
source
Plant Hydraulics Diagnostic Variables
ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction — Function
augmented_liquid_fraction(
     ν::FT,
-    S_l::FT) where {FT}
Computes the augmented liquid fraction from porosity and effective saturation.
Augmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.
source
ClimaLand.Canopy.PlantHydraulics.water_retention_curve — Function
water_retention_curve(
+    S_l::FT) where {FT}
Computes the augmented liquid fraction from porosity and effective saturation.
Augmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.
source
ClimaLand.Canopy.PlantHydraulics.water_retention_curve — Function
water_retention_curve(
     S_l::FT,
     b::FT,
     ν::FT,
-    S_s::FT) where {FT}
Returns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.
source
ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area! — Function
PlantHydraulics.root_water_flux_per_ground_area!(
     fa::ClimaCore.Fields.Field,
     s::PrognosticSoil,
     model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-)
An extension of the PlantHydraulics.root_water_flux_per_ground_area! function,  which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.
It is computed by summing the flux of water per ground area between roots and soil at each soil layer.
source
root_water_flux_per_ground_area!(
+)
An extension of the PlantHydraulics.root_water_flux_per_ground_area! function,  which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.
It is computed by summing the flux of water per ground area between roots and soil at each soil layer.
source
root_water_flux_per_ground_area!(
     fa::ClimaCore.Fields.Field,
     s::PrescribedSoil,
     model::PlantHydraulicsModel{FT},
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-) where {FT}
A method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.
The returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.
source
ClimaLand.Canopy.PlantHydraulics.flux — Function
flux(
+) where {FT}
A method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.
The returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.
source
ClimaLand.Canopy.PlantHydraulics.flux — Function
flux(
     z1,
     z2,
     ψ1,
     ψ2,
     K1,
     K2,
-) where {FT}
Computes the water flux given the absolute potential (pressure/(ρg))  at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.
We currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.
source
Plant Hydraulics Parameters
ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters — Type
PlantHydraulicsParameters
A struct for holding parameters of the PlantHydraulics Model.
  • ai_parameterization: The area index model for LAI, SAI, RAI
  • ν: porosity (m3/m3)
  • S_s: storativity (m3/m3)
  • conductivity_model: Conductivity model and parameters
  • retention_model: Water retention model and parameters
  • root_distribution: Root distribution function P(z)
source
ClimaLand.Canopy.PlantHydraulics.Weibull — Type
Weibull{FT} <: AbstractConductivityModel{FT}
A concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.
Fields
  • K_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation
  • ψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63%     of maximum xylem conductance is lost (Liu, 2020).
  • c: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).
source
ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve — Type
LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}
A concrete type specifying that a linear water retention  model is to be used; the struct contains the require parameters for this model.
When ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.
Fields
  • a: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.
source
Plant Hydraulics Methods and Types
Missing docstring.
Missing docstring for ClimaLand.PlantHydraulics.PrescribedSoilPressure. Check Documenter's build log for details.
Missing docstring.
Missing docstring for ClimaLand.PlantHydraulics.AbstractRootExtraction. Check Documenter's build log for details.

+) where {FT}

Computes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.

We currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.

source

Plant Hydraulics Parameters

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters — Type
PlantHydraulicsParameters

A struct for holding parameters of the PlantHydraulics Model.

  • ai_parameterization: The area index model for LAI, SAI, RAI

  • ν: porosity (m3/m3)

  • S_s: storativity (m3/m3)

  • conductivity_model: Conductivity model and parameters

  • retention_model: Water retention model and parameters

  • root_distribution: Root distribution function P(z)

source
ClimaLand.Canopy.PlantHydraulics.Weibull — Type
Weibull{FT} <: AbstractConductivityModel{FT}

A concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.

Fields

  • K_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation

  • ψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).

  • c: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).

source
ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve — Type
LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}

A concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.

When ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.

Fields

  • a: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.
source

Plant Hydraulics Methods and Types

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.PrescribedSoilPressure. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.AbstractRootExtraction. Check Documenter's build log for details.

diff --git a/dev/APIs/canopy/RadiativeTransfer/index.html b/dev/APIs/canopy/RadiativeTransfer/index.html index fcdd0c2a73..46676124f5 100644 --- a/dev/APIs/canopy/RadiativeTransfer/index.html +++ b/dev/APIs/canopy/RadiativeTransfer/index.html @@ -1,12 +1,12 @@ -Canopy RT · ClimaLand.jl
GitHub

Radiative Transfer

Parameters

ClimaLand.Canopy.BeerLambertParameters — Type
BeerLambertParameters{FT <: AbstractFloat}

The required parameters for the Beer-Lambert radiative transfer model.

  • α_PAR_leaf: PAR leaf reflectance (unitless)

  • α_NIR_leaf: NIR leaf reflectance

  • ϵ_canopy: Emissivity of the canopy

  • Ω: Clumping index following Braghiere (2021) (unitless)

  • λ_γ_PAR: Typical wavelength per PAR photon (m)

  • λ_γ_NIR: Typical wavelength per NIR photon (m)

  • G_Function: Leaf angle distribution function

source

Methods

Missing docstring.

Missing docstring for ClimaLand.Canopy.compute_absorbances. Check Documenter's build log for details.

ClimaLand.Canopy.plant_absorbed_pfd — Function
plant_absorbed_pfd(
+Canopy RT · ClimaLand.jl
GitHub
Radiative Transfer
Parameters
ClimaLand.Canopy.BeerLambertParameters — Type
BeerLambertParameters{FT <: AbstractFloat}
The required parameters for the Beer-Lambert radiative transfer model.
  • α_PAR_leaf: PAR leaf reflectance (unitless)
  • α_NIR_leaf: NIR leaf reflectance
  • ϵ_canopy: Emissivity of the canopy
  • Ω: Clumping index following Braghiere (2021) (unitless)
  • λ_γ_PAR: Typical wavelength per PAR photon (m)
  • λ_γ_NIR: Typical wavelength per NIR photon (m)
  • G_Function: Leaf angle distribution function
source
Methods
Missing docstring.
Missing docstring for ClimaLand.Canopy.compute_absorbances. Check Documenter's build log for details.
ClimaLand.Canopy.plant_absorbed_pfd — Function
plant_absorbed_pfd(
     RT::BeerLambertModel{FT},
     SW_IN:FT,
     α_leaf::FT,
     LAI::FT,
     K::FT,
     α_soil::FT
-)
Computes the absorbed, reflected, and transmitted photon flux density  in terms of mol photons per m^2 per  second for a radiation band.
This applies the Beer-Lambert law, which is a function of incident  radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). 
Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.
source
plant_absorbed_pfd(
+)
Computes the absorbed, reflected, and transmitted photon flux density  in terms of mol photons per m^2 per  second for a radiation band.
This applies the Beer-Lambert law, which is a function of incident  radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). 
Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.
source
plant_absorbed_pfd(
     RT::TwoStreamModel{FT},
     α_leaf,
     SW_IN::FT,
@@ -15,19 +15,19 @@
     τ_leaf,
     θs::FT,
     α_soil::FT,
-)
Computes the absorbed, transmitted, and reflected  photon flux density  in terms of mol photons  per m^2 per second for a radiation band. 
This applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all  parameters from the parameter struct of a TwoStreamModel, along with the  incident radiation, LAI, extinction coefficient K, soil albedo from the  canopy soil_driver, solar zenith angle, and τ.
Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.
source
ClimaLand.Canopy.extinction_coeff — Function
extinction_coeff(ld::FT,
-                 θs::FT) where {FT}
Computes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).
source
Missing docstring.
Missing docstring for ClimaLand.Canopy.extinction_coeff. Check Documenter's build log for details.
ClimaLand.Canopy.canopy_radiant_energy_fluxes! — Function
Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,
+)
Computes the absorbed, transmitted, and reflected  photon flux density  in terms of mol photons  per m^2 per second for a radiation band. 
This applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all  parameters from the parameter struct of a TwoStreamModel, along with the  incident radiation, LAI, extinction coefficient K, soil albedo from the  canopy soil_driver, solar zenith angle, and τ.
Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.
source
ClimaLand.Canopy.extinction_coeff — Function
extinction_coeff(ld::FT,
+                 θs::FT) where {FT}
Computes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).
source
Missing docstring.
Missing docstring for ClimaLand.Canopy.extinction_coeff. Check Documenter's build log for details.
ClimaLand.Canopy.canopy_radiant_energy_fluxes! — Function
Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,
                                      s::PrognosticSoil{F},
                                      canopy,
                                      radiation::PrescribedRadiativeFluxes,
                                      earth_param_set::PSE,
                                      Y::ClimaCore.Fields.FieldVector,
                                      t,
-                                    ) where {FT, PSE}
In standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.
In integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.
LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.
source
canopy_radiant_energy_fluxes!(p::NamedTuple,
+                                    ) where {FT, PSE}
In standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.
In integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.
LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.
source
canopy_radiant_energy_fluxes!(p::NamedTuple,
                               s::PrescribedSoil,
                               canopy,
                               radiation::PrescribedRadiativeFluxes,
                               earth_param_set::PSE,
                               Y::ClimaCore.Fields.FieldVector,
                               t,
-                             ) where {PSE}
Computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.
LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.
source

+                             ) where {PSE}

Computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

source
diff --git a/dev/APIs/canopy/StomatalConductance/index.html b/dev/APIs/canopy/StomatalConductance/index.html index f875626d43..4485b4f7e4 100644 --- a/dev/APIs/canopy/StomatalConductance/index.html +++ b/dev/APIs/canopy/StomatalConductance/index.html @@ -1,9 +1,9 @@ -Canopy Stomatal Conductance · ClimaLand.jl
GitHub

Stomatal Conductance

Parameters

ClimaLand.Canopy.MedlynConductanceParameters — Type
MedlynConductanceParameters{FT <: AbstractFloat}

The required parameters for the Medlyn stomatal conductance model.

  • Drel: Relative diffusivity of water vapor (unitless)

  • g0: Minimum stomatal conductance mol/m^2/s

  • g1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})

source

Methods

ClimaLand.Canopy.medlyn_term — Function
medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}

Computes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).

thermo_params is the Thermodynamics.jl parameter set.

source
ClimaLand.Canopy.medlyn_conductance — Function
medlyn_conductance(g0::FT,
+Canopy Stomatal Conductance · ClimaLand.jl
GitHub
Stomatal Conductance
Parameters
ClimaLand.Canopy.MedlynConductanceParameters — Type
MedlynConductanceParameters{FT <: AbstractFloat}
The required parameters for the Medlyn stomatal conductance model.
  • Drel: Relative diffusivity of water vapor (unitless)
  • g0: Minimum stomatal conductance mol/m^2/s
  • g1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})
source
Methods
ClimaLand.Canopy.medlyn_term — Function
medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}
Computes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).
thermo_params is the Thermodynamics.jl parameter set.
source
ClimaLand.Canopy.medlyn_conductance — Function
medlyn_conductance(g0::FT,
                    Drel::FT,
                    medlyn_term::FT,
                    An::FT,
-                   ca::FT) where {FT}
Computes the stomatal conductance according to Medlyn, as a function of  the minimum stomatal conductance (g0),  the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).
This returns the conductance in units of mol/m^2/s. It must be converted to  m/s using the molar density of water prior to use in SurfaceFluxes.jl.
source
ClimaLand.Canopy.upscale_leaf_conductance — Function
upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}
This currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.
TODO: Check what CLM does, and check if we can use the same function  for GPP from An, and make more general.
source
ClimaLand.Canopy.penman_monteith — Function
penman_monteith(
+                   ca::FT) where {FT}
Computes the stomatal conductance according to Medlyn, as a function of  the minimum stomatal conductance (g0),  the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).
This returns the conductance in units of mol/m^2/s. It must be converted to  m/s using the molar density of water prior to use in SurfaceFluxes.jl.
source
ClimaLand.Canopy.upscale_leaf_conductance — Function
upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}
This currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.
TODO: Check what CLM does, and check if we can use the same function  for GPP from An, and make more general.
source
ClimaLand.Canopy.penman_monteith — Function
penman_monteith(
     Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1)  
     Rn::FT, # Net irradiance (W m−2)
     G::FT, # Ground heat flux (W m−2)
@@ -14,4 +14,4 @@
     γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)
     gs::FT, # surface or stomatal conductance (m s−1)
     Lv::FT, # Volumetric latent heat of vaporization (J m-3)
-    ) where {FT}
Computes the evapotranspiration in m/s using the Penman-Monteith equation.       
source

+    ) where {FT}

Computes the evapotranspiration in m/s using the Penman-Monteith equation.

source
diff --git a/dev/APIs/shared_utilities/index.html b/dev/APIs/shared_utilities/index.html index bc9904c4e4..e034b071b1 100644 --- a/dev/APIs/shared_utilities/index.html +++ b/dev/APIs/shared_utilities/index.html @@ -1,15 +1,15 @@ -Shared Utilities · ClimaLand.jl
GitHub

Shared Utilities

Domains

ClimaLand.Domains.AbstractDomain — Type
AbstractDomain{FT <:AbstractFloat}

An abstract type for domains.

The domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.

Additionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.

source
Missing docstring.

Missing docstring for ClimaLand.Domains.AbstractLSMDomain. Check Documenter's build log for details.

ClimaLand.Domains.SphericalShell — Type
struct SphericalShell{FT} <: AbstractDomain{FT}
+Shared Utilities · ClimaLand.jl
GitHub
Shared Utilities
Domains
ClimaLand.Domains.AbstractDomain — Type
AbstractDomain{FT <:AbstractFloat}
An abstract type for domains.
The domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.
Additionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.
source
Missing docstring.
Missing docstring for ClimaLand.Domains.AbstractLSMDomain. Check Documenter's build log for details.
ClimaLand.Domains.SphericalShell — Type
struct SphericalShell{FT} <: AbstractDomain{FT}
     radius::FT
     depth::FT
     dz_tuple::Union{Tuple{FT, FT}, Nothing}
     nelements::Tuple{Int, Int}
     npolynomial::Int
-end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction),  and the resulting coordinate field.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • radius: The radius of the shell
  • depth: The radial extent of the shell
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • nelements: The number of elements to be used in the non-radial and radial directions
  • npolynomial: The polynomial order to be used in the non-radial directions
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
source
ClimaLand.Domains.SphericalSurface — Type
struct SphericalSurface{FT} <: AbstractDomain{FT}
+end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction),  and the resulting coordinate field.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • radius: The radius of the shell
  • depth: The radial extent of the shell
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • nelements: The number of elements to be used in the non-radial and radial directions
  • npolynomial: The polynomial order to be used in the non-radial directions
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
source
ClimaLand.Domains.SphericalSurface — Type
struct SphericalSurface{FT} <: AbstractDomain{FT}
     radius::FT
     nelements::Tuple{Int, Int}
     npolynomial::Int
-end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.
space is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).
Fields
  • radius: The radius of the surface
  • nelements: The number of elements to be used in the non-radial directions
  • npolynomial: The polynomial order to be used in the non-radial directions
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space
source
ClimaLand.Domains.HybridBox — Type
struct HybridBox{FT} <: AbstractDomain{FT}
+end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.
space is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).
Fields
  • radius: The radius of the surface
  • nelements: The number of elements to be used in the non-radial directions
  • npolynomial: The polynomial order to be used in the non-radial directions
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space
source
ClimaLand.Domains.HybridBox — Type
struct HybridBox{FT} <: AbstractDomain{FT}
     xlim::Tuple{FT, FT}
     ylim::Tuple{FT, FT}
     zlim::Tuple{FT, FT}
@@ -18,104 +18,104 @@
     nelements::Tuple{Int, Int, Int}
     npolynomial::Int
     periodic::Tuple{Bool, Bool}
-end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.
When longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)
  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)
  • zlim: Domain interval limits along z axis, in meters
  • longlat: When not nothing, a Tuple that contains the center long and lat.
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • nelements: Number of elements to discretize interval, (nx, ny,nz)
  • npolynomial:  Polynomial order for the horizontal directions
  • periodic: Flag indicating periodic boundaries in horizontal
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
source
ClimaLand.Domains.Column — Type
Column{FT} <: AbstractDomain{FT}
A struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • zlim: Domain interval limits, (zmin, zmax), in meters
  • nelements: Number of elements used to discretize the interval
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • boundary_names: Boundary face identifiers
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
source
ClimaLand.Domains.Plane — Type
Plane{FT} <: AbstractDomain{FT}
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.
When longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.
longlat are in degrees, with longitude going from -180 to 180.
:warning: Only independent columns are supported! (No lateral flow).
space is a NamedTuple holding the surface space (in this case, the entire Plane space).
Fields
  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)
  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)
  • longlat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long     from -180 to 180).
  • nelements: Number of elements to discretize interval, (nx, ny)
  • periodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.
  • npolynomial: Polynomial order for both x and y
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space
source
ClimaLand.Domains.Point — Type
Point{FT} <: AbstractDomain{FT}
A domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.
space is a NamedTuple holding the surface space (in this case, the Point space).
Fields
  • z_sfc: Surface elevation relative to a reference (m)
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space
source
ClimaLand.Domains.coordinates — Function
coordinates(domain::AbstractDomain)
Returns the coordinate fields for the domain as a NamedTuple.
The returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.
source
Domains.coordinates(model::AbstractLandModel)
Returns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.
For example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.
source
ClimaLand.Domains.obtain_face_space — Function
obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)
Returns the face space, if applicable, for the center space cs.
source
obtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)
Returns the face space for the CenterExtrudedFiniteDifferenceSpace cs.
source
obtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)
Returns the face space corresponding to the CenterFiniteDifferenceSpace cs.
source
ClimaLand.Domains.obtain_surface_space — Function
obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)
Returns the surface space, if applicable, for the center space cs.
source
obtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)
Returns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.
source
obtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)
Returns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.
source
ClimaLand.Domains.obtain_surface_domain — Function
obtain_surface_domain(d::AbstractDomain) where {FT}
Default method throwing an error; any domain with a corresponding domain should define a new method of this function.
source
obtain_surface_domain(c::Column{FT}) where {FT}
Returns the Point domain corresponding to the top face (surface) of the Column domain c.
source
obtain_surface_domain(b::HybridBox{FT}) where {FT}
Returns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.
source
obtain_surface_domain(s::SphericalShell{FT}) where {FT}
Returns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.
source
ClimaLand.Domains.top_center_to_surface — Function
top_center_to_surface(center_field::ClimaCore.Fields.Field)
Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.
For example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.
source
top_center_to_surface(val)
When val is a scalar (e.g. a single float or struct), returns val.
source
ClimaLand.Domains.top_face_to_surface — Function
top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)
Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.
Given a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.
source
ClimaLand.Domains.linear_interpolation_to_surface! — Function
linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)
Linearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.
source
ClimaLand.Domains.get_Δz — Function
get_Δz(z::ClimaCore.Fields.Field)
A function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.
source
Models
ClimaLand.AbstractImExModel — Type
AbstractImExModel{FT} <: AbstractModel{FT}
An abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.
source
ClimaLand.AbstractExpModel — Type
AbstractExpModel{FT} <: AbstractModel{FT}
An abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.
source
ClimaLand.make_exp_tendency — Function
make_exp_tendency(model::AbstractModel)
Returns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.
compute_exp_tendency! should be compatible with SciMLBase.jl solvers.
source
ClimaLand.make_imp_tendency — Function
make_imp_tendency(model::AbstractImExModel)
Returns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.
compute_imp_tendency! should be compatible with SciMLBase.jl solvers.
source
make_imp_tendency(model::AbstractModel)
Returns an imp_tendency that does nothing. This model type is not stepped explicity.
source
ClimaLand.make_compute_exp_tendency — Function
make_explicit_tendency(model::Soil.RichardsModel)
An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.
Construct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.
source
make_compute_exp_tendency(model::EnergyHydrology)
An extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.
This function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.
This has been written so as to work with Differential Equations.jl.
source
make_compute_exp_tendency(model::BucketModel{FT}) where {FT}
Creates the computeexptendency! function for the bucket model.
source
make_compute_exp_tendency(model::AbstractModel)
Return a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.
compute_exp_tendency! should be compatible with SciMLBase.jl solvers.
source
 ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)
Creates the computeexptendency!(dY,Y,p,t) function for the canopy component.
Since component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.
source
make_compute_exp_tendency(canopy::CanopyModel)
Creates and returns the computeexptendency! for the CanopyModel.
source
make_compute_exp_tendency(model::SoilCO2Model)
An extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.
This has been written so as to work with Differential Equations.jl.
source
make_compute_exp_tendency(model::PlantHydraulicsModel, _)
A function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.
Below, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).
Note that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.
To prevent dividing by zero, we change AI/(AI x dz)" to "AI/max(AI x dz, eps(FT))"
source
ClimaLand.make_compute_imp_tendency — Function
make_compute_imp_tendency(model::RichardsModel)
An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.
This function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.
source
make_compute_imp_tendency(model::EnergyHydrology)
An extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.
This version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.
This has been written so as to work with Differential Equations.jl.
source
make_compute_imp_tendency(model::AbstractModel)
Return a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.
compute_imp_tendency! should be compatible with SciMLBase.jl solvers.
source
ClimaLand.make_update_aux — Function
make_update_aux(model::RichardsModel)
An extension of the function make_update_aux, for the Richardson- Richards equation.
This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.
This has been written so as to work with Differential Equations.jl.
source
make_update_aux(model::EnergyHydrology)
An extension of the function make_update_aux, for the integrated soil hydrology and energy model.
This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.
This has been written so as to work with Differential Equations.jl.
source
make_update_aux(model::BucketModel{FT}) where {FT}
Creates the update_aux! function for the BucketModel.
source
make_update_aux(model::AbstractModel)
Return an update_aux! function that updates auxiliary parameters p.
source
 ClimaLand.make_update_aux(canopy::CanopyModel{FT,
+end
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.
When longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)
  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)
  • zlim: Domain interval limits along z axis, in meters
  • longlat: When not nothing, a Tuple that contains the center long and lat.
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • nelements: Number of elements to discretize interval, (nx, ny,nz)
  • npolynomial:  Polynomial order for the horizontal directions
  • periodic: Flag indicating periodic boundaries in horizontal
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
source
ClimaLand.Domains.Column — Type
Column{FT} <: AbstractDomain{FT}
A struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.
space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.
Fields
  • zlim: Domain interval limits, (zmin, zmax), in meters
  • nelements: Number of elements used to discretize the interval
  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.
  • boundary_names: Boundary face identifiers
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space
  • fields: Fields associated with the coordinates of the domain that are useful to store
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ClimaLand.Domains.Plane — Type
Plane{FT} <: AbstractDomain{FT}
A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.
When longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.
longlat are in degrees, with longitude going from -180 to 180.
:warning: Only independent columns are supported! (No lateral flow).
space is a NamedTuple holding the surface space (in this case, the entire Plane space).
Fields
  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)
  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)
  • longlat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long     from -180 to 180).
  • nelements: Number of elements to discretize interval, (nx, ny)
  • periodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.
  • npolynomial: Polynomial order for both x and y
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space
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ClimaLand.Domains.Point — Type
Point{FT} <: AbstractDomain{FT}
A domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.
space is a NamedTuple holding the surface space (in this case, the Point space).
Fields
  • z_sfc: Surface elevation relative to a reference (m)
  • space: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space
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ClimaLand.Domains.coordinates — Function
coordinates(domain::AbstractDomain)
Returns the coordinate fields for the domain as a NamedTuple.
The returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.
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Domains.coordinates(model::AbstractLandModel)
Returns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.
For example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.
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ClimaLand.Domains.obtain_face_space — Function
obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)
Returns the face space, if applicable, for the center space cs.
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obtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)
Returns the face space for the CenterExtrudedFiniteDifferenceSpace cs.
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obtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)
Returns the face space corresponding to the CenterFiniteDifferenceSpace cs.
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ClimaLand.Domains.obtain_surface_space — Function
obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)
Returns the surface space, if applicable, for the center space cs.
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obtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)
Returns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.
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obtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)
Returns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.
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ClimaLand.Domains.obtain_surface_domain — Function
obtain_surface_domain(d::AbstractDomain) where {FT}
Default method throwing an error; any domain with a corresponding domain should define a new method of this function.
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obtain_surface_domain(c::Column{FT}) where {FT}
Returns the Point domain corresponding to the top face (surface) of the Column domain c.
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obtain_surface_domain(b::HybridBox{FT}) where {FT}
Returns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.
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obtain_surface_domain(s::SphericalShell{FT}) where {FT}
Returns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.
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ClimaLand.Domains.top_center_to_surface — Function
top_center_to_surface(center_field::ClimaCore.Fields.Field)
Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.
For example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.
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top_center_to_surface(val)
When val is a scalar (e.g. a single float or struct), returns val.
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ClimaLand.Domains.top_face_to_surface — Function
top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)
Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.
Given a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.
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ClimaLand.Domains.linear_interpolation_to_surface! — Function
linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)
Linearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.
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ClimaLand.Domains.get_Δz — Function
get_Δz(z::ClimaCore.Fields.Field)
A function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.
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Models
ClimaLand.AbstractImExModel — Type
AbstractImExModel{FT} <: AbstractModel{FT}
An abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.
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ClimaLand.AbstractExpModel — Type
AbstractExpModel{FT} <: AbstractModel{FT}
An abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.
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ClimaLand.make_exp_tendency — Function
make_exp_tendency(model::AbstractModel)
Returns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.
compute_exp_tendency! should be compatible with SciMLBase.jl solvers.
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ClimaLand.make_imp_tendency — Function
make_imp_tendency(model::AbstractImExModel)
Returns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.
compute_imp_tendency! should be compatible with SciMLBase.jl solvers.
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make_imp_tendency(model::AbstractModel)
Returns an imp_tendency that does nothing. This model type is not stepped explicity.
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ClimaLand.make_compute_exp_tendency — Function
make_explicit_tendency(model::Soil.RichardsModel)
An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.
Construct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.
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make_compute_exp_tendency(model::EnergyHydrology)
An extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.
This function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.
This has been written so as to work with Differential Equations.jl.
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make_compute_exp_tendency(model::BucketModel{FT}) where {FT}
Creates the computeexptendency! function for the bucket model.
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make_compute_exp_tendency(model::AbstractModel)
Return a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.
compute_exp_tendency! should be compatible with SciMLBase.jl solvers.
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 ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)
Creates the computeexptendency!(dY,Y,p,t) function for the canopy component.
Since component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.
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make_compute_exp_tendency(canopy::CanopyModel)
Creates and returns the computeexptendency! for the CanopyModel.
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make_compute_exp_tendency(model::SoilCO2Model)
An extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.
This has been written so as to work with Differential Equations.jl.
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make_compute_exp_tendency(model::PlantHydraulicsModel, _)
A function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.
Below, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).
Note that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.
To prevent dividing by zero, we change AI/(AI x dz)" to "AI/max(AI x dz, eps(FT))"
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ClimaLand.make_compute_imp_tendency — Function
make_compute_imp_tendency(model::RichardsModel)
An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.
This function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.
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make_compute_imp_tendency(model::EnergyHydrology)
An extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.
This version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.
This has been written so as to work with Differential Equations.jl.
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make_compute_imp_tendency(model::AbstractModel)
Return a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.
compute_imp_tendency! should be compatible with SciMLBase.jl solvers.
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ClimaLand.make_update_aux — Function
make_update_aux(model::RichardsModel)
An extension of the function make_update_aux, for the Richardson- Richards equation.
This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.
This has been written so as to work with Differential Equations.jl.
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make_update_aux(model::EnergyHydrology)
An extension of the function make_update_aux, for the integrated soil hydrology and energy model.
This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.
This has been written so as to work with Differential Equations.jl.
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make_update_aux(model::BucketModel{FT}) where {FT}
Creates the update_aux! function for the BucketModel.
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make_update_aux(model::AbstractModel)
Return an update_aux! function that updates auxiliary parameters p.
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 ClimaLand.make_update_aux(canopy::CanopyModel{FT,
                                               <:AutotrophicRespirationModel,
                                               <:Union{BeerLambertModel, TwoStreamModel},
                                               <:FarquharModel,
                                               <:MedlynConductanceModel,
                                               <:PlantHydraulicsModel,},
-                          ) where {FT}
Creates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.
Please note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.
The other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.
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make_update_aux(model::SoilCO2Model)
An extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.
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ClimaLand.make_update_boundary_fluxes — Function
make_update_boundary_fluxes(model::AbstractModel)
Return an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..
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make_update_boundary_fluxes(
+                          ) where {FT}
Creates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.
Please note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.
The other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.
source
make_update_aux(model::SoilCO2Model)
An extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.
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ClimaLand.make_update_boundary_fluxes — Function
make_update_boundary_fluxes(model::AbstractModel)
Return an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..
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make_update_boundary_fluxes(
     land::LandHydrology{FT, SM, SW},
-) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}
A method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.
This function is called each ode function evaluation.
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make_update_boundary_fluxes(
+) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}
A method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.
This function is called each ode function evaluation.
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make_update_boundary_fluxes(
     land::SoilCanopyModel{FT, MM, SM, RM},
 ) where {
     FT,
     MM <: Soil.Biogeochemistry.SoilCO2Model{FT},
     SM <: Soil.RichardsModel{FT},
     RM <: Canopy.CanopyModel{FT}
-    }
A method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. 
This function is called each ode function evaluation, prior to the tendency function evaluation.
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ClimaLand.make_set_initial_cache — Function
make_set_initial_cache(model::AbstractModel)
Returns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.
In principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.
Furthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.
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ClimaLand.make_update_drivers — Function
make_update_drivers(::AbstractClimaLandDrivers)
Creates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.
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make_update_drivers(driver_tuple)
Creates and returns a function which updates the forcing variables ("drivers"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.
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make_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.
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make_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.
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make_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.
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make_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.
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ClimaLand.prognostic_vars — Function
prognostic_vars(soil::RichardsModel)
A function which returns the names of the prognostic variables of RichardsModel.
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prognostic_vars(soil::EnergyHydrology)
A function which returns the names of the prognostic variables of EnergyHydrology.
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prognostic_vars(::SnowModel)
Returns the prognostic variable names of the snow model.
For this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.
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prognostic_vars(m::AbstractModel)
Returns the prognostic variable symbols for the model in the form of a tuple.
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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ClimaLand.prognostic_vars(::AbstractCanopyComponent)
Returns the prognostic vars of the canopy component passed in as an argument.
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prognostic_vars(canopy::CanopyModel)
Returns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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prognostic_vars(model::PlantHydraulicsModel)
A function which returns the names of the prognostic variables of the PlantHydraulicsModel.
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ClimaLand.prognostic_types — Function
prognostic_types(soil::EnergyHydrology{FT}) where {FT}
A function which returns the types of the prognostic variables of EnergyHydrology.
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prognostic_types(::SnowModel{FT})
Returns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.
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prognostic_types(m::AbstractModel{FT}) where {FT}
Returns the prognostic variable types for the model in the form of a tuple.
Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType)  defined. Common examples  include
  • Float64, Float32 for scalar variables (a scalar value at each
coordinate point)
  • SVector{k,Float64} for a mutable but statically sized array of
length k at each coordinate point.
Here, the coordinate points are those returned by coordinates(model).
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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ClimaLand.prognostic_types(::AbstractCanopyComponent)
Returns the prognostic types of the canopy component passed in as an argument.
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prognostic_types(canopy::CanopyModel)
Returns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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ClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}
Defines the prognostic types for the PlantHydraulicsModel.
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ClimaLand.prognostic_domain_names — Function
prognostic_domain_names(::SnowModel)
Returns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is ":surface".
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prognosticdomainnames(m::AbstractModel)
Returns the domain names for the prognostic variables in the form of a tuple.
Examples: (:surface, :surface, :subsurface).
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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prognosticdomainnames(m::AbstractCanopyComponent)
Returns the domain names for the prognostic variables in the form of a tuple.
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ClimaLand.auxiliary_vars — Function
auxiliary_vars(soil::RichardsModel)
A function which returns the names of the auxiliary variables of RichardsModel.
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auxiliary_vars(soil::EnergyHydrology)
A function which returns the names of the auxiliary variables of EnergyHydrology.
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auxiliary_vars(::SnowModel)
Returns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.
Since the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.
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auxiliary_vars(m::AbstractModel)
Returns the auxiliary variable symbols for the model in the form of a tuple.
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ClimaLand.auxiliary_vars(::AbstractCanopyComponent)
Returns the auxiliary types of the canopy component passed in as an argument.
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auxiliary_vars(canopy::CanopyModel)
Returns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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auxiliary_vars(model::PlantHydraulicsModel)
A function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.
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ClimaLand.auxiliary_types — Function
auxiliary_types(soil::RichardsModel)
A function which returns the names of the auxiliary types of RichardsModel.
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auxiliary_types(soil::EnergyHydrology{FT}) where {FT}
A function which returns the types of the auxiliary variables of EnergyHydrology.
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auxiliary_types(m::AbstractModel{FT}) where {FT}
Returns the auxiliary variable types for the model in the form of a tuple.
Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples  include
  • Float64, Float32 for scalar variables (a scalar value at each
coordinate point)
  • SVector{k,Float64} for a mutable but statically sized array of
length k at each coordinate point.
  • Note that Arrays, MVectors are not isbits and cannot be used.
Here, the coordinate points are those returned by coordinates(model).
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ClimaLand.auxiliary_types(::AbstractCanopyComponent)
Returns the auxiliary types of the canopy component passed in as an argument.
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auxiliary_types(canopy::CanopyModel)
Returns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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ClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}
Defines the auxiliary types for the PlantHydraulicsModel.
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ClimaLand.auxiliary_domain_names — Function
auxiliary_domain_names(soil::RichardsModel)
A function which returns the names of the auxiliary domain names of RichardsModel.
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auxiliarydomainnames(m::AbstractModel)
Returns the domain names for the auxiliary variables in the form of a tuple.
Examples: (:surface, :surface, :subsurface).
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auxiliarydomainnames(m::AbstractCanopyComponent)
Returns the domain names for the auxiliary variables in the form of a tuple.
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ClimaLand.initialize_prognostic — Function
initialize_prognostic(model::AbstractModel, state::NamedTuple)
Returns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.
If a model has no prognostic variables, the returned FieldVector contains only an empty array.
The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.
Adjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.
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initialize_prognostic(
+    }
A method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. 
This function is called each ode function evaluation, prior to the tendency function evaluation.
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ClimaLand.make_set_initial_cache — Function
make_set_initial_cache(model::AbstractModel)
Returns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.
In principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.
Furthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.
source
ClimaLand.make_update_drivers — Function
make_update_drivers(::AbstractClimaLandDrivers)
Creates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.
source
make_update_drivers(driver_tuple)
Creates and returns a function which updates the forcing variables ("drivers"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.
source
make_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.
source
make_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.
source
make_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.
source
make_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}
Creates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.
source
ClimaLand.prognostic_vars — Function
prognostic_vars(soil::RichardsModel)
A function which returns the names of the prognostic variables of RichardsModel.
source
prognostic_vars(soil::EnergyHydrology)
A function which returns the names of the prognostic variables of EnergyHydrology.
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prognostic_vars(::SnowModel)
Returns the prognostic variable names of the snow model.
For this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.
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prognostic_vars(m::AbstractModel)
Returns the prognostic variable symbols for the model in the form of a tuple.
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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ClimaLand.prognostic_vars(::AbstractCanopyComponent)
Returns the prognostic vars of the canopy component passed in as an argument.
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prognostic_vars(canopy::CanopyModel)
Returns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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prognostic_vars(model::PlantHydraulicsModel)
A function which returns the names of the prognostic variables of the PlantHydraulicsModel.
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ClimaLand.prognostic_types — Function
prognostic_types(soil::EnergyHydrology{FT}) where {FT}
A function which returns the types of the prognostic variables of EnergyHydrology.
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prognostic_types(::SnowModel{FT})
Returns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.
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prognostic_types(m::AbstractModel{FT}) where {FT}
Returns the prognostic variable types for the model in the form of a tuple.
Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType)  defined. Common examples  include
  • Float64, Float32 for scalar variables (a scalar value at each
coordinate point)
  • SVector{k,Float64} for a mutable but statically sized array of
length k at each coordinate point.
Here, the coordinate points are those returned by coordinates(model).
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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ClimaLand.prognostic_types(::AbstractCanopyComponent)
Returns the prognostic types of the canopy component passed in as an argument.
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prognostic_types(canopy::CanopyModel)
Returns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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ClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}
Defines the prognostic types for the PlantHydraulicsModel.
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ClimaLand.prognostic_domain_names — Function
prognostic_domain_names(::SnowModel)
Returns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is ":surface".
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prognosticdomainnames(m::AbstractModel)
Returns the domain names for the prognostic variables in the form of a tuple.
Examples: (:surface, :surface, :subsurface).
Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.
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prognosticdomainnames(m::AbstractCanopyComponent)
Returns the domain names for the prognostic variables in the form of a tuple.
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ClimaLand.auxiliary_vars — Function
auxiliary_vars(soil::RichardsModel)
A function which returns the names of the auxiliary variables of RichardsModel.
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auxiliary_vars(soil::EnergyHydrology)
A function which returns the names of the auxiliary variables of EnergyHydrology.
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auxiliary_vars(::SnowModel)
Returns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.
Since the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.
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auxiliary_vars(m::AbstractModel)
Returns the auxiliary variable symbols for the model in the form of a tuple.
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ClimaLand.auxiliary_vars(::AbstractCanopyComponent)
Returns the auxiliary types of the canopy component passed in as an argument.
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auxiliary_vars(canopy::CanopyModel)
Returns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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auxiliary_vars(model::PlantHydraulicsModel)
A function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.
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ClimaLand.auxiliary_types — Function
auxiliary_types(soil::RichardsModel)
A function which returns the names of the auxiliary types of RichardsModel.
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auxiliary_types(soil::EnergyHydrology{FT}) where {FT}
A function which returns the types of the auxiliary variables of EnergyHydrology.
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auxiliary_types(m::AbstractModel{FT}) where {FT}
Returns the auxiliary variable types for the model in the form of a tuple.
Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples  include
  • Float64, Float32 for scalar variables (a scalar value at each
coordinate point)
  • SVector{k,Float64} for a mutable but statically sized array of
length k at each coordinate point.
  • Note that Arrays, MVectors are not isbits and cannot be used.
Here, the coordinate points are those returned by coordinates(model).
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ClimaLand.auxiliary_types(::AbstractCanopyComponent)
Returns the auxiliary types of the canopy component passed in as an argument.
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auxiliary_types(canopy::CanopyModel)
Returns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.
This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.
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ClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}
Defines the auxiliary types for the PlantHydraulicsModel.
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ClimaLand.auxiliary_domain_names — Function
auxiliary_domain_names(soil::RichardsModel)
A function which returns the names of the auxiliary domain names of RichardsModel.
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auxiliarydomainnames(m::AbstractModel)
Returns the domain names for the auxiliary variables in the form of a tuple.
Examples: (:surface, :surface, :subsurface).
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auxiliarydomainnames(m::AbstractCanopyComponent)
Returns the domain names for the auxiliary variables in the form of a tuple.
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ClimaLand.initialize_prognostic — Function
initialize_prognostic(model::AbstractModel, state::NamedTuple)
Returns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.
If a model has no prognostic variables, the returned FieldVector contains only an empty array.
The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.
Adjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.
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initialize_prognostic(
     component::AbstractCanopyComponent,
     state,
-)
Creates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component  component, stored using the name of the component.
The input state is usually a ClimaCore Field object.
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initialize_prognostic(
+)
Creates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component  component, stored using the name of the component.
The input state is usually a ClimaCore Field object.
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initialize_prognostic(
     model::CanopyModel{FT},
     coords,
-) where {FT}
Creates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.
The input state is usually a ClimaCore Field object.
This function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.
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ClimaLand.initialize_auxiliary — Function
initialize_auxiliary(model::AbstractModel, state::NamedTuple)
Returns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all  auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.
If a model has no auxiliary variables, the returned NamedTuple contains only an empty array.
The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.
Adjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.
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initialize_auxiliary(
+) where {FT}
Creates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.
The input state is usually a ClimaCore Field object.
This function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.
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ClimaLand.initialize_auxiliary — Function
initialize_auxiliary(model::AbstractModel, state::NamedTuple)
Returns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all  auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.
If a model has no auxiliary variables, the returned NamedTuple contains only an empty array.
The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.
Adjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.
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initialize_auxiliary(
     component::AbstractCanopyComponent,
     state,
-)
Creates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component  component, stored using the name of the component.
The input state is usually a ClimaCore Field object.
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initialize_auxiliary(
+)
Creates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component  component, stored using the name of the component.
The input state is usually a ClimaCore Field object.
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initialize_auxiliary(
     model::CanopyModel{FT},
     coords,
-) where {FT}
Creates the auxiliary state vector of the CanopyModel and returns  it as a ClimaCore.Fields.FieldVector.
The input coords is usually a ClimaCore Field object.
This function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.
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ClimaLand.initialize — Function
initialize(model::AbstractModel)
Creates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.
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ClimaLand.name — Function
name(model::AbstractModel)
Returns a symbol of the model component name, e.g. :soil or :vegetation.
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ClimaLand.AbstractBC — Type
AbstractBC
An abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other  convenient conditions.
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ClimaLand.source! — Function
 source!(dY::ClimaCore.Fields.FieldVector,
+) where {FT}
Creates the auxiliary state vector of the CanopyModel and returns  it as a ClimaCore.Fields.FieldVector.
The input coords is usually a ClimaCore Field object.
This function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.
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ClimaLand.initialize — Function
initialize(model::AbstractModel)
Creates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.
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ClimaLand.name — Function
name(model::AbstractModel)
Returns a symbol of the model component name, e.g. :soil or :vegetation.
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ClimaLand.AbstractBC — Type
AbstractBC
An abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other  convenient conditions.
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ClimaLand.source! — Function
 source!(dY::ClimaCore.Fields.FieldVector,
          src::PhaseChange{FT},
          Y::ClimaCore.Fields.FieldVector,
          p::NamedTuple,
          model
-         )
Computes the source terms for phase change.
source
 source!(dY::ClimaCore.Fields.FieldVector,
+         )
Computes the source terms for phase change.
source
 source!(dY::ClimaCore.Fields.FieldVector,
          src::SoilSublimation{FT},
          Y::ClimaCore.Fields.FieldVector,
          p::NamedTuple,
          model
-         )
Updates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.
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 source!(dY::ClimaCore.Fields.FieldVector,
+         )
Updates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.
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 source!(dY::ClimaCore.Fields.FieldVector,
          src::AbstractSource,
          Y::ClimaCore.Fields.FieldVector,
          p::NamedTuple
-         )::ClimaCore.Fields.Field
A stub function, which is extended by ClimaLand.
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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+         )::ClimaCore.Fields.Field
A stub function, which is extended by ClimaLand.
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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
                  src::RootExtraction,
                  Y::ClimaCore.Fields.FieldVector,
                  p::NamedTuple
-                 model::EnergyHydrology)
An extension of the ClimaLand.source! function,  which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.
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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                 model::EnergyHydrology)
An extension of the ClimaLand.source! function,  which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.
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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
                       src::MicrobeProduction,
                       Y::ClimaCore.Fields.FieldVector,
                       p::NamedTuple,
-                      params)
A method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.
source
ClimaLand.source!(
+                      params)
A method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.
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ClimaLand.source!(
     dY::ClimaCore.Fields.FieldVector,
     src::TOPMODELSubsurfaceRunoff,
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     model::AbstractSoilModel{FT},
-) where {FT}
Adjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.
The sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content,  and Rss is the runoff as a flux(m/s).
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ClimaLand.AbstractBoundary — Type
AbstractBoundary
An abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).
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ClimaLand.TopBoundary — Type
TopBoundary{} <: AbstractBoundary{}
A simple object which should be passed into a function to indicate that we are considering the top boundary.
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ClimaLand.BottomBoundary — Type
BottomBoundary{} <: AbstractBoundary{}
A simple object which should be passed into a function to indicate that we are considering the bottom boundary.
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ClimaLand.boundary_flux — Function
boundary_flux(bc::WaterFluxBC,  _...)::ClimaCore.Fields.Field
A method of boundary fluxes which returns the desired flux.
We add a field of zeros in order to convert the bc (float) into a field.
source
boundary_flux(bc::RichardsAtmosDrivenFluxBC,
+) where {FT}
Adjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.
The sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content,  and Rss is the runoff as a flux(m/s).
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ClimaLand.AbstractBoundary — Type
AbstractBoundary
An abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).
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ClimaLand.TopBoundary — Type
TopBoundary{} <: AbstractBoundary{}
A simple object which should be passed into a function to indicate that we are considering the top boundary.
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ClimaLand.BottomBoundary — Type
BottomBoundary{} <: AbstractBoundary{}
A simple object which should be passed into a function to indicate that we are considering the bottom boundary.
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ClimaLand.boundary_flux — Function
boundary_flux(bc::WaterFluxBC,  _...)::ClimaCore.Fields.Field
A method of boundary fluxes which returns the desired flux.
We add a field of zeros in order to convert the bc (float) into a field.
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boundary_flux(bc::RichardsAtmosDrivenFluxBC,
                        boundary::ClimaLand.AbstractBoundary,
                        model::RichardsModel{FT},
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field where {FT}
A method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.
If model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.
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boundary_flux(rre_bc::MoistureStateBC,
+                       )::ClimaCore.Fields.Field where {FT}
A method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.
If model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.
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boundary_flux(rre_bc::MoistureStateBC,
                        ::ClimaLand.TopBoundary,
                        model::AbstractSoilModel,
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.
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boundary_flux(rre_bc::MoistureStateBC,
+                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.
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boundary_flux(rre_bc::MoistureStateBC,
                        ::ClimaLand.BottomBoundary,
                        model::AbstractSoilModel,
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.
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boundary_flux(bc::FreeDrainage,
+                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.
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boundary_flux(bc::FreeDrainage,
                        boundary::ClimaLand.BottomBoundary,
                        model::AbstractSoilModel,
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field
A method of boundary fluxes which enforces free drainage at the bottom of the domain.
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boundary_flux(bc::HeatFluxBC,  _...)::ClimaCore.Fields.Field
A method of boundary fluxes which returns the desired flux.
We add a field of zeros in order to convert the bc (float) into a field.
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boundary_flux(heat_bc::TemperatureStateBC,
+                       )::ClimaCore.Fields.Field
A method of boundary fluxes which enforces free drainage at the bottom of the domain.
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boundary_flux(bc::HeatFluxBC,  _...)::ClimaCore.Fields.Field
A method of boundary fluxes which returns the desired flux.
We add a field of zeros in order to convert the bc (float) into a field.
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boundary_flux(heat_bc::TemperatureStateBC,
                        ::ClimaLand.TopBoundary,
                        model::EnergyHydrology,
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.
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boundary_flux(heat_bc::TemperatureStateBC,
+                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.
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boundary_flux(heat_bc::TemperatureStateBC,
                        ::ClimaLand.BottomBoundary,
                        model::EnergyHydrology,
                        Δz::ClimaCore.Fields.Field,
                        Y::ClimaCore.Fields.FieldVector,
                        p::NamedTuple,
                        t,
-                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.
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boundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field
A function which returns the correct boundary flux  given     any boundary condition (BC). 
source
function ClimaLand.boundary_flux(
+                       )::ClimaCore.Fields.Field
A method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.
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boundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field
A function which returns the correct boundary flux  given     any boundary condition (BC). 
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function ClimaLand.boundary_flux(
     bc::RunoffBC,
     ::TopBoundary,
     model::Soil.RichardsModel,
@@ -124,81 +124,81 @@
     p::NamedTuple,
     t,
     params,
-)::ClimaCore.Fields.Field
Extension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).
source
ClimaLand.boundary_flux(
+)::ClimaCore.Fields.Field
Extension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).
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ClimaLand.boundary_flux(
     bc::SoilCO2FluxBC,
     boundary::ClimaLand.AbstractBoundary,
     Δz::ClimaCore.Fields.Field,
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.
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ClimaLand.boundary_flux(
+)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.
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ClimaLand.boundary_flux(
 bc::SoilCO2StateBC,
 boundary::ClimaLand.TopBoundary,
 Δz::ClimaCore.Fields.Field,
 Y::ClimaCore.Fields.FieldVector,
 p::NamedTuple,
 t,
-)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at  top of the domain.
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ClimaLand.boundary_flux(
+)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at  top of the domain.
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ClimaLand.boundary_flux(
     bc::SoilCO2StateBC,
     boundary::ClimaLand.BottomBoundary,
     Δz::ClimaCore.Fields.Field,
     Y::ClimaCore.Fields.FieldVector,
     p::NamedTuple,
     t,
-)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.
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ClimaLand.boundary_flux(
+)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.
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ClimaLand.boundary_flux(
 bc::AtmosCO2StateBC,
 boundary::ClimaLand.TopBoundary,
 Δz::ClimaCore.Fields.Field,
 Y::ClimaCore.Fields.FieldVector,
 p::NamedTuple,
 t,
-)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.
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ClimaLand.diffusive_flux — Function
diffusive_flux(K, x_2, x_1, Δz)
Calculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.
source
ClimaLand.boundary_vars — Function
boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+)::ClimaCore.Fields.Field
A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.
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ClimaLand.diffusive_flux — Function
diffusive_flux(K, x_2, x_1, Δz)
Calculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.
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ClimaLand.boundary_vars — Function
boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
                                           <:Runoff.AbstractRunoffModel,
-                                          }, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.
These variables are updated in place in boundary_flux.
source
boundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                          }, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.
These variables are updated in place in boundary_flux.
source
boundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
                                 <:AbstractRadiativeDrivers,
                                 <:AbstractRunoffModel,
-                                }, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.
These variables are updated in place in soil_boundary_fluxes!.
source
boundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for MoistureStateBC at the top boundary.
These variables are updated in place in boundary_flux.
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boundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)
The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage for the top boundary flux fields, but which can be extended depending on the type of  boundary condition used.
For the Soil and SoilCO2 models - which solve PDEs - the  tendency functions and  updateboundaryfluxes functions are coded to access the field  :top_bc  to be present in the model cache, which is why  this is the default.  If this is not your (PDE) model's  desired behavior, you can extend this function with a new method.
Use this function in the exact same way you would use auxiliary_vars.
source
boundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)
The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
For the Soil and SoilCO2 models - which solve PDEs - the  tendency functions and  updateboundaryfluxes functions are coded to access the field  :bottom_bc  to be present in the model cache, which is why  this is the default.  If this is not your (PDE) model's  desired behavior, you can extend this function with a new method.
Use this function in the exact same way you would use auxiliary_vars.
source
ClimaLand.boundary_var_domain_names — Function
boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                }, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.
These variables are updated in place in soil_boundary_fluxes!.
source
boundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)
An extension of the boundary_vars method for MoistureStateBC at the top boundary.
These variables are updated in place in boundary_flux.
source
boundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)
The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage for the top boundary flux fields, but which can be extended depending on the type of  boundary condition used.
For the Soil and SoilCO2 models - which solve PDEs - the  tendency functions and  updateboundaryfluxes functions are coded to access the field  :top_bc  to be present in the model cache, which is why  this is the default.  If this is not your (PDE) model's  desired behavior, you can extend this function with a new method.
Use this function in the exact same way you would use auxiliary_vars.
source
boundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)
The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
For the Soil and SoilCO2 models - which solve PDEs - the  tendency functions and  updateboundaryfluxes functions are coded to access the field  :bottom_bc  to be present in the model cache, which is why  this is the default.  If this is not your (PDE) model's  desired behavior, you can extend this function with a new method.
Use this function in the exact same way you would use auxiliary_vars.
source
ClimaLand.boundary_var_domain_names — Function
boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
                                           <:Runoff.AbstractRunoffModel,
                                           },
-                          ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.
source
boundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                          ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.
source
boundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
                                               <:AbstractRadiativeDrivers,
                                               <:AbstractRunoffModel,
                                               },
-                          ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.
source
boundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.
source
boundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)
The list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage on the surface domain  for the top or bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. 
source
ClimaLand.boundary_var_types — Function
boundary_var_types(::RichardsModel{FT},
+                          ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.
source
boundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)
An extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.
source
boundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)
The list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding storage on the surface domain  for the top or bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. 
source
ClimaLand.boundary_var_types — Function
boundary_var_types(::RichardsModel{FT},
                     ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
                                                 <: Runoff.AbstractRunoffModel,
                                               },
                     ::ClimaLand.TopBoundary,
-                    ) where {FT}
An extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.
source
boundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}
The list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the  boundary flux field.
Because we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.
source
boundary_var_types(
+                    ) where {FT}
An extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.
source
boundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}
The list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the  boundary flux field.
Because we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.
source
boundary_var_types(
     ::EnergyHydrology{FT},
     ::AtmosDrivenFluxBC{
         <:PrescribedAtmosphere{FT},
         <:AbstractRadiativeDrivers{FT},
         <:AbstractRunoffModel,
     }, ::ClimaLand.TopBoundary,
-) where {FT}
An extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.
source
boundary_var_types(::RichardsModel{FT},
+) where {FT}
An extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.
source
boundary_var_types(::RichardsModel{FT},
                     ::MoistureStateBC,
                     ::ClimaLand.TopBoundary,
-                    ) where {FT}
An extension of the boundary_var_types method for MoistureStateBC at the     top boundary.
source
boundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}
The list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding a scalar variable on the surface domain  for the top or bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.
source
ClimaLand.make_jacobian — Function
make_jacobian(model::AbstractModel)
Creates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.
The default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.
Note that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.
source
ClimaLand.make_compute_jacobian — Function
ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}
Creates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.
Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).
source
ClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}
Creates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.
Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).
source
make_compute_jacobian(model::AbstractModel)
Creates and returns a function which computes the entries of the Jacobian matrix W in place.
If the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.
The default is that no updates are required, but this function must be extended for models that use implicit timestepping.
source
ClimaLand.set_dfluxBCdY! — Function
ClimaLand.set_dfluxBCdY!(
+                    ) where {FT}
An extension of the boundary_var_types method for MoistureStateBC at the     top boundary.
source
boundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}
The list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults  to adding a scalar variable on the surface domain  for the top or bottom boundary flux fields, but which can be extended depending on the type of  boundary condition used.
Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.
source
ClimaLand.make_jacobian — Function
make_jacobian(model::AbstractModel)
Creates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.
The default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.
Note that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.
source
ClimaLand.make_compute_jacobian — Function
ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}
Creates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.
Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).
source
ClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}
Creates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.
Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).
source
make_compute_jacobian(model::AbstractModel)
Creates and returns a function which computes the entries of the Jacobian matrix W in place.
If the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.
The default is that no updates are required, but this function must be extended for models that use implicit timestepping.
source
ClimaLand.set_dfluxBCdY! — Function
ClimaLand.set_dfluxBCdY!(
     model::RichardsModel,
     ::MoistureStateBC,
     boundary::ClimaLand.TopBoundary,
     Δz,
     Y,
     p,
-    t,
)
Computes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.
For Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.
source
set_dfluxBCdY!(::AbstractModel,
+    t,
)
Computes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.
For Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.
source
set_dfluxBCdY!(::AbstractModel,
               ::AbstractBC,
               ::AbstractBoundary,
-              _...)::Union{ClimaCore.Fields.FieldVector, Nothing}
A function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.
source
ClimaLand.get_drivers — Function
ClimaLand.get_drivers(model::RichardsModel)
Returns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.
source
ClimaLand.get_drivers(model::SnowModel)
Returns the driver variable symbols for the SnowModel.
source
get_drivers(model::AbstractModel)
Returns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned
source
Drivers
ClimaLand.PrescribedAtmosphere — Type
PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}
Container for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.
The default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.
Since not all models require co2 concentration, the default for that is nothing.
  • liquid_precip: Precipitation (m/s) function of time: positive by definition
  • snow_precip: Snow precipitation (m/s) function of time: positive by definition
  • T: Prescribed atmospheric temperature (function of time)  at the reference height (K)
  • u: Prescribed wind speed (function of time)  at the reference height (m/s)
  • q: Prescribed specific humidity (function of time)  at the reference height (_)
  • P: Prescribed air pressure (function of time)  at the reference height (Pa)
  • c_co2: CO2 concentration in atmosphere (mol/mol)
  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation
  • h: Reference height (m), relative to surface elevation
  • gustiness: Minimum wind speed (gustiness; m/s)
  • thermo_params: Thermodynamic parameters
source
ClimaLand.PrescribedPrecipitation — Type
PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}
Container for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).
  • liquid_precip: Precipitation (m/s) function of time: positive by definition
source
ClimaLand.PrescribedRadiativeFluxes — Type
PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}
Container for the prescribed radiation functions needed to drive land models in standalone mode.
  • SW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface
  • LW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface
  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation
  • θs: Sun zenith angle, in radians
source
ClimaLand.turbulent_fluxes — Function
turbulent_fluxes(atmos::PrescribedAtmosphere,
+              _...)::Union{ClimaCore.Fields.FieldVector, Nothing}
A function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.
source
ClimaLand.get_drivers — Function
ClimaLand.get_drivers(model::RichardsModel)
Returns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.
source
ClimaLand.get_drivers(model::SnowModel)
Returns the driver variable symbols for the SnowModel.
source
get_drivers(model::AbstractModel)
Returns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned
source
Drivers
ClimaLand.PrescribedAtmosphere — Type
PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}
Container for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.
The default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.
Since not all models require co2 concentration, the default for that is nothing.
  • liquid_precip: Precipitation (m/s) function of time: positive by definition
  • snow_precip: Snow precipitation (m/s) function of time: positive by definition
  • T: Prescribed atmospheric temperature (function of time)  at the reference height (K)
  • u: Prescribed wind speed (function of time)  at the reference height (m/s)
  • q: Prescribed specific humidity (function of time)  at the reference height (_)
  • P: Prescribed air pressure (function of time)  at the reference height (Pa)
  • c_co2: CO2 concentration in atmosphere (mol/mol)
  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation
  • h: Reference height (m), relative to surface elevation
  • gustiness: Minimum wind speed (gustiness; m/s)
  • thermo_params: Thermodynamic parameters
source
ClimaLand.PrescribedPrecipitation — Type
PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}
Container for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).
  • liquid_precip: Precipitation (m/s) function of time: positive by definition
source
ClimaLand.PrescribedRadiativeFluxes — Type
PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}
Container for the prescribed radiation functions needed to drive land models in standalone mode.
  • SW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface
  • LW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface
  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation
  • θs: Sun zenith angle, in radians
source
ClimaLand.turbulent_fluxes — Function
turbulent_fluxes(atmos::PrescribedAtmosphere,
                model::AbstractModel,
                Y::ClimaCore.Fields.FieldVector,
                p::NamedTuple,
                t
-               )
Computes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.
It solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.
source
turbulent_fluxes(atmos::CoupledAtmosphere,
+               )
Computes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.
It solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.
source
turbulent_fluxes(atmos::CoupledAtmosphere,
                 model::AbstractModel,
                 Y,
                 p,
-                t)
Computes the turbulent surface fluxes terms at the ground for a coupled simulation.
source
ClimaLand.turbulent_fluxes_at_a_point — Function
turbulent_fluxes_at_a_point(T_sfc::FT,
                             q_sfc::FT,
                             ρ_sfc::FT,
                             β_sfc::FT,
@@ -212,38 +212,38 @@
                             z_0m::FT,
                             z_0b::FT,
                             earth_param_set::EP,
-                           ) where {FT <: AbstractFloat, P}
Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc),     and air density (ρsfc), (2) Other surface properties, such as the factor     βsfc  which scales the evaporation from the potential rate     (used in bucket models), and the surface resistance rsfc (used     in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model     earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height     at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc
This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.
source
Missing docstring.
Missing docstring for ClimaLand.radiative_fluxes_at_a_point. Check Documenter's build log for details.
ClimaLand.set_atmos_ts! — Function
set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)
Fill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.
source
ClimaLand.surface_air_density — Function
surface_air_density(
+                           ) where {FT <: AbstractFloat, P}
Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc),     and air density (ρsfc), (2) Other surface properties, such as the factor     βsfc  which scales the evaporation from the potential rate     (used in bucket models), and the surface resistance rsfc (used     in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model     earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height     at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc
This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.
source
Missing docstring.
Missing docstring for ClimaLand.radiative_fluxes_at_a_point. Check Documenter's build log for details.
ClimaLand.set_atmos_ts! — Function
set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)
Fill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.
source
ClimaLand.surface_air_density — Function
surface_air_density(
                     atmos::PrescribedAtmosphere,
                     model::AbstractModel,
                     Y,
                     p,
                     t,
                     T_sfc,
-                    )
A helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.
We additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.
Extending this function for your model is only necessary if you need to compute the air density in a different way.
source
ClimaLand.surface_air_density(
+                    )
A helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.
We additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.
Extending this function for your model is only necessary if you need to compute the air density in a different way.
source
ClimaLand.surface_air_density(
                 atmos::CoupledAtmosphere,
                 model::AbstractModel,
                 Y,
                 p,
                 _...,
-            )
Returns the air density at the surface in the case of a coupled simulation.
This requires the field ρ_sfc to be present in the cache p under the name of the model.
source
ClimaLand.surface_temperature — Function
ClimaLand.surface_temperature(
+            )
Returns the air density at the surface in the case of a coupled simulation.
This requires the field ρ_sfc to be present in the cache p under the name of the model.
source
ClimaLand.surface_temperature — Function
ClimaLand.surface_temperature(
     model::EnergyHydrology{FT},
     Y,
     p,
     t,
-) where {FT}
Returns the surface temperature field of the EnergyHydrology soil model.
The assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.
source
ClimaLand.surface_temperature(model::SnowModel, Y, p)
a helper function which returns the surface temperature for the snow  model, which is stored in the aux state.
source
ClimaLand.surface_temperature(model::BucketModel, Y, p)
a helper function which returns the surface temperature for the bucket model, which is stored in the aux state.
source
surface_temperature(model::AbstractModel, Y, p, t)
A helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.
Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.
source
ClimaLand.surface_temperature(model::CanopyModel, Y, p, t)
A helper function which returns the temperature for the canopy model.
source
ClimaLand.surface_resistance — Function
ClimaLand.surface_resistance(
+) where {FT}
Returns the surface temperature field of the EnergyHydrology soil model.
The assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.
source
ClimaLand.surface_temperature(model::SnowModel, Y, p)
a helper function which returns the surface temperature for the snow  model, which is stored in the aux state.
source
ClimaLand.surface_temperature(model::BucketModel, Y, p)
a helper function which returns the surface temperature for the bucket model, which is stored in the aux state.
source
surface_temperature(model::AbstractModel, Y, p, t)
A helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.
Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.
source
ClimaLand.surface_temperature(model::CanopyModel, Y, p, t)
A helper function which returns the temperature for the canopy model.
source
ClimaLand.surface_resistance — Function
ClimaLand.surface_resistance(
     model::EnergyHydrology{FT},
     Y,
     p,
     t,
-) where {FT}
Returns the surface resistance field of the EnergyHydrology soil model.
source
surface_resistance(model::AbstractModel, Y, p, t)
A helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.
Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.
The default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.
source
ClimaLand.surface_resistance(
+) where {FT}
Returns the surface resistance field of the EnergyHydrology soil model.
source
surface_resistance(model::AbstractModel, Y, p, t)
A helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.
Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.
The default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.
source
ClimaLand.surface_resistance(
     model::CanopyModel{FT},
     Y,
     p,
     t,
-) where {FT}
Returns the stomatal resistance field of the CanopyModel canopy.
source
ClimaLand.surface_specific_humidity — Function
ClimaLand.surface_specific_humidity(
     model::EnergyHydrology{FT},
     Y,
     p,
     T_sfc,
     ρ_sfc
-) where {FT}
Returns the surface specific humidity field of the EnergyHydrology soil model.
This models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.
Over the soil ice, the specific humidity is the saturated value.
The total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.
source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)
Computes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.
source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)
a helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.
source
surface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)
A helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.
Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.
source
ClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)
A helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.
source
Missing docstring.
Missing docstring for ClimaLand.make_update_drivers. Check Documenter's build log for details.

+) where {FT}

Returns the surface specific humidity field of the EnergyHydrology soil model.

This models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.

Over the soil ice, the specific humidity is the saturated value.

The total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

Computes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

a helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.

source
surface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)

A helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)

A helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.

source
Missing docstring.

Missing docstring for ClimaLand.make_update_drivers. Check Documenter's build log for details.

diff --git a/dev/Contributing/index.html b/dev/Contributing/index.html index 82849dfe42..cda687341b 100644 --- a/dev/Contributing/index.html +++ b/dev/Contributing/index.html @@ -1,2 +1,2 @@ -Contribution guide · ClimaLand.jl
GitHub

Contributing

Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.

Some useful tips

  • When you start working on a new feature branch, make sure you start from main by running: git checkout main.
  • Make sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.
  • When your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.

Continuous integration

After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.

Automated testing

Currently a number of checks are run per commit for a given PR.

  • JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.
  • Documentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.
  • Tests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.
+Contribution guide · ClimaLand.jl
GitHub

Contributing

Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.

Some useful tips

  • When you start working on a new feature branch, make sure you start from main by running: git checkout main.
  • Make sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.
  • When your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.

Continuous integration

After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.

Automated testing

Currently a number of checks are run per commit for a given PR.

  • JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.
  • Documentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.
  • Tests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.
diff --git a/dev/diagnostics/available_diagnostics/index.html b/dev/diagnostics/available_diagnostics/index.html index 48db2510b2..c627e7fbde 100644 --- a/dev/diagnostics/available_diagnostics/index.html +++ b/dev/diagnostics/available_diagnostics/index.html @@ -278,4 +278,4 @@ ├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤ │ wsoil │ subsurface │ m │ Soil water content. │ subsurface_wate │ │ │ Water Storage │ │ │ r_storage │ -└────────────┴─────────────────┴──────────┴──────────────────────────────────┴─────────────────┘ +└────────────┴─────────────────┴──────────┴──────────────────────────────────┴─────────────────┘ diff --git a/dev/diagnostics/developers_diagnostics/index.html b/dev/diagnostics/developers_diagnostics/index.html index a5265f8e42..4786295cf3 100644 --- a/dev/diagnostics/developers_diagnostics/index.html +++ b/dev/diagnostics/developers_diagnostics/index.html @@ -41,4 +41,4 @@ t_start, short_names...; pre_output_hook! = average_pre_output_hook!, -)

will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.

+)

will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.

diff --git a/dev/diagnostics/users_diagnostics/index.html b/dev/diagnostics/users_diagnostics/index.html index fe37d005a8..9f8bef01cc 100644 --- a/dev/diagnostics/users_diagnostics/index.html +++ b/dev/diagnostics/users_diagnostics/index.html @@ -55,4 +55,4 @@ viz.plot!(fig, var) # creates an axis inside fig, and plot your var in it. -CairoMakie.save(fig) # saves the figure in current working directory +CairoMakie.save(fig) # saves the figure in current working directory diff --git a/dev/folderstructure/index.html b/dev/folderstructure/index.html index 74b1c06b3d..0edcf32bd5 100644 --- a/dev/folderstructure/index.html +++ b/dev/folderstructure/index.html @@ -1,2 +1,2 @@ -Repository structure · ClimaLand.jl
GitHub

ClimaLand folder structure

ClimaLand home directory has 5 main folders:

  • docs: contains files to generate the documentation website.
  • experiments: contains simple runs of ClimaLand models.
  • parameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc.
  • src: contains the code of ClimaLand models.
  • test: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.

and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push.

  • .buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR.
  • .dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main).
  • .github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand.

as well as 5 files:

  • .gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git.
  • LICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code.
  • Project.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file.
  • README.md: This markdown file contains the info that you can read on ClimaLand GitHub web page

/docs folder

Julia packages are recommended to have a \docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.

The folder /docs contains:

  • a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation "contribution guide" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file.
  • a make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch.

Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl.

/experiments folder

The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified.

For example, /experiments/LSM/ozark/ contains:

  • ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).
  • ozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.
  • ozark_parameters.jl: In this file, parameters values are defined.
  • ozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm.
  • ozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.

/src folder

The /src folder contains the source code of ClimaLand models. It contains 3 folders:

  • sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.
  • standalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder.
  • integrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.

As well as one file:

  • ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase.
+Repository structure · ClimaLand.jl
GitHub

ClimaLand folder structure

ClimaLand home directory has 5 main folders:

  • docs: contains files to generate the documentation website.
  • experiments: contains simple runs of ClimaLand models.
  • parameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc.
  • src: contains the code of ClimaLand models.
  • test: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.

and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push.

  • .buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR.
  • .dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main).
  • .github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand.

as well as 5 files:

  • .gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git.
  • LICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code.
  • Project.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file.
  • README.md: This markdown file contains the info that you can read on ClimaLand GitHub web page

/docs folder

Julia packages are recommended to have a \docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.

The folder /docs contains:

  • a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation "contribution guide" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file.
  • a make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch.

Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl.

/experiments folder

The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified.

For example, /experiments/LSM/ozark/ contains:

  • ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).
  • ozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.
  • ozark_parameters.jl: In this file, parameters values are defined.
  • ozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm.
  • ozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.

/src folder

The /src folder contains the source code of ClimaLand models. It contains 3 folders:

  • sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.
  • standalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder.
  • integrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.

As well as one file:

  • ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase.
diff --git a/dev/generated/integrated/soil_canopy_tutorial/index.html b/dev/generated/integrated/soil_canopy_tutorial/index.html index e526ec1aaa..ef152f6b48 100644 --- a/dev/generated/integrated/soil_canopy_tutorial/index.html +++ b/dev/generated/integrated/soil_canopy_tutorial/index.html @@ -335,4 +335,4 @@ xlim = [minimum(daily), maximum(daily)], size = (500, 700), margins = 10Plots.mm, -);

And save the output

savefig("ozark_soil_plant_flux.png");

This page was generated using Literate.jl.

+);

And save the output

savefig("ozark_soil_plant_flux.png");

This page was generated using Literate.jl.

diff --git a/dev/generated/shared_utilities/driver_tutorial/index.html b/dev/generated/shared_utilities/driver_tutorial/index.html index 170a11cd1b..372ba2e28b 100644 --- a/dev/generated/shared_utilities/driver_tutorial/index.html +++ b/dev/generated/shared_utilities/driver_tutorial/index.html @@ -46,4 +46,4 @@ @show(p.drivers);
p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])

During a simulation, the drivers are updated in place in p.drivers via a "callback", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:

updateat = collect(seconds[1]:(3600 * 3):seconds[end]);
 updatefunc = update_radiation!;
-cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);

This callback must then be provided to the simulation solve function.

This page was generated using Literate.jl.

+cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);

This callback must then be provided to the simulation solve function.

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Bucket/bucket_tutorial/index.html b/dev/generated/standalone/Bucket/bucket_tutorial/index.html index 171ef0b787..a0cb9243e9 100644 --- a/dev/generated/standalone/Bucket/bucket_tutorial/index.html +++ b/dev/generated/standalone/Bucket/bucket_tutorial/index.html @@ -142,4 +142,4 @@ ) plot!(sol.t ./ 86400, turbulent_energy_flux, label = "Turbulent fluxes") plot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = "Net flux") -savefig("energy_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png"

References

[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744

This page was generated using Literate.jl.

+savefig("energy_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png"

References

[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Bucket/coupled_bucket/index.html b/dev/generated/standalone/Bucket/coupled_bucket/index.html index 2aeb20e4c5..ab738a03f3 100644 --- a/dev/generated/standalone/Bucket/coupled_bucket/index.html +++ b/dev/generated/standalone/Bucket/coupled_bucket/index.html @@ -1,2 +1,2 @@ -Setting up a Coupled Simulation · ClimaLand.jl
GitHub

Setting up a Coupled Simulation

For more information about the bucket model, please see the bucket model tutorial.

This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.

Background

Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.

In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.

In our current setup, "passed back to the land model via the coupler" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.

In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.

Turbulent Surface Fluxes and Radiation

Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:

prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)

These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.

In the coupled case, we want different behavior. We have defined new $coupled$ types to use instead of the "prescribed" types:

struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end

Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end

similarily:

function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end

These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!

Surface air density

Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.

In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end

Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.

This page was generated using Literate.jl.

+Setting up a Coupled Simulation · ClimaLand.jl
GitHub

Setting up a Coupled Simulation

For more information about the bucket model, please see the bucket model tutorial.

This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.

Background

Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.

In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.

In our current setup, "passed back to the land model via the coupler" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.

In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.

Turbulent Surface Fluxes and Radiation

Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:

prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)

These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.

In the coupled case, we want different behavior. We have defined new $coupled$ types to use instead of the "prescribed" types:

struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end

Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end

similarily:

function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end

These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!

Surface air density

Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.

In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end

Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Canopy/canopy_tutorial/index.html b/dev/generated/standalone/Canopy/canopy_tutorial/index.html index 1f2bbbc188..195c9f7a70 100644 --- a/dev/generated/standalone/Canopy/canopy_tutorial/index.html +++ b/dev/generated/standalone/Canopy/canopy_tutorial/index.html @@ -199,4 +199,4 @@ xlim = [minimum(daily), maximum(daily)], xlabel = "days", ylabel = "Vapor Flux [mm/day]", -);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_standalone_canopy_test.png");

This page was generated using Literate.jl.

+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_standalone_canopy_test.png");

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Snow/base_tutorial/index.html b/dev/generated/standalone/Snow/base_tutorial/index.html index 8a9a3fbe4e..3e4c3ccdde 100644 --- a/dev/generated/standalone/Snow/base_tutorial/index.html +++ b/dev/generated/standalone/Snow/base_tutorial/index.html @@ -32,16 +32,16 @@ ModelTools.setoutscale!(model, 1.0);

With that, training is as simple as calling the trainmodel! function:

print("\nTraining model!\n")
 ModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);

 Training model!
-Epoch: 10 | training loss: 0.0027556566
-Epoch: 20 | training loss: 0.0026529313
-Epoch: 30 | training loss: 0.002609448
-Epoch: 40 | training loss: 0.002617809
-Epoch: 50 | training loss: 0.0025923715
-Epoch: 60 | training loss: 0.002604384
-Epoch: 70 | training loss: 0.0025663753
-Epoch: 80 | training loss: 0.002538259
-Epoch: 90 | training loss: 0.0025648254
-Epoch: 100 | training loss: 0.002541011
+Epoch: 10 | training loss: 0.0027635782
+Epoch: 20 | training loss: 0.0027531595
+Epoch: 30 | training loss: 0.002643681
+Epoch: 40 | training loss: 0.0026667549
+Epoch: 50 | training loss: 0.0027028923
+Epoch: 60 | training loss: 0.0026131957
+Epoch: 70 | training loss: 0.0026789021
+Epoch: 80 | training loss: 0.002583873
+Epoch: 90 | training loss: 0.0026229469
+Epoch: 100 | training loss: 0.0027189315

To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.

ModelTools.setoutscale!(model, out_scale)
 ModelTools.settimescale!(model, Dates.value(Δt));

For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):

Note that gaps in the data are shown as shaded regions on the plotted timeseries.

site_id = 1286
 sitedata = usedata[usedata[!, :id] .== site_id, :]
@@ -71,4 +71,4 @@
     [:black, :red],
     savename = "base_tutorial_plot2.png",
     display_plot = false,
-);

Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.

This page was generated using Literate.jl.

+);

Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Snow/base_tutorial_plot1.png b/dev/generated/standalone/Snow/base_tutorial_plot1.png index 4204457653..bc422edbb7 100644 Binary files a/dev/generated/standalone/Snow/base_tutorial_plot1.png and b/dev/generated/standalone/Snow/base_tutorial_plot1.png differ diff --git a/dev/generated/standalone/Snow/base_tutorial_plot2.png b/dev/generated/standalone/Snow/base_tutorial_plot2.png index 0dc2448555..4f24173bab 100644 Binary files a/dev/generated/standalone/Snow/base_tutorial_plot2.png and b/dev/generated/standalone/Snow/base_tutorial_plot2.png differ diff --git a/dev/generated/standalone/Snow/data_tutorial/index.html b/dev/generated/standalone/Snow/data_tutorial/index.html index 73f1eeb4c8..c971f80930 100644 --- a/dev/generated/standalone/Snow/data_tutorial/index.html +++ b/dev/generated/standalone/Snow/data_tutorial/index.html @@ -129,4 +129,4 @@ end;

With the sites complete, we condense all sites into a single DataFrame,

totaldata = deepcopy(allsites[1])
 for site in allsites[2:end]
     append!(totaldata, site)
-end

and a final CSV.write("data.csv", totaldata) call will save the file.

Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.

This page was generated using Literate.jl.

+end

and a final CSV.write("data.csv", totaldata) call will save the file.

Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/boundary_conditions/index.html b/dev/generated/standalone/Soil/boundary_conditions/index.html index c6967bcab8..9da4cbcf85 100644 --- a/dev/generated/standalone/Soil/boundary_conditions/index.html +++ b/dev/generated/standalone/Soil/boundary_conditions/index.html @@ -1,2 +1,2 @@ -Boundary conditions · ClimaLand.jl
GitHub

Boundary conditions for the soil model

In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.

Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.

Boundary conditions for Richards equation

  1. FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.

  2. WaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).

  3. MoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).

  4. RichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.

Boundary conditions for the soil heat equation

  1. HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).

  2. TemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).

Boundary conditions for the soil heat + water equations (EnergyHydrology model)

The full soil model requires boundary conditions for both Richards equation and the soil heat equation.

  1. WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).

  2. AtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.

This page was generated using Literate.jl.

+Boundary conditions · ClimaLand.jl
GitHub

Boundary conditions for the soil model

In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.

Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.

Boundary conditions for Richards equation

  1. FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.

  2. WaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).

  3. MoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).

  4. RichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.

Boundary conditions for the soil heat equation

  1. HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).

  2. TemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).

Boundary conditions for the soil heat + water equations (EnergyHydrology model)

The full soil model requires boundary conditions for both Richards equation and the soil heat equation.

  1. WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).

  2. AtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/evaporation/index.html b/dev/generated/standalone/Soil/evaporation/index.html index 1b84ffc665..36ec3a93ae 100644 --- a/dev/generated/standalone/Soil/evaporation/index.html +++ b/dev/generated/standalone/Soil/evaporation/index.html @@ -226,4 +226,4 @@ label = "Model", color = :black, ) -save("evaporation_lehmann2008_fig8b.png", fig);

This page was generated using Literate.jl.

+save("evaporation_lehmann2008_fig8b.png", fig);

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/evaporation_gilat_loess/index.html b/dev/generated/standalone/Soil/evaporation_gilat_loess/index.html index f4ae222400..ca79ab5458 100644 --- a/dev/generated/standalone/Soil/evaporation_gilat_loess/index.html +++ b/dev/generated/standalone/Soil/evaporation_gilat_loess/index.html @@ -371,4 +371,4 @@ CairoMakie.axislegend(ax3, position = :lt) CairoMakie.axislegend(ax2, position = :lt) CairoMakie.axislegend(ax1, position = :lt) -save("evaporation_gardner_fig1.png", fig2);

This page was generated using Literate.jl.

+save("evaporation_gardner_fig1.png", fig2);

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/freezing_front/index.html b/dev/generated/standalone/Soil/freezing_front/index.html index 2181f7c4bd..c1667bf535 100644 --- a/dev/generated/standalone/Soil/freezing_front/index.html +++ b/dev/generated/standalone/Soil/freezing_front/index.html @@ -174,4 +174,4 @@ ) axislegend(ax3, position = :rb) -save("mizoguchi_data_comparison.png", fig);

Discussion and Model Explanation

To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies

$ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0$

Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing ($T < T_f$)

$\frac{dp_l}{ρ_l} = L_f \frac{dT}{T},$

or

$p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)$

where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).

What this implies is that above the freezing point, the pressure is equal to $p_{l,0}$, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure $p_{l,0}$ is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):

$p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)$

where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for

$θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.$

For freezing, the freeze thaw function F_T is equal to

$F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)$

which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).

Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).

This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time

$τ_{LTE}= c̃ Δz²/κ,$

which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).

References

This page was generated using Literate.jl.

+save("mizoguchi_data_comparison.png", fig);

Discussion and Model Explanation

To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies

$ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0$

Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing ($T < T_f$)

$\frac{dp_l}{ρ_l} = L_f \frac{dT}{T},$

or

$p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)$

where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).

What this implies is that above the freezing point, the pressure is equal to $p_{l,0}$, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure $p_{l,0}$ is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):

$p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)$

where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for

$θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.$

For freezing, the freeze thaw function F_T is equal to

$F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)$

which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).

Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).

This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time

$τ_{LTE}= c̃ Δz²/κ,$

which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).

References

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/layered_soil/index.html b/dev/generated/standalone/Soil/layered_soil/index.html index c4fc782394..98b9757049 100644 --- a/dev/generated/standalone/Soil/layered_soil/index.html +++ b/dev/generated/standalone/Soil/layered_soil/index.html @@ -102,4 +102,4 @@ plot!(xlabel = "Volumeteric Water Content") -savefig("./sv62_alpha_2_inf_updated_data_climaland.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png"

This page was generated using Literate.jl.

+savefig("./sv62_alpha_2_inf_updated_data_climaland.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png"

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/richards_equation/index.html b/dev/generated/standalone/Soil/richards_equation/index.html index 938d16fd08..a90f8e41c4 100644 --- a/dev/generated/standalone/Soil/richards_equation/index.html +++ b/dev/generated/standalone/Soil/richards_equation/index.html @@ -87,4 +87,4 @@ end plot!(hydrostatic_equilibrium.(z, -0.56), z, label = "equilibrium solution"); -plot!(1e-3 .+ ϑ_l[1], z, label = "porosity");

Save the output:

savefig("equilibrium_test_ϑ_l.png");

References

This page was generated using Literate.jl.

+plot!(1e-3 .+ ϑ_l[1], z, label = "porosity");

Save the output:

savefig("equilibrium_test_ϑ_l.png");

References

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/soil_energy_hydrology/index.html b/dev/generated/standalone/Soil/soil_energy_hydrology/index.html index be8e9dff2b..9f4a99b04d 100644 --- a/dev/generated/standalone/Soil/soil_energy_hydrology/index.html +++ b/dev/generated/standalone/Soil/soil_energy_hydrology/index.html @@ -122,4 +122,4 @@ savefig("eq_moisture_plot.png");

plot(T[1], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 0d")
 plot!(T[4], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 1.5d")
 plot!(T[end], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 3d")
-savefig("eq_temperature_plot.png");

Analytic Expectations

We can determine a priori what we expect the final temperature to be in equilibrium.

Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).

Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.

One could also solve the equation for ϑ_l specified by $∂ h/∂ z = 0$ to determine the functional form of the equilibrium profile of the liquid water.

References

This page was generated using Literate.jl.

+savefig("eq_temperature_plot.png");

Analytic Expectations

We can determine a priori what we expect the final temperature to be in equilibrium.

Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).

Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.

One could also solve the equation for ϑ_l specified by $∂ h/∂ z = 0$ to determine the functional form of the equilibrium profile of the liquid water.

References

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Soil/sublimation/index.html b/dev/generated/standalone/Soil/sublimation/index.html index dc83e0e74e..83145183b4 100644 --- a/dev/generated/standalone/Soil/sublimation/index.html +++ b/dev/generated/standalone/Soil/sublimation/index.html @@ -252,4 +252,4 @@ CairoMakie.axislegend(ax3, position = :lt) CairoMakie.axislegend(ax2, position = :lt) CairoMakie.axislegend(ax1, position = :lt) -save("profiles.png", fig2);

This page was generated using Literate.jl.

+save("profiles.png", fig2);

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Usage/LSM_single_column_tutorial/index.html b/dev/generated/standalone/Usage/LSM_single_column_tutorial/index.html index 1ddefe2db4..504d581be0 100644 --- a/dev/generated/standalone/Usage/LSM_single_column_tutorial/index.html +++ b/dev/generated/standalone/Usage/LSM_single_column_tutorial/index.html @@ -79,4 +79,4 @@ compute_exp_tendency!(dY, Y, p, t) end

In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.

A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:

Y, p, coords = initialize(land);

We have volumetric liquid water fraction:

propertynames(Y.soil)
(:ϑ_l,)

and surface height of the pond:

propertynames(Y.surface_water)
(:η,)

as well as auxiliary variables for the soil:

propertynames(p.soil)
(:K, :ψ, :top_bc, :bottom_bc)

and the runoff for surface water:

propertynames(p.surface_water)
(:runoff,)

and the additional variable required in the LSM is stored here as well:

propertynames(p)
(:soil_infiltration, :soil, :surface_water)

and finally, coordinates - useful for visualization of solutions:

coords.subsurface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
   z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]

and the coordinates of the surface variables:

coords.surface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
-  z: Float32[0.0]

And we can make the tendency function as before:

land_ode! = make_exp_tendency(land);

Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.

Advantages and disadvantages

Some advantages to our interface design are as follows:

Possible disadvantages to our interface design:

This page was generated using Literate.jl.

+ z: Float32[0.0]

And we can make the tendency function as before:

land_ode! = make_exp_tendency(land);

Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.

Advantages and disadvantages

Some advantages to our interface design are as follows:

Possible disadvantages to our interface design:

This page was generated using Literate.jl.

diff --git a/dev/generated/standalone/Usage/domain_tutorial/index.html b/dev/generated/standalone/Usage/domain_tutorial/index.html index ec1d8710a9..196fec1d38 100644 --- a/dev/generated/standalone/Usage/domain_tutorial/index.html +++ b/dev/generated/standalone/Usage/domain_tutorial/index.html @@ -1,2 +1,2 @@ -Intro to ClimaLand Domains · ClimaLand.jl
GitHub

Domain Tutorial

Goals of the tutorial

The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.

Background

In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question "only" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).

For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.

In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.

ClimaLand Domains were designed with this in mind. The domains are defined so that

  1. the user can easily switch geometries, e.g. single column to global model,
  2. individual component models can be run by themselves, using a single domain,
  3. the same domains can be used to set up multi-component models (LSMs),
  4. different variables can exist on different parts of the domain.

What is a ClimaLand Domain?

A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.

Domain types

All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:

As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.

DomainSurface DomainSubsurface Domain
ColumnPointColumn
HybridBoxPlaneHybridBox
SphericalShellSphericalSurfaceSphericalShell

There is a single key method which take a ClimaLand domain as an argument.

the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.

It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.

How variable initialization depends on domains

Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.

When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.

They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.

The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].

How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.

This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.

Future work

Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into "vertical" and "horizontal" pieces.

We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.

This page was generated using Literate.jl.

  • 1finite differencing is used in the vertical, and spectral elements are used in the horizontal.
  • 2a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.
  • 3We also will support having an array-like type of variable.
+Intro to ClimaLand Domains · ClimaLand.jl
GitHub

Domain Tutorial

Goals of the tutorial

The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.

Background

In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question "only" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).

For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.

In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.

ClimaLand Domains were designed with this in mind. The domains are defined so that

  1. the user can easily switch geometries, e.g. single column to global model,
  2. individual component models can be run by themselves, using a single domain,
  3. the same domains can be used to set up multi-component models (LSMs),
  4. different variables can exist on different parts of the domain.

What is a ClimaLand Domain?

A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.

Domain types

All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:

As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.

DomainSurface DomainSubsurface Domain
ColumnPointColumn
HybridBoxPlaneHybridBox
SphericalShellSphericalSurfaceSphericalShell

There is a single key method which take a ClimaLand domain as an argument.

the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.

It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.

How variable initialization depends on domains

Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.

When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.

They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.

The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].

How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.

This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.

Future work

Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into "vertical" and "horizontal" pieces.

We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.

This page was generated using Literate.jl.

  • 1finite differencing is used in the vertical, and spectral elements are used in the horizontal.
  • 2a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.
  • 3We also will support having an array-like type of variable.
diff --git a/dev/generated/standalone/Usage/model_tutorial/index.html b/dev/generated/standalone/Usage/model_tutorial/index.html index 0b87f9c907..458823fb71 100644 --- a/dev/generated/standalone/Usage/model_tutorial/index.html +++ b/dev/generated/standalone/Usage/model_tutorial/index.html @@ -130,4 +130,4 @@ Y, (t0, tf), p, -);

Solve command:

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);

The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.

This page was generated using Literate.jl.

+);

Solve command:

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);

The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.

This page was generated using Literate.jl.

diff --git a/dev/getting_started/index.html b/dev/getting_started/index.html index a657770b3c..09a3f76d73 100644 --- a/dev/getting_started/index.html +++ b/dev/getting_started/index.html @@ -29,4 +29,4 @@ • co2_diffusivity • MicrobeProduction • SoilCO2StateBC • microbe_source • PrescribedMet • SoilDrivers • volumetric_air_content • SoilCO2FluxBC -• AbstractSoilDriver • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

+• AbstractSoilDriver • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

diff --git a/dev/index.html b/dev/index.html index 279d81bccd..6b5b2ff060 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · ClimaLand.jl
GitHub

ClimaLand.jl Documentation (v0.14.3)

Introduction

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
+Home · ClimaLand.jl
GitHub

ClimaLand.jl Documentation (v0.14.3)

Introduction

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
diff --git a/dev/search_index.js b/dev/search_index.js index 71f6b19454..fffd7c4333 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"APIs/canopy/RadiativeTransfer/#Radiative-Transfer","page":"Canopy RT","title":"Radiative Transfer","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#Parameters","page":"Canopy RT","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.BeerLambertParameters","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.BeerLambertParameters","page":"Canopy RT","title":"ClimaLand.Canopy.BeerLambertParameters","text":"BeerLambertParameters{FT <: AbstractFloat}\n\nThe required parameters for the Beer-Lambert radiative transfer model.\n\nα_PAR_leaf: PAR leaf reflectance (unitless)\nα_NIR_leaf: NIR leaf reflectance\nϵ_canopy: Emissivity of the canopy\nΩ: Clumping index following Braghiere (2021) (unitless)\nλ_γ_PAR: Typical wavelength per PAR photon (m)\nλ_γ_NIR: Typical wavelength per NIR photon (m)\nG_Function: Leaf angle distribution function\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/RadiativeTransfer/#Methods","page":"Canopy RT","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.compute_absorbances\nClimaLand.Canopy.plant_absorbed_pfd\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.canopy_radiant_energy_fluxes!","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.plant_absorbed_pfd","page":"Canopy RT","title":"ClimaLand.Canopy.plant_absorbed_pfd","text":"plant_absorbed_pfd(\n RT::BeerLambertModel{FT},\n SW_IN:FT,\n α_leaf::FT,\n LAI::FT,\n K::FT,\n α_soil::FT\n)\n\nComputes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.\n\nThis applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). \n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\nplant_absorbed_pfd(\n RT::TwoStreamModel{FT},\n α_leaf,\n SW_IN::FT,\n LAI::FT,\n K::FT,\n τ_leaf,\n θs::FT,\n α_soil::FT,\n)\n\nComputes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band. \n\nThis applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.\n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.extinction_coeff","page":"Canopy RT","title":"ClimaLand.Canopy.extinction_coeff","text":"extinction_coeff(ld::FT,\n θs::FT) where {FT}\n\nComputes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.canopy_radiant_energy_fluxes!","page":"Canopy RT","title":"ClimaLand.Canopy.canopy_radiant_energy_fluxes!","text":"Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrognosticSoil{F},\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {FT, PSE}\n\nIn standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.\n\nIn integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\ncanopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrescribedSoil,\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {PSE}\n\nComputes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/available_diagnostics/#Available-diagnostic-variables","page":"Available diagnostics","title":"Available diagnostic variables","text":"","category":"section"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"Autogenerate table of available diagnostics:","category":"page"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"include(\"make_diagnostic_table.jl\")","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"ref_time = DateTime(2005) # required argument, but not used in this case\nSW_d = (t) -> 0\nLW_d = (t) -> 301.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 5\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)\n Y.soil.θ_i .= 0\n T = FT(296.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 13)\ndt = Float64(900.0)","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"900.0","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the tendency functions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\nevaporation_data =\n ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();\nref_soln_E = readdlm(evaporation_data, ',')\nref_soln_E_350mm = ref_soln_E[2:end, 1:2]\ndata_dates = ref_soln_E_350mm[:, 1]\ndata_e = ref_soln_E_350mm[:, 2];\n\nfig = Figure(size = (800, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Evaporation rate (mm/d)\",\n title = \"Bare soil evaporation\",\n)\nCairoMakie.xlims!(minimum(data_dates), maximum(data_dates))\nCairoMakie.lines!(\n ax,\n FT.(data_dates),\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nax = Axis(\n fig[1, 2],\n xlabel = \"Mass (g)\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nA_col = π * (0.027)^2\nmass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col\nmass_loss =\n [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]\nCairoMakie.lines!(\n ax,\n cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n mass_loss,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nsave(\"evaporation_lehmann2008_fig8b.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#General-setup","page":"Intro to standalone models","title":"General setup","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Your model defines a system of equations of the following form:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in vecY. Generically, we will speak of the functions vecf as tendencies; these can be functions of the prognostic state, of space vecx, and of time t, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-cache-(\"auxiliary-variables\")","page":"Intro to standalone models","title":"The cache (\"auxiliary variables\")","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"There are typically quantities, which depend on the state vector vecY, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require \"a lot\" of memory to store (e.g., most variables in global runs). Allocating memory \"on-the-fly\" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Denoting the cache as vecp, your equations may be rewritten as:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecp vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"vecp(vecx t) = vecg(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables vecp at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported (vecY cannot be zero dimensional).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-Model","page":"Intro to standalone models","title":"The Model","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to \"imex\" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as \"explicit\" or \"implicit\" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Let's first import some needed packages.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaTimeSteppers as CTS\nusing SciMLBase\nusing Plots\nusing ClimaCore\nusing ClimaLand","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Import the functions we are extending for our model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaLand:\n name,\n make_exp_tendency,\n make_compute_exp_tendency,\n make_update_aux,\n make_update_boundary_fluxes,\n prognostic_vars,\n prognostic_types,\n prognostic_domain_names,\n auxiliary_vars,\n auxiliary_types,\n auxiliary_domain_names","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}\n \"van Genuchten model parameters\"\n vGmodel::ClimaLand.Soil.vanGenuchten{FT}\n \"Porosity [unitless]\"\n ν::FT\n \"Residual water fraction [unitless]\"\n θ_r::FT\n \"Saturated hydraulic conductiity [m/s]\"\n Ksat::FT\n \"Surface flux, used as boundary condition [m/s]\"\n F_sfc::FT\n \"Domain of the model\"\n domain::D\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"For reasons that will be clear momentarily, let's also define the name of the model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.name(model::RichardsTutorialModel) = :soil;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Explicit-tendency","page":"Intro to standalone models","title":"Explicit tendency","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracpartial ϑ_lpartial t = - -K(θ) (ψ(θ)+z)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)\n function compute_exp_tendency!(dY, Y, p, t)\n gradc2f = ClimaCore.Operators.GradientC2F()\n interpc2f = ClimaCore.Operators.InterpolateC2F()\n FT = FTfromY(Y)\n divf2c = ClimaCore.Operators.DivergenceF2C(\n top = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(model.F_sfc),\n ),\n bottom = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(FT(0)),\n ),\n )\n\n @. dY.soil.θ =\n -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))\n end\n return compute_exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"A couple of notes: the vector vecdY contains the evaluation of the tendency function for each variable in vecY. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-prognostic-state-vector-\\vec{Y}-and-cache-\\vec{p}","page":"Intro to standalone models","title":"The prognostic state vector vecY and cache vecp","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We have given the state vector vecY a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);\nClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);\nClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.auxiliary_vars(::RichardsTutorialModel) =\n (:K, :ψ, :top_flux, :bottom_flux, :z)\nClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =\n (FT, FT, FT, FT, FT);\nClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =\n (:subsurface, :subsurface, :surface, :surface, :subsurface);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Updating-the-cache","page":"Intro to standalone models","title":"Updating the cache","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_update_aux(model::RichardsTutorialModel)\n function update_aux!(p, Y, t)\n p.soil.z .=\n ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step\n @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(\n model.vGmodel,\n model.Ksat,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n @. p.soil.ψ = ClimaLand.Soil.matric_potential(\n model.vGmodel,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n end\n return update_aux!\nend;\n\nfunction ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)\n function update_boundary_fluxes!(p, Y, t)\n FT = ClimaLand.FTfromY(Y)\n p.soil.top_flux .= model.F_sfc\n p.soil.bottom_flux .= FT(0)\n end\n return update_boundary_fluxes!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The default tendency function in ClimaLand for any AbstractModel carries out the following:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function make_exp_tendency(model::AbstractModel)\n update_aux! = make_update_aux(model)\n update_boundary_fluxes! = make_update_boundary_fluxes(model)\n compute_exp_tendency! = make_compute_exp_tendency(model)\n function exp_tendency!(dY,Y,p,t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY,Y,p,t)\n end\n return exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone \"component\" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-a-simulation","page":"Intro to standalone models","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create a model instance.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"FT = Float32\nvGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)\nKsat = FT(4.0e-7)\nν = 0.5f0\nθ_r = 0.0f0\nF_sfc = FT(-3.0e-8)\ndomain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)\nsoil = RichardsTutorialModel{Float32, typeof(domain)}(\n vGmodel,\n ν,\n θ_r,\n Ksat,\n F_sfc,\n domain,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The same is true for p:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"p.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"(K = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:\n Float32[0.0], bottom_flux = Float32-valued Field:\n Float32[0.0], z = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here we now update Y in place with initial conditions of our choosing.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil.θ = 0.25f0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set initial cache variable values, and inspect values:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, 0.0);\n@show p.soil.K\n\n@show p.soil.ψ\n\n@show p.soil.top_flux","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Float32-valued Field:\n Float32[-3.0f-8]","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Next up is to create the exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"exp_tendency! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-the-simulation","page":"Intro to standalone models","title":"Running the simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set the initial and end times, timestep:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"t0 = 0.0;\ntf = 7 * 24 * 3600.0;\ndt = 1800.0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Select the timestepping algorithm we want to use from CTS.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"SciMLBase problem statement using CTS.jl internals:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Solve command:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand","page":"ClimaLand","title":"ClimaLand","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/ClimaLand/#LSM-Model-Types-and-methods","page":"ClimaLand","title":"LSM Model Types and methods","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.SoilPlantHydrologyModel\nClimaLand.LandSoilBiogeochemistry\nClimaLand.LandHydrology\nClimaLand.make_interactions_update_aux\nClimaLand.initialize_interactions\nClimaLand.land_components\nClimaLand.lsm_aux_vars\nClimaLand.lsm_aux_types\nClimaLand.lsm_aux_domain_names\nClimaLand.domain_name","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.LandSoilBiogeochemistry","page":"ClimaLand","title":"ClimaLand.LandSoilBiogeochemistry","text":"struct LandSoilBiogeochemistry{\n FT,\n SEH <: Soil.EnergyHydrology{FT},\n SB <: Soil.Biogeochemistry.SoilCO2Model{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.\n\nsoil: The soil model\nsoilco2: The biochemistry model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.LandHydrology","page":"ClimaLand","title":"ClimaLand.LandHydrology","text":"struct LandHydrology{\n FT,\n SM <: Soil.AbstractSoilModel{FT},\n SW <: Pond.AbstractSurfaceWaterModel{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil and surface water component.\n\nsoil: The soil model\nsurface_water: The surface water model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.land_components","page":"ClimaLand","title":"ClimaLand.land_components","text":"land_components(land::AbstractLandModel)\n\nReturns the component names of the land model, by calling propertynames(land).\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_vars","page":"ClimaLand","title":"ClimaLand.lsm_aux_vars","text":"lsmauxvars(m::AbstractLandModel)\n\nReturns the additional aux variable symbols for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_vars(m::SoilCanopyModel)\n\nThe names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_types","page":"ClimaLand","title":"ClimaLand.lsm_aux_types","text":"lsmauxtypes(m::AbstractLandModel)\n\nReturns the shared additional aux variable types for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_types(m::SoilCanopyModel)\n\nThe types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_domain_names","page":"ClimaLand","title":"ClimaLand.lsm_aux_domain_names","text":"lsmauxdomain_names(m::AbstractLandModel)\n\nReturns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.\n\nThis is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.\n\n\n\n\n\nlsm_aux_domain_names(m::SoilCanopyModel)\n\nThe domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#Land-Hydrology","page":"ClimaLand","title":"Land Hydrology","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.infiltration_capacity\nClimaLand.infiltration_at_point\nClimaLand.PrognosticRunoff\nClimaLand.RunoffBC","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_capacity","page":"ClimaLand","title":"ClimaLand.infiltration_capacity","text":"function infiltration_capacity(\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n)\n\nFunction which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.\n\nDefined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_at_point","page":"ClimaLand","title":"ClimaLand.infiltration_at_point","text":"infiltration_at_point(η::FT, i_c::FT, P::FT)\n\nReturns the infiltration given pond height η, infiltration capacity, and precipitation.\n\nThis is defined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticRunoff","page":"ClimaLand","title":"ClimaLand.PrognosticRunoff","text":"PrognosticRunoff <: Pond.AbstractSurfaceRunoff\n\nConcrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.\n\nThis is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RunoffBC","page":"ClimaLand","title":"ClimaLand.RunoffBC","text":"RunoffBC <: Soil.AbstractSoilBC\n\nConcrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.\n\nThis is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#SoilCanopyModel","page":"ClimaLand","title":"SoilCanopyModel","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticSoil\nClimaLand.RootExtraction","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticSoil","page":"ClimaLand","title":"ClimaLand.PrognosticSoil","text":" PrognosticSoil{FT} <: AbstractSoilDriver\n\nConcrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.\n\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RootExtraction","page":"ClimaLand","title":"ClimaLand.RootExtraction","text":"RootExtraction{FT} <: Soil.AbstractSoilSource{FT}\n\nConcrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.\n\nThis is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#LandSoilBiogeochemistry","page":"ClimaLand","title":"LandSoilBiogeochemistry","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticMet","category":"page"},{"location":"APIs/shared_utilities/#Shared-Utilities","page":"Shared Utilities","title":"Shared Utilities","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/shared_utilities/#Domains","page":"Shared Utilities","title":"Domains","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.Domains.AbstractDomain\nClimaLand.Domains.AbstractLSMDomain\nClimaLand.Domains.SphericalShell\nClimaLand.Domains.SphericalSurface\nClimaLand.Domains.HybridBox\nClimaLand.Domains.Column\nClimaLand.Domains.Plane\nClimaLand.Domains.Point\nClimaLand.Domains.coordinates\nClimaLand.Domains.obtain_face_space\nClimaLand.Domains.obtain_surface_space\nClimaLand.Domains.obtain_surface_domain\nClimaLand.Domains.top_center_to_surface\nClimaLand.Domains.top_face_to_surface\nClimaLand.Domains.linear_interpolation_to_surface!\nClimaLand.Domains.get_Δz\n","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.AbstractDomain","page":"Shared Utilities","title":"ClimaLand.Domains.AbstractDomain","text":"AbstractDomain{FT <:AbstractFloat}\n\nAn abstract type for domains.\n\nThe domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.\n\nAdditionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalShell","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalShell","text":"struct SphericalShell{FT} <: AbstractDomain{FT}\n radius::FT\n depth::FT\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nradius: The radius of the shell\ndepth: The radial extent of the shell\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: The number of elements to be used in the non-radial and radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalSurface","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalSurface","text":"struct SphericalSurface{FT} <: AbstractDomain{FT}\n radius::FT\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).\n\nFields\n\nradius: The radius of the surface\nnelements: The number of elements to be used in the non-radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.HybridBox","page":"Shared Utilities","title":"ClimaLand.Domains.HybridBox","text":"struct HybridBox{FT} <: AbstractDomain{FT}\n xlim::Tuple{FT, FT}\n ylim::Tuple{FT, FT}\n zlim::Tuple{FT, FT}\n longlat::Union{Nothing, Tuple{FT, FT}},\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int, Int}\n npolynomial::Int\n periodic::Tuple{Bool, Bool}\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.\n\nWhen longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nzlim: Domain interval limits along z axis, in meters\nlonglat: When not nothing, a Tuple that contains the center long and lat.\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: Number of elements to discretize interval, (nx, ny,nz)\nnpolynomial: Polynomial order for the horizontal directions\nperiodic: Flag indicating periodic boundaries in horizontal\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Column","page":"Shared Utilities","title":"ClimaLand.Domains.Column","text":"Column{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nzlim: Domain interval limits, (zmin, zmax), in meters\nnelements: Number of elements used to discretize the interval\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nboundary_names: Boundary face identifiers\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Plane","page":"Shared Utilities","title":"ClimaLand.Domains.Plane","text":"Plane{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.\n\nWhen longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.\n\nlonglat are in degrees, with longitude going from -180 to 180.\n\n:warning: Only independent columns are supported! (No lateral flow).\n\nspace is a NamedTuple holding the surface space (in this case, the entire Plane space).\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nlonglat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).\nnelements: Number of elements to discretize interval, (nx, ny)\nperiodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.\nnpolynomial: Polynomial order for both x and y\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Point","page":"Shared Utilities","title":"ClimaLand.Domains.Point","text":"Point{FT} <: AbstractDomain{FT}\n\nA domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.\n\nspace is a NamedTuple holding the surface space (in this case, the Point space).\n\nFields\n\nz_sfc: Surface elevation relative to a reference (m)\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.coordinates","page":"Shared Utilities","title":"ClimaLand.Domains.coordinates","text":"coordinates(domain::AbstractDomain)\n\nReturns the coordinate fields for the domain as a NamedTuple.\n\nThe returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.\n\n\n\n\n\nDomains.coordinates(model::AbstractLandModel)\n\nReturns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.\n\nFor example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_face_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_face_space","text":"obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the face space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the face space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_space","text":"obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the surface space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_domain","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_domain","text":"obtain_surface_domain(d::AbstractDomain) where {FT}\n\nDefault method throwing an error; any domain with a corresponding domain should define a new method of this function.\n\n\n\n\n\nobtain_surface_domain(c::Column{FT}) where {FT}\n\nReturns the Point domain corresponding to the top face (surface) of the Column domain c.\n\n\n\n\n\nobtain_surface_domain(b::HybridBox{FT}) where {FT}\n\nReturns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.\n\n\n\n\n\nobtain_surface_domain(s::SphericalShell{FT}) where {FT}\n\nReturns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_center_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_center_to_surface","text":"top_center_to_surface(center_field::ClimaCore.Fields.Field)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.\n\nFor example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\ntop_center_to_surface(val)\n\nWhen val is a scalar (e.g. a single float or struct), returns val.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_face_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_face_to_surface","text":"top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.\n\nGiven a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.linear_interpolation_to_surface!","page":"Shared Utilities","title":"ClimaLand.Domains.linear_interpolation_to_surface!","text":"linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)\n\nLinearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.get_Δz","page":"Shared Utilities","title":"ClimaLand.Domains.get_Δz","text":"get_Δz(z::ClimaCore.Fields.Field)\n\nA function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Models","page":"Shared Utilities","title":"Models","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.AbstractModel\nClimaLand.AbstractImExModel\nClimaLand.AbstractExpModel\nClimaLand.make_exp_tendency\nClimaLand.make_imp_tendency\nClimaLand.make_compute_exp_tendency\nClimaLand.make_compute_imp_tendency\nClimaLand.make_update_aux\nClimaLand.make_update_boundary_fluxes\nClimaLand.make_set_initial_cache\nClimaLand.make_update_drivers\nClimaLand.prognostic_vars\nClimaLand.prognostic_types\nClimaLand.prognostic_domain_names\nClimaLand.auxiliary_vars\nClimaLand.auxiliary_types\nClimaLand.auxiliary_domain_names\nClimaLand.initialize_prognostic\nClimaLand.initialize_auxiliary\nClimaLand.initialize\nClimaLand.name\nClimaLand.AbstractBC\nClimaLand.AbstractSource\nClimaLand.source!\nClimaLand.AbstractBoundary\nClimaLand.TopBoundary\nClimaLand.BottomBoundary\nClimaLand.boundary_flux\nClimaLand.diffusive_flux\nClimaLand.boundary_vars\nClimaLand.boundary_var_domain_names\nClimaLand.boundary_var_types\nClimaLand.make_jacobian\nClimaLand.make_compute_jacobian\nClimaLand.set_dfluxBCdY!\nClimaLand.get_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractModel","page":"Shared Utilities","title":"ClimaLand.AbstractModel","text":"abstract type AbstractModel{FT <: AbstractFloat}\n\nAn abstract type for all models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractImExModel","page":"Shared Utilities","title":"ClimaLand.AbstractImExModel","text":"AbstractImExModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractExpModel","page":"Shared Utilities","title":"ClimaLand.AbstractExpModel","text":"AbstractExpModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.make_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_exp_tendency","text":"make_exp_tendency(model::AbstractModel)\n\nReturns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_imp_tendency","text":"make_imp_tendency(model::AbstractImExModel)\n\nReturns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\nmake_imp_tendency(model::AbstractModel)\n\nReturns an imp_tendency that does nothing. This model type is not stepped explicity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_exp_tendency","text":"make_explicit_tendency(model::Soil.RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nConstruct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.\n\n\n\n\n\nmake_compute_exp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::BucketModel{FT}) where {FT}\n\nCreates the computeexptendency! function for the bucket model.\n\n\n\n\n\nmake_compute_exp_tendency(model::AbstractModel)\n\nReturn a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)\n\nCreates the computeexptendency!(dY,Y,p,t) function for the canopy component.\n\nSince component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.\n\n\n\n\n\nmake_compute_exp_tendency(canopy::CanopyModel)\n\nCreates and returns the computeexptendency! for the CanopyModel.\n\n\n\n\n\nmake_compute_exp_tendency(model::SoilCO2Model)\n\nAn extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::PlantHydraulicsModel, _)\n\nA function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.\n\nBelow, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).\n\nNote that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.\n\nTo prevent dividing by zero, we change AI/(AI x dz)\" to \"AI/max(AI x dz, eps(FT))\"\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_imp_tendency","text":"make_compute_imp_tendency(model::RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.\n\n\n\n\n\nmake_compute_imp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_imp_tendency(model::AbstractModel)\n\nReturn a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_aux","page":"Shared Utilities","title":"ClimaLand.make_update_aux","text":"make_update_aux(model::RichardsModel)\n\nAn extension of the function make_update_aux, for the Richardson- Richards equation.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::EnergyHydrology)\n\nAn extension of the function make_update_aux, for the integrated soil hydrology and energy model.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::BucketModel{FT}) where {FT}\n\nCreates the update_aux! function for the BucketModel.\n\n\n\n\n\nmake_update_aux(model::AbstractModel)\n\nReturn an update_aux! function that updates auxiliary parameters p.\n\n\n\n\n\n ClimaLand.make_update_aux(canopy::CanopyModel{FT,\n <:AutotrophicRespirationModel,\n <:Union{BeerLambertModel, TwoStreamModel},\n <:FarquharModel,\n <:MedlynConductanceModel,\n <:PlantHydraulicsModel,},\n ) where {FT}\n\nCreates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.\n\nPlease note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.\n\nThe other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.\n\n\n\n\n\nmake_update_aux(model::SoilCO2Model)\n\nAn extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_boundary_fluxes","page":"Shared Utilities","title":"ClimaLand.make_update_boundary_fluxes","text":"make_update_boundary_fluxes(model::AbstractModel)\n\nReturn an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::LandHydrology{FT, SM, SW},\n) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}\n\nA method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.\n\nThis function is called each ode function evaluation.\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::SoilCanopyModel{FT, MM, SM, RM},\n) where {\n FT,\n MM <: Soil.Biogeochemistry.SoilCO2Model{FT},\n SM <: Soil.RichardsModel{FT},\n RM <: Canopy.CanopyModel{FT}\n }\n\nA method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. \n\nThis function is called each ode function evaluation, prior to the tendency function evaluation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_set_initial_cache","page":"Shared Utilities","title":"ClimaLand.make_set_initial_cache","text":"make_set_initial_cache(model::AbstractModel)\n\nReturns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.\n\nIn principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.\n\nFurthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_drivers","page":"Shared Utilities","title":"ClimaLand.make_update_drivers","text":"make_update_drivers(::AbstractClimaLandDrivers)\n\nCreates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.\n\n\n\n\n\nmake_update_drivers(driver_tuple)\n\nCreates and returns a function which updates the forcing variables (\"drivers\"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.\n\n\n\n\n\nmake_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.\n\n\n\n\n\nmake_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.\n\n\n\n\n\nmake_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.\n\n\n\n\n\nmake_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_vars","page":"Shared Utilities","title":"ClimaLand.prognostic_vars","text":"prognostic_vars(soil::RichardsModel)\n\nA function which returns the names of the prognostic variables of RichardsModel.\n\n\n\n\n\nprognostic_vars(soil::EnergyHydrology)\n\nA function which returns the names of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_vars(::SnowModel)\n\nReturns the prognostic variable names of the snow model.\n\nFor this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.\n\n\n\n\n\nprognostic_vars(m::AbstractModel)\n\nReturns the prognostic variable symbols for the model in the form of a tuple.\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_vars(::AbstractCanopyComponent)\n\nReturns the prognostic vars of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_vars(canopy::CanopyModel)\n\nReturns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nprognostic_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the prognostic variables of the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_types","page":"Shared Utilities","title":"ClimaLand.prognostic_types","text":"prognostic_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_types(::SnowModel{FT})\n\nReturns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.\n\n\n\n\n\nprognostic_types(m::AbstractModel{FT}) where {FT}\n\nReturns the prognostic variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nHere, the coordinate points are those returned by coordinates(model).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_types(::AbstractCanopyComponent)\n\nReturns the prognostic types of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_types(canopy::CanopyModel)\n\nReturns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the prognostic types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_domain_names","page":"Shared Utilities","title":"ClimaLand.prognostic_domain_names","text":"prognostic_domain_names(::SnowModel)\n\nReturns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is \":surface\".\n\n\n\n\n\nprognosticdomainnames(m::AbstractModel)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nprognosticdomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_vars","page":"Shared Utilities","title":"ClimaLand.auxiliary_vars","text":"auxiliary_vars(soil::RichardsModel)\n\nA function which returns the names of the auxiliary variables of RichardsModel.\n\n\n\n\n\nauxiliary_vars(soil::EnergyHydrology)\n\nA function which returns the names of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_vars(::SnowModel)\n\nReturns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.\n\nSince the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.\n\n\n\n\n\nauxiliary_vars(m::AbstractModel)\n\nReturns the auxiliary variable symbols for the model in the form of a tuple.\n\n\n\n\n\nClimaLand.auxiliary_vars(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_vars(canopy::CanopyModel)\n\nReturns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nauxiliary_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_types","page":"Shared Utilities","title":"ClimaLand.auxiliary_types","text":"auxiliary_types(soil::RichardsModel)\n\nA function which returns the names of the auxiliary types of RichardsModel.\n\n\n\n\n\nauxiliary_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_types(m::AbstractModel{FT}) where {FT}\n\nReturns the auxiliary variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nNote that Arrays, MVectors are not isbits and cannot be used.\n\nHere, the coordinate points are those returned by coordinates(model).\n\n\n\n\n\nClimaLand.auxiliary_types(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_types(canopy::CanopyModel)\n\nReturns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the auxiliary types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_domain_names","page":"Shared Utilities","title":"ClimaLand.auxiliary_domain_names","text":"auxiliary_domain_names(soil::RichardsModel)\n\nA function which returns the names of the auxiliary domain names of RichardsModel.\n\n\n\n\n\nauxiliarydomainnames(m::AbstractModel)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\n\n\n\n\nauxiliarydomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_prognostic","page":"Shared Utilities","title":"ClimaLand.initialize_prognostic","text":"initialize_prognostic(model::AbstractModel, state::NamedTuple)\n\nReturns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.\n\nIf a model has no prognostic variables, the returned FieldVector contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_prognostic(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_prognostic(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input state is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_auxiliary","page":"Shared Utilities","title":"ClimaLand.initialize_auxiliary","text":"initialize_auxiliary(model::AbstractModel, state::NamedTuple)\n\nReturns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.\n\nIf a model has no auxiliary variables, the returned NamedTuple contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_auxiliary(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_auxiliary(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input coords is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize","page":"Shared Utilities","title":"ClimaLand.initialize","text":"initialize(model::AbstractModel)\n\nCreates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.name","page":"Shared Utilities","title":"ClimaLand.name","text":"name(model::AbstractModel)\n\nReturns a symbol of the model component name, e.g. :soil or :vegetation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBC","page":"Shared Utilities","title":"ClimaLand.AbstractBC","text":"AbstractBC\n\nAn abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractSource","page":"Shared Utilities","title":"ClimaLand.AbstractSource","text":"AbstractSource{FT <: AbstractFloat}\n\nAn abstract type for types of source terms.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.source!","page":"Shared Utilities","title":"ClimaLand.source!","text":" source!(dY::ClimaCore.Fields.FieldVector,\n src::PhaseChange{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nComputes the source terms for phase change.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::SoilSublimation{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nUpdates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::AbstractSource,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n )::ClimaCore.Fields.Field\n\nA stub function, which is extended by ClimaLand.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::RootExtraction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n model::EnergyHydrology)\n\nAn extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::MicrobeProduction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n params)\n\nA method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.\n\n\n\n\n\nClimaLand.source!(\n dY::ClimaCore.Fields.FieldVector,\n src::TOPMODELSubsurfaceRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model::AbstractSoilModel{FT},\n) where {FT}\n\nAdjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.\n\nThe sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBoundary","page":"Shared Utilities","title":"ClimaLand.AbstractBoundary","text":"AbstractBoundary\n\nAn abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.TopBoundary","page":"Shared Utilities","title":"ClimaLand.TopBoundary","text":"TopBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the top boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.BottomBoundary","page":"Shared Utilities","title":"ClimaLand.BottomBoundary","text":"BottomBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the bottom boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_flux","page":"Shared Utilities","title":"ClimaLand.boundary_flux","text":"boundary_flux(bc::WaterFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(bc::RichardsAtmosDrivenFluxBC,\n boundary::ClimaLand.AbstractBoundary,\n model::RichardsModel{FT},\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field where {FT}\n\nA method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.\n\nIf model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(bc::FreeDrainage,\n boundary::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which enforces free drainage at the bottom of the domain.\n\n\n\n\n\nboundary_flux(bc::HeatFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.BottomBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field\n\nA function which returns the correct boundary flux given any boundary condition (BC). \n\n\n\n\n\nfunction ClimaLand.boundary_flux(\n bc::RunoffBC,\n ::TopBoundary,\n model::Soil.RichardsModel,\n Δz::FT,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n params,\n)::ClimaCore.Fields.Field\n\nExtension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2FluxBC,\n boundary::ClimaLand.AbstractBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::SoilCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2StateBC,\n boundary::ClimaLand.BottomBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::AtmosCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.diffusive_flux","page":"Shared Utilities","title":"ClimaLand.diffusive_flux","text":"diffusive_flux(K, x_2, x_1, Δz)\n\nCalculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_vars","page":"Shared Utilities","title":"ClimaLand.boundary_vars","text":"boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.\n\nThese variables are updated in place in soil_boundary_fluxes!.\n\n\n\n\n\nboundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for MoistureStateBC at the top boundary.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\nboundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_domain_names","page":"Shared Utilities","title":"ClimaLand.boundary_var_domain_names","text":"boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.\n\n\n\n\n\nboundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)\n\nThe list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. \n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_types","page":"Shared Utilities","title":"ClimaLand.boundary_var_types","text":"boundary_var_types(::RichardsModel{FT},\n ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <: Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.\n\nBecause we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.\n\n\n\n\n\nboundary_var_types(\n ::EnergyHydrology{FT},\n ::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere{FT},\n <:AbstractRadiativeDrivers{FT},\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary,\n) where {FT}\n\nAn extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.\n\n\n\n\n\nboundary_var_types(::RichardsModel{FT},\n ::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_jacobian","page":"Shared Utilities","title":"ClimaLand.make_jacobian","text":"make_jacobian(model::AbstractModel)\n\nCreates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.\n\nThe default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.\n\nNote that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_jacobian","page":"Shared Utilities","title":"ClimaLand.make_compute_jacobian","text":"ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nmake_compute_jacobian(model::AbstractModel)\n\nCreates and returns a function which computes the entries of the Jacobian matrix W in place.\n\nIf the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.\n\nThe default is that no updates are required, but this function must be extended for models that use implicit timestepping.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_dfluxBCdY!","page":"Shared Utilities","title":"ClimaLand.set_dfluxBCdY!","text":"ClimaLand.set_dfluxBCdY!(\n model::RichardsModel,\n ::MoistureStateBC,\n boundary::ClimaLand.TopBoundary,\n Δz,\n Y,\n p,\n t,\n\n)\n\nComputes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.\n\nFor Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.\n\n\n\n\n\nset_dfluxBCdY!(::AbstractModel,\n ::AbstractBC,\n ::AbstractBoundary,\n _...)::Union{ClimaCore.Fields.FieldVector, Nothing}\n\nA function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.get_drivers","page":"Shared Utilities","title":"ClimaLand.get_drivers","text":"ClimaLand.get_drivers(model::RichardsModel)\n\nReturns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.\n\n\n\n\n\nClimaLand.get_drivers(model::SnowModel)\n\nReturns the driver variable symbols for the SnowModel.\n\n\n\n\n\nget_drivers(model::AbstractModel)\n\nReturns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Drivers","page":"Shared Utilities","title":"Drivers","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.PrescribedAtmosphere\nClimaLand.PrescribedPrecipitation\nClimaLand.PrescribedRadiativeFluxes\nClimaLand.PrescribedSoilOrganicCarbon\nClimaLand.CoupledAtmosphere\nClimaLand.CoupledRadiativeFluxes\nClimaLand.AbstractAtmosphericDrivers\nClimaLand.AbstractRadiativeDrivers\nClimaLand.turbulent_fluxes\nClimaLand.turbulent_fluxes_at_a_point\nClimaLand.radiative_fluxes_at_a_point\nClimaLand.set_atmos_ts!\nClimaLand.surface_air_density\nClimaLand.surface_temperature\nClimaLand.surface_resistance\nClimaLand.surface_specific_humidity\nClimaLand.make_update_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedAtmosphere","page":"Shared Utilities","title":"ClimaLand.PrescribedAtmosphere","text":"PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.\n\nThe default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.\n\nSince not all models require co2 concentration, the default for that is nothing.\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\nsnow_precip: Snow precipitation (m/s) function of time: positive by definition\nT: Prescribed atmospheric temperature (function of time) at the reference height (K)\nu: Prescribed wind speed (function of time) at the reference height (m/s)\nq: Prescribed specific humidity (function of time) at the reference height (_)\nP: Prescribed air pressure (function of time) at the reference height (Pa)\nc_co2: CO2 concentration in atmosphere (mol/mol)\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nh: Reference height (m), relative to surface elevation\ngustiness: Minimum wind speed (gustiness; m/s)\nthermo_params: Thermodynamic parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedPrecipitation","page":"Shared Utilities","title":"ClimaLand.PrescribedPrecipitation","text":"PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.PrescribedRadiativeFluxes","text":"PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}\n\nContainer for the prescribed radiation functions needed to drive land models in standalone mode.\n\nSW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface\nLW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nθs: Sun zenith angle, in radians\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedSoilOrganicCarbon","page":"Shared Utilities","title":"ClimaLand.PrescribedSoilOrganicCarbon","text":" PrescribedSoilOrganicCarbon{FT}\n\nA type for prescribing soil organic carbon.\n\nsoc: Soil organic carbon, function of time and space: kg C/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledAtmosphere","page":"Shared Utilities","title":"ClimaLand.CoupledAtmosphere","text":"CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.CoupledRadiativeFluxes","text":"CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractAtmosphericDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractAtmosphericDrivers","text":" AbstractAtmosphericDrivers{FT}\n\nAn abstract type of atmospheric drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractRadiativeDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractRadiativeDrivers","text":" AbstractRadiativeDrivers{FT}\n\nAn abstract type of radiative drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes","text":"turbulent_fluxes(atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t\n )\n\nComputes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.\n\nIt solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.\n\n\n\n\n\nturbulent_fluxes(atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t)\n\nComputes the turbulent surface fluxes terms at the ground for a coupled simulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes_at_a_point","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes_at_a_point","text":"turbulent_fluxes_at_a_point(T_sfc::FT,\n q_sfc::FT,\n ρ_sfc::FT,\n β_sfc::FT,\n h_sfc::FT,\n r_sfc::FT,\n d_sfc::FT,\n ts_in,\n u::FT,\n h::FT,\n gustiness::FT,\n z_0m::FT,\n z_0b::FT,\n earth_param_set::EP,\n ) where {FT <: AbstractFloat, P}\n\nComputes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc\n\nThis returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_atmos_ts!","page":"Shared Utilities","title":"ClimaLand.set_atmos_ts!","text":"set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)\n\nFill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_air_density","page":"Shared Utilities","title":"ClimaLand.surface_air_density","text":"surface_air_density(\n atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t,\n T_sfc,\n )\n\nA helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.\n\nWe additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.\n\nExtending this function for your model is only necessary if you need to compute the air density in a different way.\n\n\n\n\n\nClimaLand.surface_air_density(\n atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n _...,\n )\n\nReturns the air density at the surface in the case of a coupled simulation.\n\nThis requires the field ρ_sfc to be present in the cache p under the name of the model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_temperature","page":"Shared Utilities","title":"ClimaLand.surface_temperature","text":"ClimaLand.surface_temperature(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface temperature field of the EnergyHydrology soil model.\n\nThe assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.\n\n\n\n\n\nClimaLand.surface_temperature(model::SnowModel, Y, p)\n\na helper function which returns the surface temperature for the snow model, which is stored in the aux state.\n\n\n\n\n\nClimaLand.surface_temperature(model::BucketModel, Y, p)\n\na helper function which returns the surface temperature for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_temperature(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_temperature(model::CanopyModel, Y, p, t)\n\nA helper function which returns the temperature for the canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_resistance","page":"Shared Utilities","title":"ClimaLand.surface_resistance","text":"ClimaLand.surface_resistance(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface resistance field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_resistance(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\nThe default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.\n\n\n\n\n\nClimaLand.surface_resistance(\n model::CanopyModel{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the stomatal resistance field of the CanopyModel canopy.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_specific_humidity","page":"Shared Utilities","title":"ClimaLand.surface_specific_humidity","text":"ClimaLand.surface_specific_humidity(\n model::EnergyHydrology{FT},\n Y,\n p,\n T_sfc,\n ρ_sfc\n) where {FT}\n\nReturns the surface specific humidity field of the EnergyHydrology soil model.\n\nThis models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.\n\nOver the soil ice, the specific humidity is the saturated value.\n\nThe total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\nComputes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\na helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)\n\nA helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)\n\nA helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.\n\n\n\n\n\n","category":"function"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Farquhar-Model","page":"Farquhar model","title":"Farquhar Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO_2 into the leaf, the assimilation of CO_2 during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO_2 concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Rubisco-limited-rate","page":"Farquhar model","title":"Rubisco limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_1(T c_a VPD) =\nbegincases\n V_cmax(T) frac(c_i(T c_a VPD) - Gamma^*(T))(c_i(T c_a VPD) + K_c(T)*(1+o_iK_o(T))) textfor C3\n V_cmax(T) textfor C4\nendcases\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The dependence on the atmospheric CO_2 concentration c_a (mol/mol) and vapor pressure deficit VPD arise in the expression for c_i,","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n c_i(T c_a VPD) = max(c_a(1-1m(VPD)) Gamma^*(T))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where and m is the Medlyn factor (see Stomatal Conductance).","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We also have","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":" Gamma^*(T) = Gamma^*_25expleft(Delta H_Gamma^*fracT - T_oT_o R Tright)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where Delta H_Gamma^* is the activation energy per mol for Gamma^*.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Light-limited-rate","page":"Farquhar model","title":"Light limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_2 =\nbegincases\n J(T PAR) (c_i - Gamma^*)4(c_i + 2 Gamma^*) textfor C3\n J(T PAR) textfor C4\nendcases \nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where J is the rate of electron transport, which has units of mol photon per m^2 per s. It depends on PAR via APAR, as described below, and on T via the dependence on J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"J is given by the root of the equation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n theta_j J^2 - (I + J_max) J + I J_max = 0 nonumber \n I = fracphi2 (APAR) nonumber \n J_max(T) = V_cmax(T)times e expleft(Delta H_J_maxfracT - T_oT_o R Tright)nonumber \nJ(T PAR) = frac(I + J_max - sqrt(I + J_max)^2 - 4theta_j I times J_max2theta_j\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where phi = 06 and theta_j = 09 are the quantum yield of photosystem II and a curvature function (Bonan's book), and Delta H_J_max is the energy of activation of J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The total net carbon assimilation (A_n, mol CO_2 m^-2 s^-1) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nA_n(T PAR VPD c_a) = textmax(0 textmin(a_1 beta a_2) - R_d)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where beta is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R_d is the leaf dark respiration calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n R_d25(psi_l) = f V_cmax25beta(psi_l) nonumber \n R_d (T psi_l) = R_d25(psi_l)expleft(Delta H_R_dfracT - T_oT_o R Tright)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where f = 0015 is a constant, Delta H_R_d is the energy of activation for R_d, and finally Vcmax is calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\nV_cmax(T) = V_cmax25 expleft(Delta H_VcmaxfracT - T_oT_o R Tright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"with V_cmax25 is a parameter (Vcmax at the reference temperature 25 C), and Delta H_Vcmax = 65330 Jmol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The moisture stress factor is related to the leaf water potential psi_l as","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n beta = frac1+ exp(s_c psi_c)1+ exp(s_c(psi_c - psi_l))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where s_c = 4MPa^-1, psi_c = -2MPa, and psi_l is the leaf water potential computed by the plant hydraulics model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nGPP(T PAR c_a VPD theta_s) = A_n (1 - exp(-K LAI Omega))K\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This is not currently needed by other components, but is used for offline validation of the model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We need to supply the following parameters and “drivers\"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"K_c25 and K_o25, V_cmax 25, Gamma^*_25phi, theta_j, o_i, s_c, psi_c\npsi_l, to compute beta\nTemperature T, PAR, c_a, VPD, theta_s.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Output Symbol Unit Range\nTotal net carbon assimilation A_n μmol CO_2 m^-2 s^-1 0–25","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nTemperature T °C 0–50","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Parameters Symbol Unit Range\nMoisture stress β - 0-1\nLeaf Area Index LAI m² m⁻² 1–10\nCO_2 concentration c_a ppm 300e–500\nVapor pressure deficit VPD kPa 1-10","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Constants Symbol Unit Value\nZenith angle θ_s rad 0.6\nLeaf angle distribution l_d - 0.5\nCanopy reflectance ρ_leaf - 0.1\nClumping index Ω - 0.69\nCO_2 compensation at 25°C Γ^*_25 mol/mol 4.275e-5\nEnergy of activation for Γ^* ΔH_Γ^* J/mol 37830\nStandard temperature T_o K 298.15\nUniversal gas constant R J/mol 8.314\nThe maximum rate of carboxylation of Rubisco V_cmax25 mol CO_2 m^-2 s^-1 5e-5\nEnergy of activation for J_max ΔH_J_max J/mol 43540\nCurvature parameter, a fitting constant to compute J θ_j - 0.9\nThe quantum yied of photosystem II phi - 0.6\nEnergy of activation for V_cmax ΔH_V_cmax J/mol 58520\nSlope parameter for stomatal conductance models g_1 - 141\nMichaelis Menten constant for CO_2 and at 25\u000e°C K_c25 mol/mol 4.049e-4\nEnergy of activation for CO_2 ΔH_K_c J/mol 79430\nMichaelis Menten constant for O_2 at 25 °C\u000e K_o25 mmol/mol 0.2874\nEnergy of activation for O_2 ΔH_K_o J/mol 36380\nIntercellular O_2 concentration o_i mol/mol 0.209\nConstant factor appearing the dark respiration term\u000e f - 0.015\nEnergy of activation for R_d ΔH_R_d J/mol 43390","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Soil-Biogeochemistry","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"CurrentModule = ClimaLand.Soil.Biogeochemistry","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Model-Structure","page":"Soil Biogeochemistry","title":"Model Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2Model","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","text":"SoilCO2Model\n\nA model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type NamedTuple\nsources: A tuple of sources, each of type AbstractSource\ndrivers: Drivers\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Parameter-Structure","page":"Soil Biogeochemistry","title":"Parameter Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","text":"SoilCO2ModelParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SoilCO2Model.\n\nAll of these parameters are currently treated as global constants.\n\nD_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)\nD_liq: Diffusivity of soil C substrate in liquid (unitless)\nα_sx: Pre-exponential factor (kg C m-3 s-1)\nEa_sx: Activation energy (J mol-1)\nkM_sx: Michaelis constant (kg C m-3)\nkM_o2: Michaelis constant for O2 (m3 m-3)\nO2_a: Volumetric fraction of O₂ in the soil air, dimensionless\nD_oa: Diffusion coefficient of oxygen in air, dimensionless\np_sx: Fraction of soil carbon that is considered soluble, dimensionless\nearth_param_set: Physical constants used Clima-wide\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Model-specific-Types","page":"Soil Biogeochemistry","title":"Model-specific Types","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction\nClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC\nClimaLand.Soil.Biogeochemistry.SoilCO2StateBC\nClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC\nClimaLand.Soil.Biogeochemistry.AbstractSoilDriver\nClimaLand.Soil.Biogeochemistry.SoilDrivers\nClimaLand.Soil.Biogeochemistry.PrescribedMet","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.MicrobeProduction","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction","text":"MicrobeProduction{FT} <: AbstractCarbonSource{FT}\n\nStruct for the microbe production of CO2, appearing as a source term in the differential equation.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","text":"SoilCO2FluxBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 flux boundary condition, which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","text":"SoilCO2StateBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 state boundary condition (kg CO2 m−3), which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","text":"AtmosCO2StateBC <: ClimaLand.AbstractBC\n\nSet the CO2 concentration to the atmospheric one.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","text":"AbstractSoilDriver\n\nAn abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilDrivers","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilDrivers","text":"SoilDrivers\n\nA container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).\n\nmet: Soil temperature and moisture drivers - Prescribed or Prognostic\nsoc: Soil SOM driver - Prescribed only\natmos: Prescribed atmospheric variables\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.PrescribedMet","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.PrescribedMet","text":"PrescribedMet <: AbstractSoilDriver\n\nA container which holds the prescribed functions for soil temperature and moisture.\n\nThis is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.\n\ntemperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat\nvolumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat\nν: Soil porosity (m³ m⁻³)\nθ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)\nb: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Functions-of-State","page":"Soil Biogeochemistry","title":"Functions of State","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content\nClimaLand.Soil.Biogeochemistry.co2_diffusivity\nClimaLand.Soil.Biogeochemistry.microbe_source","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.volumetric_air_content","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content","text":"volumetric_air_content(θ_w::FT,\n ν::FT,\n ) where {FT}\n\nComputes the volumetric air content (θ_a) in the soil, which is related to the total soil porosity (ν) and volumetric soil water content (θ_w = θ_l+θ_i).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.co2_diffusivity","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.co2_diffusivity","text":"co2_diffusivity(\n T_soil::FT,\n θ_w::FT,\n P_sfc::FT,\n θ_a100::FT,\n b::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT},\n ) where {FT}\n\nComputes the diffusivity of CO₂ within the soil (D).\n\nFirst, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.\n\nThis parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.microbe_source","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.microbe_source","text":"microbe_source(T_soil::FT,\n θ_l::FT,\n Csom::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT}\n ) where {FT}\n\nComputes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#Extendible-Functions","page":"Soil Biogeochemistry","title":"Extendible Functions","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.soil_moisture\nClimaLand.Soil.Biogeochemistry.soil_temperature\nClimaLand.Soil.Biogeochemistry.soil_SOM_C","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_moisture","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_moisture","text":"soil_moisture(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.\n\n\n\n\n\nsoil_moisture(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil moisture at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_temperature","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_temperature","text":"soil_temperature(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the prognostic soil temperature.\n\n\n\n\n\nsoil_temperature(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil temperature at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/users_diagnostics/#Using-ClimaLand-Diagnostics-when-running-a-simulation","page":"For users","title":"Using ClimaLand Diagnostics when running a simulation","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"This is where ClimaLand Diagnostics comes in for users.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.","category":"page"},{"location":"diagnostics/users_diagnostics/#Default-Diagnostics","page":"For users","title":"Default Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#define-an-output-folder","page":"For users","title":"define an output folder","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path(\"base_output_dir/\")","category":"page"},{"location":"diagnostics/users_diagnostics/#define-a-space","page":"For users","title":"define a space","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"space = bucket_domain.space.subsurface","category":"page"},{"location":"diagnostics/users_diagnostics/#define-your-writter","page":"For users","title":"define your writter","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"providing the space and output_dir defined in steps 1. and 2.","category":"page"},{"location":"diagnostics/users_diagnostics/#make-your-diagnostics-on-your-model,-using-your-writter,-and-define-a-callback","page":"For users","title":"make your diagnostics on your model, using your writter, and define a callback","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"t0 = 0 # the starting time of your simulation\n\nreference_date = DateTime(2024) # reference_date is the DateTime of your starting time\n\ndiags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)\n\ndiagnostic_handler =\n ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)\n\ndiag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics have now been written in netcdf files in your output folder.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.","category":"page"},{"location":"diagnostics/users_diagnostics/#Custom-Diagnostics","page":"For users","title":"Custom Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When defining a custom diagnostic, follow these steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-compute-your-diagnostic-variable-from-your-model-state-and-cache.","page":"For users","title":"Define how to compute your diagnostic variable from your model state and cache.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)\n else\n out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf\n end\nend","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"@diagnostic_compute \"latent_heat_flux\" BucketModel p.bucket.turbulent_fluxes.lhf","category":"page"},{"location":"diagnostics/users_diagnostics/#Add-that-diagnostic(s)-variable-to-your-list-of-variables","page":"For users","title":"Add that diagnostic(s) variable to your list of variables","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":" add_diagnostic_variable!(\n short_name = \"bor\",\n long_name = \"Bowen ratio\",\n standard_name = \"bowen_ratio\",\n units = \"\",\n comments = \"Ratio of sensible to latent heat flux.\",\n compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),\n)\n\nadd_diagnostic_variable!(\n short_name = \"lhf\",\n long_name = \"Latent Heat Flux\",\n standard_name = \"latent_heat_flux\",\n units = \"W m^-2\",\n comments = \"Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.\",\n compute! = (out, Y, p, t) ->\n compute_latent_heat_flux!(out, Y, p, t, land_model),\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-schedule-your-variables.-For-example,-you-want-the-seasonal-maximum-of-your-variables,-where-season-is-defined-as-90-days.","page":"For users","title":"Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(\n 90 * 24 * 60 * 60 * one(t_start),\n max,\n output_writer,\n t_start,\n short_names...,\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-a-function-to-return-your-ScheduledDiagnostics","page":"For users","title":"Define a function to return your ScheduledDiagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now, you can call your schedule with your variables.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"my_custom_diagnostics = [\"lhf\", \"bor\"]\n\ndiags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)","category":"page"},{"location":"diagnostics/users_diagnostics/#Analyze-your-simulation-output","page":"For users","title":"Analyze your simulation output","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Here is an example of how to plot a variable:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"import ClimaAnalysis\n\nimport ClimaAnalysis.Visualize as viz\n\nimport CairoMakie # the plotting package used by ClimaAnalysis\n\nsimdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.\n\nvar = get(simdir; \"lhf\") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.\n\nfig = CairoMakie.Figure() # creates an empty figure object\n\nviz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.\n\nCairoMakie.save(fig) # saves the figure in current working directory","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/#Optimality-Model","page":"Optimality model","title":"Optimality Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation (V_textcmax). ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates V_textcmax and J_textmax as a function of environmental variables as follows:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n V_textcmax^* = varphi I left(fracmm_cright)left(fracoverlineomega^*8thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega^* = 1 + overlineomega - sqrt(1 + overlineomega)^2 - 4thetaoverlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega = -(1 - 2theta) + sqrt(1 - theta)left(frac1frac4cmleft(1 - thetafrac4cmright) - 4thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n c = fracm8thetaleft(1 - fracvarphi I + J_textmax - 2thetavarphi Isqrt(varphi I + J_textmax)^2 - 4thetavarphi I J_textmaxright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n J_textmax = varphi I overlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m = fracC_i - Gamma^*C_i + 2Gamma^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n C_i = Gamma^* + (C_a - Gamma^*)fracxixi + sqrtD_g\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n xi = sqrtbeta fracK + Gamma^*16eta^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n K = K_cleft(1 + fracO_iK_oright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m_c = fracC_i - Gamma^*C_i + K\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Gamma^*","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"is the CO_2 compensation point in the absence of mitochondrial respiration","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n Gamma^* = Gamma^*_0 f(T Delta H_a) pp_0\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where Gamma^*_0 = 4332 Pa, p is the atmospheric pressure, p_0 = 101325 Pa, and Delta H_a = 37830 J/mol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"varphi is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.\ntheta is the curvature of the light response curve (dimensionless). Estimated at 0.85.\nbeta is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"For Smith et al. (2019) Vcmax model:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Altitude\nD_g is the vapor pressure deficit (VPD) at altitude\nC_a is the CO_2 partial pressure\nI is the incident photosynthetically active photon flux (PAR)\nT is the temperature","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Introduction-to-the-Canopy-Model","page":"Standalone Canopy","title":"Introduction to the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Preliminary-Setup","page":"Standalone Canopy","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load External Packages:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing StaticArrays\nusing ClimaLand\nusing ClimaLand.Domains: Point\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Setup-the-Canopy-Model","page":"Standalone Canopy","title":"Setup the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4) # kg/m^2\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Point(; z_sfc = FT(0.0))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}\n Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"time_offset = 7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Site latitude and longitude","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"-92.2f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Height of the sensor at the site","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"atmos_h = FT(32)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"32.0f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_soil0 = FT(0.0)\n\nsoil_driver = PrescribedSoil(\n FT;\n root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),\n ψ = t -> ψ_soil0,\n α_PAR = FT(0.2),\n α_NIR = FT(0.4),\n T = t -> 298.0,\n ϵ = FT(0.99),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"rt_params = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = FT(0.1),\n α_NIR_leaf = FT(0.45),\n τ_PAR_leaf = FT(0.05),\n τ_NIR_leaf = FT(0.25),\n Ω = FT(0.69),\n λ_γ_PAR = FT(5e-7),\n λ_γ_NIR = FT(1.65e-6),\n)\n\nrt_model = TwoStreamModel{FT}(rt_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for conductance model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))\n\nstomatal_model = MedlynConductanceModel{FT}(cond_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for photosynthesis model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))\n\nphotosynthesis_model = FarquharModel{FT}(photo_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for autotrophic respiration model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"AR_params = AutotrophicRespirationParameters(FT)\nAR_model = AutotrophicRespirationModel{FT}(AR_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for plant hydraulics model are more complicated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"LAI = 4.2\nLAIfunction = (t) -> LAI\nSAI = FT(0.00242)\nf_root_to_shoot = FT(3.5)\nRAI = FT((SAI + LAI) * f_root_to_shoot)\nai_parameterization =\n PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)\nrooting_depth = FT(1.0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the root distribution function p(z):","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"function root_distribution(z::T; rooting_depth = rooting_depth) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"K_sat_plant = FT(1.8e-8)\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use these values to populate the parameters of the PlantHydraulics model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ν = FT(0.7)\nS_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = ν,\n S_s = S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the remaining variables required for the plant hydraulics model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_surfaces = compartment_surfaces,\n compartment_midpoints = compartment_midpoints,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"canopy = ClimaLand.Canopy.CanopyModel{FT}(;\n parameters = shared_params,\n domain = land_domain,\n autotrophic_respiration = AR_model,\n radiative_transfer = rt_model,\n photosynthesis = photosynthesis_model,\n conductance = stomatal_model,\n hydraulics = plant_hydraulics,\n soil_driver = soil_driver,\n atmos = atmos,\n radiation = radiation,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Y, p, coords = ClimaLand.initialize(canopy)\nexp_tendency! = make_exp_tendency(canopy);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide initial conditions for the canopy hydraulics model","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n ν,\n S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"t0 = 0.0\nN_days = 364\ntf = t0 + 3600 * 24 * N_days\ndt = 225.0;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the cache variables for the canopy using the initial conditions and initial time.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"set_initial_cache! = make_set_initial_cache(canopy)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"n = 16\nsaveat = Array(t0:(n * dt):tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the callback function which updates the forcing variables, or drivers.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"updateat = Array(t0:1800:tf)\nmodel_drivers = ClimaLand.get_drivers(canopy)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a timestepping algorithm and setup the ODE problem.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"timestepper = CTS.RK4();\node_algo = CTS.ExplicitAlgorithm(timestepper)\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Create-some-plots","page":"Standalone Canopy","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We can now plot the data produced in the simulation. For example, GPP:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Transpiration plot:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Show the two plots together:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Save the output:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"savefig(\"ozark_standalone_canopy_test.png\");","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"(Image: )","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Introduction-to-the-Land-Bucket-Model","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"All equation variables are defined immediately below. We have:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wdt = -I","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wsdt = -(P_liq + σM + (1-σ) E_soil - I)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd σSdt = -(P_snow + σE_snow - σM)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ρc fracpartial Tpartial t = κ_soil fracpartial Tpartial z","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_bot = 00 = -κ_soil fracpartial Tpartial z_z = z_bot","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(1-σ) (R_n+ SHF + LHF)_soil + σG_undersnow = -κ_soil fracpartial Tpartial z_z = z_sfc","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"G_undersnow = (R_n+ SHF + LHF)_snow - F_intosnow","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_intosnow = -ρ_l L_f0 (P_snow + E_snow-M)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"R_n = -(1-α)*SW -LW + σ_SB T_sfc^4","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_soil = β(W W_f) E(q_sat(T_sfc ρ_sfc liquid)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_snow = E(q_sat(T_sfc ρ_sfc ice)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Simulating-a-standalone-bucket-model","page":"Introduction to the Land Bucket Model","title":"Simulating a standalone bucket model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaCore","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaParams as CP","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use Insolation to calculate solar zenith angle and solar insolation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaLand.Bucket:\n BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo\nusing ClimaLand.Domains: coordinates, Column\nusing ClimaLand:\n initialize,\n make_update_aux,\n make_exp_tendency,\n make_set_initial_cache,\n PrescribedAtmosphere,\n PrescribedRadiativeFluxes\nusing ClimaUtilities.TimeVaryingInputs: TimeVaryingInput","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also want to plot the solution","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Plots","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"And we need to use the DateTime type to store reference times","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Dates\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaLand\nimport ClimaLand.Parameters as LP\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"soil_depth = FT(3.5);\nbucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);\nsurface_space = bucket_domain.space.surface","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}\n Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"α_bareground_func = (coordinate_point) -> 0.2;\nα_snow = FT(0.8);\nalbedo =\n PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The critical snow level setting the scale for when we interpolate between snow and surface albedo","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"σS_c = FT(0.2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The field capacity of the soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W_f = FT(0.15);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Roughness lengths (meters)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"z_0m = FT(1e-2);\nz_0b = FT(1e-3);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Thermal parameters of soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"κ_soil = FT(0.7);\nρc_soil = FT(2e6);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Snow melt timescale","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"τc = FT(3600);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Simulation start time, end time, and timestep","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"t0 = 0.0;\ntf = 7 * 86400;\nΔt = 3600.0;\n\nbucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ref_time = DateTime(2005);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Here we define the model drivers Prescribed atmospheric variables","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Precipitation:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"precip = (t) -> 0;\nsnow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Diurnal temperature variations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Constant otherwise:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"u_atmos = (t) -> 3.0;\nq_atmos = (t) -> 0.005;\nh_atmos = FT(2);\nP_atmos = (t) -> 101325;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"bucket_atmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(snow_precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));\nLW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;\nbucket_rad = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"model = BucketModel(\n parameters = bucket_parameters,\n domain = bucket_domain,\n atmosphere = bucket_atmos,\n radiation = bucket_rad,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Like all ClimaLand models, we set up the state vector using initialize:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y, p, coords = initialize(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We can inspect the prognostic and auxiliary variables of the model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.prognostic_vars(model)\nY.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:W, :T, :Ws, :σS)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.auxiliary_vars(model)\np.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Next is to set initial conditions.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y.bucket.T .= FT(270);\nY.bucket.W .= FT(0.05);\nY.bucket.Ws .= FT(0.0);\nY.bucket.σS .= FT(0.08);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also set the initial values of the cache here:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"set_initial_cache! = make_set_initial_cache(model);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"exp_tendency! = make_exp_tendency(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Now we choose our timestepping algorithm.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then we can set up the simulation and solve it:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"saveat = collect(t0:Δt:tf);\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\nsaving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);\nupdateat = copy(saveat)\nmodel_drivers = ClimaLand.get_drivers(model)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];\nWs = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];\nσS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];\nT_sfc =\n [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];\nevaporation = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]\n for k in 1:length(sol.t)\n];\nR_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The turbulent energy flux is the sum of latent and sensible heat fluxes.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"LHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for\n k in 1:length(sol.t)\n];\nSHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for\n k in 1:length(sol.t)\n];\nturbulent_energy_flux = SHF .+ LHF\n\nplot(\n sol.t ./ 86400,\n W,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"W (m)\",\n title = \"Land water storage (m)\",\n)\nsavefig(\"w.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n σS,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"σS (m)\",\n title = \"Area weighted SWE (m) \",\n)\nsavefig(\"swe.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n snow_precip.(sol.t),\n label = \"Net precipitation\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (m/s)\",\n title = \"Surface water fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, evaporation, label = \"Sublimation/Evaporation\")\nsavefig(\"water_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n T_sfc,\n title = \"Surface Temperatures\",\n label = \"Ground temperature\",\n xlabel = \"time (days)\",\n ylabel = \"T_sfc (K)\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, T_atmos.(sol.t), label = \"Atmospheric Temperature\")\nsavefig(\"t.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n R_n,\n label = \"Net radiative flux\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (W/m^2)\",\n title = \"Surface energy fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, turbulent_energy_flux, label = \"Turbulent fluxes\")\nplot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = \"Net flux\")\nsavefig(\"energy_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#References","page":"Introduction to the Land Bucket Model","title":"References","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/#The-Two-Stream-Scheme","page":"Two-Stream model","title":"The Two-Stream Scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"-overlinemu(dI^uparrow)dL + 1 - (1 - beta)omegaI^uparrow - omega beta I^downarrow = omega overlinemu K beta_0 exp(-KL)\n-overlinemu(dI^downarrow)dL + 1 - (1 - beta)omegaI^downarrow - omega beta I^uparrow = omega overlinemu K (1-beta_0) exp(-KL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"int_0^1mu^primeG(mu^prime)dmu^prime","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = frach_1exp(-KL)sigma + h_2exp(-hL) + h_3exp(hL)\nI^downarrow = frach_4exp(-KL)sigma + h_5exp(-hL) + h_6exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = h_7exp(-hL) + h_8exp(hL)\nI^downarrow = h_9exp(-hL) + h_10exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Output Symmbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500\nAbsorbed Near-Infrared Radiation ANIR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Parameters Symbol Unit Range\nCanopy PAR Reflectance alpha_PAR_leaf - 0.0–1.0\nCanopy NIR Reflectance alpha_NIR_leaf - 0.0–1.0\nCanopy PAR Transmittance tau_PAR_leaf - 0.0–1.0\nCanopy NIR Transmittance tau_NIR_leaf - 0.0–1.0\nCanopy Emissivity ϵ_canopy - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5\nTypical wavelength per photon PAR lambda_gamma_PAR m 5e-7\nTypical wavelength per photon NIR lambda_gamma_NIR m 1.65e-6","category":"page"},{"location":"APIs/Bucket/#Bucket","page":"Bucket Model","title":"Bucket","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"CurrentModule = ClimaLand.Bucket","category":"page"},{"location":"APIs/Bucket/#Types","page":"Bucket Model","title":"Types","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.BucketModelParameters\nClimaLand.Bucket.PrescribedBaregroundAlbedo\nClimaLand.Bucket.PrescribedSurfaceAlbedo\nClimaLand.Bucket.BucketModel","category":"page"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModelParameters","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModelParameters","text":"struct BucketModelParameters{\n FT <: AbstractFloat,\n PSE,\n}\n\nContainer for holding the parameters of the bucket model.\n\nκ_soil: Conductivity of the soil (W/K/m); constant\nρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant\nalbedo: Albedo Model\nσS_c: Critical σSWE amount (m) where surface transitions from to snow-covered\nf_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)\nW_f: Capacity of the land bucket (m)\nf_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)\np: Exponent used in β decay (unitless)\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nτc: τc timescale on which snow melts\nearth_param_set: Earth Parameter set; physical constants, etc\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedBaregroundAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedBaregroundAlbedo","text":"PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel\n\nAn albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedSurfaceAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedSurfaceAlbedo","text":"PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}\n <: AbstractBucketAlbedoModel\n\nAn albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.\n\nNote that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModel","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModel","text":"struct BucketModel{\n FT,\n PS <: BucketModelParameters{FT},\n ATM <: AbstractAtmosphericDrivers{FT},\n RAD <: AbstractRadiativeDrivers{FT},\n D,\n } <: AbstractBucketModel{FT}\n\nConcrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.\n\nparameters: Parameters required by the bucket model\natmos: The atmospheric drivers: Prescribed or Coupled\nradiation: The radiation drivers: Prescribed or Coupled\ndomain: The domain of the model\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#Misc-Functions","page":"Bucket Model","title":"Misc Functions","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.surface_albedo\nClimaLand.Bucket.beta_factor","category":"page"},{"location":"APIs/Bucket/#ClimaLand.surface_albedo","page":"Bucket Model","title":"ClimaLand.surface_albedo","text":"ClimaLand.surface_albedo(\n model::EnergyHydrology{FT},\n Y,\n p,\n) where {FT}\n\nReturns the surface albedo field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_albedo(model::SnowModel, Y, p)\n\nA helper function which computes and returns the snow albedo.\n\n\n\n\n\nsurface_albedo(model::BucketModel, Y, p)\n\nReturns the bulk surface albedo, which gets updated in update_aux via next_albedo.\n\n\n\n\n\nsurface_albedo(model::AbstractModel, Y, p)\n\nA helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Bucket/#ClimaLand.Bucket.beta_factor","page":"Bucket Model","title":"ClimaLand.Bucket.beta_factor","text":"beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}\n\nComputes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:\n\nβ = (x/xc)^p x < xc 1 otherwise\n\nwhere x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.\n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Coupled-heat-and-water-equations-tending-towards-equilibrium","page":"Energy and Hydrology","title":"Coupled heat and water equations tending towards equilibrium","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"θ_i is the volumetric ice fraction, and","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We will solve this equation in an effectively 1-d domain with z -10, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- κ T(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-κ T(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T(t = 0 z) = T_min + (T_max-T_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-K h(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ(t = 0 z) = ϑ_min + (ϑ_max-ϑ_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"where C T_min T_max ϑ_min and ϑ_max are constants.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy hz = 0 and Tz = 0. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Import-necessary-modules","page":"Energy and Hydrology","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"External (non - CliMA) modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"import SciMLBase\nusing Statistics\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"FT = Float32\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Create-the-model","page":"Energy and Hydrology","title":"Create the model","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν = FT(0.395)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.395f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν_ss_quartz = FT(0.92)\nν_ss_om = FT(0.0)\nν_ss_gravel = FT(0.0)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.0f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Ksat = FT(4.42 / 3600 / 100) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.89)\nvg_α = FT(7.5) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nθ_r = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat = Ksat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We also need to pick a domain on which to solve the equations:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"zmax = FT(0)\nzmin = FT(-1.0)\nnelems = 50\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Water boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_water_flux = WaterFluxBC((p, t) -> 0.0)\nbottom_water_flux = WaterFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary conditions for the heat equation:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_heat_flux = HeatFluxBC((p, t) -> 0.0)\nbottom_heat_flux = HeatFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We wrap up all of those in a WaterHeatBC struct:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"boundary_fluxes = (;\n top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),\n bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Set-up-the-simulation","page":"Energy and Hydrology","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"function init_soil!(Y, z, params)\n ν = params.ν\n θ_r = params.θ_r\n FT = eltype(Y.soil.ϑ_l)\n zmax = FT(0)\n zmin = FT(-1)\n\n theta_max = FT(ν * 0.5)\n theta_min = FT(ν * 0.4)\n T_max = FT(289.0)\n T_min = FT(288.0)\n\n c = FT(20.0)\n @. Y.soil.ϑ_l =\n theta_min +\n (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)\n Y.soil.θ_i .= FT(0.0)\n\n T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))\n\n θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)\n ρc_s =\n Soil.volumetric_heat_capacity.(\n θ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 72);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We use ClimaTimesteppers.jl for carrying out the time integration.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a timestepper and set up the ODE problem:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"dt = Float64(1000.0);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"saveat = collect(t0:FT(30000):tf)\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\ncb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Extract output","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z = parent(coords.subsurface.z)\nt = parent(sol.t)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nT = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Let's look at the initial and final times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(ϑ_l[1], z, xlabel = \"ϑ_l\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(ϑ_l[4], z, label = \"t = 1.5d\")\nplot!(ϑ_l[end], z, label = \"t = 3d\")\nsavefig(\"eq_moisture_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(T[1], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(T[4], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 1.5d\")\nplot!(T[end], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 3d\")\nsavefig(\"eq_temperature_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Analytic-Expectations","page":"Energy and Hydrology","title":"Analytic Expectations","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can determine a priori what we expect the final temperature to be in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"One could also solve the equation for ϑ_l specified by h z = 0 to determine the functional form of the equilibrium profile of the liquid water.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#References","page":"Energy and Hydrology","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.\nBalland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"using Plots\nimport ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing ClimaCore\nimport ClimaParams as CP\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nFT = Float64;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define simulation times","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"t0 = Float64(0)\ntf = Float64(60 * 60)\ndt = Float64(30);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define the domain","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"zmax = FT(0)\nzmin = FT(-1.1)\nnelems = 75\nΔ = FT((zmax - zmin) / nelems / 2)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();\nparameter_data = readdlm(data_file, ',');","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m\nksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s\nvgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).\nvgn = reverse(parameter_data[5, :])\nresidual_frac = reverse(parameter_data[2, :])\nporosity = reverse(parameter_data[3, :]);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Create fields corresponding to the parameter","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)\nK_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)\nθ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"hcm = SpaceVaryingInput(\n depth,\n (; α = vgα, n = vgn),\n soil_domain.space.subsurface,\n vanGenuchten{FT},\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The parameter struct:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"params = ClimaLand.Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"function top_flux_function(p, t)\n return -0.0001033\nend\ntop_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)\nbottom_bc = ClimaLand.Soil.FreeDrainage()\nboundary_fluxes = (; top = top_bc, bottom = bottom_bc)\nsoil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial the state vectors, and set initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"We also set the initial conditions of the auxiliary state here:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"stepper = CTS.ARS111()\n@assert FT in (Float32, Float64)\nerr = (FT == Float64) ? 1e-8 : 1e-4\nconvergence_cond = CTS.MaximumError(err)\nconv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 10,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n convergence_checker = conv_checker,\n ),\n)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = make_jacobian(soil)\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);\n\nz = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]\nplot(ϑ_l[1], z, label = \"initial\", color = \"grey\", aspect_ratio = 0.8)\nplot!(ϑ_l[2], z, label = \"8min\", color = \"orange\")\nplot!(ϑ_l[3], z, label = \"16min\", color = \"red\")\nplot!(ϑ_l[4], z, label = \"24min\", color = \"teal\")\nplot!(ϑ_l[5], z, label = \"32min\", color = \"blue\")\nplot!(ϑ_l[6], z, label = \"40min\", color = \"purple\")\nplot!(ϑ_l[7], z, label = \"60min\", color = \"green\")\nscatter!(porosity, depth, label = \"Porosity\")\nplot!(legend = :bottomright)\n\nplot!(xlim = [0, 0.7])\n\nplot!(\n ylim = [-1.1, 0],\n yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],\n)\n\nplot!(ylabel = \"Depth (m)\")\n\nplot!(xlabel = \"Volumeteric Water Content\")\n\nsavefig(\"./sv62_alpha_2_inf_updated_data_climaland.png\")","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy","page":"Canopy Models","title":"Canopy","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Structs","page":"Canopy Models","title":"Canopy Model Structs","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.CanopyModel\nClimaLand.Canopy.SharedCanopyParameters","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.CanopyModel","page":"Canopy Models","title":"ClimaLand.Canopy.CanopyModel","text":" CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}\n\nThe model struct for the canopy, which contains\n\nthe canopy model domain (a point for site-level simulations, or\n\nan extended surface (plane/spherical surface) for regional or global simulations.\n\nsubcomponent model type for radiative transfer. This is of type\n\nAbstractRadiationModel.\n\nsubcomponent model type for photosynthesis. This is of type\n\nAbstractPhotosynthesisModel, and currently only the FarquharModel is supported.\n\nsubcomponent model type for stomatal conductance. This is of type\n\nAbstractStomatalConductanceModel and currently only the MedlynModel is supported\n\nsubcomponent model type for plant hydraulics. This is of type\n\nAbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.\n\nsubcomponent model type for canopy energy. This is of type\n\nAbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.\n\nsubcomponent model type for canopy SIF. prognostically.\ncanopy model parameters, which include parameters that are shared\n\nbetween canopy model components or those needed to compute boundary fluxes.\n\nThe atmospheric conditions, which are either prescribed\n\n(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).\n\nThe radiative flux conditions, which are either prescribed\n\n(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).\n\nThe soil conditions, which are either prescribed (of type PrecribedSoil, for\n\nrunning the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)\n\nNote that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.\n\nautotrophic_respiration: Autotrophic respiration model, a canopy component model\nradiative_transfer: Radiative transfer model, a canopy component model\nphotosynthesis: Photosynthesis model, a canopy component model\nconductance: Stomatal conductance model, a canopy component model\nhydraulics: Plant hydraulics model, a canopy component model\nenergy: Energy balance model, a canopy component model\nsif: SIF model, a canopy component model\natmos: Atmospheric forcing: prescribed or coupled\nradiation: Radiative forcing: prescribed or coupled\nsoil_driver: Soil pressure: prescribed or prognostic\nparameters: Shared canopy parameters between component models\ndomain: Canopy model domain\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.SharedCanopyParameters","page":"Canopy Models","title":"ClimaLand.Canopy.SharedCanopyParameters","text":"SharedCanopyParameters{FT <: AbstractFloat, PSE}\n\nA place to store shared parameters that are required by multiple canopy components.\n\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nearth_param_set: Earth param set\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Fluxes","page":"Canopy Models","title":"Canopy Model Fluxes","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.DiagnosticTranspiration","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","page":"Canopy Models","title":"ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","text":"DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Soil-Drivers","page":"Canopy Models","title":"Canopy Model Soil Drivers","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.AbstractSoilDriver\nClimaLand.Canopy.PrescribedSoil","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.AbstractSoilDriver","page":"Canopy Models","title":"ClimaLand.Canopy.AbstractSoilDriver","text":"An abstract type of soil drivers of the canopy model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PrescribedSoil","page":"Canopy Models","title":"ClimaLand.Canopy.PrescribedSoil","text":" PrescribedSoil <: AbstractSoilDriver\n\nA container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.\n\nroot_depths: The depth of the root tips, in meters\nψ: Prescribed soil potential (m) in the root zone a function of time\nT: Prescribed soil surface temperature (K) as a function of time\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\nϵ: Soil emissivity\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-model","page":"Boundary conditions","title":"Boundary conditions for the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-Richards-equation","page":"Boundary conditions","title":"Boundary conditions for Richards equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.\nWaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).\nMoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).\nRichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-equation","page":"Boundary conditions","title":"Boundary conditions for the soil heat equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).\nTemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-water-equations-(EnergyHydrology-model)","page":"Boundary conditions","title":"Boundary conditions for the soil heat + water equations (EnergyHydrology model)","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"The full soil model requires boundary conditions for both Richards equation and the soil heat equation.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).\nAtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"This page was generated using Literate.jl.","category":"page"},{"location":"folderstructure/#ClimaLand-folder-structure","page":"Repository structure","title":"ClimaLand folder structure","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand home directory has 5 main folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"docs: contains files to generate the documentation website.\nexperiments: contains simple runs of ClimaLand models. \nparameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc. \nsrc: contains the code of ClimaLand models. \ntest: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR. \n.dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main). \n.github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"as well as 5 files:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git. \nLICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code. \nProject.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file. \nREADME.md: This markdown file contains the info that you can read on ClimaLand GitHub web page","category":"page"},{"location":"folderstructure/#/docs-folder","page":"Repository structure","title":"/docs folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Julia packages are recommended to have a \\docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The folder /docs contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation \"contribution guide\" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file. \na make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl. ","category":"page"},{"location":"folderstructure/#/experiments-folder","page":"Repository structure","title":"/experiments folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"For example, /experiments/LSM/ozark/ contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).\nozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.\nozark_parameters.jl: In this file, parameters values are defined. \nozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm. \nozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.","category":"page"},{"location":"folderstructure/#/src-folder","page":"Repository structure","title":"/src folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The /src folder contains the source code of ClimaLand models. It contains 3 folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.\nstandalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder. \nintegrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"As well as one file:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase. ","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Stomatal-Conductance","page":"Canopy Stomatal Conductance","title":"Stomatal Conductance","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Parameters","page":"Canopy Stomatal Conductance","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.MedlynConductanceParameters","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.MedlynConductanceParameters","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.MedlynConductanceParameters","text":"MedlynConductanceParameters{FT <: AbstractFloat}\n\nThe required parameters for the Medlyn stomatal conductance model.\n\nDrel: Relative diffusivity of water vapor (unitless)\ng0: Minimum stomatal conductance mol/m^2/s\ng1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/StomatalConductance/#Methods","page":"Canopy Stomatal Conductance","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.medlyn_term\nClimaLand.Canopy.medlyn_conductance\nClimaLand.Canopy.upscale_leaf_conductance\nClimaLand.Canopy.penman_monteith","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_term","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_term","text":"medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}\n\nComputes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).\n\nthermo_params is the Thermodynamics.jl parameter set.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_conductance","text":"medlyn_conductance(g0::FT,\n Drel::FT,\n medlyn_term::FT,\n An::FT,\n ca::FT) where {FT}\n\nComputes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).\n\nThis returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.upscale_leaf_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.upscale_leaf_conductance","text":"upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}\n\nThis currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.\n\nTODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.penman_monteith","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.penman_monteith","text":"penman_monteith(\n Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1) \n Rn::FT, # Net irradiance (W m−2)\n G::FT, # Ground heat flux (W m−2)\n ρa::FT, # Dry air density (kg m−3)\n cp::FT, # Specific heat capacity of air (J kg−1 K−1) \n VPD::FT, # vapor pressure deficit (Pa)\n ga::FT, # atmospheric conductance (m s−1)\n γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)\n gs::FT, # surface or stomatal conductance (m s−1)\n Lv::FT, # Volumetric latent heat of vaporization (J m-3)\n ) where {FT}\n\nComputes the evapotranspiration in m/s using the Penman-Monteith equation. \n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-Tutorial","page":"Intro to ClimaLand Domains","title":"Domain Tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/#Goals-of-the-tutorial","page":"Intro to ClimaLand Domains","title":"Goals of the tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Background","page":"Intro to ClimaLand Domains","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question \"only\" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"ClimaLand Domains were designed with this in mind. The domains are defined so that","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the user can easily switch geometries, e.g. single column to global model,\nindividual component models can be run by themselves, using a single domain,\nthe same domains can be used to set up multi-component models (LSMs),\ndifferent variables can exist on different parts of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#What-is-a-ClimaLand-Domain?","page":"Intro to ClimaLand Domains","title":"What is a ClimaLand Domain?","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-types","page":"Intro to ClimaLand Domains","title":"Domain types","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"0D: Domains.Point\n1D: Domains.Column\n2D: Domains.Plane, Domains.SphericalSurface\n3D: Domains.HybridBox, Domains.SphericalShell.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Domain Surface Domain Subsurface Domain\nColumn Point Column\nHybridBox Plane HybridBox\nSphericalShell SphericalSurface SphericalShell","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"There is a single key method which take a ClimaLand domain as an argument.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"coordinates(domain): under the hood, this function uses","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#How-variable-initialization-depends-on-domains","page":"Intro to ClimaLand Domains","title":"How variable initialization depends on domains","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Future-work","page":"Intro to ClimaLand Domains","title":"Future work","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into \"vertical\" and \"horizontal\" pieces.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[1]: finite differencing is used in the vertical, and spectral elements are used in the horizontal.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[2]: a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[3]: We also will support having an array-like type of variable.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Coupling-the-CliMA-Canopy-and-Soil-Hydraulics-Models","page":"Coupled Canopy and Soil","title":"Coupling the CliMA Canopy and Soil Hydraulics Models","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Preliminary-Setup","page":"Coupled Canopy and Soil","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load External Packages:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Soil.Biogeochemistry\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Setup the domain for the model:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4)\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Column(; zlim = (zmin, zmax), nelements = nelements);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"time_offset = 7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Site latitude and longitude","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"-92.2f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Height of the sensor at the site","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"atmos_h = FT(32) # m","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"32.0f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Setup-the-Coupled-Canopy-and-Soil-Physics-Model","page":"Coupled Canopy and Soil","title":"Setup the Coupled Canopy and Soil Physics Model","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the parameters for the soil model and provide them to the model parameters struct:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil_ν = FT(0.5) # m3/m3\nsoil_K_sat = FT(4e-7) # m/s\nsoil_S_s = FT(1e-3) # 1/m\nsoil_vg_n = FT(2.05) # unitless\nsoil_vg_α = FT(0.04) # inverse meters\nθ_r = FT(0.067); # m3/m3","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil heat transfer parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ν_ss_quartz = FT(0.1)\nν_ss_om = FT(0.1)\nν_ss_gravel = FT(0.0);\nz_0m_soil = FT(0.1)\nz_0b_soil = FT(0.1)\nsoil_ϵ = FT(0.98)\nsoil_α_PAR = FT(0.2)\nsoil_α_NIR = FT(0.4)\n\nsoil_domain = land_domain\nsoil_ps = Soil.EnergyHydrologyParameters(\n FT;\n ν = soil_ν,\n ν_ss_om = ν_ss_om,\n ν_ss_quartz = ν_ss_quartz,\n ν_ss_gravel = ν_ss_gravel,\n hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),\n K_sat = soil_K_sat,\n S_s = soil_S_s,\n θ_r = θ_r,\n earth_param_set = earth_param_set,\n z_0m = z_0m_soil,\n z_0b = z_0b_soil,\n emissivity = soil_ϵ,\n PAR_albedo = soil_α_PAR,\n NIR_albedo = soil_α_NIR,\n);\n\nsoil_args = (domain = soil_domain, parameters = soil_ps)\nsoil_model_type = Soil.EnergyHydrology{FT}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ClimaLand.Soil.EnergyHydrology{Float32}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}\n\nsoilco2_ps = SoilCO2ModelParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil microbes args","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))\n\nsoilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()\nsoilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);\nsoilco2_sources = (MicrobeProduction{FT}(),);\n\nsoilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);\n\nsoilco2_args = (;\n boundary_conditions = soilco2_boundary_conditions,\n sources = soilco2_sources,\n domain = soil_domain,\n parameters = soilco2_ps,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_types = (;\n autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},\n radiative_transfer = Canopy.TwoStreamModel{FT},\n photosynthesis = Canopy.FarquharModel{FT},\n conductance = Canopy.MedlynConductanceModel{FT},\n hydraulics = Canopy.PlantHydraulicsModel{FT},\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"autotrophic_respiration_args =\n (; parameters = AutotrophicRespirationParameters(FT))\n\nradiative_transfer_args = (;\n parameters = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = 0.1,\n α_NIR_leaf = 0.45,\n τ_PAR_leaf = 0.05,\n τ_NIR_leaf = 0.25,\n Ω = 0.69,\n )\n)\n\nconductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))\n\nphotosynthesis_args =\n (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));\n\nK_sat_plant = FT(1.8e-8)\nRAI = (SAI + maxLAI) * f_root_to_shoot;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Note: LAIfunction was determined from data in the script we included above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)\nfunction root_distribution(z::T; rooting_depth = FT(1.0)) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m\nend\n\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)\n\nplant_ν = FT(0.7)\nplant_S_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = plant_ν,\n S_s = plant_S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n)\n\nplant_hydraulics_args = (\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_midpoints = compartment_midpoints,\n compartment_surfaces = compartment_surfaces,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_args = (;\n autotrophic_respiration = autotrophic_respiration_args,\n radiative_transfer = radiative_transfer_args,\n photosynthesis = photosynthesis_args,\n conductance = conductance_args,\n hydraulics = plant_hydraulics_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We also need to provide the shared parameter struct to the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n)\ncanopy_domain = obtain_surface_domain(land_domain)\ncanopy_model_args = (; parameters = shared_params, domain = canopy_domain);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)\n\nland = SoilCanopyModel{FT}(;\n soilco2_type = soilco2_type,\n soilco2_args = soilco2_args,\n land_args = land_input,\n soil_model_type = soil_model_type,\n soil_args = soil_args,\n canopy_component_types = canopy_component_types,\n canopy_component_args = canopy_component_args,\n canopy_model_args = canopy_model_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y, p, coords = initialize(land);\nexp_tendency! = make_exp_tendency(land);\nimp_tendency! = make_imp_tendency(land);\njacobian! = make_jacobian(land);\njac_kwargs =\n (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We need to provide initial conditions for the soil and canopy hydraulics models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y.soil.ϑ_l = FT(0.4)\nY.soil.θ_i = FT(0.0)\nT_0 = FT(288.7)\nρc_s =\n volumetric_heat_capacity.(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n land.soil.parameters.ρc_ds,\n earth_param_set,\n )\nY.soil.ρe_int =\n volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)\n\nY.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air\n\nψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n plant_ν,\n plant_S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .=\n augmented_liquid_fraction.(plant_ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"t0 = Float64(150 * 3600 * 24)# start mid year\nN_days = 100\ntf = t0 + Float64(3600 * 24 * N_days)\ndt = Float64(30)\nn = 120\nsaveat = Array(t0:(n * dt):tf)\n\ntimestepper = CTS.ARS343()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now set the initial values for the cache variables for the combined soil and plant model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"set_initial_cache! = make_set_initial_cache(land)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Set the callbacks, which govern how often we save output, and how often we update the forcing data (\"drivers\")","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"sv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nmodel_drivers = ClimaLand.get_drivers(land)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\nupdateat = Array(t0:1800:tf)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Carry out the simulation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(\n prob,\n ode_algo;\n dt = dt,\n callback = cb,\n adaptive = false,\n saveat = saveat,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Plotting","page":"Coupled Canopy and Soil","title":"Plotting","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Transpiration plot:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Show the two plots together:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_canopy_flux_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"plt1 = Plots.plot(size = (500, 700));\nϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]\nplt1 = Plots.plot(\n daily,\n ϑ_l_10,\n label = \"10 cm\",\n xlabel = \"Days\",\n ylabel = \"SWC [m/m]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n color = \"blue\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],\n label = \"20cm\",\n color = \"red\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],\n label = \"30cm\",\n color = \"purple\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"root_stem_flux = [\n sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for\n k in 1:length(sol.t)\n]\nplt1 = Plots.plot(\n daily,\n root_stem_flux,\n label = \"soil-root-stem water flux\",\n ylabel = \"Water flux[mm/day]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And save the output","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_plant_flux.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"diagnostics/developers_diagnostics/#ClimaLand-Diagnostics:-why-and-how","page":"For developers","title":"ClimaLand Diagnostics: why and how","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in \"K\" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Diagnostics.jl defines the module,\ndiagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"compute methods are defined in a separate file, for example, bucket_compute_methods.jl.\nstandard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.\ndefault_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Compute-methods","page":"For developers","title":"Compute methods","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.α_sfc)\n else\n out .= p.bucket.α_sfc\n end\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@diagnostic_compute \"albedo\" BucketModel p.bucket.α\\_sfc","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Define-diagnostics","page":"For developers","title":"Define diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!(\n short_name = \"alpha\",\n long_name = \"Albedo\",\n standard_name = \"albedo\",\n units = \"\",\n compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),\n )","category":"page"},{"location":"diagnostics/developers_diagnostics/#Default-diagnostics","page":"For developers","title":"Default diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"function default_diagnostics(land_model::BucketModel, t_start; output_writer)\n\n define_diagnostics!(land_model)\n\n bucket_diagnostics = [\n \"alpha\",\n \"rn\",\n \"tsfc\",\n \"qsfc\",\n \"lhf\",\n \"rae\",\n \"shf\",\n \"vflux\",\n \"rhosfc\",\n \"t\",\n \"w\",\n \"ws\",\n \"sigmas\",\n ]\n\n default_outputs =\n hourly_averages(bucket_diagnostics...; output_writer, t_start)\n return [default_outputs...]\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = [\"gpp\", \"ct\", \"lai\", \"swc\", \"si\"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Standard-diagnostic-frequencies","page":"For developers","title":"Standard diagnostic frequencies","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(\n 60 * 60 * one(t_start),\n (+),\n output_writer,\n t_start,\n short_names...;\n pre_output_hook! = average_pre_output_hook!,\n)","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.","category":"page"},{"location":"","page":"Home","title":"Home","text":"using Pkg\nio = IOBuffer()\nv = Pkg.installed()[\"ClimaLand\"]\nprint(io, \"\"\"\n # ClimaLand.jl Documentation (v$(v))\n\n \"\"\")\nimport Markdown\nMarkdown.parse(String(take!(io)))","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand can be run coupled (or \"online\") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data (\"offline\").","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.","category":"page"},{"location":"#Important-Links","page":"Home","title":"Important Links","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CliMA Homepage\nCliMA GitHub Organisation\nClimaCoupler\nClimaAnalysis\nJulia Homepage","category":"page"},{"location":"#Documentation-for-Users-and-Developers","page":"Home","title":"Documentation for Users and Developers","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.","category":"page"},{"location":"#Physical-units","page":"Home","title":"Physical units","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Note that CliMA, in all its repositories, uses Standard Units, reminded below","category":"page"},{"location":"","page":"Home","title":"Home","text":"Quantity Unit Name SI Symbol SI Unit Equivalent\nLength Meter m 1 m\nMass Kilogram kg 1 kg\nTime Second s 1 s\nTemperature Kelvin K 1 K\nAmount of Substance Mole mol 1 mol\nEnergy Joule J 1 J = 1 N·m\nPower Watt W 1 W = 1 J/s\nPressure Pascal Pa 1 Pa = 1 N/m²\nFrequency Hertz Hz 1 Hz = 1 s⁻¹","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 270.0^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"T_air = FT(270.0)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 12\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sources = (PhaseChange{FT}(),);\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)\n Y.soil.θ_i .= 0\n T = FT(275.0)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 4)\ndt = Float64(5)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"5.0","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping functions:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = ClimaLand.make_jacobian(soil)\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\n\ntimestepper = CTS.ARS111()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsub = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n sv.saveval[k].soil.ice_frac,\n )[1] for k in 1:length(sol.t)\n]\n\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (400, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Rate (mm/d)\",\n title = \"Vapor Fluxes\",\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n sub .* (1000 * 3600 * 24),\n label = \"Sublimation\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Evaporation\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nsave(\"water_fluxes.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Temperature\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(260, 280)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 3, 4]\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax1,\n parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Ice\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax2,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Liquid Water\", xlabel = \"\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax3,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"profiles.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Soil/#Soil-Models","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"CurrentModule = ClimaLand.Soil","category":"page"},{"location":"APIs/Soil/#Soil-Models-2","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilModel\nClimaLand.Soil.RichardsModel\nClimaLand.Soil.EnergyHydrology","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilModel","text":"AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}\n\nThe abstract type for all soil models.\n\nCurrently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsModel","text":"RichardsModel\n\nA model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.\n\nA variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).\n\nIf you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrology","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrology","text":"EnergyHydrology <: AbstractSoilModel\n\nA model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.\n\nA variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.\n\nparameters: The parameter sets\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Parameter-Structs","page":"Soil Energy and Hydrology","title":"Soil Parameter Structs","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.RichardsParameters\nClimaLand.Soil.EnergyHydrologyParameters","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsParameters","text":"RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}\n\nA struct for storing parameters of the RichardsModel.\n\nν: The porosity of the soil (m^3/m^3)\nhydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrologyParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrologyParameters","text":"EnergyHydrologyParameters{\n FT <: AbstractFloat,\n F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n C,\n PSE,\n }\n\nA parameter structure for the integrated soil water and energy equation system.\n\nNote that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:\n\nSurface parameters: albedo in each wavelength band (SF)\nScalar parameters: emissivity, α, β, γ, γT_ref, Ω,\n\nroughness lengths z0, dds ) (FT)\n\nParameters defined in the interior: all else (F)\n\nκ_dry: The dry soil thermal conductivity, W/m/K\nκ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K\nκ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K\nρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)\nν: The porosity of the soil (m^3/m^3)\nν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)\nν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)\nν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)\nα: The parameter α used in computing Kersten number, unitless\nβ: The parameter β used in computing Kersten number, unitless\nhydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\nΩ: Ice impedance factor for the hydraulic conductivity\nγ: Coefficient of viscosity factor for the hydraulic conductivity\nγT_ref: Reference temperature for the viscosity factor\nPAR_albedo: Soil PAR Albedo\nNIR_albedo: Soil NIR Albedo\nemissivity: Soil Emissivity\nz_0m: Roughness length for momentum\nz_0b: Roughness length for scalars\nd_ds: Maximum dry soil layer thickness under evaporation (m)\nearth_param_set: Physical constants and clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Hydrology-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Hydrology Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_liquid_fraction\nClimaLand.Soil.pressure_head\nClimaLand.Soil.hydraulic_conductivity\nClimaLand.Soil.impedance_factor\nClimaLand.Soil.viscosity_factor\nClimaLand.Soil.effective_saturation\nClimaLand.Soil.matric_potential\nClimaLand.Soil.dψdϑ\nClimaLand.Soil.inverse_matric_potential\nClimaLand.Soil.AbstractSoilHydrologyClosure\nClimaLand.Soil.vanGenuchten\nClimaLand.Soil.BrooksCorey","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_liquid_fraction","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_liquid_fraction","text":"volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}\n\nA pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.pressure_head","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.pressure_head","text":"pressure_head(\n cm::vanGenuchten{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\npressure_head(\n cm::BrooksCorey{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.hydraulic_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.hydraulic_conductivity","text":" hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.\n\n\n\n\n\n hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.impedance_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.impedance_factor","text":"impedance_factor(\n f_i::FT,\n Ω::FT\n) where {FT}\n\nReturns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.viscosity_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.viscosity_factor","text":"viscosity_factor(\n T::FT,\n γ::FT,\n γT_ref::FT,\n) where {FT}\n\nReturns the multiplicative factor which accounts for the temperature dependence of the conductivity.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.effective_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.effective_saturation","text":"effective_saturation(porosity::FT, ϑ_l::FT, θr::FT) where {FT}\n\nA point-wise function computing the effective saturation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.matric_potential","text":" matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.dψdϑ","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dψdϑ","text":"dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.\n\n\n\n\n\ndψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.inverse_matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.inverse_matric_potential","text":" inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilHydrologyClosure","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilHydrologyClosure","text":"AbstractSoilHydrologyClosure{FT <: AbstractFloat}\n\nThe abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.\n\nTo add a new parameterization, methods are required for:\n\nmatric_potential,\ninversematricpotential,\npressure_head,\ndψdϑ,\nhydraulic_conductivity.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.vanGenuchten","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.vanGenuchten","text":"vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).\n\nα: The inverse of the air entry potential (1/m)\nn: The van Genuchten pore-size distribution index (unitless)\nm: The van Genuchten parameter m = 1 - 1/n (unitless)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.BrooksCorey","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.BrooksCorey","text":"BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).\n\nc: The pore-size distribution index (unitless)\nψb: The air entry matric potential, when S=1 (m)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Heat-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Heat Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_heat_capacity\nClimaLand.Soil.κ_solid\nClimaLand.Soil.κ_sat_frozen\nClimaLand.Soil.κ_sat_unfrozen\nClimaLand.Soil.κ_sat\nClimaLand.Soil.κ_dry\nClimaLand.Soil.kersten_number\nClimaLand.Soil.relative_saturation\nClimaLand.Soil.volumetric_internal_energy\nClimaLand.Soil.volumetric_internal_energy_liq\nClimaLand.Soil.temperature_from_ρe_int\nClimaLand.Soil.thermal_conductivity\nClimaLand.Soil.phase_change_source\nClimaLand.Soil.thermal_time","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_heat_capacity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_heat_capacity","text":"volumetric_heat_capacity(\n θ_l::FT,\n θ_i::FT,\n ρc_ds::FT,\n earth_param_set::EP,\n) where {FT,EP}\n\nCompute the expression for volumetric heat capacity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_solid","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_solid","text":"κ_solid(ν_ss_om::FT,\n ν_ss_quartz::FT,\n κ_om::FT,\n κ_quartz::FT,\n κ_minerals::FT) where {FT}\n\nComputes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_frozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_frozen","text":"function κ_sat_frozen(\n κ_solid::FT,\n ν::FT,\n κ_ice::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated frozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_unfrozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_unfrozen","text":"function κ_sat_unfrozen(\n κ_solid::FT,\n ν::FT,\n κ_l::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated unfrozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat","text":"κ_sat(\n θ_l::FT,\n θ_i::FT,\n κ_sat_unfrozen::FT,\n κ_sat_frozen::FT\n) where {FT}\n\nCompute the expression for saturated thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_dry","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_dry","text":"function κ_dry(ρp::FT,\n ν::FT,\n κ_solid::FT,\n κ_air::FT;\n a = FT(0.053)) where {FT}\n\nComputes the thermal conductivity of dry soil according to the model of Balland and Arp.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.kersten_number","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.kersten_number","text":"kersten_number(\n θ_i::FT,\n S_r::FT,\n α::FT,\n β::FT,\n ν_ss_om::FT,\n ν_ss_quartz::FT,\n ν_ss_gravel::FT,\n ) where {FT}\n\nCompute the expression for the Kersten number, using the Balland and Arp model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.relative_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.relative_saturation","text":"relative_saturation(\n θ_l::FT,\n θ_i::FT,\n ν::FT\n) where {FT}\n\nCompute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy","text":"volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy_liq","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy_liq","text":"volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.temperature_from_ρe_int","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.temperature_from_ρe_int","text":"temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_conductivity","text":"thermal_conductivity(\n κ_dry::FT,\n K_e::FT,\n κ_sat::FT\n) where {FT}\n\nCompute the expression for thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.phase_change_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.phase_change_source","text":"phase_change_source(\n θ_l::FT,\n θ_i::FT,\n T::FT,\n τ::FT,\n ν::FT,\n θ_r::FT,\n hydrology_cm::C,\n earth_param_set::EP,\n) where {FT, EP, C}\n\nReturns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.\n\nNote that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_time","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_time","text":"thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}\n\nReturns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Surface-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Surface Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.soil.soil_resistance\nClimaLand.Soil.dry_soil_layer_thickness\nClimaLand.Soil.soil_tortuosity","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.dry_soil_layer_thickness","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dry_soil_layer_thickness","text":"dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}\n\nReturns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_tortuosity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_tortuosity","text":"soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}\n\nComputes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.\n\nSee Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Runoff-Types-and-Methods","page":"Soil Energy and Hydrology","title":"Soil Runoff Types and Methods","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.NoRunoff\nClimaLand.Soil.SurfaceRunoff\nClimaLand.Soil.TOPMODELRunoff\nClimaLand.Soil.TOPMODELSubsurfaceRunoff\nClimaLand.Soil.subsurface_runoff_source\nClimaLand.Soil.update_runoff!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.NoRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.NoRunoff","text":"NoRunoff <: AbstractRunoffModel\n\nA concrete type of soil runoff model; the default choice, which does not include any runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.SurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.SurfaceRunoff","text":"SurfaceRunoff <: AbstractRunoffModel\n\nA simple model for runoff appropriate for single column runs.\n\nOnly surface runoff is computed, using a combination of Dunne and Hortonian runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELRunoff","text":"TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel\n\nThe TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.\n\nThe runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\nf_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.\nsubsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","text":"TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}\n\nThe TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.\n\nThe runoff flux is given by Equation 12 of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nR_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.subsurface_runoff_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.subsurface_runoff_source","text":"subsurface_runoff_source(runoff::AbstractRunoffModel)\n\nA helper function which returns the subsurface source of the runoff model runoff.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.update_runoff!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.update_runoff!","text":"update_runoff!(p, runoff::NoRunoff, _...)\n\nUpdates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.\n\n\n\n\n\nupdate_runoff!(\n p,\n runoff::SurfaceRunoff,\n Y,\n t,\n model::AbstractSoilModel,\n\n)\n\nThe update_runoff! function for the SurfaceRunoff model.\n\nUpdates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration\n\n\n\n\n\nupdate_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)\n\nUpdates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-BC-Methods-and-Types","page":"Soil Energy and Hydrology","title":"Soil BC Methods and Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.MoistureStateBC\nClimaLand.Soil.HeatFluxBC\nClimaLand.Soil.WaterFluxBC\nClimaLand.Soil.TemperatureStateBC\nClimaLand.Soil.FreeDrainage\nClimaLand.Soil.RichardsAtmosDrivenFluxBC\nClimaLand.Soil.AtmosDrivenFluxBC\nClimaLand.Soil.WaterHeatBC\nClimaLand.Soil.soil_boundary_fluxes!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.MoistureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.MoistureStateBC","text":"MoistureStateBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.HeatFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.HeatFluxBC","text":"HeatFluxBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterFluxBC","text":"WaterFluxBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.TemperatureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.TemperatureStateBC","text":"TemperatureStateBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.FreeDrainage","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.FreeDrainage","text":"FreeDrainage <: AbstractWaterBC\n\nA concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsAtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsAtmosDrivenFluxBC","text":"RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC\n\nA concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.\n\nIf you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.\n\nprecip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.AtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AtmosDrivenFluxBC","text":"AtmosDrivenFluxBC{\n A <: AbstractAtmosphericDrivers,\n B <: AbstractRadiativeDrivers,\n R <: AbstractRunoffModel\n} <: AbstractEnergyHydrologyBC\n\nA concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.\n\nThis choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.\n\natmos: The atmospheric conditions driving the model\nradiation: The radiative fluxes driving the model\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterHeatBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterHeatBC","text":"WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:\n AbstractEnergyHydrologyBC\n\nA general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_boundary_fluxes!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_boundary_fluxes!","text":"soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)\n\nupdates the boundary fluxes for ϑl and ρeint.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere,\n <:PrescribedRadiativeFluxes,\n },\n boundary::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz,\n Y,\n p,\n t,\n)\n\nReturns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.\n\nIf you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.\n\nThis function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},\n boundary::ClimaLand.TopBoundary,\n soil::EnergyHydrology{FT},\n Δz,\n Y,\n p,\n t,\n) where {FT}\n\nA method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Source-Types","page":"Soil Energy and Hydrology","title":"Soil Source Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilSource\nClimaLand.Soil.PhaseChange\nClimaLand.Soil.RootExtraction","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilSource","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilSource","text":"AbstractSoilSource{FT} <: ClimaLand.AbstractSource{FT}\n\nAn abstract type for types of source terms for the soil equations.\n\nIn standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.PhaseChange","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.PhaseChange","text":"PhaseChange{FT} <: AbstractSoilSource{FT}\n\nPhaseChange source type.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Jacobian-Structures","page":"Soil Energy and Hydrology","title":"Soil Jacobian Structures","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.ImplicitEquationJacobian","category":"page"},{"location":"APIs/Soil/#ClimaLand.ImplicitEquationJacobian","page":"Soil Energy and Hydrology","title":"ClimaLand.ImplicitEquationJacobian","text":"ImplicitEquationJacobian{M, S}\n\nA struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.\n\nmatrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.\n\nNote that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.\n\n\n\n\n\n","category":"type"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Radiative-transfer-scheme","page":"Beer model","title":"Radiative transfer scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Beer's-law","page":"Beer model","title":"Beer's law","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"APAR(PAR theta_s) = (PAR)(1 - rho_leaf)(1 - e^(-K(theta_s) LAI Omega))","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, ρ_leaf is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, θ_s is the zenith angle, and Ω is the clumping index following Braghiere (2021). K, Ω and ρ_leaf are all unitless. LAI is in m² m⁻². In order to compute K, we need θ_s in radians and the leaf angle distribution l_d (unitless). K is then defined as","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"K = l_dmax(cos(theta_s) epsilon)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"so that at night, when 3π/2 > θ_s > π/2, K is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Output Symbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Parameters Symbol Unit Range\nCanopy reflectance ρ_leaf - 0.0–1.0\nExtinction coefficient K - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Interactive-APAR(PAR,-LAI,-ρ_{leaf},-K,-Ω)","page":"Beer model","title":"Interactive APAR(PAR, LAI, ρ_leaf, K, Ω)","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Modeling-a-freezing-front-in-unsaturated-soil","page":"Phase Changes","title":"Modeling a freezing front in unsaturated soil","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν ) -fracF_Tρ_l","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac θ_i t = fracF_Tρ_i","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Here","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_i is the volumetric ice fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν denotes parameters relating to soil type, such as porosity, and","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T is the freeze-thaw term.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We solve these equations in an effectively 1-d domain with z -020, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z = 0) = 28 Wm^2K (T - 26715K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z= -02) = -3 Wm^2K (T - 27985K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T(t = 0 z) = 27985 K","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"-K h(t z = -02) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l(t = 0 z) = 033.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Import-necessary-modules","page":"Phase Changes","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS\nusing DelimitedFiles\nusing CairoMakie\n\nusing ClimaCore\nimport ClimaParams as CP\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Preliminary set-up","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"FT = Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν = FT(0.535)\nK_sat = FT(3.2e-6) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.48)\nvg_α = FT(1.11) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"You could also try the Brooks and Corey model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"#ψb = FT(-0.6)\n#c = FT(0.43)\n#hcm = BrooksCorey(;ψb = ψb, c = c);\nθ_r = FT(0.05)\nν_ss_om = FT(0.3)\nν_ss_quartz = FT(0.7)\nν_ss_gravel = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose the domain and discretization:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zmax = FT(0)\nzmin = FT(-0.2)\nnelems = 20\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)\nfunction top_heat_flux(p, t)\n FT = eltype(p.soil.T)\n p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))\n T_c = ClimaCore.Fields.level(p.soil.T, p_len)\n return @. FT(28 * (T_c - 267.15))\nend\nfunction bottom_heat_flux(p, t)\n FT = eltype(p.soil.T)\n T_c = ClimaCore.Fields.level(p.soil.T, 1)\n return @. FT(-3 * (T_c - 279.85))\nend\ntop_heat_flux_bc = HeatFluxBC(top_heat_flux)\nbottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)\nboundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),\n bottom = WaterHeatBC(;\n water = zero_water_flux_bc,\n heat = bottom_heat_flux_bc,\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sources = (PhaseChange{FT}(),);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can package this up in the EnergyHydrology model struct:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Running-a-simulation","page":"Phase Changes","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Once we have the model, we can initialize the state vectors and obtain the coordinates","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Y, p, coords = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"After which, we can specify the initial condition function, and initialze the variables:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"function init_soil!(Ysoil, z, params)\n ν = params.ν\n FT = eltype(Ysoil.soil.ϑ_l)\n Ysoil.soil.ϑ_l .= FT(0.33)\n Ysoil.soil.θ_i .= FT(0.0)\n T = FT(279.85)\n ρc_s = Soil.volumetric_heat_capacity(\n FT(0.33),\n FT(0.0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Ysoil.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n FT(0.0),\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 50);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the tendency function, and choose a timestep, and timestepper:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ndt = Float64(100)\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 3,\n update_j = CTS.UpdateEvery(CTS.NewTimeStep),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Comparison-to-data","page":"Phase Changes","title":"Comparison to data","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();\nds = readdlm(dataset_path, ',')\nhours = ds[:, 1][2:end]\nvwc = ds[:, 2][2:end] ./ 100.0\ndepth = ds[:, 3][2:end]\nmask_12h = hours .== 12\nmask_24h = hours .== 24\nmask_50h = hours .== 50;\n\nfig = Figure(size = (900, 300))\nax1 = Axis(\n fig[1, 1],\n title = \"12 hours\",\n xlabel = L\"θ_l + θ_i\",\n ylabel = \"Soil depth (m)\",\n)\nlimits!(ax1, 0.2, 0.5, -0.2, 0.0)\nax2 = Axis(\n fig[1, 2],\n title = \"24 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax2, 0.2, 0.5, -0.2, 0.0)\nax3 = Axis(\n fig[1, 3],\n title = \"50 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax3, 0.2, 0.5, -0.2, 0.0)\n\n\nz = parent(coords.subsurface.z)[:];\n\nscatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = \"\", color = \"purple\")\nlines!(\n ax1,\n parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\n\nscatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = \"\", color = \"purple\")\nlines!(\n ax2,\n parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\nscatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = \"Data\", color = \"purple\")\nlines!(\n ax3,\n parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],\n z,\n label = \"Simulation\",\n color = :green,\n)\naxislegend(ax3, position = :rb)\n\nsave(\"mizoguchi_data_comparison.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Discussion-and-Model-Explanation","page":"Phase Changes","title":"Discussion and Model Explanation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρ_l partial_tϑ_l + ρ_i partial_tθ_i = -F_T + F_T = 0","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing (T T_f)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"fracdp_lρ_l = L_f fracdTT","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"or","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l = p_l0 + L_f ρ_l fracT-T_fT_f mathcalH(T_f-T)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"What this implies is that above the freezing point, the pressure is equal to p_l0, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure p_l0 is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l0 = ρ_l g ψ(θ_l+ρ_iθ_iρ_l)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_l^* = (ν-θ_r) ψ^-1(p_l(ρ_l g)) + θ_r","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"For freezing, the freeze thaw function F_T is equal to","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T = frac1τ ρ_l (θ_l-θ_l^*) mathcalH(T_f-T) mathcalH(θ_l-θ_l^*)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"τ_LTE= c Δz²κ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#References","page":"Phase Changes","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.\nHansson et al., Vadose Zone Journal 3:693–704 (2004).\nM. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).\nKurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)\nWatanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58\nPainter and Karra, Vadose Zone Journal (2014) 13 (4)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/#Scraping-SNOTEL-Data","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"section"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We begin by importing all required packages:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"metadata = DataTools.snotel_metadata();\nmetacols = [\"id\", \"name\", \"state\", \"elev\", \"lat\", \"lon\"]\nDataFrames.rename!(metadata, Symbol.(metacols));","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"example_ID = 1030\nexample_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]\nexample_data = DataTools.scrape_site_paper(example_ID, example_state);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"const inch2meter = 0.0254\nconst kmphr2mps = 5.0 / 18.0\n\nfilter_val = Dict{Symbol, Tuple{Real, Real}}(\n :SWE => (0.0, 250.0),\n :z => (0.0, 420.0),\n :precip => (0.0, 250.0),\n :rel_hum_avg => (10.0, 100.0),\n :sol_rad_avg => (0.0, 1500.0),\n :wind_speed_avg => (0.0, 216.0),\n :air_temp_avg => (-40.0, 60.0),\n)\n\nscales = Dict{Symbol, Real}(\n :SWE => inch2meter,\n :z => inch2meter,\n :precip => inch2meter,\n :rel_hum_avg => 0.01,\n :wind_speed_avg => kmphr2mps,\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. \"start\" refers to 1850-01-01 or the first available date, while \"end\" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"* Indicates alternative handling of the rectify_daily_hourly() function.\n^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.\nA Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.\nT Requires a site that already has had the temperature bias correction at the portal level as of May 2024.\nX Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"good_stations = Dict{Int, Tuple{String, String}}(\n #306 => (\"start\", \"end\"), #*\n 316 => (\"start\", \"end\"),\n 344 => (\"start\", \"end\"),\n #=367 => (\"start\", \"end\"),\n 395 => (\"start\", \"end\"),\n 457 => (\"start\", \"end\"),\n 482 => (\"start\", \"end\"),\n 491 => (\"start\", \"end\"),\n 515 => (\"start\", \"2023-06-02\"), #X\n 532 => (\"start\", \"end\"),\n 551 => (\"start\", \"end\"),\n 571 => (\"start\", \"end\"),\n 599 => (\"start\", \"end\"),\n 608 => (\"start\", \"end\"),\n 613 => (\"start\", \"end\"),\n 641 => (\"start\", \"end\"), #A^\n 665 => (\"start\", \"end\"),\n 708 => (\"start\", \"end\"),\n 715 => (\"start\", \"end\"),\n 734 => (\"start\", \"end\"),\n 737 => (\"start\", \"end\"),\n 744 => (\"start\", \"end\"),\n 832 => (\"start\", \"end\"),\n 845 => (\"start\", \"end\"),\n 854 => (\"start\", \"end\"),\n 857 => (\"start\", \"end\"),\n 921 => (\"start\", \"end\"),\n 922 => (\"start\", \"end\"),\n 927 => (\"start\", \"end\"),\n 942 => (\"start\", \"end\"),\n 963 => (\"start\", \"end\"), #A^\n 969 => (\"start\", \"end\"),\n 974 => (\"start\", \"end\"),\n 978 => (\"start\", \"end\"), #*\n 1030 => (\"start\", \"end\"),\n 1035 => (\"start\", \"end\"), #A^\n 1053 => (\"start\", \"end\"),\n 1070 => (\"start\", \"end\"), #A^T\n 1083 => (\"start\", \"end\"),\n 1091 => (\"start\", \"end\"), #A^T\n 1092 => (\"start\", \"end\"), #A^T\n 1105 => (\"start\", \"end\"),\n 1122 => (\"start\", \"end\"), #*\n 1123 => (\"start\", \"end\"),\n 1159 => (\"start\", \"end\"),\n 1168 => (\"start\", \"end\"),\n 1170 => (\"start\", \"end\"),\n 1254 => (\"start\", \"end\"),\n 1286 => (\"start\", \"end\"),\n 2080 => (\"start\", \"end\"), #A^\n 2170 => (\"start\", \"end\"), #^\n =#\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We then loop through each site to scrape and follow an automated data pipeline, consisting of:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Extracting the daily and hourly timeseries from the site\nApplying the sensor bounds over each data timeseries (i.e. remove sensor error)\nConverting the hourly dataset into a daily dataset\nCoalescing the converted-hourly and daily data into one dataset\nScaling all data to the appropriate metric units\nRestricting data to complete cases\nMaking the differential variables ( fracdzdt, etc.)\nResetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip\nAttaching appropriate metadata","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"allsites = Any[];\nfor site in sort(collect(keys(good_stations)))\n state = metadata[metadata[!, :id] .== site, :state][1]\n start_date = good_stations[site][1]\n end_date = good_stations[site][2]\n\n hourly = DataTools.apply_bounds(\n DataTools.sitedata_hourly(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n hourly[!, :id] .= site\n #hourly = DataTools.bcqc_hourly(hourly)\n hourly_d = DataTools.hourly2daily(hourly)\n #DataFrames.allowmissing!(hourly_d)\n #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)\n #hourly_d[sflags, :sol_rad_avg] .= missing\n\n daily = DataTools.apply_bounds(\n DataTools.sitedata_daily(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n daily[!, :id] .= site\n gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)\n #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)\n #gap_daily = DataTools.d_impute(gap_daily)\n daily_scaled = DataTools.scale_cols(gap_daily, scales)\n daily_clean = daily_scaled[completecases(daily_scaled), :]\n daily_clean = DataTools.makediffs(daily_clean, Day(1))\n good_vals = daily_clean[!, :dprecipdt] .>= 0.0\n daily_clean[(!).(good_vals), :dprecipdt] .= 0.0\n daily_clean = daily_clean[!, Not(:precip)]\n #show(describe(daily_clean), allrows = true, allcols = true)\n #print(\"\\nSIZE: \", nrow(daily_clean), \"\\n\")\n\n daily_clean[!, :id] .= site\n daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]\n daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]\n daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]\n\n push!(allsites, daily_clean)\nend;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"With the sites complete, we condense all sites into a single DataFrame,","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"totaldata = deepcopy(allsites[1])\nfor site in allsites[2:end]\n append!(totaldata, site)\nend","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"and a final CSV.write(\"data.csv\", totaldata) call will save the file.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Setting-up-a-Coupled-Simulation","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"For more information about the bucket model, please see the bucket model tutorial.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Background","page":"Setting up a Coupled Simulation","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In our current setup, \"passed back to the land model via the coupler\" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Turbulent-Surface-Fluxes-and-Radiation","page":"Setting up a Coupled Simulation","title":"Turbulent Surface Fluxes and Radiation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we want different behavior. We have defined new coupled types to use instead of the \"prescribed\" types:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"similarily:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Surface-air-density","page":"Setting up a Coupled Simulation","title":"Surface air density","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Regridder/#Bucket","page":"Parameter Dataset Tools","title":"Bucket","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"CurrentModule = ClimaLand.Regridder","category":"page"},{"location":"APIs/Regridder/#Functions","page":"Parameter Dataset Tools","title":"Functions","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll\nClimaLand.Regridder.swap_space","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Hydrostatic-Equilibrium-test-for-Richards-Equation","page":"Richards Equation","title":"Hydrostatic Equilibrium test for Richards Equation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"frac ϑ_l t = K (ϑ_l ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We will solve this equation in a 1-d domain with z -50, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"-K h(t z = -5) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ(t = 0 z) = ν-0001","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"θ_i(t = 0 z) = 00","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"where nu is the porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Preliminary-setup","page":"Richards Equation","title":"Preliminary setup","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load external packages","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"import SciMLBase\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-soil-model","page":"Richards Equation","title":"Set up the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"First, we define the parameters: porosity \\nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K_sat = FT(0.0443 / (3600 * 100))\nS_s = FT(1e-3)\nν = FT(0.495)\nvg_α = FT(2.6)\nvg_n = FT(2)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0)\nparams = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"zmax = FT(0)\nzmin = FT(-5)\nnelems = 10\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nbottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nboundary_conditions = (; top = surface_flux, bottom = bottom_flux);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, in this case we don't have any sources, so we pass an empty tuple:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"soil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_conditions,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = ClimaLand.make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-simulation","page":"Richards Equation","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y.soil.ϑ_l .= FT(0.494);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 24 * 36);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"dt = Float64(1e3);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"stepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we set up the information used for our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Create-some-plots","page":"Richards Equation","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t = sol.t ./ (60 * 60 * 24);\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nz = parent(coords.subsurface.z)\nplot(\n ϑ_l[1],\n z,\n label = string(\"t = \", string(t[1]), \"days\"),\n xlim = [0.47, 0.501],\n ylabel = \"z\",\n xlabel = \"ϑ_l\",\n legend = :bottomleft,\n title = \"Equilibrium test\",\n);\nplot!(ϑ_l[end], z, label = string(\"t = \", string(t[end]), \"days\"));\nfunction hydrostatic_equilibrium(z, z_interface)\n ν = 0.495\n S_s = 1e-3\n α = 2.6\n n = 2.0\n m = 0.5\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nplot!(hydrostatic_equilibrium.(z, -0.56), z, label = \"equilibrium solution\");\n\nplot!(1e-3 .+ ϑ_l[1], z, label = \"porosity\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Save the output:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"savefig(\"equilibrium_test_ϑ_l.png\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#References","page":"Richards Equation","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#PlantHydraulics","page":"Plant Hydraulics","title":"PlantHydraulics","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"CurrentModule = ClimaLand.PlantHydraulics","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#Models","page":"Plant Hydraulics","title":"Models","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsModel","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","text":"PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}\n\nDefines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.\n\nThis model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can \"turn off\" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:\n\nn_leaf = 1\nn_stem = 0\nLAI = SAI = RAI = 0.\n\nA plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.\n\nFinally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.\n\nn_stem: The number of stem compartments for the plant; can be zero\nn_leaf: The number of leaf compartments for the plant; must be >=1\ncompartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters\ncompartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.\ncompartment_labels: The label (:stem or :leaf) of each compartment\nparameters: Parameters required by the Plant Hydraulics model\ntranspiration: The transpiration model, of type AbstractTranspiration\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Diagnostic-Variables","page":"Plant Hydraulics","title":"Plant Hydraulics Diagnostic Variables","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.effective_saturation\nClimaLand.PlantHydraulics.augmented_liquid_fraction\nClimaLand.PlantHydraulics.water_retention_curve\nClimaLand.PlantHydraulics.inverse_water_retention_curve\nClimaLand.PlantHydraulics.root_water_flux_per_ground_area!\nClimaLand.PlantHydraulics.flux\nClimaLand.PlantHydraulics.hydraulic_conductivity","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.effective_saturation","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.effective_saturation","text":"effective_saturation(\n ν::FT,\n ϑ_l::FT) where {FT}\n\nComputes the effective saturation given the augmented liquid fraction.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","text":"augmented_liquid_fraction(\n ν::FT,\n S_l::FT) where {FT}\n\nComputes the augmented liquid fraction from porosity and effective saturation.\n\nAugmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.water_retention_curve","text":"water_retention_curve(\n S_l::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","text":"inverse_water_retention_curve(\n ψ::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the effective saturation given the potential at a point, according to the linear retention curve model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","text":"PlantHydraulics.root_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrognosticSoil,\n model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nAn extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.\n\nIt is computed by summing the flux of water per ground area between roots and soil at each soil layer.\n\n\n\n\n\nroot_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.\n\nThe returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.flux","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.flux","text":"flux(\n z1,\n z2,\n ψ1,\n ψ2,\n K1,\n K2,\n) where {FT}\n\nComputes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.\n\nWe currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","text":"hydraulic_conductivity(conductivity_params::Weibull{FT}, ψ::FT) where {FT}\n\nComputes the hydraulic conductivity at a point, using the Weibull formulation, given the potential ψ.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Parameters","page":"Plant Hydraulics","title":"Plant Hydraulics Parameters","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsParameters\nClimaLand.PlantHydraulics.Weibull\nClimaLand.PlantHydraulics.LinearRetentionCurve","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","text":"PlantHydraulicsParameters\n\nA struct for holding parameters of the PlantHydraulics Model.\n\nai_parameterization: The area index model for LAI, SAI, RAI\nν: porosity (m3/m3)\nS_s: storativity (m3/m3)\nconductivity_model: Conductivity model and parameters\nretention_model: Water retention model and parameters\nroot_distribution: Root distribution function P(z)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.Weibull","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.Weibull","text":"Weibull{FT} <: AbstractConductivityModel{FT}\n\nA concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.\n\nFields\n\nK_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation\nψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).\nc: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","text":"LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}\n\nA concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.\n\nWhen ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.\n\nFields\n\na: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Methods-and-Types","page":"Plant Hydraulics","title":"Plant Hydraulics Methods and Types","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PrescribedSoilPressure\nClimaLand.PlantHydraulics.PrescribedTranspiration\nClimaLand.PlantHydraulics.AbstractRootExtraction","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","text":"PrescribedTranspiration{FT, F <: Function} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch when computing the transpiration from the leaves, in the case where transpiration is prescribed.\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's load the required modules:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"using ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Pond\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-I-Soil-Hydrology","page":"Intro to multi-component models","title":"The individual component models I - Soil Hydrology","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The RR equation for the volumetric water content of soil is given by","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S(xyz t)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In order to solve this, one must specify:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"boundary conditions,\nrelevant parameters (closure models for K and ψ),\na domain and a spatial discretization scheme,\nadditional source terms S, if applicable,\na time-stepping algorithm,\ninitial conditions.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"For the RR equation, we can create this as follows. First, we specify parameters:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ν = FT(0.495);\nK_sat = FT(0.0443 / 3600 / 100); # m/s\nS_s = FT(1e-3); #inverse meters\nvg_n = FT(2.0);\nvg_α = FT(2.6); # inverse meters\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0);\nsoil_ps = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next, let's define the spatial domain and discretization:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"zmax = FT(0);\nzmin = FT(-1);\nnelems = 20;\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And boundary conditions and source terms (none currently):","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"top_flux_bc = WaterFluxBC((p, t) -> 0.0);\nbot_flux_bc = WaterFluxBC((p, t) -> 0.0);\nsources = ()\nboundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(top = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#1#2\"}(Main.var\"##494\".var\"#1#2\"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#3#4\"}(Main.var\"##494\".var\"#3#4\"()))","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"With this information, we can make our model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil = Soil.RichardsModel{FT}(;\n parameters = soil_ps,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_soil, p_soil, coords_soil = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and we can set up the tendency function using the default as well,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_ode! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which computes, for the column domain,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-frac z (-Kfrac(ψ+z) z)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"for each value of ϑ on the mesh of our soil_domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function soil_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place\n update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place\n compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-II-Surface-Water","page":"Intro to multi-component models","title":"The individual component models II - Surface Water","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond model has a single variable, the pond height η, which satisfies the ODE:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"frac η t = -(P - I) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To write down the pond equations, we need to specify","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"P\nI","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s\n\ninfiltration(t) = -(1e-6) #m/s\npond_model =\n Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Again we can initialize the state vector and auxiliary vectors:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_pond, p_pond, coords_pond = initialize(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We can make the tendency function in the same way, for stepping the state forward in time:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"pond_ode! = make_exp_tendency(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond_ode! function works in the same way as for the soil model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function pond_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # falls back to default; does nothing\n update_boundary_fluxes!(p,Y,t) # p.surface_water.runoff in place\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#An-LSM-with-pond-and-soil:","page":"Intro to multi-component models","title":"An LSM with pond and soil:","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The LSM model must contain everything needed to write down the joint system of equations","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial etapartial t = -(P(t) - I(ϑ η P)) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmax z = I(ϑ η P)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmin z = 00","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"As in the standalone cases, defining our model requires specifying","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"parameters,\ndomains, discretizations\nprecipitation,\nboundary conditions,\nsources in the soil equation, if any.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's make our single column domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());\nsurface_water_args = NamedTuple();","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Atmospheric drivers don't \"belong\" to either component alone:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_args = (precip = precipitation,);\nland = LandHydrology{FT}(;\n land_args = land_args,\n soil_model_type = Soil.RichardsModel{FT},\n soil_args = soil_args,\n surface_water_model_type = Pond.PondModel{FT},\n surface_water_args = surface_water_args,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_aux(land)\n soil_update_aux! = make_update_aux(land.soil)\n surface_update_aux! = make_update_aux(land.surface_water)\n function update_aux!(p,Y,t)\n surface_update_aux!(p,Y,t) # does nothing to `p`\n soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ\n end\n return update_aux!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_boundary_fluxes(land)\n update_soil_bf! = make_update_boundary_fluxes(land.soil)\n update_pond_bf! = make_update_boundary_fluxes(land.surface_water)\n function update_boundary_fluxes!(p,Y,t)\n p.soil_infiltration = compute_infiltration(Y,p, t)\n update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration\n update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration\n end\n return update_boundary_fluxes!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and similarily for the compute_exp_tendency! functions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_compute_exp_tendency(land)\n soil_compute_exp_tendency! = make_update_aux(land.soil)\n surface_compute_exp_tendency! = make_update_aux(land.surface_water)\n function compute_exp_tendency!(dY,Y,p,t)\n surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η\n soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ\n end\n return compute_exp_tendency!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The exp_tendency! for the land model is then again just","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function exp_tendency!(dY, Y, p, t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"initialize(land.soil)\ninitialize(land.surface_water)\ninitializes additional auxiliary variables, like p.soil_infiltration\nappend these into Y, p, and coords:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y, p, coords = initialize(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We have volumetric liquid water fraction:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:ϑ_l,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and surface height of the pond:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:η,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"as well as auxiliary variables for the soil:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:K, :ψ, :top_bc, :bottom_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the runoff for surface water:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:runoff,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the additional variable required in the LSM is stored here as well:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:soil_infiltration, :soil, :surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and finally, coordinates - useful for visualization of solutions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.subsurface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the coordinates of the surface variables:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.surface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[0.0]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And we can make the tendency function as before:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_ode! = make_exp_tendency(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#Advantages-and-disadvantages","page":"Intro to multi-component models","title":"Advantages and disadvantages","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Some advantages to our interface design are as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.\nlikewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.\nthe exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).\nalthough we wrote it here in a hardwired fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.\nthe order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.\nthe code is also modular in terms of swapping out a simple component model for a more complex version.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Possible disadvantages to our interface design:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.\nTo accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.\nstandalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.\nwe need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"The same experiment is carried out 3 times","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"No evaporation (zero flux boundary conditions)\nWith evaporation but no drainage (Ksat = 0)\nWith evaporation and drainage","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Parameters","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"K_sat = FT(0.01 / 3600 / 24)\nvg_n = FT(1.45)\nvg_α = FT(1.5)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.4)\nθ_r = FT(0.04)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(0.3)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)# 10mm\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 294.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}(Main.var\"##317\".var\"#1#2\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}(Main.var\"##317\".var\"#3#4\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), nothing)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Atmos","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}(Main.var\"##317\".var\"#7#8\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}(Main.var\"##317\".var\"#9#10\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}(Main.var\"##317\".var\"#11#12\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}(Main.var\"##317\".var\"#13#14\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}(ClimaLand.var\"#21#24\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Simulation setup - no evaporation Boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"zero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nno_flux_boundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);\n\nt0 = Float64(0)\ntf = Float64(24 * 3600 * 15)\ndt = Float64(900.0)\nΔz = 0.01\nzmax = FT(0)\nzmin = FT(-1.6)\nnelems = Int((zmax - zmin) / Δz)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_flux_boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Y, p, cds = initialize(soil)\nfunction estimated_ic(z)\n 0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= estimated_ic.(z)\n Y.soil.θ_i .= 0\n T = FT(294.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\ncb = SciMLBase.CallbackSet(saving_cb);\n\nsol_no_evap =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Repeat with evaporation and drainage This requires different initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nevap_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n)\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = evap_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nsoil_exp_tendency! = make_exp_tendency(soil)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];\n\n# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"bottom_water_bc = MoistureStateBC((p, t) -> 0.35)\nno_drainage_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),\n)\nzmax = FT(0)\nzmin = FT(-0.16)\nnelems = Int((zmax - zmin) / Δz)\ndt = Float64(10.0)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_drainage_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z_no_evap, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol_no_drainage =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap_no_drainage = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"savepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (800, 400))\nax = Axis(fig[1, 1], xlabel = \"Day\", ylabel = \"Evaporation rate (mm/d)\")\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"With drainage\",\n color = :red,\n)\nCairoMakie.lines!(\n ax,\n sol_no_drainage.t ./ 3600 ./ 24,\n evap_no_drainage .* (1000 * 3600 * 24),\n label = \"No drainage\",\n color = :blue,\n)\n\nCairoMakie.axislegend(ax)\nax2 = Axis(fig[1, 2], xlabel = \"Day\", ylabel = \"Cumulative evaporation (mm)\")\nCairoMakie.lines!(\n ax2,\n sol.t ./ 3600 ./ 24,\n cumsum(evap) .* (1000 * 3600),\n color = :red,\n)\nCairoMakie.lines!(\n ax2,\n sol_no_drainage.t ./ 3600 ./ 24,\n cumsum(evap_no_drainage) .* (1000 * 3600),\n color = :blue,\n)\nsave(\"evaporation_lehmann2024_figS6.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Drainage only\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.4)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 10]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax1,\n parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Evap+Drainage\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.3, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 1, 2, 5, 13]\nfor i in 1:1:5\n CairoMakie.lines!(\n ax2,\n parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Evap only\", xlabel = \"Volumetric Water Content\")\nCairoMakie.ylims!(-0.15, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 2, 9, 14]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax3,\n parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n label = \"$(days[i]) days\",\n parent(z_no_evap)[:],\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"evaporation_gardner_fig1.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"Snow Model","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"CurrentModule = ClimaLand.Snow","category":"page"},{"location":"APIs/Snow/#Snow-Parameters","page":"Snow Model","title":"Snow Parameters","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"ClimaLand.Snow.SnowParameters","category":"page"},{"location":"APIs/Snow/#ClimaLand.Snow.SnowParameters","page":"Snow Model","title":"ClimaLand.Snow.SnowParameters","text":"SnowParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SnowModel.\n\nNote that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.\n\nρ_snow: Density of snow (kg/m^3)\nz_0m: Roughness length over snow for momentum (m)\nz_0b: Roughness length over snow for scalars (m)\nα_snow: Albedo of snow (unitless)\nϵ_snow: Emissivity of snow (unitless)\nθ_r: Volumetric holding capacity of water in snow (unitless)\nKsat: Hydraulic conductivity of wet snow (m/s)\nκ_ice: Thermal conductivity of ice (W/m/K)\nΔt: Timestep of the model (s)\nρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)\nearth_param_set: Clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Snow/#Snow-Functions-of-State","page":"Snow Model","title":"Snow Functions of State","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff\t ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Stomatal-conductance","page":"Medlyn model","title":"Stomatal conductance","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"This section describes multiple models of stomatal conductance implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Medlyn-Model","page":"Medlyn model","title":"Medlyn Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\nT = -rho_a g_mathrmeff leftq_a- q_v(T_mathrmleaf rho_mathrmsfc) right\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where T is the transpiration (mass flux of water vapor), q_a is the specific humidity at the lowest level of the atmosphere, q_v(T_mathrmT_leaf rho_mathrmsfc) is the saturated specific humidity over liquid water, given the temperature of the leave T_leaf and air density at the surface rho_sfc. We will approximate T_leaf = T_a and rho_mathrmsfc = rho_a.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"We also need the effective conductivity, given by","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n g_mathrmeff = frac1g_mathrmae^-1+g_mathrms^-1\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g_ae is the aerodynamic conductance, computed by the MOST solve, and g_s is the stomatal conductance to water vapor per unit ground area. The units of all conductances are ms.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginalign\ng_sm(PAR T VPD c_a) = g_0m + D_rel times m fracA_n(PAR T VPD c_a)c_anonumber \ng_s = fracg_smrho_m\nendalign","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where D_rel =16 (unitless) is the relative diffusivity of water vapor with respect to CO_2, rho_m is the molar density of water, and m is the Medlyn factor,","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n m = left( 1 + fracg_1sqrtVPD right) \nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A_n as the biochemical demand for CO_2 calculated using the photosynthesis model (Farquhar, 1980; Equation \\eqref{eq:an}; units of molar flux). The resulting units are ms. g{0,m}$ is a minimum molar conductivity. (subscript m indicates molar).","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Constants Symbol Unit Value\nRelative diffusivity of water vapor D_rel - 1.6\nMinimum stomatal conductance g_0 mol/m^2/s 1e-4\nSlope parameter g_1 sqrtPa 790","category":"page"},{"location":"standalone/README/#DynamicDocs","page":"DynamicDocs","title":"DynamicDocs","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Plant-Hydraulics","page":"Van Genuchten model","title":"Plant Hydraulics","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The plant hydraulics code solves for the volumetric water content in the stem and leaf (theta_stem and theta_leaf). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Van-Genuchten-Model","page":"Van Genuchten model","title":"Van Genuchten Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The volume flux of water q (m/s) between compartments with centers at two heights, z_1 and z_2, is given by Darcy's law as","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n q = -int_z_1^z_2 k(psi) dh\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where h = psi+z is the head (in meters), and k is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\\footnote{Double check this - the units of k in our code are ms.}","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\nq = -int_h_1^h_2 k(psi) dh approx -frack_1(psi_1) + k_2(psi_2)2 * (psi_2 - psi_1) + (z_2 - z_1)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"In order to close the set of equations, the user will have to specify k(psi) and a function psi(theta). In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of K_sat","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The change of water volume (m^3) V, in the compartments is then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd V_w stemdt = q_rootssigma_roots - q_stemsigma_stem nonumber \n fracd V_w leafdt = q_stemsigma_stem - tau sigma_leaf\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where tau is a transpiration volume flux per unit emitting area, and sigma is the total emitting/conducting area\\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals N. We can make use of the fact that NsigmaA, where A is the area of the ground those N individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Then we have:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd v_stemdt = q_rootsRAI - q_stemSAI nonumber \n fracd v_leafdt = q_stemSAI - tau LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where v now represents the volume of water in that compartment (of a bulk plant) per unit ground area.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We also need to convert from the variable v to psi, in order to compute root extraction with the soil. To do so, we can convert v to the volumetric water content, and from theta to psi using a van Genuchten relationship. To convert, let the volume of water per area of compartment be V_wstem, and H the typical ``length\" of the compartment. Then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n theta_stem=fracV_wstemA_ground times fracA_groundA_stem times frac1H_stem = fracv_stemH_stem times SAI \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Substituting in the volumetric water content, we have","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd theta_stemdt = fracq_rootsRAI - q_stemSAIH_stem SAI nonumber \n fracd theta_leafdt = fracq_stemSAI - tau LAIH_leaf LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction P(z), satisfying int P(z) dz = 1. Instead of having a single root at one discrete location, we can distribute the root system over different depths using P(z). The total flux from roots between z and z+dz is given by","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n dq_roots(z) = -P(z) dz int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"so that the net flux for the plant system would sum over this","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n q_roots = -int_z_min^z_sfc fracdq_roots(z)dzdz \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where z_min is the minimum soil layer of the simulation. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n S(z) = -(RAI) dq_roots(z)dz = (RAI) P(z) int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sign change occurs in the expression for S(z) because a positive value of q_roots indicates flow from the soil to the plant. This is a sink term for the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The model needs the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Drivers Symbol Unit Range\nA function of simulation time t given the leaf area index LAI m²/m² 0–10\nThe constant stem area index SAI m²/m² 0–10\nThe constant root area index RAI m²/m² 0–10","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Microbial-respiration","page":"DAMM model","title":"Microbial respiration","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Dual-Arrhenius-Michaelis-Menten","page":"DAMM model","title":"Dual Arrhenius Michaelis-Menten","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration (Rh) as a function of soil temperature (T_s) and soil moisture (theta).","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The rate of respiration, Rh, is expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n Rh = V_textmaxs_xMM_s_xMM_O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where V_textmaxs_x is the maximum potential rate of respiration, MM_s_x represents the availability of substrate, and MM_O_2 is used as the oxygen limitation factor. MM_s_x and MM_O_2 are between 0 (limiting) and 1 (non limiting). V_textmaxs_x, MM_s_x, and MM_O_2 are expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n V_textmaxs_x = alpha_s_x exp(frac-Ea_s_xRT_s)\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textsx = fracs_xkM_s_x+s_x\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textO_2 = fracO_2kM_O_2+O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where alpha_s_x is the pre-exponential factor, Ea_s_x is the activation energy of the reaction, R is the gas constant, and T_s is soil temperature. s_x is the concentration of all soluble substrate, and O_2 is the oxygen concentration. kM_s_x and kM_O_2 are the Michaelis constant for soil and oxygen, respectively.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, s_x is calculated as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n s_x = p_s_xtimesC_somtimes D_liqtimestheta^3\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where C_som is the total amount of soil organic carbon, and p_s_x is the fraction of C_som that is soluble. D_liq is the diffusion coefficient of the soluble carbon. theta is soil moisture.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of O_2 depends on the diffusion of gases within the soil, which is calculated as below:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n O_2 = D_Oatimes O_2a times porosity_air^43\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where D_Oa is the diffusion coefficient for O_2 in air, O_2a is the volume fraction of O_2 in air, and porosity_air is the air-filled porosity.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The air-filled porosity is calculated by subtracting the soil moisture from the total porosity (nu):","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n porosity_air = nu - theta\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"To sum up, the model has the following parameters:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Output Symbol Unit Range\nHeterotrophic respiration Rh mumol m^-2 s^-2 0–25","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Drivers Symbol Unit Range\nSoil temperature T_s C -20–50\nSoil moisture theta m^3 m^-3 0.0–1.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Parameters Symbol Unit Range\nSoil porosity nu m^3 m^-3 0.0–1.0\nPre-exponential factor alpha_s_x kg C m^-3 s^-1 100e3–300e3\nActivation energy Ea_s_x Jmol^-1 50e3–70e3\nMichaelis constant for soil kM_s_x kg C m^-3 1e-10–0.1\nMichaelis constant for O_2 kM_O_2 m^3 m^-3 1e-10–0.1\nVolumetric fraction of O_2 in the soil air content O_2_a - 0.005–0.5\nFraction of soil carbon that is considered soluble p_s_x - 0.005–0.5\nSoil organic C C_som kg C m^-3 1.0–10.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Constants Symbol Unit Value\nAir-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa) O_a100 - 0.1816\nDiffusivity of soil C substrate in liquid D_liq - 3.17\nDiffusion coefficient of oxygen in air D_Oa - 1.67","category":"page"},{"location":"Contributing/#Contributing","page":"Contribution guide","title":"Contributing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.","category":"page"},{"location":"Contributing/#Some-useful-tips","page":"Contribution guide","title":"Some useful tips","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"When you start working on a new feature branch, make sure you start from main by running: git checkout main.\nMake sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.\nWhen your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.","category":"page"},{"location":"Contributing/#Continuous-integration","page":"Contribution guide","title":"Continuous integration","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.","category":"page"},{"location":"Contributing/#Automated-testing","page":"Contribution guide","title":"Automated testing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Currently a number of checks are run per commit for a given PR.","category":"page"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.\nDocumentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.\nTests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Canopy-Energy-Model","page":"Canopy Energy","title":"Canopy Energy Model","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Methods","page":"Canopy Energy","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.canopy_temperature\nClimaLand.Canopy.root_energy_flux_per_ground_area!","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.canopy_temperature","page":"Canopy Energy","title":"ClimaLand.Canopy.canopy_temperature","text":"canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.\n\n\n\n\n\ncanopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.root_energy_flux_per_ground_area!","page":"Canopy Energy","title":"ClimaLand.Canopy.root_energy_flux_per_ground_area!","text":"root_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrognosticSoil{F},\n model::Canopy.AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT, F}\n\nA method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).\n\nNote that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\nroot_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.\n\nBackground information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#Types","page":"Canopy Energy","title":"Types","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.AbstractCanopyEnergyModel\nClimaLand.Canopy.PrescribedCanopyTempModel\nClimaLand.Canopy.BigLeafEnergyModel","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.PrescribedCanopyTempModel","page":"Canopy Energy","title":"ClimaLand.Canopy.PrescribedCanopyTempModel","text":"PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}\n\nA model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.\n\nNo equation for the energy of the canopy is solved.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.BigLeafEnergyModel","page":"Canopy Energy","title":"ClimaLand.Canopy.BigLeafEnergyModel","text":"BigLeafEnergyModel{FT} <: AbstractCanopyEnergyModel{FT}\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#SurfaceWater","page":"Surface Water Models","title":"SurfaceWater","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"CurrentModule = ClimaLand.Pond","category":"page"},{"location":"APIs/SurfaceWater/#Models","page":"Surface Water Models","title":"Models","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PondModel","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PondModel","page":"Surface Water Models","title":"ClimaLand.Pond.PondModel","text":"PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}\n\nA stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.\n\ndomain: The domain for the pond model\nrunoff: The runoff model for the pond model\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#Methods-and-Types","page":"Surface Water Models","title":"Methods and Types","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PrescribedRunoff\nClimaLand.Pond.surface_runoff","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PrescribedRunoff","page":"Surface Water Models","title":"ClimaLand.Pond.PrescribedRunoff","text":"PrescribedRunoff{F1 <: Function, F2 <: Function} <: AbstractSurfaceRunoff\n\nThe required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.surface_runoff","page":"Surface Water Models","title":"ClimaLand.Pond.surface_runoff","text":"function Pond.surface_runoff(\n runoff::PrognosticRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nExtension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#Autotrophic-Respiration","page":"Canopy Autotrophic Respiration","title":"Autotrophic Respiration","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#Parameters","page":"Canopy Autotrophic Respiration","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.AutotrophicRespirationParameters","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.AutotrophicRespirationParameters","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.AutotrophicRespirationParameters","text":"AutotrophicRespirationParameters{FT<:AbstractFloat}\n\nThe required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. \"The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics.\" Geoscientific Model Development 4.3 (2011): 701-722.\n\nne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)\nηsl: Live stem wood coefficient (kg C m-3)\nσl: Specific leaf density (kg C m-2 [leaf])\nμr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\nμs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1\nRel: Relative contribution or Rgrowth (-)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/AutotrophicRespiration/#Methods","page":"Canopy Autotrophic Respiration","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.nitrogen_content\nClimaLand.Canopy.plant_respiration_maintenance\nClimaLand.Canopy.plant_respiration_growth","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.nitrogen_content","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.nitrogen_content","text":"nitrogen_content(\n ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)\n Vcmax25::FT, #\n LAI::FT, # Leaf area index\n SAI::FT,\n RAI::FT,\n ηsl::FT, # live stem wood coefficient (kg C m-3) \n h::FT, # canopy height (m)\n σl::FT # Specific leaf density (kg C m-2 [leaf])\n μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\n μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 \n ) where {FT}\n\nComputes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_maintenance","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_maintenance","text":"plant_respiration_maintenance(\n Rd::FT, # Dark respiration\n β::FT, # Soil moisture factor\n Nl::FT, # Nitrogen content of leafs\n Nr::FT, # Nitrogen content of roots\n Ns::FT, # Nitrogen content of stems\n ) where {FT}\n\nComputes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_growth","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_growth","text":"plant_respiration_growth(\n Rel::FT, # Factor of relative contribution\n An::FT, # Net photosynthesis\n Rpm::FT # Plant maintenance respiration\n ) where {FT}\n\nComputes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.\n\n\n\n\n\n","category":"function"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Using-atmospheric-and-radiative-drivers","page":"Intro to forced site-level runs","title":"Using atmospheric and radiative drivers","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers\"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Types-of-forcing-data","page":"Intro to forced site-level runs","title":"Types of forcing data","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Creating-site-level-drivers-for-radiation","page":"Intro to forced site-level runs","title":"Creating site-level drivers for radiation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"using Dates\nusing Insolation # for computing zenith angle given lat, lon, time.\nusing ClimaLand\nimport ClimaLand.Parameters as LP\nimport ClimaParams","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the local_datetime array is read in from the data file.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Timezone (offset of local time from UTC in hrs)","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"time_offset = 7;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Site latitude and longitude","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"lat = 38.7441; # degree\nlong = -92.2000; # degree","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"ref_time = local_datetime[1] + Dates.Hour(time_offset);\ndata_dt = 3600.0;\nseconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));\nLW_d = 5.67 * 10^(-8) .* T .^ 4;\nSW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"LW_d = TimeVaryingInput(seconds, LW_d)\nSW_d = TimeVaryingInput(seconds, SW_d);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"earth_param_set = LP.LandParameters(Float64);\ninsol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation\nfunction zenith_angle(\n t,\n ref_time;\n latitude = lat,\n longitude = long,\n insol_params = insol_params,\n)\n current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC\n\n d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(\n current_datetime,\n ref_time,\n insol_params,\n ))\n\n\n return Insolation.instantaneous_zenith_angle(\n d,\n δ,\n η_UTC,\n longitude,\n latitude,\n )[1]\nend;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"radiation = ClimaLand.PrescribedRadiativeFluxes(\n Float64,\n SW_d,\n LW_d,\n ref_time;\n θs = zenith_angle,\n);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Updating-the-driver-variables-during-the-simulation","page":"Intro to forced site-level runs","title":"Updating the driver variables during the simulation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"In order to update them, we can make use of default update functions:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"update_radiation! = ClimaLand.make_update_drivers(radiation)\nt0 = seconds[1] # midnight local time\nupdate_radiation!(p, t0);\n@show(p.drivers);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])\n","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"During a simulation, the drivers are updated in place in p.drivers via a \"callback\", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"updateat = collect(seconds[1]:(3600 * 3):seconds[end]);\nupdatefunc = update_radiation!;\ncb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This callback must then be provided to the simulation solve function.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Photosynthesis","page":"Canopy Photosynthesis","title":"Photosynthesis","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Parameters","page":"Canopy Photosynthesis","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.SIFParameters\nClimaLand.Canopy.FarquharParameters\nClimaLand.Canopy.OptimalityFarquharParameters","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.SIFParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.SIFParameters","text":"SIFParameters{FT<:AbstractFloat}\n\nThe required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.\n\nkf: The rate coefficient for florescence, unitless\nkd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nmin_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkp: Rate coefficient for photochemical quenching\nkappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\nkappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.FarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.FarquharParameters","text":"FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}\n\nThe required parameters for the Farquhar photosynthesis model.\n\nVcmax25: Vcmax at 25 °C (mol CO2/m^2/s)\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercelluar O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]\npc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]\nmechanism: Photosynthesis mechanism: C3 or C4\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.OptimalityFarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.OptimalityFarquharParameters","text":"OptimalityFarquharParameters{FT<:AbstractFloat}\n\nThe required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.\n\nmechanism: Photosynthesis mechanism: C3 only\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercellular O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Fitting constant to compute the moisture stress factor (Pa^{-1})\npc: Fitting constant to compute the moisture stress factor (Pa)\nc: Constant describing cost of maintaining electron transport (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#Methods","page":"Canopy Photosynthesis","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.arrhenius_function\nClimaLand.Canopy.intercellular_co2\nClimaLand.Canopy.co2_compensation\nClimaLand.Canopy.rubisco_assimilation\nClimaLand.Canopy.light_assimilation\nClimaLand.Canopy.C3\nClimaLand.Canopy.C4\nClimaLand.Canopy.max_electron_transport\nClimaLand.Canopy.electron_transport\nClimaLand.Canopy.net_photosynthesis\nClimaLand.Canopy.optimality_max_photosynthetic_rates\nClimaLand.Canopy.moisture_stress\nClimaLand.Canopy.dark_respiration\nClimaLand.Canopy.compute_GPP\nClimaLand.Canopy.MM_Kc\nClimaLand.Canopy.MM_Ko\nClimaLand.Canopy.compute_Vcmax","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.arrhenius_function","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.arrhenius_function","text":"arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)\n\nComputes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.intercellular_co2","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.intercellular_co2","text":"intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}\n\nComputes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.co2_compensation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.co2_compensation","text":"co2_compensation(Γstar25::FT,\n ΔHΓstar::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.rubisco_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.rubisco_assimilation","text":"rubisco_assimilation(::C3,\n Vcmax::FT,\n ci::FT,\n Γstar::FT,\n Kc::FT,\n Ko::FT,\n oi::FT) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).\n\nThe empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nrubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.light_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.light_assimilation","text":"light_assimilation(::C3,\n J::FT,\n ci::FT,\n Γstar::FT) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nlight_assimilation(::C4, J::FT, _...) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C3","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C3","text":"C3 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C4","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C4","text":"C4 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.max_electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.max_electron_transport","text":"max_electron_transport(Vcmax::FT) where {FT}\n\nComputes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.electron_transport","text":"electron_transport(APAR::FT,\n Jmax::FT,\n θj::FT,\n ϕ::FT) where {FT}\n\nComputes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). \n\nSee Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.net_photosynthesis","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.net_photosynthesis","text":"net_photosynthesis(Ac::FT,\n Aj::FT,\n Rd::FT,\n β::FT) where {FT}\n\nComputes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). \n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.optimality_max_photosynthetic_rates","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.optimality_max_photosynthetic_rates","text":"optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)\n\nComputes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.\n\nSee Smith et al. 2019.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.moisture_stress","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.moisture_stress","text":"moisture_stress(pl::FT,\n sc::FT,\n pc::FT) where {FT}\n\nComputes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) . \n\nSee Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.dark_respiration","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.dark_respiration","text":"dark_respiration(Vcmax25::FT,\n β::FT,\n f::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_GPP","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_GPP","text":"compute_GPP(An::FT,\n K::FT,\n LAI::FT,\n Ω::FT) where {FT}\n\nComputes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Kc","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Kc","text":"MM_Kc(Kc25::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Ko","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Ko","text":"MM_Ko(Ko25::FT,\n ΔHko::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_Vcmax","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_Vcmax","text":"compute_Vcmax(Vcmax25::FT,\n T::FT,\n To::FT,\n R::FT,\n ep5::FT) where {FT}\n\nComputes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"getting_started/#Getting-Started","page":"Getting Started","title":"Getting Started","text":"","category":"section"},{"location":"getting_started/#For-Users","page":"Getting Started","title":"For Users","text":"","category":"section"},{"location":"getting_started/#Installation","page":"Getting Started","title":"Installation","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"julia> ] # Enter Package REPL mode\nPkg> add ClimaLand # Install ClimaLand\nPkg> # Go back to Julia REPL mode\nJulia> using ClimaLand","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.","category":"page"},{"location":"getting_started/#Parameterization","page":"Getting Started","title":"Parameterization","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Let's start with a basic example: compute canopy gross photosynthesis (GPP).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\n@doc ClimaLand.Canopy.compute_GPP","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"import ClimaLand.Canopy as canopy\ncanopy.compute_GPP(5.0, 0.5, 3.0, 0.7)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Et voilà!","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.","category":"page"},{"location":"getting_started/#ClimaLand-structure","page":"Getting Started","title":"ClimaLand structure","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ClimaLand contains multiple modules. They are listed below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using MethodAnalysis, ClimaLand\nchild_modules(ClimaLand)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To explore what modules, functions and types are exported in a particular module, you can use About.jl:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\nusing About\nabout(ClimaLand.Soil.Biogeochemistry)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/#Seasonal-Snow-Timeseries-Generation-with-a-Neural-Network","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"section"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The updates of the neural snow model follow the equation","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"fracdzdt = mathcalMleft(z SWE φ R v T_air P_snowright)","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"where","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"t is the time (s),\nz is the snow depth (m),\nmathcalM is the neural network,\nSWE is the Snow Water Equivalent, or the height of water if all the snow melted (m),\nφ is the relative humidity (0-1),\nR is the solar radiation (W/m²).\nv is the wind speed (W/m²).\nT_air is the air temperature (degrees C).\nP_snow is the water equivalent rate of snow precipitation (m/s).","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The model is a 1D model to permit utilization over any desired grid resolution and shape.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"┌ Warning: Package cuDNN not found in current path.\n│ - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia.\n│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.\n└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools\nModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\ncode_dir = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Snow\")\ninclude(joinpath(code_dir, \"display_tools.jl\"));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter n as outlined in the associated parameter, and the two loss function hyperparameters n_1, n_2.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"n = 4\nn1 = 2\nn2 = 4;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the fracdzdt column.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"pred_vars = [\n :z,\n :SWE,\n :rel_hum_avg,\n :sol_rad_avg,\n :wind_speed_avg,\n :air_temp_avg,\n :dprecipdt_snow,\n]\ntarget = :dzdt;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"nfeatures = length(pred_vars)\nz_idx = 1\np_idx = 7;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"training_data_download_link = \"https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv\"\ntesting_data_download_link = \"https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv\"\ndata_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)\nvaldata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)\nΔt = Second(86400)\nhole_thresh = 5;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"usedata = DataTools.prep_data(data_train);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of \"0\" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"out_scale = maximum(abs.(usedata[!, target]))\nin_scales = std.(eachcol(select(usedata, pred_vars)))\nx_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)\nps = ModelTools.get_model_ps(model);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)\nModelTools.setoutscale!(model, 1.0);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With that, training is as simple as calling the trainmodel! function:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"print(\"\\nTraining model!\\n\")\nModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"\nTraining model!\nEpoch: 10 | training loss: 0.0027556566\nEpoch: 20 | training loss: 0.0026529313\nEpoch: 30 | training loss: 0.002609448\nEpoch: 40 | training loss: 0.002617809\nEpoch: 50 | training loss: 0.0025923715\nEpoch: 60 | training loss: 0.002604384\nEpoch: 70 | training loss: 0.0025663753\nEpoch: 80 | training loss: 0.002538259\nEpoch: 90 | training loss: 0.0025648254\nEpoch: 100 | training loss: 0.002541011\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.setoutscale!(model, out_scale)\nModelTools.settimescale!(model, Dates.value(Δt));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Note that gaps in the data are shown as shaded regions on the plotted timeseries.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = 1286\nsitedata = usedata[usedata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Slagamount Lakes, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot1.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = \"1070\" #string format for the testing ids is due to non-numerical testing site codes.\nsitedata = valdata[valdata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Anchorage Hillside, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot2.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This page was generated using Literate.jl.","category":"page"}] +[{"location":"APIs/canopy/RadiativeTransfer/#Radiative-Transfer","page":"Canopy RT","title":"Radiative Transfer","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#Parameters","page":"Canopy RT","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.BeerLambertParameters","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.BeerLambertParameters","page":"Canopy RT","title":"ClimaLand.Canopy.BeerLambertParameters","text":"BeerLambertParameters{FT <: AbstractFloat}\n\nThe required parameters for the Beer-Lambert radiative transfer model.\n\nα_PAR_leaf: PAR leaf reflectance (unitless)\nα_NIR_leaf: NIR leaf reflectance\nϵ_canopy: Emissivity of the canopy\nΩ: Clumping index following Braghiere (2021) (unitless)\nλ_γ_PAR: Typical wavelength per PAR photon (m)\nλ_γ_NIR: Typical wavelength per NIR photon (m)\nG_Function: Leaf angle distribution function\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/RadiativeTransfer/#Methods","page":"Canopy RT","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.compute_absorbances\nClimaLand.Canopy.plant_absorbed_pfd\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.canopy_radiant_energy_fluxes!","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.plant_absorbed_pfd","page":"Canopy RT","title":"ClimaLand.Canopy.plant_absorbed_pfd","text":"plant_absorbed_pfd(\n RT::BeerLambertModel{FT},\n SW_IN:FT,\n α_leaf::FT,\n LAI::FT,\n K::FT,\n α_soil::FT\n)\n\nComputes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.\n\nThis applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). \n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\nplant_absorbed_pfd(\n RT::TwoStreamModel{FT},\n α_leaf,\n SW_IN::FT,\n LAI::FT,\n K::FT,\n τ_leaf,\n θs::FT,\n α_soil::FT,\n)\n\nComputes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band. \n\nThis applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.\n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.extinction_coeff","page":"Canopy RT","title":"ClimaLand.Canopy.extinction_coeff","text":"extinction_coeff(ld::FT,\n θs::FT) where {FT}\n\nComputes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.canopy_radiant_energy_fluxes!","page":"Canopy RT","title":"ClimaLand.Canopy.canopy_radiant_energy_fluxes!","text":"Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrognosticSoil{F},\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {FT, PSE}\n\nIn standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.\n\nIn integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\ncanopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrescribedSoil,\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {PSE}\n\nComputes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/available_diagnostics/#Available-diagnostic-variables","page":"Available diagnostics","title":"Available diagnostic variables","text":"","category":"section"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"Autogenerate table of available diagnostics:","category":"page"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"include(\"make_diagnostic_table.jl\")","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"ref_time = DateTime(2005) # required argument, but not used in this case\nSW_d = (t) -> 0\nLW_d = (t) -> 301.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 5\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)\n Y.soil.θ_i .= 0\n T = FT(296.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 13)\ndt = Float64(900.0)","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"900.0","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the tendency functions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\nevaporation_data =\n ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();\nref_soln_E = readdlm(evaporation_data, ',')\nref_soln_E_350mm = ref_soln_E[2:end, 1:2]\ndata_dates = ref_soln_E_350mm[:, 1]\ndata_e = ref_soln_E_350mm[:, 2];\n\nfig = Figure(size = (800, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Evaporation rate (mm/d)\",\n title = \"Bare soil evaporation\",\n)\nCairoMakie.xlims!(minimum(data_dates), maximum(data_dates))\nCairoMakie.lines!(\n ax,\n FT.(data_dates),\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nax = Axis(\n fig[1, 2],\n xlabel = \"Mass (g)\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nA_col = π * (0.027)^2\nmass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col\nmass_loss =\n [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]\nCairoMakie.lines!(\n ax,\n cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n mass_loss,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nsave(\"evaporation_lehmann2008_fig8b.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#General-setup","page":"Intro to standalone models","title":"General setup","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Your model defines a system of equations of the following form:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in vecY. Generically, we will speak of the functions vecf as tendencies; these can be functions of the prognostic state, of space vecx, and of time t, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-cache-(\"auxiliary-variables\")","page":"Intro to standalone models","title":"The cache (\"auxiliary variables\")","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"There are typically quantities, which depend on the state vector vecY, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require \"a lot\" of memory to store (e.g., most variables in global runs). Allocating memory \"on-the-fly\" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Denoting the cache as vecp, your equations may be rewritten as:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecp vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"vecp(vecx t) = vecg(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables vecp at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported (vecY cannot be zero dimensional).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-Model","page":"Intro to standalone models","title":"The Model","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to \"imex\" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as \"explicit\" or \"implicit\" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Let's first import some needed packages.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaTimeSteppers as CTS\nusing SciMLBase\nusing Plots\nusing ClimaCore\nusing ClimaLand","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Import the functions we are extending for our model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaLand:\n name,\n make_exp_tendency,\n make_compute_exp_tendency,\n make_update_aux,\n make_update_boundary_fluxes,\n prognostic_vars,\n prognostic_types,\n prognostic_domain_names,\n auxiliary_vars,\n auxiliary_types,\n auxiliary_domain_names","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}\n \"van Genuchten model parameters\"\n vGmodel::ClimaLand.Soil.vanGenuchten{FT}\n \"Porosity [unitless]\"\n ν::FT\n \"Residual water fraction [unitless]\"\n θ_r::FT\n \"Saturated hydraulic conductiity [m/s]\"\n Ksat::FT\n \"Surface flux, used as boundary condition [m/s]\"\n F_sfc::FT\n \"Domain of the model\"\n domain::D\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"For reasons that will be clear momentarily, let's also define the name of the model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.name(model::RichardsTutorialModel) = :soil;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Explicit-tendency","page":"Intro to standalone models","title":"Explicit tendency","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracpartial ϑ_lpartial t = - -K(θ) (ψ(θ)+z)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)\n function compute_exp_tendency!(dY, Y, p, t)\n gradc2f = ClimaCore.Operators.GradientC2F()\n interpc2f = ClimaCore.Operators.InterpolateC2F()\n FT = FTfromY(Y)\n divf2c = ClimaCore.Operators.DivergenceF2C(\n top = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(model.F_sfc),\n ),\n bottom = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(FT(0)),\n ),\n )\n\n @. dY.soil.θ =\n -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))\n end\n return compute_exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"A couple of notes: the vector vecdY contains the evaluation of the tendency function for each variable in vecY. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-prognostic-state-vector-\\vec{Y}-and-cache-\\vec{p}","page":"Intro to standalone models","title":"The prognostic state vector vecY and cache vecp","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We have given the state vector vecY a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);\nClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);\nClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.auxiliary_vars(::RichardsTutorialModel) =\n (:K, :ψ, :top_flux, :bottom_flux, :z)\nClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =\n (FT, FT, FT, FT, FT);\nClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =\n (:subsurface, :subsurface, :surface, :surface, :subsurface);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Updating-the-cache","page":"Intro to standalone models","title":"Updating the cache","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_update_aux(model::RichardsTutorialModel)\n function update_aux!(p, Y, t)\n p.soil.z .=\n ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step\n @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(\n model.vGmodel,\n model.Ksat,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n @. p.soil.ψ = ClimaLand.Soil.matric_potential(\n model.vGmodel,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n end\n return update_aux!\nend;\n\nfunction ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)\n function update_boundary_fluxes!(p, Y, t)\n FT = ClimaLand.FTfromY(Y)\n p.soil.top_flux .= model.F_sfc\n p.soil.bottom_flux .= FT(0)\n end\n return update_boundary_fluxes!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The default tendency function in ClimaLand for any AbstractModel carries out the following:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function make_exp_tendency(model::AbstractModel)\n update_aux! = make_update_aux(model)\n update_boundary_fluxes! = make_update_boundary_fluxes(model)\n compute_exp_tendency! = make_compute_exp_tendency(model)\n function exp_tendency!(dY,Y,p,t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY,Y,p,t)\n end\n return exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone \"component\" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-a-simulation","page":"Intro to standalone models","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create a model instance.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"FT = Float32\nvGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)\nKsat = FT(4.0e-7)\nν = 0.5f0\nθ_r = 0.0f0\nF_sfc = FT(-3.0e-8)\ndomain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)\nsoil = RichardsTutorialModel{Float32, typeof(domain)}(\n vGmodel,\n ν,\n θ_r,\n Ksat,\n F_sfc,\n domain,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The same is true for p:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"p.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"(K = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:\n Float32[0.0], bottom_flux = Float32-valued Field:\n Float32[0.0], z = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here we now update Y in place with initial conditions of our choosing.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil.θ = 0.25f0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set initial cache variable values, and inspect values:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, 0.0);\n@show p.soil.K\n\n@show p.soil.ψ\n\n@show p.soil.top_flux","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Float32-valued Field:\n Float32[-3.0f-8]","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Next up is to create the exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"exp_tendency! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-the-simulation","page":"Intro to standalone models","title":"Running the simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set the initial and end times, timestep:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"t0 = 0.0;\ntf = 7 * 24 * 3600.0;\ndt = 1800.0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Select the timestepping algorithm we want to use from CTS.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"SciMLBase problem statement using CTS.jl internals:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Solve command:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand","page":"ClimaLand","title":"ClimaLand","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/ClimaLand/#LSM-Model-Types-and-methods","page":"ClimaLand","title":"LSM Model Types and methods","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.SoilPlantHydrologyModel\nClimaLand.LandSoilBiogeochemistry\nClimaLand.LandHydrology\nClimaLand.make_interactions_update_aux\nClimaLand.initialize_interactions\nClimaLand.land_components\nClimaLand.lsm_aux_vars\nClimaLand.lsm_aux_types\nClimaLand.lsm_aux_domain_names\nClimaLand.domain_name","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.LandSoilBiogeochemistry","page":"ClimaLand","title":"ClimaLand.LandSoilBiogeochemistry","text":"struct LandSoilBiogeochemistry{\n FT,\n SEH <: Soil.EnergyHydrology{FT},\n SB <: Soil.Biogeochemistry.SoilCO2Model{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.\n\nsoil: The soil model\nsoilco2: The biochemistry model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.LandHydrology","page":"ClimaLand","title":"ClimaLand.LandHydrology","text":"struct LandHydrology{\n FT,\n SM <: Soil.AbstractSoilModel{FT},\n SW <: Pond.AbstractSurfaceWaterModel{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil and surface water component.\n\nsoil: The soil model\nsurface_water: The surface water model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.land_components","page":"ClimaLand","title":"ClimaLand.land_components","text":"land_components(land::AbstractLandModel)\n\nReturns the component names of the land model, by calling propertynames(land).\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_vars","page":"ClimaLand","title":"ClimaLand.lsm_aux_vars","text":"lsmauxvars(m::AbstractLandModel)\n\nReturns the additional aux variable symbols for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_vars(m::SoilCanopyModel)\n\nThe names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_types","page":"ClimaLand","title":"ClimaLand.lsm_aux_types","text":"lsmauxtypes(m::AbstractLandModel)\n\nReturns the shared additional aux variable types for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_types(m::SoilCanopyModel)\n\nThe types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_domain_names","page":"ClimaLand","title":"ClimaLand.lsm_aux_domain_names","text":"lsmauxdomain_names(m::AbstractLandModel)\n\nReturns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.\n\nThis is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.\n\n\n\n\n\nlsm_aux_domain_names(m::SoilCanopyModel)\n\nThe domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#Land-Hydrology","page":"ClimaLand","title":"Land Hydrology","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.infiltration_capacity\nClimaLand.infiltration_at_point\nClimaLand.PrognosticRunoff\nClimaLand.RunoffBC","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_capacity","page":"ClimaLand","title":"ClimaLand.infiltration_capacity","text":"function infiltration_capacity(\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n)\n\nFunction which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.\n\nDefined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_at_point","page":"ClimaLand","title":"ClimaLand.infiltration_at_point","text":"infiltration_at_point(η::FT, i_c::FT, P::FT)\n\nReturns the infiltration given pond height η, infiltration capacity, and precipitation.\n\nThis is defined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticRunoff","page":"ClimaLand","title":"ClimaLand.PrognosticRunoff","text":"PrognosticRunoff <: Pond.AbstractSurfaceRunoff\n\nConcrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.\n\nThis is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RunoffBC","page":"ClimaLand","title":"ClimaLand.RunoffBC","text":"RunoffBC <: Soil.AbstractSoilBC\n\nConcrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.\n\nThis is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#SoilCanopyModel","page":"ClimaLand","title":"SoilCanopyModel","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticSoil\nClimaLand.RootExtraction","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticSoil","page":"ClimaLand","title":"ClimaLand.PrognosticSoil","text":" PrognosticSoil{FT} <: AbstractSoilDriver\n\nConcrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.\n\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RootExtraction","page":"ClimaLand","title":"ClimaLand.RootExtraction","text":"RootExtraction{FT} <: Soil.AbstractSoilSource{FT}\n\nConcrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.\n\nThis is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#LandSoilBiogeochemistry","page":"ClimaLand","title":"LandSoilBiogeochemistry","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticMet","category":"page"},{"location":"APIs/shared_utilities/#Shared-Utilities","page":"Shared Utilities","title":"Shared Utilities","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/shared_utilities/#Domains","page":"Shared Utilities","title":"Domains","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.Domains.AbstractDomain\nClimaLand.Domains.AbstractLSMDomain\nClimaLand.Domains.SphericalShell\nClimaLand.Domains.SphericalSurface\nClimaLand.Domains.HybridBox\nClimaLand.Domains.Column\nClimaLand.Domains.Plane\nClimaLand.Domains.Point\nClimaLand.Domains.coordinates\nClimaLand.Domains.obtain_face_space\nClimaLand.Domains.obtain_surface_space\nClimaLand.Domains.obtain_surface_domain\nClimaLand.Domains.top_center_to_surface\nClimaLand.Domains.top_face_to_surface\nClimaLand.Domains.linear_interpolation_to_surface!\nClimaLand.Domains.get_Δz\n","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.AbstractDomain","page":"Shared Utilities","title":"ClimaLand.Domains.AbstractDomain","text":"AbstractDomain{FT <:AbstractFloat}\n\nAn abstract type for domains.\n\nThe domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.\n\nAdditionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalShell","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalShell","text":"struct SphericalShell{FT} <: AbstractDomain{FT}\n radius::FT\n depth::FT\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nradius: The radius of the shell\ndepth: The radial extent of the shell\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: The number of elements to be used in the non-radial and radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalSurface","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalSurface","text":"struct SphericalSurface{FT} <: AbstractDomain{FT}\n radius::FT\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).\n\nFields\n\nradius: The radius of the surface\nnelements: The number of elements to be used in the non-radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.HybridBox","page":"Shared Utilities","title":"ClimaLand.Domains.HybridBox","text":"struct HybridBox{FT} <: AbstractDomain{FT}\n xlim::Tuple{FT, FT}\n ylim::Tuple{FT, FT}\n zlim::Tuple{FT, FT}\n longlat::Union{Nothing, Tuple{FT, FT}},\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int, Int}\n npolynomial::Int\n periodic::Tuple{Bool, Bool}\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.\n\nWhen longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nzlim: Domain interval limits along z axis, in meters\nlonglat: When not nothing, a Tuple that contains the center long and lat.\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: Number of elements to discretize interval, (nx, ny,nz)\nnpolynomial: Polynomial order for the horizontal directions\nperiodic: Flag indicating periodic boundaries in horizontal\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Column","page":"Shared Utilities","title":"ClimaLand.Domains.Column","text":"Column{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nzlim: Domain interval limits, (zmin, zmax), in meters\nnelements: Number of elements used to discretize the interval\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nboundary_names: Boundary face identifiers\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Plane","page":"Shared Utilities","title":"ClimaLand.Domains.Plane","text":"Plane{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.\n\nWhen longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.\n\nlonglat are in degrees, with longitude going from -180 to 180.\n\n:warning: Only independent columns are supported! (No lateral flow).\n\nspace is a NamedTuple holding the surface space (in this case, the entire Plane space).\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nlonglat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).\nnelements: Number of elements to discretize interval, (nx, ny)\nperiodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.\nnpolynomial: Polynomial order for both x and y\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Point","page":"Shared Utilities","title":"ClimaLand.Domains.Point","text":"Point{FT} <: AbstractDomain{FT}\n\nA domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.\n\nspace is a NamedTuple holding the surface space (in this case, the Point space).\n\nFields\n\nz_sfc: Surface elevation relative to a reference (m)\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.coordinates","page":"Shared Utilities","title":"ClimaLand.Domains.coordinates","text":"coordinates(domain::AbstractDomain)\n\nReturns the coordinate fields for the domain as a NamedTuple.\n\nThe returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.\n\n\n\n\n\nDomains.coordinates(model::AbstractLandModel)\n\nReturns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.\n\nFor example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_face_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_face_space","text":"obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the face space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the face space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_space","text":"obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the surface space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_domain","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_domain","text":"obtain_surface_domain(d::AbstractDomain) where {FT}\n\nDefault method throwing an error; any domain with a corresponding domain should define a new method of this function.\n\n\n\n\n\nobtain_surface_domain(c::Column{FT}) where {FT}\n\nReturns the Point domain corresponding to the top face (surface) of the Column domain c.\n\n\n\n\n\nobtain_surface_domain(b::HybridBox{FT}) where {FT}\n\nReturns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.\n\n\n\n\n\nobtain_surface_domain(s::SphericalShell{FT}) where {FT}\n\nReturns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_center_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_center_to_surface","text":"top_center_to_surface(center_field::ClimaCore.Fields.Field)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.\n\nFor example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\ntop_center_to_surface(val)\n\nWhen val is a scalar (e.g. a single float or struct), returns val.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_face_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_face_to_surface","text":"top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.\n\nGiven a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.linear_interpolation_to_surface!","page":"Shared Utilities","title":"ClimaLand.Domains.linear_interpolation_to_surface!","text":"linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)\n\nLinearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.get_Δz","page":"Shared Utilities","title":"ClimaLand.Domains.get_Δz","text":"get_Δz(z::ClimaCore.Fields.Field)\n\nA function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Models","page":"Shared Utilities","title":"Models","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.AbstractModel\nClimaLand.AbstractImExModel\nClimaLand.AbstractExpModel\nClimaLand.make_exp_tendency\nClimaLand.make_imp_tendency\nClimaLand.make_compute_exp_tendency\nClimaLand.make_compute_imp_tendency\nClimaLand.make_update_aux\nClimaLand.make_update_boundary_fluxes\nClimaLand.make_set_initial_cache\nClimaLand.make_update_drivers\nClimaLand.prognostic_vars\nClimaLand.prognostic_types\nClimaLand.prognostic_domain_names\nClimaLand.auxiliary_vars\nClimaLand.auxiliary_types\nClimaLand.auxiliary_domain_names\nClimaLand.initialize_prognostic\nClimaLand.initialize_auxiliary\nClimaLand.initialize\nClimaLand.name\nClimaLand.AbstractBC\nClimaLand.AbstractSource\nClimaLand.source!\nClimaLand.AbstractBoundary\nClimaLand.TopBoundary\nClimaLand.BottomBoundary\nClimaLand.boundary_flux\nClimaLand.diffusive_flux\nClimaLand.boundary_vars\nClimaLand.boundary_var_domain_names\nClimaLand.boundary_var_types\nClimaLand.make_jacobian\nClimaLand.make_compute_jacobian\nClimaLand.set_dfluxBCdY!\nClimaLand.get_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractModel","page":"Shared Utilities","title":"ClimaLand.AbstractModel","text":"abstract type AbstractModel{FT <: AbstractFloat}\n\nAn abstract type for all models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractImExModel","page":"Shared Utilities","title":"ClimaLand.AbstractImExModel","text":"AbstractImExModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractExpModel","page":"Shared Utilities","title":"ClimaLand.AbstractExpModel","text":"AbstractExpModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.make_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_exp_tendency","text":"make_exp_tendency(model::AbstractModel)\n\nReturns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_imp_tendency","text":"make_imp_tendency(model::AbstractImExModel)\n\nReturns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\nmake_imp_tendency(model::AbstractModel)\n\nReturns an imp_tendency that does nothing. This model type is not stepped explicity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_exp_tendency","text":"make_explicit_tendency(model::Soil.RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nConstruct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.\n\n\n\n\n\nmake_compute_exp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::BucketModel{FT}) where {FT}\n\nCreates the computeexptendency! function for the bucket model.\n\n\n\n\n\nmake_compute_exp_tendency(model::AbstractModel)\n\nReturn a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)\n\nCreates the computeexptendency!(dY,Y,p,t) function for the canopy component.\n\nSince component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.\n\n\n\n\n\nmake_compute_exp_tendency(canopy::CanopyModel)\n\nCreates and returns the computeexptendency! for the CanopyModel.\n\n\n\n\n\nmake_compute_exp_tendency(model::SoilCO2Model)\n\nAn extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::PlantHydraulicsModel, _)\n\nA function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.\n\nBelow, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).\n\nNote that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.\n\nTo prevent dividing by zero, we change AI/(AI x dz)\" to \"AI/max(AI x dz, eps(FT))\"\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_imp_tendency","text":"make_compute_imp_tendency(model::RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.\n\n\n\n\n\nmake_compute_imp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_imp_tendency(model::AbstractModel)\n\nReturn a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_aux","page":"Shared Utilities","title":"ClimaLand.make_update_aux","text":"make_update_aux(model::RichardsModel)\n\nAn extension of the function make_update_aux, for the Richardson- Richards equation.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::EnergyHydrology)\n\nAn extension of the function make_update_aux, for the integrated soil hydrology and energy model.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::BucketModel{FT}) where {FT}\n\nCreates the update_aux! function for the BucketModel.\n\n\n\n\n\nmake_update_aux(model::AbstractModel)\n\nReturn an update_aux! function that updates auxiliary parameters p.\n\n\n\n\n\n ClimaLand.make_update_aux(canopy::CanopyModel{FT,\n <:AutotrophicRespirationModel,\n <:Union{BeerLambertModel, TwoStreamModel},\n <:FarquharModel,\n <:MedlynConductanceModel,\n <:PlantHydraulicsModel,},\n ) where {FT}\n\nCreates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.\n\nPlease note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.\n\nThe other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.\n\n\n\n\n\nmake_update_aux(model::SoilCO2Model)\n\nAn extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_boundary_fluxes","page":"Shared Utilities","title":"ClimaLand.make_update_boundary_fluxes","text":"make_update_boundary_fluxes(model::AbstractModel)\n\nReturn an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::LandHydrology{FT, SM, SW},\n) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}\n\nA method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.\n\nThis function is called each ode function evaluation.\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::SoilCanopyModel{FT, MM, SM, RM},\n) where {\n FT,\n MM <: Soil.Biogeochemistry.SoilCO2Model{FT},\n SM <: Soil.RichardsModel{FT},\n RM <: Canopy.CanopyModel{FT}\n }\n\nA method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. \n\nThis function is called each ode function evaluation, prior to the tendency function evaluation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_set_initial_cache","page":"Shared Utilities","title":"ClimaLand.make_set_initial_cache","text":"make_set_initial_cache(model::AbstractModel)\n\nReturns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.\n\nIn principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.\n\nFurthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_drivers","page":"Shared Utilities","title":"ClimaLand.make_update_drivers","text":"make_update_drivers(::AbstractClimaLandDrivers)\n\nCreates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.\n\n\n\n\n\nmake_update_drivers(driver_tuple)\n\nCreates and returns a function which updates the forcing variables (\"drivers\"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.\n\n\n\n\n\nmake_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.\n\n\n\n\n\nmake_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.\n\n\n\n\n\nmake_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.\n\n\n\n\n\nmake_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_vars","page":"Shared Utilities","title":"ClimaLand.prognostic_vars","text":"prognostic_vars(soil::RichardsModel)\n\nA function which returns the names of the prognostic variables of RichardsModel.\n\n\n\n\n\nprognostic_vars(soil::EnergyHydrology)\n\nA function which returns the names of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_vars(::SnowModel)\n\nReturns the prognostic variable names of the snow model.\n\nFor this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.\n\n\n\n\n\nprognostic_vars(m::AbstractModel)\n\nReturns the prognostic variable symbols for the model in the form of a tuple.\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_vars(::AbstractCanopyComponent)\n\nReturns the prognostic vars of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_vars(canopy::CanopyModel)\n\nReturns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nprognostic_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the prognostic variables of the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_types","page":"Shared Utilities","title":"ClimaLand.prognostic_types","text":"prognostic_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_types(::SnowModel{FT})\n\nReturns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.\n\n\n\n\n\nprognostic_types(m::AbstractModel{FT}) where {FT}\n\nReturns the prognostic variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nHere, the coordinate points are those returned by coordinates(model).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_types(::AbstractCanopyComponent)\n\nReturns the prognostic types of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_types(canopy::CanopyModel)\n\nReturns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the prognostic types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_domain_names","page":"Shared Utilities","title":"ClimaLand.prognostic_domain_names","text":"prognostic_domain_names(::SnowModel)\n\nReturns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is \":surface\".\n\n\n\n\n\nprognosticdomainnames(m::AbstractModel)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nprognosticdomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_vars","page":"Shared Utilities","title":"ClimaLand.auxiliary_vars","text":"auxiliary_vars(soil::RichardsModel)\n\nA function which returns the names of the auxiliary variables of RichardsModel.\n\n\n\n\n\nauxiliary_vars(soil::EnergyHydrology)\n\nA function which returns the names of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_vars(::SnowModel)\n\nReturns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.\n\nSince the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.\n\n\n\n\n\nauxiliary_vars(m::AbstractModel)\n\nReturns the auxiliary variable symbols for the model in the form of a tuple.\n\n\n\n\n\nClimaLand.auxiliary_vars(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_vars(canopy::CanopyModel)\n\nReturns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nauxiliary_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_types","page":"Shared Utilities","title":"ClimaLand.auxiliary_types","text":"auxiliary_types(soil::RichardsModel)\n\nA function which returns the names of the auxiliary types of RichardsModel.\n\n\n\n\n\nauxiliary_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_types(m::AbstractModel{FT}) where {FT}\n\nReturns the auxiliary variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nNote that Arrays, MVectors are not isbits and cannot be used.\n\nHere, the coordinate points are those returned by coordinates(model).\n\n\n\n\n\nClimaLand.auxiliary_types(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_types(canopy::CanopyModel)\n\nReturns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the auxiliary types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_domain_names","page":"Shared Utilities","title":"ClimaLand.auxiliary_domain_names","text":"auxiliary_domain_names(soil::RichardsModel)\n\nA function which returns the names of the auxiliary domain names of RichardsModel.\n\n\n\n\n\nauxiliarydomainnames(m::AbstractModel)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\n\n\n\n\nauxiliarydomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_prognostic","page":"Shared Utilities","title":"ClimaLand.initialize_prognostic","text":"initialize_prognostic(model::AbstractModel, state::NamedTuple)\n\nReturns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.\n\nIf a model has no prognostic variables, the returned FieldVector contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_prognostic(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_prognostic(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input state is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_auxiliary","page":"Shared Utilities","title":"ClimaLand.initialize_auxiliary","text":"initialize_auxiliary(model::AbstractModel, state::NamedTuple)\n\nReturns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.\n\nIf a model has no auxiliary variables, the returned NamedTuple contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_auxiliary(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_auxiliary(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input coords is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize","page":"Shared Utilities","title":"ClimaLand.initialize","text":"initialize(model::AbstractModel)\n\nCreates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.name","page":"Shared Utilities","title":"ClimaLand.name","text":"name(model::AbstractModel)\n\nReturns a symbol of the model component name, e.g. :soil or :vegetation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBC","page":"Shared Utilities","title":"ClimaLand.AbstractBC","text":"AbstractBC\n\nAn abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractSource","page":"Shared Utilities","title":"ClimaLand.AbstractSource","text":"AbstractSource{FT <: AbstractFloat}\n\nAn abstract type for types of source terms.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.source!","page":"Shared Utilities","title":"ClimaLand.source!","text":" source!(dY::ClimaCore.Fields.FieldVector,\n src::PhaseChange{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nComputes the source terms for phase change.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::SoilSublimation{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nUpdates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::AbstractSource,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n )::ClimaCore.Fields.Field\n\nA stub function, which is extended by ClimaLand.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::RootExtraction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n model::EnergyHydrology)\n\nAn extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::MicrobeProduction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n params)\n\nA method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.\n\n\n\n\n\nClimaLand.source!(\n dY::ClimaCore.Fields.FieldVector,\n src::TOPMODELSubsurfaceRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model::AbstractSoilModel{FT},\n) where {FT}\n\nAdjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.\n\nThe sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBoundary","page":"Shared Utilities","title":"ClimaLand.AbstractBoundary","text":"AbstractBoundary\n\nAn abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.TopBoundary","page":"Shared Utilities","title":"ClimaLand.TopBoundary","text":"TopBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the top boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.BottomBoundary","page":"Shared Utilities","title":"ClimaLand.BottomBoundary","text":"BottomBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the bottom boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_flux","page":"Shared Utilities","title":"ClimaLand.boundary_flux","text":"boundary_flux(bc::WaterFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(bc::RichardsAtmosDrivenFluxBC,\n boundary::ClimaLand.AbstractBoundary,\n model::RichardsModel{FT},\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field where {FT}\n\nA method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.\n\nIf model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(bc::FreeDrainage,\n boundary::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which enforces free drainage at the bottom of the domain.\n\n\n\n\n\nboundary_flux(bc::HeatFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.BottomBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field\n\nA function which returns the correct boundary flux given any boundary condition (BC). \n\n\n\n\n\nfunction ClimaLand.boundary_flux(\n bc::RunoffBC,\n ::TopBoundary,\n model::Soil.RichardsModel,\n Δz::FT,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n params,\n)::ClimaCore.Fields.Field\n\nExtension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2FluxBC,\n boundary::ClimaLand.AbstractBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::SoilCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2StateBC,\n boundary::ClimaLand.BottomBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::AtmosCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.diffusive_flux","page":"Shared Utilities","title":"ClimaLand.diffusive_flux","text":"diffusive_flux(K, x_2, x_1, Δz)\n\nCalculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_vars","page":"Shared Utilities","title":"ClimaLand.boundary_vars","text":"boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.\n\nThese variables are updated in place in soil_boundary_fluxes!.\n\n\n\n\n\nboundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for MoistureStateBC at the top boundary.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\nboundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_domain_names","page":"Shared Utilities","title":"ClimaLand.boundary_var_domain_names","text":"boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.\n\n\n\n\n\nboundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)\n\nThe list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. \n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_types","page":"Shared Utilities","title":"ClimaLand.boundary_var_types","text":"boundary_var_types(::RichardsModel{FT},\n ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <: Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.\n\nBecause we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.\n\n\n\n\n\nboundary_var_types(\n ::EnergyHydrology{FT},\n ::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere{FT},\n <:AbstractRadiativeDrivers{FT},\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary,\n) where {FT}\n\nAn extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.\n\n\n\n\n\nboundary_var_types(::RichardsModel{FT},\n ::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_jacobian","page":"Shared Utilities","title":"ClimaLand.make_jacobian","text":"make_jacobian(model::AbstractModel)\n\nCreates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.\n\nThe default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.\n\nNote that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_jacobian","page":"Shared Utilities","title":"ClimaLand.make_compute_jacobian","text":"ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nmake_compute_jacobian(model::AbstractModel)\n\nCreates and returns a function which computes the entries of the Jacobian matrix W in place.\n\nIf the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.\n\nThe default is that no updates are required, but this function must be extended for models that use implicit timestepping.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_dfluxBCdY!","page":"Shared Utilities","title":"ClimaLand.set_dfluxBCdY!","text":"ClimaLand.set_dfluxBCdY!(\n model::RichardsModel,\n ::MoistureStateBC,\n boundary::ClimaLand.TopBoundary,\n Δz,\n Y,\n p,\n t,\n\n)\n\nComputes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.\n\nFor Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.\n\n\n\n\n\nset_dfluxBCdY!(::AbstractModel,\n ::AbstractBC,\n ::AbstractBoundary,\n _...)::Union{ClimaCore.Fields.FieldVector, Nothing}\n\nA function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.get_drivers","page":"Shared Utilities","title":"ClimaLand.get_drivers","text":"ClimaLand.get_drivers(model::RichardsModel)\n\nReturns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.\n\n\n\n\n\nClimaLand.get_drivers(model::SnowModel)\n\nReturns the driver variable symbols for the SnowModel.\n\n\n\n\n\nget_drivers(model::AbstractModel)\n\nReturns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Drivers","page":"Shared Utilities","title":"Drivers","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.PrescribedAtmosphere\nClimaLand.PrescribedPrecipitation\nClimaLand.PrescribedRadiativeFluxes\nClimaLand.PrescribedSoilOrganicCarbon\nClimaLand.CoupledAtmosphere\nClimaLand.CoupledRadiativeFluxes\nClimaLand.AbstractAtmosphericDrivers\nClimaLand.AbstractRadiativeDrivers\nClimaLand.turbulent_fluxes\nClimaLand.turbulent_fluxes_at_a_point\nClimaLand.radiative_fluxes_at_a_point\nClimaLand.set_atmos_ts!\nClimaLand.surface_air_density\nClimaLand.surface_temperature\nClimaLand.surface_resistance\nClimaLand.surface_specific_humidity\nClimaLand.make_update_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedAtmosphere","page":"Shared Utilities","title":"ClimaLand.PrescribedAtmosphere","text":"PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.\n\nThe default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.\n\nSince not all models require co2 concentration, the default for that is nothing.\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\nsnow_precip: Snow precipitation (m/s) function of time: positive by definition\nT: Prescribed atmospheric temperature (function of time) at the reference height (K)\nu: Prescribed wind speed (function of time) at the reference height (m/s)\nq: Prescribed specific humidity (function of time) at the reference height (_)\nP: Prescribed air pressure (function of time) at the reference height (Pa)\nc_co2: CO2 concentration in atmosphere (mol/mol)\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nh: Reference height (m), relative to surface elevation\ngustiness: Minimum wind speed (gustiness; m/s)\nthermo_params: Thermodynamic parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedPrecipitation","page":"Shared Utilities","title":"ClimaLand.PrescribedPrecipitation","text":"PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.PrescribedRadiativeFluxes","text":"PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}\n\nContainer for the prescribed radiation functions needed to drive land models in standalone mode.\n\nSW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface\nLW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nθs: Sun zenith angle, in radians\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedSoilOrganicCarbon","page":"Shared Utilities","title":"ClimaLand.PrescribedSoilOrganicCarbon","text":" PrescribedSoilOrganicCarbon{FT}\n\nA type for prescribing soil organic carbon.\n\nsoc: Soil organic carbon, function of time and space: kg C/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledAtmosphere","page":"Shared Utilities","title":"ClimaLand.CoupledAtmosphere","text":"CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.CoupledRadiativeFluxes","text":"CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractAtmosphericDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractAtmosphericDrivers","text":" AbstractAtmosphericDrivers{FT}\n\nAn abstract type of atmospheric drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractRadiativeDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractRadiativeDrivers","text":" AbstractRadiativeDrivers{FT}\n\nAn abstract type of radiative drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes","text":"turbulent_fluxes(atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t\n )\n\nComputes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.\n\nIt solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.\n\n\n\n\n\nturbulent_fluxes(atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t)\n\nComputes the turbulent surface fluxes terms at the ground for a coupled simulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes_at_a_point","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes_at_a_point","text":"turbulent_fluxes_at_a_point(T_sfc::FT,\n q_sfc::FT,\n ρ_sfc::FT,\n β_sfc::FT,\n h_sfc::FT,\n r_sfc::FT,\n d_sfc::FT,\n ts_in,\n u::FT,\n h::FT,\n gustiness::FT,\n z_0m::FT,\n z_0b::FT,\n earth_param_set::EP,\n ) where {FT <: AbstractFloat, P}\n\nComputes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc\n\nThis returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_atmos_ts!","page":"Shared Utilities","title":"ClimaLand.set_atmos_ts!","text":"set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)\n\nFill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_air_density","page":"Shared Utilities","title":"ClimaLand.surface_air_density","text":"surface_air_density(\n atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t,\n T_sfc,\n )\n\nA helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.\n\nWe additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.\n\nExtending this function for your model is only necessary if you need to compute the air density in a different way.\n\n\n\n\n\nClimaLand.surface_air_density(\n atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n _...,\n )\n\nReturns the air density at the surface in the case of a coupled simulation.\n\nThis requires the field ρ_sfc to be present in the cache p under the name of the model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_temperature","page":"Shared Utilities","title":"ClimaLand.surface_temperature","text":"ClimaLand.surface_temperature(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface temperature field of the EnergyHydrology soil model.\n\nThe assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.\n\n\n\n\n\nClimaLand.surface_temperature(model::SnowModel, Y, p)\n\na helper function which returns the surface temperature for the snow model, which is stored in the aux state.\n\n\n\n\n\nClimaLand.surface_temperature(model::BucketModel, Y, p)\n\na helper function which returns the surface temperature for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_temperature(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_temperature(model::CanopyModel, Y, p, t)\n\nA helper function which returns the temperature for the canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_resistance","page":"Shared Utilities","title":"ClimaLand.surface_resistance","text":"ClimaLand.surface_resistance(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface resistance field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_resistance(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\nThe default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.\n\n\n\n\n\nClimaLand.surface_resistance(\n model::CanopyModel{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the stomatal resistance field of the CanopyModel canopy.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_specific_humidity","page":"Shared Utilities","title":"ClimaLand.surface_specific_humidity","text":"ClimaLand.surface_specific_humidity(\n model::EnergyHydrology{FT},\n Y,\n p,\n T_sfc,\n ρ_sfc\n) where {FT}\n\nReturns the surface specific humidity field of the EnergyHydrology soil model.\n\nThis models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.\n\nOver the soil ice, the specific humidity is the saturated value.\n\nThe total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\nComputes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\na helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)\n\nA helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)\n\nA helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.\n\n\n\n\n\n","category":"function"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Farquhar-Model","page":"Farquhar model","title":"Farquhar Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO_2 into the leaf, the assimilation of CO_2 during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO_2 concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Rubisco-limited-rate","page":"Farquhar model","title":"Rubisco limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_1(T c_a VPD) =\nbegincases\n V_cmax(T) frac(c_i(T c_a VPD) - Gamma^*(T))(c_i(T c_a VPD) + K_c(T)*(1+o_iK_o(T))) textfor C3\n V_cmax(T) textfor C4\nendcases\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The dependence on the atmospheric CO_2 concentration c_a (mol/mol) and vapor pressure deficit VPD arise in the expression for c_i,","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n c_i(T c_a VPD) = max(c_a(1-1m(VPD)) Gamma^*(T))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where and m is the Medlyn factor (see Stomatal Conductance).","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We also have","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":" Gamma^*(T) = Gamma^*_25expleft(Delta H_Gamma^*fracT - T_oT_o R Tright)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where Delta H_Gamma^* is the activation energy per mol for Gamma^*.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Light-limited-rate","page":"Farquhar model","title":"Light limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_2 =\nbegincases\n J(T PAR) (c_i - Gamma^*)4(c_i + 2 Gamma^*) textfor C3\n J(T PAR) textfor C4\nendcases \nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where J is the rate of electron transport, which has units of mol photon per m^2 per s. It depends on PAR via APAR, as described below, and on T via the dependence on J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"J is given by the root of the equation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n theta_j J^2 - (I + J_max) J + I J_max = 0 nonumber \n I = fracphi2 (APAR) nonumber \n J_max(T) = V_cmax(T)times e expleft(Delta H_J_maxfracT - T_oT_o R Tright)nonumber \nJ(T PAR) = frac(I + J_max - sqrt(I + J_max)^2 - 4theta_j I times J_max2theta_j\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where phi = 06 and theta_j = 09 are the quantum yield of photosystem II and a curvature function (Bonan's book), and Delta H_J_max is the energy of activation of J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The total net carbon assimilation (A_n, mol CO_2 m^-2 s^-1) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nA_n(T PAR VPD c_a) = textmax(0 textmin(a_1 beta a_2) - R_d)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where beta is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R_d is the leaf dark respiration calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n R_d25(psi_l) = f V_cmax25beta(psi_l) nonumber \n R_d (T psi_l) = R_d25(psi_l)expleft(Delta H_R_dfracT - T_oT_o R Tright)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where f = 0015 is a constant, Delta H_R_d is the energy of activation for R_d, and finally Vcmax is calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\nV_cmax(T) = V_cmax25 expleft(Delta H_VcmaxfracT - T_oT_o R Tright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"with V_cmax25 is a parameter (Vcmax at the reference temperature 25 C), and Delta H_Vcmax = 65330 Jmol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The moisture stress factor is related to the leaf water potential psi_l as","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n beta = frac1+ exp(s_c psi_c)1+ exp(s_c(psi_c - psi_l))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where s_c = 4MPa^-1, psi_c = -2MPa, and psi_l is the leaf water potential computed by the plant hydraulics model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nGPP(T PAR c_a VPD theta_s) = A_n (1 - exp(-K LAI Omega))K\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This is not currently needed by other components, but is used for offline validation of the model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We need to supply the following parameters and “drivers\"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"K_c25 and K_o25, V_cmax 25, Gamma^*_25phi, theta_j, o_i, s_c, psi_c\npsi_l, to compute beta\nTemperature T, PAR, c_a, VPD, theta_s.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Output Symbol Unit Range\nTotal net carbon assimilation A_n μmol CO_2 m^-2 s^-1 0–25","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nTemperature T °C 0–50","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Parameters Symbol Unit Range\nMoisture stress β - 0-1\nLeaf Area Index LAI m² m⁻² 1–10\nCO_2 concentration c_a ppm 300e–500\nVapor pressure deficit VPD kPa 1-10","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Constants Symbol Unit Value\nZenith angle θ_s rad 0.6\nLeaf angle distribution l_d - 0.5\nCanopy reflectance ρ_leaf - 0.1\nClumping index Ω - 0.69\nCO_2 compensation at 25°C Γ^*_25 mol/mol 4.275e-5\nEnergy of activation for Γ^* ΔH_Γ^* J/mol 37830\nStandard temperature T_o K 298.15\nUniversal gas constant R J/mol 8.314\nThe maximum rate of carboxylation of Rubisco V_cmax25 mol CO_2 m^-2 s^-1 5e-5\nEnergy of activation for J_max ΔH_J_max J/mol 43540\nCurvature parameter, a fitting constant to compute J θ_j - 0.9\nThe quantum yied of photosystem II phi - 0.6\nEnergy of activation for V_cmax ΔH_V_cmax J/mol 58520\nSlope parameter for stomatal conductance models g_1 - 141\nMichaelis Menten constant for CO_2 and at 25\u000e°C K_c25 mol/mol 4.049e-4\nEnergy of activation for CO_2 ΔH_K_c J/mol 79430\nMichaelis Menten constant for O_2 at 25 °C\u000e K_o25 mmol/mol 0.2874\nEnergy of activation for O_2 ΔH_K_o J/mol 36380\nIntercellular O_2 concentration o_i mol/mol 0.209\nConstant factor appearing the dark respiration term\u000e f - 0.015\nEnergy of activation for R_d ΔH_R_d J/mol 43390","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Soil-Biogeochemistry","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"CurrentModule = ClimaLand.Soil.Biogeochemistry","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Model-Structure","page":"Soil Biogeochemistry","title":"Model Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2Model","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","text":"SoilCO2Model\n\nA model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type NamedTuple\nsources: A tuple of sources, each of type AbstractSource\ndrivers: Drivers\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Parameter-Structure","page":"Soil Biogeochemistry","title":"Parameter Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","text":"SoilCO2ModelParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SoilCO2Model.\n\nAll of these parameters are currently treated as global constants.\n\nD_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)\nD_liq: Diffusivity of soil C substrate in liquid (unitless)\nα_sx: Pre-exponential factor (kg C m-3 s-1)\nEa_sx: Activation energy (J mol-1)\nkM_sx: Michaelis constant (kg C m-3)\nkM_o2: Michaelis constant for O2 (m3 m-3)\nO2_a: Volumetric fraction of O₂ in the soil air, dimensionless\nD_oa: Diffusion coefficient of oxygen in air, dimensionless\np_sx: Fraction of soil carbon that is considered soluble, dimensionless\nearth_param_set: Physical constants used Clima-wide\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Model-specific-Types","page":"Soil Biogeochemistry","title":"Model-specific Types","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction\nClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC\nClimaLand.Soil.Biogeochemistry.SoilCO2StateBC\nClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC\nClimaLand.Soil.Biogeochemistry.AbstractSoilDriver\nClimaLand.Soil.Biogeochemistry.SoilDrivers\nClimaLand.Soil.Biogeochemistry.PrescribedMet","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.MicrobeProduction","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction","text":"MicrobeProduction{FT} <: AbstractCarbonSource{FT}\n\nStruct for the microbe production of CO2, appearing as a source term in the differential equation.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","text":"SoilCO2FluxBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 flux boundary condition, which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","text":"SoilCO2StateBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 state boundary condition (kg CO2 m−3), which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","text":"AtmosCO2StateBC <: ClimaLand.AbstractBC\n\nSet the CO2 concentration to the atmospheric one.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","text":"AbstractSoilDriver\n\nAn abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilDrivers","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilDrivers","text":"SoilDrivers\n\nA container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).\n\nmet: Soil temperature and moisture drivers - Prescribed or Prognostic\nsoc: Soil SOM driver - Prescribed only\natmos: Prescribed atmospheric variables\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.PrescribedMet","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.PrescribedMet","text":"PrescribedMet <: AbstractSoilDriver\n\nA container which holds the prescribed functions for soil temperature and moisture.\n\nThis is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.\n\ntemperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat\nvolumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat\nν: Soil porosity (m³ m⁻³)\nθ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)\nb: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Functions-of-State","page":"Soil Biogeochemistry","title":"Functions of State","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content\nClimaLand.Soil.Biogeochemistry.co2_diffusivity\nClimaLand.Soil.Biogeochemistry.microbe_source","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.volumetric_air_content","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content","text":"volumetric_air_content(θ_w::FT,\n ν::FT,\n ) where {FT}\n\nComputes the volumetric air content (θ_a) in the soil, which is related to the total soil porosity (ν) and volumetric soil water content (θ_w = θ_l+θ_i).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.co2_diffusivity","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.co2_diffusivity","text":"co2_diffusivity(\n T_soil::FT,\n θ_w::FT,\n P_sfc::FT,\n θ_a100::FT,\n b::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT},\n ) where {FT}\n\nComputes the diffusivity of CO₂ within the soil (D).\n\nFirst, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.\n\nThis parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.microbe_source","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.microbe_source","text":"microbe_source(T_soil::FT,\n θ_l::FT,\n Csom::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT}\n ) where {FT}\n\nComputes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#Extendible-Functions","page":"Soil Biogeochemistry","title":"Extendible Functions","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.soil_moisture\nClimaLand.Soil.Biogeochemistry.soil_temperature\nClimaLand.Soil.Biogeochemistry.soil_SOM_C","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_moisture","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_moisture","text":"soil_moisture(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.\n\n\n\n\n\nsoil_moisture(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil moisture at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_temperature","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_temperature","text":"soil_temperature(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the prognostic soil temperature.\n\n\n\n\n\nsoil_temperature(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil temperature at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/users_diagnostics/#Using-ClimaLand-Diagnostics-when-running-a-simulation","page":"For users","title":"Using ClimaLand Diagnostics when running a simulation","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"This is where ClimaLand Diagnostics comes in for users.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.","category":"page"},{"location":"diagnostics/users_diagnostics/#Default-Diagnostics","page":"For users","title":"Default Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#define-an-output-folder","page":"For users","title":"define an output folder","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path(\"base_output_dir/\")","category":"page"},{"location":"diagnostics/users_diagnostics/#define-a-space","page":"For users","title":"define a space","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"space = bucket_domain.space.subsurface","category":"page"},{"location":"diagnostics/users_diagnostics/#define-your-writter","page":"For users","title":"define your writter","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"providing the space and output_dir defined in steps 1. and 2.","category":"page"},{"location":"diagnostics/users_diagnostics/#make-your-diagnostics-on-your-model,-using-your-writter,-and-define-a-callback","page":"For users","title":"make your diagnostics on your model, using your writter, and define a callback","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"t0 = 0 # the starting time of your simulation\n\nreference_date = DateTime(2024) # reference_date is the DateTime of your starting time\n\ndiags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)\n\ndiagnostic_handler =\n ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)\n\ndiag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics have now been written in netcdf files in your output folder.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.","category":"page"},{"location":"diagnostics/users_diagnostics/#Custom-Diagnostics","page":"For users","title":"Custom Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When defining a custom diagnostic, follow these steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-compute-your-diagnostic-variable-from-your-model-state-and-cache.","page":"For users","title":"Define how to compute your diagnostic variable from your model state and cache.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)\n else\n out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf\n end\nend","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"@diagnostic_compute \"latent_heat_flux\" BucketModel p.bucket.turbulent_fluxes.lhf","category":"page"},{"location":"diagnostics/users_diagnostics/#Add-that-diagnostic(s)-variable-to-your-list-of-variables","page":"For users","title":"Add that diagnostic(s) variable to your list of variables","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":" add_diagnostic_variable!(\n short_name = \"bor\",\n long_name = \"Bowen ratio\",\n standard_name = \"bowen_ratio\",\n units = \"\",\n comments = \"Ratio of sensible to latent heat flux.\",\n compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),\n)\n\nadd_diagnostic_variable!(\n short_name = \"lhf\",\n long_name = \"Latent Heat Flux\",\n standard_name = \"latent_heat_flux\",\n units = \"W m^-2\",\n comments = \"Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.\",\n compute! = (out, Y, p, t) ->\n compute_latent_heat_flux!(out, Y, p, t, land_model),\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-schedule-your-variables.-For-example,-you-want-the-seasonal-maximum-of-your-variables,-where-season-is-defined-as-90-days.","page":"For users","title":"Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(\n 90 * 24 * 60 * 60 * one(t_start),\n max,\n output_writer,\n t_start,\n short_names...,\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-a-function-to-return-your-ScheduledDiagnostics","page":"For users","title":"Define a function to return your ScheduledDiagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now, you can call your schedule with your variables.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"my_custom_diagnostics = [\"lhf\", \"bor\"]\n\ndiags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)","category":"page"},{"location":"diagnostics/users_diagnostics/#Analyze-your-simulation-output","page":"For users","title":"Analyze your simulation output","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Here is an example of how to plot a variable:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"import ClimaAnalysis\n\nimport ClimaAnalysis.Visualize as viz\n\nimport CairoMakie # the plotting package used by ClimaAnalysis\n\nsimdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.\n\nvar = get(simdir; \"lhf\") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.\n\nfig = CairoMakie.Figure() # creates an empty figure object\n\nviz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.\n\nCairoMakie.save(fig) # saves the figure in current working directory","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/#Optimality-Model","page":"Optimality model","title":"Optimality Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation (V_textcmax). ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates V_textcmax and J_textmax as a function of environmental variables as follows:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n V_textcmax^* = varphi I left(fracmm_cright)left(fracoverlineomega^*8thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega^* = 1 + overlineomega - sqrt(1 + overlineomega)^2 - 4thetaoverlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega = -(1 - 2theta) + sqrt(1 - theta)left(frac1frac4cmleft(1 - thetafrac4cmright) - 4thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n c = fracm8thetaleft(1 - fracvarphi I + J_textmax - 2thetavarphi Isqrt(varphi I + J_textmax)^2 - 4thetavarphi I J_textmaxright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n J_textmax = varphi I overlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m = fracC_i - Gamma^*C_i + 2Gamma^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n C_i = Gamma^* + (C_a - Gamma^*)fracxixi + sqrtD_g\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n xi = sqrtbeta fracK + Gamma^*16eta^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n K = K_cleft(1 + fracO_iK_oright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m_c = fracC_i - Gamma^*C_i + K\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Gamma^*","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"is the CO_2 compensation point in the absence of mitochondrial respiration","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n Gamma^* = Gamma^*_0 f(T Delta H_a) pp_0\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where Gamma^*_0 = 4332 Pa, p is the atmospheric pressure, p_0 = 101325 Pa, and Delta H_a = 37830 J/mol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"varphi is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.\ntheta is the curvature of the light response curve (dimensionless). Estimated at 0.85.\nbeta is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"For Smith et al. (2019) Vcmax model:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Altitude\nD_g is the vapor pressure deficit (VPD) at altitude\nC_a is the CO_2 partial pressure\nI is the incident photosynthetically active photon flux (PAR)\nT is the temperature","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Introduction-to-the-Canopy-Model","page":"Standalone Canopy","title":"Introduction to the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Preliminary-Setup","page":"Standalone Canopy","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load External Packages:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing StaticArrays\nusing ClimaLand\nusing ClimaLand.Domains: Point\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Setup-the-Canopy-Model","page":"Standalone Canopy","title":"Setup the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4) # kg/m^2\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Point(; z_sfc = FT(0.0))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}\n Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"time_offset = 7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Site latitude and longitude","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"-92.2f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Height of the sensor at the site","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"atmos_h = FT(32)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"32.0f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_soil0 = FT(0.0)\n\nsoil_driver = PrescribedSoil(\n FT;\n root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),\n ψ = t -> ψ_soil0,\n α_PAR = FT(0.2),\n α_NIR = FT(0.4),\n T = t -> 298.0,\n ϵ = FT(0.99),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"rt_params = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = FT(0.1),\n α_NIR_leaf = FT(0.45),\n τ_PAR_leaf = FT(0.05),\n τ_NIR_leaf = FT(0.25),\n Ω = FT(0.69),\n λ_γ_PAR = FT(5e-7),\n λ_γ_NIR = FT(1.65e-6),\n)\n\nrt_model = TwoStreamModel{FT}(rt_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for conductance model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))\n\nstomatal_model = MedlynConductanceModel{FT}(cond_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for photosynthesis model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))\n\nphotosynthesis_model = FarquharModel{FT}(photo_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for autotrophic respiration model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"AR_params = AutotrophicRespirationParameters(FT)\nAR_model = AutotrophicRespirationModel{FT}(AR_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for plant hydraulics model are more complicated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"LAI = 4.2\nLAIfunction = (t) -> LAI\nSAI = FT(0.00242)\nf_root_to_shoot = FT(3.5)\nRAI = FT((SAI + LAI) * f_root_to_shoot)\nai_parameterization =\n PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)\nrooting_depth = FT(1.0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the root distribution function p(z):","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"function root_distribution(z::T; rooting_depth = rooting_depth) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"K_sat_plant = FT(1.8e-8)\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use these values to populate the parameters of the PlantHydraulics model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ν = FT(0.7)\nS_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = ν,\n S_s = S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the remaining variables required for the plant hydraulics model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_surfaces = compartment_surfaces,\n compartment_midpoints = compartment_midpoints,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"canopy = ClimaLand.Canopy.CanopyModel{FT}(;\n parameters = shared_params,\n domain = land_domain,\n autotrophic_respiration = AR_model,\n radiative_transfer = rt_model,\n photosynthesis = photosynthesis_model,\n conductance = stomatal_model,\n hydraulics = plant_hydraulics,\n soil_driver = soil_driver,\n atmos = atmos,\n radiation = radiation,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Y, p, coords = ClimaLand.initialize(canopy)\nexp_tendency! = make_exp_tendency(canopy);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide initial conditions for the canopy hydraulics model","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n ν,\n S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"t0 = 0.0\nN_days = 364\ntf = t0 + 3600 * 24 * N_days\ndt = 225.0;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the cache variables for the canopy using the initial conditions and initial time.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"set_initial_cache! = make_set_initial_cache(canopy)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"n = 16\nsaveat = Array(t0:(n * dt):tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the callback function which updates the forcing variables, or drivers.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"updateat = Array(t0:1800:tf)\nmodel_drivers = ClimaLand.get_drivers(canopy)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a timestepping algorithm and setup the ODE problem.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"timestepper = CTS.RK4();\node_algo = CTS.ExplicitAlgorithm(timestepper)\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Create-some-plots","page":"Standalone Canopy","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We can now plot the data produced in the simulation. For example, GPP:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Transpiration plot:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Show the two plots together:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Save the output:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"savefig(\"ozark_standalone_canopy_test.png\");","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"(Image: )","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Introduction-to-the-Land-Bucket-Model","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"All equation variables are defined immediately below. We have:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wdt = -I","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wsdt = -(P_liq + σM + (1-σ) E_soil - I)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd σSdt = -(P_snow + σE_snow - σM)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ρc fracpartial Tpartial t = κ_soil fracpartial Tpartial z","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_bot = 00 = -κ_soil fracpartial Tpartial z_z = z_bot","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(1-σ) (R_n+ SHF + LHF)_soil + σG_undersnow = -κ_soil fracpartial Tpartial z_z = z_sfc","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"G_undersnow = (R_n+ SHF + LHF)_snow - F_intosnow","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_intosnow = -ρ_l L_f0 (P_snow + E_snow-M)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"R_n = -(1-α)*SW -LW + σ_SB T_sfc^4","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_soil = β(W W_f) E(q_sat(T_sfc ρ_sfc liquid)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_snow = E(q_sat(T_sfc ρ_sfc ice)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Simulating-a-standalone-bucket-model","page":"Introduction to the Land Bucket Model","title":"Simulating a standalone bucket model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaCore","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaParams as CP","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use Insolation to calculate solar zenith angle and solar insolation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaLand.Bucket:\n BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo\nusing ClimaLand.Domains: coordinates, Column\nusing ClimaLand:\n initialize,\n make_update_aux,\n make_exp_tendency,\n make_set_initial_cache,\n PrescribedAtmosphere,\n PrescribedRadiativeFluxes\nusing ClimaUtilities.TimeVaryingInputs: TimeVaryingInput","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also want to plot the solution","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Plots","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"And we need to use the DateTime type to store reference times","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Dates\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaLand\nimport ClimaLand.Parameters as LP\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"soil_depth = FT(3.5);\nbucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);\nsurface_space = bucket_domain.space.surface","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}\n Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"α_bareground_func = (coordinate_point) -> 0.2;\nα_snow = FT(0.8);\nalbedo =\n PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The critical snow level setting the scale for when we interpolate between snow and surface albedo","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"σS_c = FT(0.2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The field capacity of the soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W_f = FT(0.15);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Roughness lengths (meters)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"z_0m = FT(1e-2);\nz_0b = FT(1e-3);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Thermal parameters of soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"κ_soil = FT(0.7);\nρc_soil = FT(2e6);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Snow melt timescale","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"τc = FT(3600);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Simulation start time, end time, and timestep","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"t0 = 0.0;\ntf = 7 * 86400;\nΔt = 3600.0;\n\nbucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ref_time = DateTime(2005);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Here we define the model drivers Prescribed atmospheric variables","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Precipitation:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"precip = (t) -> 0;\nsnow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Diurnal temperature variations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Constant otherwise:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"u_atmos = (t) -> 3.0;\nq_atmos = (t) -> 0.005;\nh_atmos = FT(2);\nP_atmos = (t) -> 101325;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"bucket_atmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(snow_precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));\nLW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;\nbucket_rad = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"model = BucketModel(\n parameters = bucket_parameters,\n domain = bucket_domain,\n atmosphere = bucket_atmos,\n radiation = bucket_rad,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Like all ClimaLand models, we set up the state vector using initialize:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y, p, coords = initialize(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We can inspect the prognostic and auxiliary variables of the model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.prognostic_vars(model)\nY.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:W, :T, :Ws, :σS)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.auxiliary_vars(model)\np.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Next is to set initial conditions.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y.bucket.T .= FT(270);\nY.bucket.W .= FT(0.05);\nY.bucket.Ws .= FT(0.0);\nY.bucket.σS .= FT(0.08);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also set the initial values of the cache here:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"set_initial_cache! = make_set_initial_cache(model);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"exp_tendency! = make_exp_tendency(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Now we choose our timestepping algorithm.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then we can set up the simulation and solve it:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"saveat = collect(t0:Δt:tf);\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\nsaving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);\nupdateat = copy(saveat)\nmodel_drivers = ClimaLand.get_drivers(model)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];\nWs = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];\nσS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];\nT_sfc =\n [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];\nevaporation = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]\n for k in 1:length(sol.t)\n];\nR_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The turbulent energy flux is the sum of latent and sensible heat fluxes.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"LHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for\n k in 1:length(sol.t)\n];\nSHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for\n k in 1:length(sol.t)\n];\nturbulent_energy_flux = SHF .+ LHF\n\nplot(\n sol.t ./ 86400,\n W,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"W (m)\",\n title = \"Land water storage (m)\",\n)\nsavefig(\"w.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n σS,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"σS (m)\",\n title = \"Area weighted SWE (m) \",\n)\nsavefig(\"swe.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n snow_precip.(sol.t),\n label = \"Net precipitation\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (m/s)\",\n title = \"Surface water fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, evaporation, label = \"Sublimation/Evaporation\")\nsavefig(\"water_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n T_sfc,\n title = \"Surface Temperatures\",\n label = \"Ground temperature\",\n xlabel = \"time (days)\",\n ylabel = \"T_sfc (K)\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, T_atmos.(sol.t), label = \"Atmospheric Temperature\")\nsavefig(\"t.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n R_n,\n label = \"Net radiative flux\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (W/m^2)\",\n title = \"Surface energy fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, turbulent_energy_flux, label = \"Turbulent fluxes\")\nplot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = \"Net flux\")\nsavefig(\"energy_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#References","page":"Introduction to the Land Bucket Model","title":"References","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/#The-Two-Stream-Scheme","page":"Two-Stream model","title":"The Two-Stream Scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"-overlinemu(dI^uparrow)dL + 1 - (1 - beta)omegaI^uparrow - omega beta I^downarrow = omega overlinemu K beta_0 exp(-KL)\n-overlinemu(dI^downarrow)dL + 1 - (1 - beta)omegaI^downarrow - omega beta I^uparrow = omega overlinemu K (1-beta_0) exp(-KL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"int_0^1mu^primeG(mu^prime)dmu^prime","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = frach_1exp(-KL)sigma + h_2exp(-hL) + h_3exp(hL)\nI^downarrow = frach_4exp(-KL)sigma + h_5exp(-hL) + h_6exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = h_7exp(-hL) + h_8exp(hL)\nI^downarrow = h_9exp(-hL) + h_10exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Output Symmbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500\nAbsorbed Near-Infrared Radiation ANIR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Parameters Symbol Unit Range\nCanopy PAR Reflectance alpha_PAR_leaf - 0.0–1.0\nCanopy NIR Reflectance alpha_NIR_leaf - 0.0–1.0\nCanopy PAR Transmittance tau_PAR_leaf - 0.0–1.0\nCanopy NIR Transmittance tau_NIR_leaf - 0.0–1.0\nCanopy Emissivity ϵ_canopy - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5\nTypical wavelength per photon PAR lambda_gamma_PAR m 5e-7\nTypical wavelength per photon NIR lambda_gamma_NIR m 1.65e-6","category":"page"},{"location":"APIs/Bucket/#Bucket","page":"Bucket Model","title":"Bucket","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"CurrentModule = ClimaLand.Bucket","category":"page"},{"location":"APIs/Bucket/#Types","page":"Bucket Model","title":"Types","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.BucketModelParameters\nClimaLand.Bucket.PrescribedBaregroundAlbedo\nClimaLand.Bucket.PrescribedSurfaceAlbedo\nClimaLand.Bucket.BucketModel","category":"page"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModelParameters","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModelParameters","text":"struct BucketModelParameters{\n FT <: AbstractFloat,\n PSE,\n}\n\nContainer for holding the parameters of the bucket model.\n\nκ_soil: Conductivity of the soil (W/K/m); constant\nρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant\nalbedo: Albedo Model\nσS_c: Critical σSWE amount (m) where surface transitions from to snow-covered\nf_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)\nW_f: Capacity of the land bucket (m)\nf_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)\np: Exponent used in β decay (unitless)\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nτc: τc timescale on which snow melts\nearth_param_set: Earth Parameter set; physical constants, etc\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedBaregroundAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedBaregroundAlbedo","text":"PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel\n\nAn albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedSurfaceAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedSurfaceAlbedo","text":"PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}\n <: AbstractBucketAlbedoModel\n\nAn albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.\n\nNote that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModel","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModel","text":"struct BucketModel{\n FT,\n PS <: BucketModelParameters{FT},\n ATM <: AbstractAtmosphericDrivers{FT},\n RAD <: AbstractRadiativeDrivers{FT},\n D,\n } <: AbstractBucketModel{FT}\n\nConcrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.\n\nparameters: Parameters required by the bucket model\natmos: The atmospheric drivers: Prescribed or Coupled\nradiation: The radiation drivers: Prescribed or Coupled\ndomain: The domain of the model\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#Misc-Functions","page":"Bucket Model","title":"Misc Functions","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.surface_albedo\nClimaLand.Bucket.beta_factor","category":"page"},{"location":"APIs/Bucket/#ClimaLand.surface_albedo","page":"Bucket Model","title":"ClimaLand.surface_albedo","text":"ClimaLand.surface_albedo(\n model::EnergyHydrology{FT},\n Y,\n p,\n) where {FT}\n\nReturns the surface albedo field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_albedo(model::SnowModel, Y, p)\n\nA helper function which computes and returns the snow albedo.\n\n\n\n\n\nsurface_albedo(model::BucketModel, Y, p)\n\nReturns the bulk surface albedo, which gets updated in update_aux via next_albedo.\n\n\n\n\n\nsurface_albedo(model::AbstractModel, Y, p)\n\nA helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Bucket/#ClimaLand.Bucket.beta_factor","page":"Bucket Model","title":"ClimaLand.Bucket.beta_factor","text":"beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}\n\nComputes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:\n\nβ = (x/xc)^p x < xc 1 otherwise\n\nwhere x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.\n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Coupled-heat-and-water-equations-tending-towards-equilibrium","page":"Energy and Hydrology","title":"Coupled heat and water equations tending towards equilibrium","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"θ_i is the volumetric ice fraction, and","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We will solve this equation in an effectively 1-d domain with z -10, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- κ T(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-κ T(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T(t = 0 z) = T_min + (T_max-T_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-K h(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ(t = 0 z) = ϑ_min + (ϑ_max-ϑ_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"where C T_min T_max ϑ_min and ϑ_max are constants.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy hz = 0 and Tz = 0. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Import-necessary-modules","page":"Energy and Hydrology","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"External (non - CliMA) modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"import SciMLBase\nusing Statistics\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"FT = Float32\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Create-the-model","page":"Energy and Hydrology","title":"Create the model","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν = FT(0.395)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.395f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν_ss_quartz = FT(0.92)\nν_ss_om = FT(0.0)\nν_ss_gravel = FT(0.0)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.0f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Ksat = FT(4.42 / 3600 / 100) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.89)\nvg_α = FT(7.5) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nθ_r = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat = Ksat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We also need to pick a domain on which to solve the equations:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"zmax = FT(0)\nzmin = FT(-1.0)\nnelems = 50\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Water boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_water_flux = WaterFluxBC((p, t) -> 0.0)\nbottom_water_flux = WaterFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary conditions for the heat equation:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_heat_flux = HeatFluxBC((p, t) -> 0.0)\nbottom_heat_flux = HeatFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We wrap up all of those in a WaterHeatBC struct:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"boundary_fluxes = (;\n top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),\n bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Set-up-the-simulation","page":"Energy and Hydrology","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"function init_soil!(Y, z, params)\n ν = params.ν\n θ_r = params.θ_r\n FT = eltype(Y.soil.ϑ_l)\n zmax = FT(0)\n zmin = FT(-1)\n\n theta_max = FT(ν * 0.5)\n theta_min = FT(ν * 0.4)\n T_max = FT(289.0)\n T_min = FT(288.0)\n\n c = FT(20.0)\n @. Y.soil.ϑ_l =\n theta_min +\n (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)\n Y.soil.θ_i .= FT(0.0)\n\n T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))\n\n θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)\n ρc_s =\n Soil.volumetric_heat_capacity.(\n θ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 72);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We use ClimaTimesteppers.jl for carrying out the time integration.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a timestepper and set up the ODE problem:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"dt = Float64(1000.0);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"saveat = collect(t0:FT(30000):tf)\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\ncb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Extract output","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z = parent(coords.subsurface.z)\nt = parent(sol.t)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nT = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Let's look at the initial and final times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(ϑ_l[1], z, xlabel = \"ϑ_l\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(ϑ_l[4], z, label = \"t = 1.5d\")\nplot!(ϑ_l[end], z, label = \"t = 3d\")\nsavefig(\"eq_moisture_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(T[1], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(T[4], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 1.5d\")\nplot!(T[end], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 3d\")\nsavefig(\"eq_temperature_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Analytic-Expectations","page":"Energy and Hydrology","title":"Analytic Expectations","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can determine a priori what we expect the final temperature to be in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"One could also solve the equation for ϑ_l specified by h z = 0 to determine the functional form of the equilibrium profile of the liquid water.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#References","page":"Energy and Hydrology","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.\nBalland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"using Plots\nimport ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing ClimaCore\nimport ClimaParams as CP\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nFT = Float64;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define simulation times","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"t0 = Float64(0)\ntf = Float64(60 * 60)\ndt = Float64(30);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define the domain","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"zmax = FT(0)\nzmin = FT(-1.1)\nnelems = 75\nΔ = FT((zmax - zmin) / nelems / 2)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();\nparameter_data = readdlm(data_file, ',');","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m\nksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s\nvgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).\nvgn = reverse(parameter_data[5, :])\nresidual_frac = reverse(parameter_data[2, :])\nporosity = reverse(parameter_data[3, :]);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Create fields corresponding to the parameter","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)\nK_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)\nθ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"hcm = SpaceVaryingInput(\n depth,\n (; α = vgα, n = vgn),\n soil_domain.space.subsurface,\n vanGenuchten{FT},\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The parameter struct:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"params = ClimaLand.Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"function top_flux_function(p, t)\n return -0.0001033\nend\ntop_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)\nbottom_bc = ClimaLand.Soil.FreeDrainage()\nboundary_fluxes = (; top = top_bc, bottom = bottom_bc)\nsoil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial the state vectors, and set initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"We also set the initial conditions of the auxiliary state here:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"stepper = CTS.ARS111()\n@assert FT in (Float32, Float64)\nerr = (FT == Float64) ? 1e-8 : 1e-4\nconvergence_cond = CTS.MaximumError(err)\nconv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 10,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n convergence_checker = conv_checker,\n ),\n)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = make_jacobian(soil)\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);\n\nz = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]\nplot(ϑ_l[1], z, label = \"initial\", color = \"grey\", aspect_ratio = 0.8)\nplot!(ϑ_l[2], z, label = \"8min\", color = \"orange\")\nplot!(ϑ_l[3], z, label = \"16min\", color = \"red\")\nplot!(ϑ_l[4], z, label = \"24min\", color = \"teal\")\nplot!(ϑ_l[5], z, label = \"32min\", color = \"blue\")\nplot!(ϑ_l[6], z, label = \"40min\", color = \"purple\")\nplot!(ϑ_l[7], z, label = \"60min\", color = \"green\")\nscatter!(porosity, depth, label = \"Porosity\")\nplot!(legend = :bottomright)\n\nplot!(xlim = [0, 0.7])\n\nplot!(\n ylim = [-1.1, 0],\n yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],\n)\n\nplot!(ylabel = \"Depth (m)\")\n\nplot!(xlabel = \"Volumeteric Water Content\")\n\nsavefig(\"./sv62_alpha_2_inf_updated_data_climaland.png\")","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy","page":"Canopy Models","title":"Canopy","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Structs","page":"Canopy Models","title":"Canopy Model Structs","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.CanopyModel\nClimaLand.Canopy.SharedCanopyParameters","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.CanopyModel","page":"Canopy Models","title":"ClimaLand.Canopy.CanopyModel","text":" CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}\n\nThe model struct for the canopy, which contains\n\nthe canopy model domain (a point for site-level simulations, or\n\nan extended surface (plane/spherical surface) for regional or global simulations.\n\nsubcomponent model type for radiative transfer. This is of type\n\nAbstractRadiationModel.\n\nsubcomponent model type for photosynthesis. This is of type\n\nAbstractPhotosynthesisModel, and currently only the FarquharModel is supported.\n\nsubcomponent model type for stomatal conductance. This is of type\n\nAbstractStomatalConductanceModel and currently only the MedlynModel is supported\n\nsubcomponent model type for plant hydraulics. This is of type\n\nAbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.\n\nsubcomponent model type for canopy energy. This is of type\n\nAbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.\n\nsubcomponent model type for canopy SIF. prognostically.\ncanopy model parameters, which include parameters that are shared\n\nbetween canopy model components or those needed to compute boundary fluxes.\n\nThe atmospheric conditions, which are either prescribed\n\n(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).\n\nThe radiative flux conditions, which are either prescribed\n\n(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).\n\nThe soil conditions, which are either prescribed (of type PrecribedSoil, for\n\nrunning the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)\n\nNote that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.\n\nautotrophic_respiration: Autotrophic respiration model, a canopy component model\nradiative_transfer: Radiative transfer model, a canopy component model\nphotosynthesis: Photosynthesis model, a canopy component model\nconductance: Stomatal conductance model, a canopy component model\nhydraulics: Plant hydraulics model, a canopy component model\nenergy: Energy balance model, a canopy component model\nsif: SIF model, a canopy component model\natmos: Atmospheric forcing: prescribed or coupled\nradiation: Radiative forcing: prescribed or coupled\nsoil_driver: Soil pressure: prescribed or prognostic\nparameters: Shared canopy parameters between component models\ndomain: Canopy model domain\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.SharedCanopyParameters","page":"Canopy Models","title":"ClimaLand.Canopy.SharedCanopyParameters","text":"SharedCanopyParameters{FT <: AbstractFloat, PSE}\n\nA place to store shared parameters that are required by multiple canopy components.\n\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nearth_param_set: Earth param set\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Fluxes","page":"Canopy Models","title":"Canopy Model Fluxes","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.DiagnosticTranspiration","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","page":"Canopy Models","title":"ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","text":"DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Soil-Drivers","page":"Canopy Models","title":"Canopy Model Soil Drivers","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.AbstractSoilDriver\nClimaLand.Canopy.PrescribedSoil","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.AbstractSoilDriver","page":"Canopy Models","title":"ClimaLand.Canopy.AbstractSoilDriver","text":"An abstract type of soil drivers of the canopy model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PrescribedSoil","page":"Canopy Models","title":"ClimaLand.Canopy.PrescribedSoil","text":" PrescribedSoil <: AbstractSoilDriver\n\nA container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.\n\nroot_depths: The depth of the root tips, in meters\nψ: Prescribed soil potential (m) in the root zone a function of time\nT: Prescribed soil surface temperature (K) as a function of time\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\nϵ: Soil emissivity\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-model","page":"Boundary conditions","title":"Boundary conditions for the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-Richards-equation","page":"Boundary conditions","title":"Boundary conditions for Richards equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.\nWaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).\nMoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).\nRichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-equation","page":"Boundary conditions","title":"Boundary conditions for the soil heat equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).\nTemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-water-equations-(EnergyHydrology-model)","page":"Boundary conditions","title":"Boundary conditions for the soil heat + water equations (EnergyHydrology model)","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"The full soil model requires boundary conditions for both Richards equation and the soil heat equation.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).\nAtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"This page was generated using Literate.jl.","category":"page"},{"location":"folderstructure/#ClimaLand-folder-structure","page":"Repository structure","title":"ClimaLand folder structure","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand home directory has 5 main folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"docs: contains files to generate the documentation website.\nexperiments: contains simple runs of ClimaLand models. \nparameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc. \nsrc: contains the code of ClimaLand models. \ntest: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR. \n.dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main). \n.github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"as well as 5 files:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git. \nLICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code. \nProject.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file. \nREADME.md: This markdown file contains the info that you can read on ClimaLand GitHub web page","category":"page"},{"location":"folderstructure/#/docs-folder","page":"Repository structure","title":"/docs folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Julia packages are recommended to have a \\docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The folder /docs contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation \"contribution guide\" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file. \na make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl. ","category":"page"},{"location":"folderstructure/#/experiments-folder","page":"Repository structure","title":"/experiments folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"For example, /experiments/LSM/ozark/ contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).\nozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.\nozark_parameters.jl: In this file, parameters values are defined. \nozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm. \nozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.","category":"page"},{"location":"folderstructure/#/src-folder","page":"Repository structure","title":"/src folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The /src folder contains the source code of ClimaLand models. It contains 3 folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.\nstandalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder. \nintegrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"As well as one file:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase. ","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Stomatal-Conductance","page":"Canopy Stomatal Conductance","title":"Stomatal Conductance","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Parameters","page":"Canopy Stomatal Conductance","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.MedlynConductanceParameters","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.MedlynConductanceParameters","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.MedlynConductanceParameters","text":"MedlynConductanceParameters{FT <: AbstractFloat}\n\nThe required parameters for the Medlyn stomatal conductance model.\n\nDrel: Relative diffusivity of water vapor (unitless)\ng0: Minimum stomatal conductance mol/m^2/s\ng1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/StomatalConductance/#Methods","page":"Canopy Stomatal Conductance","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.medlyn_term\nClimaLand.Canopy.medlyn_conductance\nClimaLand.Canopy.upscale_leaf_conductance\nClimaLand.Canopy.penman_monteith","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_term","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_term","text":"medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}\n\nComputes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).\n\nthermo_params is the Thermodynamics.jl parameter set.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_conductance","text":"medlyn_conductance(g0::FT,\n Drel::FT,\n medlyn_term::FT,\n An::FT,\n ca::FT) where {FT}\n\nComputes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).\n\nThis returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.upscale_leaf_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.upscale_leaf_conductance","text":"upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}\n\nThis currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.\n\nTODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.penman_monteith","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.penman_monteith","text":"penman_monteith(\n Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1) \n Rn::FT, # Net irradiance (W m−2)\n G::FT, # Ground heat flux (W m−2)\n ρa::FT, # Dry air density (kg m−3)\n cp::FT, # Specific heat capacity of air (J kg−1 K−1) \n VPD::FT, # vapor pressure deficit (Pa)\n ga::FT, # atmospheric conductance (m s−1)\n γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)\n gs::FT, # surface or stomatal conductance (m s−1)\n Lv::FT, # Volumetric latent heat of vaporization (J m-3)\n ) where {FT}\n\nComputes the evapotranspiration in m/s using the Penman-Monteith equation. \n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-Tutorial","page":"Intro to ClimaLand Domains","title":"Domain Tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/#Goals-of-the-tutorial","page":"Intro to ClimaLand Domains","title":"Goals of the tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Background","page":"Intro to ClimaLand Domains","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question \"only\" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"ClimaLand Domains were designed with this in mind. The domains are defined so that","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the user can easily switch geometries, e.g. single column to global model,\nindividual component models can be run by themselves, using a single domain,\nthe same domains can be used to set up multi-component models (LSMs),\ndifferent variables can exist on different parts of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#What-is-a-ClimaLand-Domain?","page":"Intro to ClimaLand Domains","title":"What is a ClimaLand Domain?","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-types","page":"Intro to ClimaLand Domains","title":"Domain types","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"0D: Domains.Point\n1D: Domains.Column\n2D: Domains.Plane, Domains.SphericalSurface\n3D: Domains.HybridBox, Domains.SphericalShell.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Domain Surface Domain Subsurface Domain\nColumn Point Column\nHybridBox Plane HybridBox\nSphericalShell SphericalSurface SphericalShell","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"There is a single key method which take a ClimaLand domain as an argument.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"coordinates(domain): under the hood, this function uses","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#How-variable-initialization-depends-on-domains","page":"Intro to ClimaLand Domains","title":"How variable initialization depends on domains","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Future-work","page":"Intro to ClimaLand Domains","title":"Future work","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into \"vertical\" and \"horizontal\" pieces.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[1]: finite differencing is used in the vertical, and spectral elements are used in the horizontal.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[2]: a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[3]: We also will support having an array-like type of variable.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Coupling-the-CliMA-Canopy-and-Soil-Hydraulics-Models","page":"Coupled Canopy and Soil","title":"Coupling the CliMA Canopy and Soil Hydraulics Models","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Preliminary-Setup","page":"Coupled Canopy and Soil","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load External Packages:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Soil.Biogeochemistry\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Setup the domain for the model:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4)\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Column(; zlim = (zmin, zmax), nelements = nelements);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"time_offset = 7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Site latitude and longitude","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"-92.2f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Height of the sensor at the site","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"atmos_h = FT(32) # m","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"32.0f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Setup-the-Coupled-Canopy-and-Soil-Physics-Model","page":"Coupled Canopy and Soil","title":"Setup the Coupled Canopy and Soil Physics Model","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the parameters for the soil model and provide them to the model parameters struct:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil_ν = FT(0.5) # m3/m3\nsoil_K_sat = FT(4e-7) # m/s\nsoil_S_s = FT(1e-3) # 1/m\nsoil_vg_n = FT(2.05) # unitless\nsoil_vg_α = FT(0.04) # inverse meters\nθ_r = FT(0.067); # m3/m3","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil heat transfer parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ν_ss_quartz = FT(0.1)\nν_ss_om = FT(0.1)\nν_ss_gravel = FT(0.0);\nz_0m_soil = FT(0.1)\nz_0b_soil = FT(0.1)\nsoil_ϵ = FT(0.98)\nsoil_α_PAR = FT(0.2)\nsoil_α_NIR = FT(0.4)\n\nsoil_domain = land_domain\nsoil_ps = Soil.EnergyHydrologyParameters(\n FT;\n ν = soil_ν,\n ν_ss_om = ν_ss_om,\n ν_ss_quartz = ν_ss_quartz,\n ν_ss_gravel = ν_ss_gravel,\n hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),\n K_sat = soil_K_sat,\n S_s = soil_S_s,\n θ_r = θ_r,\n earth_param_set = earth_param_set,\n z_0m = z_0m_soil,\n z_0b = z_0b_soil,\n emissivity = soil_ϵ,\n PAR_albedo = soil_α_PAR,\n NIR_albedo = soil_α_NIR,\n);\n\nsoil_args = (domain = soil_domain, parameters = soil_ps)\nsoil_model_type = Soil.EnergyHydrology{FT}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ClimaLand.Soil.EnergyHydrology{Float32}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}\n\nsoilco2_ps = SoilCO2ModelParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil microbes args","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))\n\nsoilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()\nsoilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);\nsoilco2_sources = (MicrobeProduction{FT}(),);\n\nsoilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);\n\nsoilco2_args = (;\n boundary_conditions = soilco2_boundary_conditions,\n sources = soilco2_sources,\n domain = soil_domain,\n parameters = soilco2_ps,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_types = (;\n autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},\n radiative_transfer = Canopy.TwoStreamModel{FT},\n photosynthesis = Canopy.FarquharModel{FT},\n conductance = Canopy.MedlynConductanceModel{FT},\n hydraulics = Canopy.PlantHydraulicsModel{FT},\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"autotrophic_respiration_args =\n (; parameters = AutotrophicRespirationParameters(FT))\n\nradiative_transfer_args = (;\n parameters = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = 0.1,\n α_NIR_leaf = 0.45,\n τ_PAR_leaf = 0.05,\n τ_NIR_leaf = 0.25,\n Ω = 0.69,\n )\n)\n\nconductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))\n\nphotosynthesis_args =\n (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));\n\nK_sat_plant = FT(1.8e-8)\nRAI = (SAI + maxLAI) * f_root_to_shoot;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Note: LAIfunction was determined from data in the script we included above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)\nfunction root_distribution(z::T; rooting_depth = FT(1.0)) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m\nend\n\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)\n\nplant_ν = FT(0.7)\nplant_S_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = plant_ν,\n S_s = plant_S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n)\n\nplant_hydraulics_args = (\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_midpoints = compartment_midpoints,\n compartment_surfaces = compartment_surfaces,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_args = (;\n autotrophic_respiration = autotrophic_respiration_args,\n radiative_transfer = radiative_transfer_args,\n photosynthesis = photosynthesis_args,\n conductance = conductance_args,\n hydraulics = plant_hydraulics_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We also need to provide the shared parameter struct to the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n)\ncanopy_domain = obtain_surface_domain(land_domain)\ncanopy_model_args = (; parameters = shared_params, domain = canopy_domain);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)\n\nland = SoilCanopyModel{FT}(;\n soilco2_type = soilco2_type,\n soilco2_args = soilco2_args,\n land_args = land_input,\n soil_model_type = soil_model_type,\n soil_args = soil_args,\n canopy_component_types = canopy_component_types,\n canopy_component_args = canopy_component_args,\n canopy_model_args = canopy_model_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y, p, coords = initialize(land);\nexp_tendency! = make_exp_tendency(land);\nimp_tendency! = make_imp_tendency(land);\njacobian! = make_jacobian(land);\njac_kwargs =\n (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We need to provide initial conditions for the soil and canopy hydraulics models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y.soil.ϑ_l = FT(0.4)\nY.soil.θ_i = FT(0.0)\nT_0 = FT(288.7)\nρc_s =\n volumetric_heat_capacity.(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n land.soil.parameters.ρc_ds,\n earth_param_set,\n )\nY.soil.ρe_int =\n volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)\n\nY.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air\n\nψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n plant_ν,\n plant_S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .=\n augmented_liquid_fraction.(plant_ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"t0 = Float64(150 * 3600 * 24)# start mid year\nN_days = 100\ntf = t0 + Float64(3600 * 24 * N_days)\ndt = Float64(30)\nn = 120\nsaveat = Array(t0:(n * dt):tf)\n\ntimestepper = CTS.ARS343()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now set the initial values for the cache variables for the combined soil and plant model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"set_initial_cache! = make_set_initial_cache(land)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Set the callbacks, which govern how often we save output, and how often we update the forcing data (\"drivers\")","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"sv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nmodel_drivers = ClimaLand.get_drivers(land)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\nupdateat = Array(t0:1800:tf)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Carry out the simulation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(\n prob,\n ode_algo;\n dt = dt,\n callback = cb,\n adaptive = false,\n saveat = saveat,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Plotting","page":"Coupled Canopy and Soil","title":"Plotting","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Transpiration plot:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Show the two plots together:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_canopy_flux_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"plt1 = Plots.plot(size = (500, 700));\nϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]\nplt1 = Plots.plot(\n daily,\n ϑ_l_10,\n label = \"10 cm\",\n xlabel = \"Days\",\n ylabel = \"SWC [m/m]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n color = \"blue\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],\n label = \"20cm\",\n color = \"red\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],\n label = \"30cm\",\n color = \"purple\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"root_stem_flux = [\n sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for\n k in 1:length(sol.t)\n]\nplt1 = Plots.plot(\n daily,\n root_stem_flux,\n label = \"soil-root-stem water flux\",\n ylabel = \"Water flux[mm/day]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And save the output","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_plant_flux.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"diagnostics/developers_diagnostics/#ClimaLand-Diagnostics:-why-and-how","page":"For developers","title":"ClimaLand Diagnostics: why and how","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in \"K\" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Diagnostics.jl defines the module,\ndiagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"compute methods are defined in a separate file, for example, bucket_compute_methods.jl.\nstandard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.\ndefault_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Compute-methods","page":"For developers","title":"Compute methods","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.α_sfc)\n else\n out .= p.bucket.α_sfc\n end\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@diagnostic_compute \"albedo\" BucketModel p.bucket.α\\_sfc","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Define-diagnostics","page":"For developers","title":"Define diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!(\n short_name = \"alpha\",\n long_name = \"Albedo\",\n standard_name = \"albedo\",\n units = \"\",\n compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),\n )","category":"page"},{"location":"diagnostics/developers_diagnostics/#Default-diagnostics","page":"For developers","title":"Default diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"function default_diagnostics(land_model::BucketModel, t_start; output_writer)\n\n define_diagnostics!(land_model)\n\n bucket_diagnostics = [\n \"alpha\",\n \"rn\",\n \"tsfc\",\n \"qsfc\",\n \"lhf\",\n \"rae\",\n \"shf\",\n \"vflux\",\n \"rhosfc\",\n \"t\",\n \"w\",\n \"ws\",\n \"sigmas\",\n ]\n\n default_outputs =\n hourly_averages(bucket_diagnostics...; output_writer, t_start)\n return [default_outputs...]\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = [\"gpp\", \"ct\", \"lai\", \"swc\", \"si\"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Standard-diagnostic-frequencies","page":"For developers","title":"Standard diagnostic frequencies","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(\n 60 * 60 * one(t_start),\n (+),\n output_writer,\n t_start,\n short_names...;\n pre_output_hook! = average_pre_output_hook!,\n)","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.","category":"page"},{"location":"","page":"Home","title":"Home","text":"using Pkg\nio = IOBuffer()\nv = Pkg.installed()[\"ClimaLand\"]\nprint(io, \"\"\"\n # ClimaLand.jl Documentation (v$(v))\n\n \"\"\")\nimport Markdown\nMarkdown.parse(String(take!(io)))","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand can be run coupled (or \"online\") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data (\"offline\").","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.","category":"page"},{"location":"#Important-Links","page":"Home","title":"Important Links","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CliMA Homepage\nCliMA GitHub Organisation\nClimaCoupler\nClimaAnalysis\nJulia Homepage","category":"page"},{"location":"#Documentation-for-Users-and-Developers","page":"Home","title":"Documentation for Users and Developers","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.","category":"page"},{"location":"#Physical-units","page":"Home","title":"Physical units","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Note that CliMA, in all its repositories, uses Standard Units, reminded below","category":"page"},{"location":"","page":"Home","title":"Home","text":"Quantity Unit Name SI Symbol SI Unit Equivalent\nLength Meter m 1 m\nMass Kilogram kg 1 kg\nTime Second s 1 s\nTemperature Kelvin K 1 K\nAmount of Substance Mole mol 1 mol\nEnergy Joule J 1 J = 1 N·m\nPower Watt W 1 W = 1 J/s\nPressure Pascal Pa 1 Pa = 1 N/m²\nFrequency Hertz Hz 1 Hz = 1 s⁻¹","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 270.0^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"T_air = FT(270.0)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 12\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sources = (PhaseChange{FT}(),);\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)\n Y.soil.θ_i .= 0\n T = FT(275.0)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 4)\ndt = Float64(5)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"5.0","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping functions:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = ClimaLand.make_jacobian(soil)\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\n\ntimestepper = CTS.ARS111()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsub = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n sv.saveval[k].soil.ice_frac,\n )[1] for k in 1:length(sol.t)\n]\n\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (400, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Rate (mm/d)\",\n title = \"Vapor Fluxes\",\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n sub .* (1000 * 3600 * 24),\n label = \"Sublimation\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Evaporation\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nsave(\"water_fluxes.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Temperature\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(260, 280)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 3, 4]\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax1,\n parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Ice\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax2,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Liquid Water\", xlabel = \"\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax3,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"profiles.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Soil/#Soil-Models","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"CurrentModule = ClimaLand.Soil","category":"page"},{"location":"APIs/Soil/#Soil-Models-2","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilModel\nClimaLand.Soil.RichardsModel\nClimaLand.Soil.EnergyHydrology","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilModel","text":"AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}\n\nThe abstract type for all soil models.\n\nCurrently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsModel","text":"RichardsModel\n\nA model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.\n\nA variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).\n\nIf you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrology","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrology","text":"EnergyHydrology <: AbstractSoilModel\n\nA model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.\n\nA variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.\n\nparameters: The parameter sets\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Parameter-Structs","page":"Soil Energy and Hydrology","title":"Soil Parameter Structs","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.RichardsParameters\nClimaLand.Soil.EnergyHydrologyParameters","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsParameters","text":"RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}\n\nA struct for storing parameters of the RichardsModel.\n\nν: The porosity of the soil (m^3/m^3)\nhydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrologyParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrologyParameters","text":"EnergyHydrologyParameters{\n FT <: AbstractFloat,\n F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n C,\n PSE,\n }\n\nA parameter structure for the integrated soil water and energy equation system.\n\nNote that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:\n\nSurface parameters: albedo in each wavelength band (SF)\nScalar parameters: emissivity, α, β, γ, γT_ref, Ω,\n\nroughness lengths z0, dds ) (FT)\n\nParameters defined in the interior: all else (F)\n\nκ_dry: The dry soil thermal conductivity, W/m/K\nκ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K\nκ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K\nρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)\nν: The porosity of the soil (m^3/m^3)\nν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)\nν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)\nν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)\nα: The parameter α used in computing Kersten number, unitless\nβ: The parameter β used in computing Kersten number, unitless\nhydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\nΩ: Ice impedance factor for the hydraulic conductivity\nγ: Coefficient of viscosity factor for the hydraulic conductivity\nγT_ref: Reference temperature for the viscosity factor\nPAR_albedo: Soil PAR Albedo\nNIR_albedo: Soil NIR Albedo\nemissivity: Soil Emissivity\nz_0m: Roughness length for momentum\nz_0b: Roughness length for scalars\nd_ds: Maximum dry soil layer thickness under evaporation (m)\nearth_param_set: Physical constants and clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Hydrology-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Hydrology Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_liquid_fraction\nClimaLand.Soil.pressure_head\nClimaLand.Soil.hydraulic_conductivity\nClimaLand.Soil.impedance_factor\nClimaLand.Soil.viscosity_factor\nClimaLand.Soil.effective_saturation\nClimaLand.Soil.matric_potential\nClimaLand.Soil.dψdϑ\nClimaLand.Soil.inverse_matric_potential\nClimaLand.Soil.AbstractSoilHydrologyClosure\nClimaLand.Soil.vanGenuchten\nClimaLand.Soil.BrooksCorey","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_liquid_fraction","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_liquid_fraction","text":"volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}\n\nA pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.pressure_head","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.pressure_head","text":"pressure_head(\n cm::vanGenuchten{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\npressure_head(\n cm::BrooksCorey{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.hydraulic_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.hydraulic_conductivity","text":" hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.\n\n\n\n\n\n hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.impedance_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.impedance_factor","text":"impedance_factor(\n f_i::FT,\n Ω::FT\n) where {FT}\n\nReturns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.viscosity_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.viscosity_factor","text":"viscosity_factor(\n T::FT,\n γ::FT,\n γT_ref::FT,\n) where {FT}\n\nReturns the multiplicative factor which accounts for the temperature dependence of the conductivity.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.effective_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.effective_saturation","text":"effective_saturation(porosity::FT, ϑ_l::FT, θr::FT) where {FT}\n\nA point-wise function computing the effective saturation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.matric_potential","text":" matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.dψdϑ","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dψdϑ","text":"dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.\n\n\n\n\n\ndψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.inverse_matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.inverse_matric_potential","text":" inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilHydrologyClosure","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilHydrologyClosure","text":"AbstractSoilHydrologyClosure{FT <: AbstractFloat}\n\nThe abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.\n\nTo add a new parameterization, methods are required for:\n\nmatric_potential,\ninversematricpotential,\npressure_head,\ndψdϑ,\nhydraulic_conductivity.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.vanGenuchten","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.vanGenuchten","text":"vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).\n\nα: The inverse of the air entry potential (1/m)\nn: The van Genuchten pore-size distribution index (unitless)\nm: The van Genuchten parameter m = 1 - 1/n (unitless)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.BrooksCorey","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.BrooksCorey","text":"BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).\n\nc: The pore-size distribution index (unitless)\nψb: The air entry matric potential, when S=1 (m)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Heat-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Heat Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_heat_capacity\nClimaLand.Soil.κ_solid\nClimaLand.Soil.κ_sat_frozen\nClimaLand.Soil.κ_sat_unfrozen\nClimaLand.Soil.κ_sat\nClimaLand.Soil.κ_dry\nClimaLand.Soil.kersten_number\nClimaLand.Soil.relative_saturation\nClimaLand.Soil.volumetric_internal_energy\nClimaLand.Soil.volumetric_internal_energy_liq\nClimaLand.Soil.temperature_from_ρe_int\nClimaLand.Soil.thermal_conductivity\nClimaLand.Soil.phase_change_source\nClimaLand.Soil.thermal_time","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_heat_capacity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_heat_capacity","text":"volumetric_heat_capacity(\n θ_l::FT,\n θ_i::FT,\n ρc_ds::FT,\n earth_param_set::EP,\n) where {FT,EP}\n\nCompute the expression for volumetric heat capacity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_solid","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_solid","text":"κ_solid(ν_ss_om::FT,\n ν_ss_quartz::FT,\n κ_om::FT,\n κ_quartz::FT,\n κ_minerals::FT) where {FT}\n\nComputes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_frozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_frozen","text":"function κ_sat_frozen(\n κ_solid::FT,\n ν::FT,\n κ_ice::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated frozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_unfrozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_unfrozen","text":"function κ_sat_unfrozen(\n κ_solid::FT,\n ν::FT,\n κ_l::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated unfrozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat","text":"κ_sat(\n θ_l::FT,\n θ_i::FT,\n κ_sat_unfrozen::FT,\n κ_sat_frozen::FT\n) where {FT}\n\nCompute the expression for saturated thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_dry","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_dry","text":"function κ_dry(ρp::FT,\n ν::FT,\n κ_solid::FT,\n κ_air::FT;\n a = FT(0.053)) where {FT}\n\nComputes the thermal conductivity of dry soil according to the model of Balland and Arp.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.kersten_number","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.kersten_number","text":"kersten_number(\n θ_i::FT,\n S_r::FT,\n α::FT,\n β::FT,\n ν_ss_om::FT,\n ν_ss_quartz::FT,\n ν_ss_gravel::FT,\n ) where {FT}\n\nCompute the expression for the Kersten number, using the Balland and Arp model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.relative_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.relative_saturation","text":"relative_saturation(\n θ_l::FT,\n θ_i::FT,\n ν::FT\n) where {FT}\n\nCompute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy","text":"volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy_liq","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy_liq","text":"volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.temperature_from_ρe_int","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.temperature_from_ρe_int","text":"temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_conductivity","text":"thermal_conductivity(\n κ_dry::FT,\n K_e::FT,\n κ_sat::FT\n) where {FT}\n\nCompute the expression for thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.phase_change_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.phase_change_source","text":"phase_change_source(\n θ_l::FT,\n θ_i::FT,\n T::FT,\n τ::FT,\n ν::FT,\n θ_r::FT,\n hydrology_cm::C,\n earth_param_set::EP,\n) where {FT, EP, C}\n\nReturns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.\n\nNote that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_time","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_time","text":"thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}\n\nReturns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Surface-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Surface Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.soil.soil_resistance\nClimaLand.Soil.dry_soil_layer_thickness\nClimaLand.Soil.soil_tortuosity","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.dry_soil_layer_thickness","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dry_soil_layer_thickness","text":"dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}\n\nReturns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_tortuosity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_tortuosity","text":"soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}\n\nComputes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.\n\nSee Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Runoff-Types-and-Methods","page":"Soil Energy and Hydrology","title":"Soil Runoff Types and Methods","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.NoRunoff\nClimaLand.Soil.SurfaceRunoff\nClimaLand.Soil.TOPMODELRunoff\nClimaLand.Soil.TOPMODELSubsurfaceRunoff\nClimaLand.Soil.subsurface_runoff_source\nClimaLand.Soil.update_runoff!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.NoRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.NoRunoff","text":"NoRunoff <: AbstractRunoffModel\n\nA concrete type of soil runoff model; the default choice, which does not include any runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.SurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.SurfaceRunoff","text":"SurfaceRunoff <: AbstractRunoffModel\n\nA simple model for runoff appropriate for single column runs.\n\nOnly surface runoff is computed, using a combination of Dunne and Hortonian runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELRunoff","text":"TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel\n\nThe TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.\n\nThe runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\nf_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.\nsubsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","text":"TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}\n\nThe TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.\n\nThe runoff flux is given by Equation 12 of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nR_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.subsurface_runoff_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.subsurface_runoff_source","text":"subsurface_runoff_source(runoff::AbstractRunoffModel)\n\nA helper function which returns the subsurface source of the runoff model runoff.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.update_runoff!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.update_runoff!","text":"update_runoff!(p, runoff::NoRunoff, _...)\n\nUpdates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.\n\n\n\n\n\nupdate_runoff!(\n p,\n runoff::SurfaceRunoff,\n Y,\n t,\n model::AbstractSoilModel,\n\n)\n\nThe update_runoff! function for the SurfaceRunoff model.\n\nUpdates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration\n\n\n\n\n\nupdate_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)\n\nUpdates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-BC-Methods-and-Types","page":"Soil Energy and Hydrology","title":"Soil BC Methods and Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.MoistureStateBC\nClimaLand.Soil.HeatFluxBC\nClimaLand.Soil.WaterFluxBC\nClimaLand.Soil.TemperatureStateBC\nClimaLand.Soil.FreeDrainage\nClimaLand.Soil.RichardsAtmosDrivenFluxBC\nClimaLand.Soil.AtmosDrivenFluxBC\nClimaLand.Soil.WaterHeatBC\nClimaLand.Soil.soil_boundary_fluxes!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.MoistureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.MoistureStateBC","text":"MoistureStateBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.HeatFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.HeatFluxBC","text":"HeatFluxBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterFluxBC","text":"WaterFluxBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.TemperatureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.TemperatureStateBC","text":"TemperatureStateBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.FreeDrainage","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.FreeDrainage","text":"FreeDrainage <: AbstractWaterBC\n\nA concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsAtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsAtmosDrivenFluxBC","text":"RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC\n\nA concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.\n\nIf you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.\n\nprecip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.AtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AtmosDrivenFluxBC","text":"AtmosDrivenFluxBC{\n A <: AbstractAtmosphericDrivers,\n B <: AbstractRadiativeDrivers,\n R <: AbstractRunoffModel\n} <: AbstractEnergyHydrologyBC\n\nA concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.\n\nThis choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.\n\natmos: The atmospheric conditions driving the model\nradiation: The radiative fluxes driving the model\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterHeatBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterHeatBC","text":"WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:\n AbstractEnergyHydrologyBC\n\nA general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_boundary_fluxes!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_boundary_fluxes!","text":"soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)\n\nupdates the boundary fluxes for ϑl and ρeint.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere,\n <:PrescribedRadiativeFluxes,\n },\n boundary::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz,\n Y,\n p,\n t,\n)\n\nReturns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.\n\nIf you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.\n\nThis function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},\n boundary::ClimaLand.TopBoundary,\n soil::EnergyHydrology{FT},\n Δz,\n Y,\n p,\n t,\n) where {FT}\n\nA method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Source-Types","page":"Soil Energy and Hydrology","title":"Soil Source Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilSource\nClimaLand.Soil.PhaseChange\nClimaLand.Soil.RootExtraction","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilSource","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilSource","text":"AbstractSoilSource{FT} <: ClimaLand.AbstractSource{FT}\n\nAn abstract type for types of source terms for the soil equations.\n\nIn standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.PhaseChange","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.PhaseChange","text":"PhaseChange{FT} <: AbstractSoilSource{FT}\n\nPhaseChange source type.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Jacobian-Structures","page":"Soil Energy and Hydrology","title":"Soil Jacobian Structures","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.ImplicitEquationJacobian","category":"page"},{"location":"APIs/Soil/#ClimaLand.ImplicitEquationJacobian","page":"Soil Energy and Hydrology","title":"ClimaLand.ImplicitEquationJacobian","text":"ImplicitEquationJacobian{M, S}\n\nA struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.\n\nmatrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.\n\nNote that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.\n\n\n\n\n\n","category":"type"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Radiative-transfer-scheme","page":"Beer model","title":"Radiative transfer scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Beer's-law","page":"Beer model","title":"Beer's law","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"APAR(PAR theta_s) = (PAR)(1 - rho_leaf)(1 - e^(-K(theta_s) LAI Omega))","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, ρ_leaf is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, θ_s is the zenith angle, and Ω is the clumping index following Braghiere (2021). K, Ω and ρ_leaf are all unitless. LAI is in m² m⁻². In order to compute K, we need θ_s in radians and the leaf angle distribution l_d (unitless). K is then defined as","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"K = l_dmax(cos(theta_s) epsilon)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"so that at night, when 3π/2 > θ_s > π/2, K is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Output Symbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Parameters Symbol Unit Range\nCanopy reflectance ρ_leaf - 0.0–1.0\nExtinction coefficient K - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Interactive-APAR(PAR,-LAI,-ρ_{leaf},-K,-Ω)","page":"Beer model","title":"Interactive APAR(PAR, LAI, ρ_leaf, K, Ω)","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Modeling-a-freezing-front-in-unsaturated-soil","page":"Phase Changes","title":"Modeling a freezing front in unsaturated soil","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν ) -fracF_Tρ_l","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac θ_i t = fracF_Tρ_i","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Here","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_i is the volumetric ice fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν denotes parameters relating to soil type, such as porosity, and","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T is the freeze-thaw term.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We solve these equations in an effectively 1-d domain with z -020, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z = 0) = 28 Wm^2K (T - 26715K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z= -02) = -3 Wm^2K (T - 27985K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T(t = 0 z) = 27985 K","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"-K h(t z = -02) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l(t = 0 z) = 033.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Import-necessary-modules","page":"Phase Changes","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS\nusing DelimitedFiles\nusing CairoMakie\n\nusing ClimaCore\nimport ClimaParams as CP\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Preliminary set-up","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"FT = Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν = FT(0.535)\nK_sat = FT(3.2e-6) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.48)\nvg_α = FT(1.11) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"You could also try the Brooks and Corey model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"#ψb = FT(-0.6)\n#c = FT(0.43)\n#hcm = BrooksCorey(;ψb = ψb, c = c);\nθ_r = FT(0.05)\nν_ss_om = FT(0.3)\nν_ss_quartz = FT(0.7)\nν_ss_gravel = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose the domain and discretization:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zmax = FT(0)\nzmin = FT(-0.2)\nnelems = 20\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)\nfunction top_heat_flux(p, t)\n FT = eltype(p.soil.T)\n p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))\n T_c = ClimaCore.Fields.level(p.soil.T, p_len)\n return @. FT(28 * (T_c - 267.15))\nend\nfunction bottom_heat_flux(p, t)\n FT = eltype(p.soil.T)\n T_c = ClimaCore.Fields.level(p.soil.T, 1)\n return @. FT(-3 * (T_c - 279.85))\nend\ntop_heat_flux_bc = HeatFluxBC(top_heat_flux)\nbottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)\nboundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),\n bottom = WaterHeatBC(;\n water = zero_water_flux_bc,\n heat = bottom_heat_flux_bc,\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sources = (PhaseChange{FT}(),);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can package this up in the EnergyHydrology model struct:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Running-a-simulation","page":"Phase Changes","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Once we have the model, we can initialize the state vectors and obtain the coordinates","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Y, p, coords = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"After which, we can specify the initial condition function, and initialze the variables:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"function init_soil!(Ysoil, z, params)\n ν = params.ν\n FT = eltype(Ysoil.soil.ϑ_l)\n Ysoil.soil.ϑ_l .= FT(0.33)\n Ysoil.soil.θ_i .= FT(0.0)\n T = FT(279.85)\n ρc_s = Soil.volumetric_heat_capacity(\n FT(0.33),\n FT(0.0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Ysoil.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n FT(0.0),\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 50);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the tendency function, and choose a timestep, and timestepper:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ndt = Float64(100)\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 3,\n update_j = CTS.UpdateEvery(CTS.NewTimeStep),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Comparison-to-data","page":"Phase Changes","title":"Comparison to data","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();\nds = readdlm(dataset_path, ',')\nhours = ds[:, 1][2:end]\nvwc = ds[:, 2][2:end] ./ 100.0\ndepth = ds[:, 3][2:end]\nmask_12h = hours .== 12\nmask_24h = hours .== 24\nmask_50h = hours .== 50;\n\nfig = Figure(size = (900, 300))\nax1 = Axis(\n fig[1, 1],\n title = \"12 hours\",\n xlabel = L\"θ_l + θ_i\",\n ylabel = \"Soil depth (m)\",\n)\nlimits!(ax1, 0.2, 0.5, -0.2, 0.0)\nax2 = Axis(\n fig[1, 2],\n title = \"24 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax2, 0.2, 0.5, -0.2, 0.0)\nax3 = Axis(\n fig[1, 3],\n title = \"50 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax3, 0.2, 0.5, -0.2, 0.0)\n\n\nz = parent(coords.subsurface.z)[:];\n\nscatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = \"\", color = \"purple\")\nlines!(\n ax1,\n parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\n\nscatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = \"\", color = \"purple\")\nlines!(\n ax2,\n parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\nscatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = \"Data\", color = \"purple\")\nlines!(\n ax3,\n parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],\n z,\n label = \"Simulation\",\n color = :green,\n)\naxislegend(ax3, position = :rb)\n\nsave(\"mizoguchi_data_comparison.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Discussion-and-Model-Explanation","page":"Phase Changes","title":"Discussion and Model Explanation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρ_l partial_tϑ_l + ρ_i partial_tθ_i = -F_T + F_T = 0","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing (T T_f)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"fracdp_lρ_l = L_f fracdTT","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"or","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l = p_l0 + L_f ρ_l fracT-T_fT_f mathcalH(T_f-T)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"What this implies is that above the freezing point, the pressure is equal to p_l0, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure p_l0 is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l0 = ρ_l g ψ(θ_l+ρ_iθ_iρ_l)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_l^* = (ν-θ_r) ψ^-1(p_l(ρ_l g)) + θ_r","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"For freezing, the freeze thaw function F_T is equal to","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T = frac1τ ρ_l (θ_l-θ_l^*) mathcalH(T_f-T) mathcalH(θ_l-θ_l^*)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"τ_LTE= c Δz²κ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#References","page":"Phase Changes","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.\nHansson et al., Vadose Zone Journal 3:693–704 (2004).\nM. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).\nKurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)\nWatanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58\nPainter and Karra, Vadose Zone Journal (2014) 13 (4)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/#Scraping-SNOTEL-Data","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"section"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We begin by importing all required packages:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"metadata = DataTools.snotel_metadata();\nmetacols = [\"id\", \"name\", \"state\", \"elev\", \"lat\", \"lon\"]\nDataFrames.rename!(metadata, Symbol.(metacols));","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"example_ID = 1030\nexample_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]\nexample_data = DataTools.scrape_site_paper(example_ID, example_state);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"const inch2meter = 0.0254\nconst kmphr2mps = 5.0 / 18.0\n\nfilter_val = Dict{Symbol, Tuple{Real, Real}}(\n :SWE => (0.0, 250.0),\n :z => (0.0, 420.0),\n :precip => (0.0, 250.0),\n :rel_hum_avg => (10.0, 100.0),\n :sol_rad_avg => (0.0, 1500.0),\n :wind_speed_avg => (0.0, 216.0),\n :air_temp_avg => (-40.0, 60.0),\n)\n\nscales = Dict{Symbol, Real}(\n :SWE => inch2meter,\n :z => inch2meter,\n :precip => inch2meter,\n :rel_hum_avg => 0.01,\n :wind_speed_avg => kmphr2mps,\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. \"start\" refers to 1850-01-01 or the first available date, while \"end\" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"* Indicates alternative handling of the rectify_daily_hourly() function.\n^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.\nA Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.\nT Requires a site that already has had the temperature bias correction at the portal level as of May 2024.\nX Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"good_stations = Dict{Int, Tuple{String, String}}(\n #306 => (\"start\", \"end\"), #*\n 316 => (\"start\", \"end\"),\n 344 => (\"start\", \"end\"),\n #=367 => (\"start\", \"end\"),\n 395 => (\"start\", \"end\"),\n 457 => (\"start\", \"end\"),\n 482 => (\"start\", \"end\"),\n 491 => (\"start\", \"end\"),\n 515 => (\"start\", \"2023-06-02\"), #X\n 532 => (\"start\", \"end\"),\n 551 => (\"start\", \"end\"),\n 571 => (\"start\", \"end\"),\n 599 => (\"start\", \"end\"),\n 608 => (\"start\", \"end\"),\n 613 => (\"start\", \"end\"),\n 641 => (\"start\", \"end\"), #A^\n 665 => (\"start\", \"end\"),\n 708 => (\"start\", \"end\"),\n 715 => (\"start\", \"end\"),\n 734 => (\"start\", \"end\"),\n 737 => (\"start\", \"end\"),\n 744 => (\"start\", \"end\"),\n 832 => (\"start\", \"end\"),\n 845 => (\"start\", \"end\"),\n 854 => (\"start\", \"end\"),\n 857 => (\"start\", \"end\"),\n 921 => (\"start\", \"end\"),\n 922 => (\"start\", \"end\"),\n 927 => (\"start\", \"end\"),\n 942 => (\"start\", \"end\"),\n 963 => (\"start\", \"end\"), #A^\n 969 => (\"start\", \"end\"),\n 974 => (\"start\", \"end\"),\n 978 => (\"start\", \"end\"), #*\n 1030 => (\"start\", \"end\"),\n 1035 => (\"start\", \"end\"), #A^\n 1053 => (\"start\", \"end\"),\n 1070 => (\"start\", \"end\"), #A^T\n 1083 => (\"start\", \"end\"),\n 1091 => (\"start\", \"end\"), #A^T\n 1092 => (\"start\", \"end\"), #A^T\n 1105 => (\"start\", \"end\"),\n 1122 => (\"start\", \"end\"), #*\n 1123 => (\"start\", \"end\"),\n 1159 => (\"start\", \"end\"),\n 1168 => (\"start\", \"end\"),\n 1170 => (\"start\", \"end\"),\n 1254 => (\"start\", \"end\"),\n 1286 => (\"start\", \"end\"),\n 2080 => (\"start\", \"end\"), #A^\n 2170 => (\"start\", \"end\"), #^\n =#\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We then loop through each site to scrape and follow an automated data pipeline, consisting of:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Extracting the daily and hourly timeseries from the site\nApplying the sensor bounds over each data timeseries (i.e. remove sensor error)\nConverting the hourly dataset into a daily dataset\nCoalescing the converted-hourly and daily data into one dataset\nScaling all data to the appropriate metric units\nRestricting data to complete cases\nMaking the differential variables ( fracdzdt, etc.)\nResetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip\nAttaching appropriate metadata","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"allsites = Any[];\nfor site in sort(collect(keys(good_stations)))\n state = metadata[metadata[!, :id] .== site, :state][1]\n start_date = good_stations[site][1]\n end_date = good_stations[site][2]\n\n hourly = DataTools.apply_bounds(\n DataTools.sitedata_hourly(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n hourly[!, :id] .= site\n #hourly = DataTools.bcqc_hourly(hourly)\n hourly_d = DataTools.hourly2daily(hourly)\n #DataFrames.allowmissing!(hourly_d)\n #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)\n #hourly_d[sflags, :sol_rad_avg] .= missing\n\n daily = DataTools.apply_bounds(\n DataTools.sitedata_daily(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n daily[!, :id] .= site\n gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)\n #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)\n #gap_daily = DataTools.d_impute(gap_daily)\n daily_scaled = DataTools.scale_cols(gap_daily, scales)\n daily_clean = daily_scaled[completecases(daily_scaled), :]\n daily_clean = DataTools.makediffs(daily_clean, Day(1))\n good_vals = daily_clean[!, :dprecipdt] .>= 0.0\n daily_clean[(!).(good_vals), :dprecipdt] .= 0.0\n daily_clean = daily_clean[!, Not(:precip)]\n #show(describe(daily_clean), allrows = true, allcols = true)\n #print(\"\\nSIZE: \", nrow(daily_clean), \"\\n\")\n\n daily_clean[!, :id] .= site\n daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]\n daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]\n daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]\n\n push!(allsites, daily_clean)\nend;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"With the sites complete, we condense all sites into a single DataFrame,","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"totaldata = deepcopy(allsites[1])\nfor site in allsites[2:end]\n append!(totaldata, site)\nend","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"and a final CSV.write(\"data.csv\", totaldata) call will save the file.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Setting-up-a-Coupled-Simulation","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"For more information about the bucket model, please see the bucket model tutorial.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Background","page":"Setting up a Coupled Simulation","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In our current setup, \"passed back to the land model via the coupler\" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Turbulent-Surface-Fluxes-and-Radiation","page":"Setting up a Coupled Simulation","title":"Turbulent Surface Fluxes and Radiation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we want different behavior. We have defined new coupled types to use instead of the \"prescribed\" types:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"similarily:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Surface-air-density","page":"Setting up a Coupled Simulation","title":"Surface air density","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Regridder/#Bucket","page":"Parameter Dataset Tools","title":"Bucket","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"CurrentModule = ClimaLand.Regridder","category":"page"},{"location":"APIs/Regridder/#Functions","page":"Parameter Dataset Tools","title":"Functions","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll\nClimaLand.Regridder.swap_space","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Hydrostatic-Equilibrium-test-for-Richards-Equation","page":"Richards Equation","title":"Hydrostatic Equilibrium test for Richards Equation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"frac ϑ_l t = K (ϑ_l ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We will solve this equation in a 1-d domain with z -50, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"-K h(t z = -5) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ(t = 0 z) = ν-0001","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"θ_i(t = 0 z) = 00","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"where nu is the porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Preliminary-setup","page":"Richards Equation","title":"Preliminary setup","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load external packages","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"import SciMLBase\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-soil-model","page":"Richards Equation","title":"Set up the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"First, we define the parameters: porosity \\nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K_sat = FT(0.0443 / (3600 * 100))\nS_s = FT(1e-3)\nν = FT(0.495)\nvg_α = FT(2.6)\nvg_n = FT(2)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0)\nparams = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"zmax = FT(0)\nzmin = FT(-5)\nnelems = 10\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nbottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nboundary_conditions = (; top = surface_flux, bottom = bottom_flux);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, in this case we don't have any sources, so we pass an empty tuple:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"soil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_conditions,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = ClimaLand.make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-simulation","page":"Richards Equation","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y.soil.ϑ_l .= FT(0.494);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 24 * 36);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"dt = Float64(1e3);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"stepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we set up the information used for our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Create-some-plots","page":"Richards Equation","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t = sol.t ./ (60 * 60 * 24);\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nz = parent(coords.subsurface.z)\nplot(\n ϑ_l[1],\n z,\n label = string(\"t = \", string(t[1]), \"days\"),\n xlim = [0.47, 0.501],\n ylabel = \"z\",\n xlabel = \"ϑ_l\",\n legend = :bottomleft,\n title = \"Equilibrium test\",\n);\nplot!(ϑ_l[end], z, label = string(\"t = \", string(t[end]), \"days\"));\nfunction hydrostatic_equilibrium(z, z_interface)\n ν = 0.495\n S_s = 1e-3\n α = 2.6\n n = 2.0\n m = 0.5\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nplot!(hydrostatic_equilibrium.(z, -0.56), z, label = \"equilibrium solution\");\n\nplot!(1e-3 .+ ϑ_l[1], z, label = \"porosity\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Save the output:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"savefig(\"equilibrium_test_ϑ_l.png\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#References","page":"Richards Equation","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#PlantHydraulics","page":"Plant Hydraulics","title":"PlantHydraulics","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"CurrentModule = ClimaLand.PlantHydraulics","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#Models","page":"Plant Hydraulics","title":"Models","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsModel","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","text":"PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}\n\nDefines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.\n\nThis model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can \"turn off\" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:\n\nn_leaf = 1\nn_stem = 0\nLAI = SAI = RAI = 0.\n\nA plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.\n\nFinally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.\n\nn_stem: The number of stem compartments for the plant; can be zero\nn_leaf: The number of leaf compartments for the plant; must be >=1\ncompartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters\ncompartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.\ncompartment_labels: The label (:stem or :leaf) of each compartment\nparameters: Parameters required by the Plant Hydraulics model\ntranspiration: The transpiration model, of type AbstractTranspiration\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Diagnostic-Variables","page":"Plant Hydraulics","title":"Plant Hydraulics Diagnostic Variables","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.effective_saturation\nClimaLand.PlantHydraulics.augmented_liquid_fraction\nClimaLand.PlantHydraulics.water_retention_curve\nClimaLand.PlantHydraulics.inverse_water_retention_curve\nClimaLand.PlantHydraulics.root_water_flux_per_ground_area!\nClimaLand.PlantHydraulics.flux\nClimaLand.PlantHydraulics.hydraulic_conductivity","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.effective_saturation","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.effective_saturation","text":"effective_saturation(\n ν::FT,\n ϑ_l::FT) where {FT}\n\nComputes the effective saturation given the augmented liquid fraction.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","text":"augmented_liquid_fraction(\n ν::FT,\n S_l::FT) where {FT}\n\nComputes the augmented liquid fraction from porosity and effective saturation.\n\nAugmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.water_retention_curve","text":"water_retention_curve(\n S_l::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","text":"inverse_water_retention_curve(\n ψ::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the effective saturation given the potential at a point, according to the linear retention curve model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","text":"PlantHydraulics.root_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrognosticSoil,\n model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nAn extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.\n\nIt is computed by summing the flux of water per ground area between roots and soil at each soil layer.\n\n\n\n\n\nroot_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.\n\nThe returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.flux","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.flux","text":"flux(\n z1,\n z2,\n ψ1,\n ψ2,\n K1,\n K2,\n) where {FT}\n\nComputes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.\n\nWe currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","text":"hydraulic_conductivity(conductivity_params::Weibull{FT}, ψ::FT) where {FT}\n\nComputes the hydraulic conductivity at a point, using the Weibull formulation, given the potential ψ.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Parameters","page":"Plant Hydraulics","title":"Plant Hydraulics Parameters","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsParameters\nClimaLand.PlantHydraulics.Weibull\nClimaLand.PlantHydraulics.LinearRetentionCurve","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","text":"PlantHydraulicsParameters\n\nA struct for holding parameters of the PlantHydraulics Model.\n\nai_parameterization: The area index model for LAI, SAI, RAI\nν: porosity (m3/m3)\nS_s: storativity (m3/m3)\nconductivity_model: Conductivity model and parameters\nretention_model: Water retention model and parameters\nroot_distribution: Root distribution function P(z)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.Weibull","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.Weibull","text":"Weibull{FT} <: AbstractConductivityModel{FT}\n\nA concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.\n\nFields\n\nK_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation\nψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).\nc: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","text":"LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}\n\nA concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.\n\nWhen ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.\n\nFields\n\na: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Methods-and-Types","page":"Plant Hydraulics","title":"Plant Hydraulics Methods and Types","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PrescribedSoilPressure\nClimaLand.PlantHydraulics.PrescribedTranspiration\nClimaLand.PlantHydraulics.AbstractRootExtraction","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","text":"PrescribedTranspiration{FT, F <: Function} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch when computing the transpiration from the leaves, in the case where transpiration is prescribed.\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's load the required modules:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"using ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Pond\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-I-Soil-Hydrology","page":"Intro to multi-component models","title":"The individual component models I - Soil Hydrology","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The RR equation for the volumetric water content of soil is given by","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S(xyz t)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In order to solve this, one must specify:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"boundary conditions,\nrelevant parameters (closure models for K and ψ),\na domain and a spatial discretization scheme,\nadditional source terms S, if applicable,\na time-stepping algorithm,\ninitial conditions.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"For the RR equation, we can create this as follows. First, we specify parameters:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ν = FT(0.495);\nK_sat = FT(0.0443 / 3600 / 100); # m/s\nS_s = FT(1e-3); #inverse meters\nvg_n = FT(2.0);\nvg_α = FT(2.6); # inverse meters\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0);\nsoil_ps = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next, let's define the spatial domain and discretization:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"zmax = FT(0);\nzmin = FT(-1);\nnelems = 20;\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And boundary conditions and source terms (none currently):","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"top_flux_bc = WaterFluxBC((p, t) -> 0.0);\nbot_flux_bc = WaterFluxBC((p, t) -> 0.0);\nsources = ()\nboundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(top = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#1#2\"}(Main.var\"##494\".var\"#1#2\"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#3#4\"}(Main.var\"##494\".var\"#3#4\"()))","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"With this information, we can make our model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil = Soil.RichardsModel{FT}(;\n parameters = soil_ps,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_soil, p_soil, coords_soil = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and we can set up the tendency function using the default as well,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_ode! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which computes, for the column domain,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-frac z (-Kfrac(ψ+z) z)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"for each value of ϑ on the mesh of our soil_domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function soil_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place\n update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place\n compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-II-Surface-Water","page":"Intro to multi-component models","title":"The individual component models II - Surface Water","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond model has a single variable, the pond height η, which satisfies the ODE:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"frac η t = -(P - I) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To write down the pond equations, we need to specify","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"P\nI","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s\n\ninfiltration(t) = -(1e-6) #m/s\npond_model =\n Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Again we can initialize the state vector and auxiliary vectors:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_pond, p_pond, coords_pond = initialize(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We can make the tendency function in the same way, for stepping the state forward in time:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"pond_ode! = make_exp_tendency(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond_ode! function works in the same way as for the soil model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function pond_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # falls back to default; does nothing\n update_boundary_fluxes!(p,Y,t) # p.surface_water.runoff in place\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#An-LSM-with-pond-and-soil:","page":"Intro to multi-component models","title":"An LSM with pond and soil:","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The LSM model must contain everything needed to write down the joint system of equations","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial etapartial t = -(P(t) - I(ϑ η P)) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmax z = I(ϑ η P)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmin z = 00","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"As in the standalone cases, defining our model requires specifying","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"parameters,\ndomains, discretizations\nprecipitation,\nboundary conditions,\nsources in the soil equation, if any.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's make our single column domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());\nsurface_water_args = NamedTuple();","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Atmospheric drivers don't \"belong\" to either component alone:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_args = (precip = precipitation,);\nland = LandHydrology{FT}(;\n land_args = land_args,\n soil_model_type = Soil.RichardsModel{FT},\n soil_args = soil_args,\n surface_water_model_type = Pond.PondModel{FT},\n surface_water_args = surface_water_args,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_aux(land)\n soil_update_aux! = make_update_aux(land.soil)\n surface_update_aux! = make_update_aux(land.surface_water)\n function update_aux!(p,Y,t)\n surface_update_aux!(p,Y,t) # does nothing to `p`\n soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ\n end\n return update_aux!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_boundary_fluxes(land)\n update_soil_bf! = make_update_boundary_fluxes(land.soil)\n update_pond_bf! = make_update_boundary_fluxes(land.surface_water)\n function update_boundary_fluxes!(p,Y,t)\n p.soil_infiltration = compute_infiltration(Y,p, t)\n update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration\n update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration\n end\n return update_boundary_fluxes!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and similarily for the compute_exp_tendency! functions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_compute_exp_tendency(land)\n soil_compute_exp_tendency! = make_update_aux(land.soil)\n surface_compute_exp_tendency! = make_update_aux(land.surface_water)\n function compute_exp_tendency!(dY,Y,p,t)\n surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η\n soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ\n end\n return compute_exp_tendency!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The exp_tendency! for the land model is then again just","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function exp_tendency!(dY, Y, p, t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"initialize(land.soil)\ninitialize(land.surface_water)\ninitializes additional auxiliary variables, like p.soil_infiltration\nappend these into Y, p, and coords:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y, p, coords = initialize(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We have volumetric liquid water fraction:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:ϑ_l,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and surface height of the pond:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:η,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"as well as auxiliary variables for the soil:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:K, :ψ, :top_bc, :bottom_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the runoff for surface water:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:runoff,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the additional variable required in the LSM is stored here as well:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:soil_infiltration, :soil, :surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and finally, coordinates - useful for visualization of solutions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.subsurface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the coordinates of the surface variables:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.surface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[0.0]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And we can make the tendency function as before:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_ode! = make_exp_tendency(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#Advantages-and-disadvantages","page":"Intro to multi-component models","title":"Advantages and disadvantages","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Some advantages to our interface design are as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.\nlikewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.\nthe exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).\nalthough we wrote it here in a hardwired fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.\nthe order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.\nthe code is also modular in terms of swapping out a simple component model for a more complex version.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Possible disadvantages to our interface design:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.\nTo accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.\nstandalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.\nwe need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"The same experiment is carried out 3 times","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"No evaporation (zero flux boundary conditions)\nWith evaporation but no drainage (Ksat = 0)\nWith evaporation and drainage","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Parameters","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"K_sat = FT(0.01 / 3600 / 24)\nvg_n = FT(1.45)\nvg_α = FT(1.5)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.4)\nθ_r = FT(0.04)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(0.3)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)# 10mm\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 294.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}(Main.var\"##317\".var\"#1#2\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}(Main.var\"##317\".var\"#3#4\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), nothing)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Atmos","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}(Main.var\"##317\".var\"#7#8\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}(Main.var\"##317\".var\"#9#10\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}(Main.var\"##317\".var\"#11#12\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}(Main.var\"##317\".var\"#13#14\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}(ClimaLand.var\"#21#24\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Simulation setup - no evaporation Boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"zero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nno_flux_boundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);\n\nt0 = Float64(0)\ntf = Float64(24 * 3600 * 15)\ndt = Float64(900.0)\nΔz = 0.01\nzmax = FT(0)\nzmin = FT(-1.6)\nnelems = Int((zmax - zmin) / Δz)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_flux_boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Y, p, cds = initialize(soil)\nfunction estimated_ic(z)\n 0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= estimated_ic.(z)\n Y.soil.θ_i .= 0\n T = FT(294.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\ncb = SciMLBase.CallbackSet(saving_cb);\n\nsol_no_evap =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Repeat with evaporation and drainage This requires different initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nevap_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n)\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = evap_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nsoil_exp_tendency! = make_exp_tendency(soil)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];\n\n# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"bottom_water_bc = MoistureStateBC((p, t) -> 0.35)\nno_drainage_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),\n)\nzmax = FT(0)\nzmin = FT(-0.16)\nnelems = Int((zmax - zmin) / Δz)\ndt = Float64(10.0)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_drainage_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z_no_evap, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol_no_drainage =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap_no_drainage = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"savepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (800, 400))\nax = Axis(fig[1, 1], xlabel = \"Day\", ylabel = \"Evaporation rate (mm/d)\")\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"With drainage\",\n color = :red,\n)\nCairoMakie.lines!(\n ax,\n sol_no_drainage.t ./ 3600 ./ 24,\n evap_no_drainage .* (1000 * 3600 * 24),\n label = \"No drainage\",\n color = :blue,\n)\n\nCairoMakie.axislegend(ax)\nax2 = Axis(fig[1, 2], xlabel = \"Day\", ylabel = \"Cumulative evaporation (mm)\")\nCairoMakie.lines!(\n ax2,\n sol.t ./ 3600 ./ 24,\n cumsum(evap) .* (1000 * 3600),\n color = :red,\n)\nCairoMakie.lines!(\n ax2,\n sol_no_drainage.t ./ 3600 ./ 24,\n cumsum(evap_no_drainage) .* (1000 * 3600),\n color = :blue,\n)\nsave(\"evaporation_lehmann2024_figS6.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Drainage only\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.4)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 10]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax1,\n parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Evap+Drainage\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.3, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 1, 2, 5, 13]\nfor i in 1:1:5\n CairoMakie.lines!(\n ax2,\n parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Evap only\", xlabel = \"Volumetric Water Content\")\nCairoMakie.ylims!(-0.15, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 2, 9, 14]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax3,\n parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n label = \"$(days[i]) days\",\n parent(z_no_evap)[:],\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"evaporation_gardner_fig1.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"Snow Model","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"CurrentModule = ClimaLand.Snow","category":"page"},{"location":"APIs/Snow/#Snow-Parameters","page":"Snow Model","title":"Snow Parameters","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"ClimaLand.Snow.SnowParameters","category":"page"},{"location":"APIs/Snow/#ClimaLand.Snow.SnowParameters","page":"Snow Model","title":"ClimaLand.Snow.SnowParameters","text":"SnowParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SnowModel.\n\nNote that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.\n\nρ_snow: Density of snow (kg/m^3)\nz_0m: Roughness length over snow for momentum (m)\nz_0b: Roughness length over snow for scalars (m)\nα_snow: Albedo of snow (unitless)\nϵ_snow: Emissivity of snow (unitless)\nθ_r: Volumetric holding capacity of water in snow (unitless)\nKsat: Hydraulic conductivity of wet snow (m/s)\nκ_ice: Thermal conductivity of ice (W/m/K)\nΔt: Timestep of the model (s)\nρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)\nearth_param_set: Clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Snow/#Snow-Functions-of-State","page":"Snow Model","title":"Snow Functions of State","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff\t ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Stomatal-conductance","page":"Medlyn model","title":"Stomatal conductance","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"This section describes multiple models of stomatal conductance implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Medlyn-Model","page":"Medlyn model","title":"Medlyn Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\nT = -rho_a g_mathrmeff leftq_a- q_v(T_mathrmleaf rho_mathrmsfc) right\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where T is the transpiration (mass flux of water vapor), q_a is the specific humidity at the lowest level of the atmosphere, q_v(T_mathrmT_leaf rho_mathrmsfc) is the saturated specific humidity over liquid water, given the temperature of the leave T_leaf and air density at the surface rho_sfc. We will approximate T_leaf = T_a and rho_mathrmsfc = rho_a.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"We also need the effective conductivity, given by","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n g_mathrmeff = frac1g_mathrmae^-1+g_mathrms^-1\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g_ae is the aerodynamic conductance, computed by the MOST solve, and g_s is the stomatal conductance to water vapor per unit ground area. The units of all conductances are ms.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginalign\ng_sm(PAR T VPD c_a) = g_0m + D_rel times m fracA_n(PAR T VPD c_a)c_anonumber \ng_s = fracg_smrho_m\nendalign","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where D_rel =16 (unitless) is the relative diffusivity of water vapor with respect to CO_2, rho_m is the molar density of water, and m is the Medlyn factor,","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n m = left( 1 + fracg_1sqrtVPD right) \nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A_n as the biochemical demand for CO_2 calculated using the photosynthesis model (Farquhar, 1980; Equation \\eqref{eq:an}; units of molar flux). The resulting units are ms. g{0,m}$ is a minimum molar conductivity. (subscript m indicates molar).","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Constants Symbol Unit Value\nRelative diffusivity of water vapor D_rel - 1.6\nMinimum stomatal conductance g_0 mol/m^2/s 1e-4\nSlope parameter g_1 sqrtPa 790","category":"page"},{"location":"standalone/README/#DynamicDocs","page":"DynamicDocs","title":"DynamicDocs","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Plant-Hydraulics","page":"Van Genuchten model","title":"Plant Hydraulics","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The plant hydraulics code solves for the volumetric water content in the stem and leaf (theta_stem and theta_leaf). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Van-Genuchten-Model","page":"Van Genuchten model","title":"Van Genuchten Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The volume flux of water q (m/s) between compartments with centers at two heights, z_1 and z_2, is given by Darcy's law as","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n q = -int_z_1^z_2 k(psi) dh\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where h = psi+z is the head (in meters), and k is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\\footnote{Double check this - the units of k in our code are ms.}","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\nq = -int_h_1^h_2 k(psi) dh approx -frack_1(psi_1) + k_2(psi_2)2 * (psi_2 - psi_1) + (z_2 - z_1)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"In order to close the set of equations, the user will have to specify k(psi) and a function psi(theta). In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of K_sat","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The change of water volume (m^3) V, in the compartments is then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd V_w stemdt = q_rootssigma_roots - q_stemsigma_stem nonumber \n fracd V_w leafdt = q_stemsigma_stem - tau sigma_leaf\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where tau is a transpiration volume flux per unit emitting area, and sigma is the total emitting/conducting area\\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals N. We can make use of the fact that NsigmaA, where A is the area of the ground those N individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Then we have:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd v_stemdt = q_rootsRAI - q_stemSAI nonumber \n fracd v_leafdt = q_stemSAI - tau LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where v now represents the volume of water in that compartment (of a bulk plant) per unit ground area.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We also need to convert from the variable v to psi, in order to compute root extraction with the soil. To do so, we can convert v to the volumetric water content, and from theta to psi using a van Genuchten relationship. To convert, let the volume of water per area of compartment be V_wstem, and H the typical ``length\" of the compartment. Then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n theta_stem=fracV_wstemA_ground times fracA_groundA_stem times frac1H_stem = fracv_stemH_stem times SAI \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Substituting in the volumetric water content, we have","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd theta_stemdt = fracq_rootsRAI - q_stemSAIH_stem SAI nonumber \n fracd theta_leafdt = fracq_stemSAI - tau LAIH_leaf LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction P(z), satisfying int P(z) dz = 1. Instead of having a single root at one discrete location, we can distribute the root system over different depths using P(z). The total flux from roots between z and z+dz is given by","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n dq_roots(z) = -P(z) dz int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"so that the net flux for the plant system would sum over this","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n q_roots = -int_z_min^z_sfc fracdq_roots(z)dzdz \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where z_min is the minimum soil layer of the simulation. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n S(z) = -(RAI) dq_roots(z)dz = (RAI) P(z) int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sign change occurs in the expression for S(z) because a positive value of q_roots indicates flow from the soil to the plant. This is a sink term for the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The model needs the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Drivers Symbol Unit Range\nA function of simulation time t given the leaf area index LAI m²/m² 0–10\nThe constant stem area index SAI m²/m² 0–10\nThe constant root area index RAI m²/m² 0–10","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Microbial-respiration","page":"DAMM model","title":"Microbial respiration","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Dual-Arrhenius-Michaelis-Menten","page":"DAMM model","title":"Dual Arrhenius Michaelis-Menten","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration (Rh) as a function of soil temperature (T_s) and soil moisture (theta).","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The rate of respiration, Rh, is expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n Rh = V_textmaxs_xMM_s_xMM_O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where V_textmaxs_x is the maximum potential rate of respiration, MM_s_x represents the availability of substrate, and MM_O_2 is used as the oxygen limitation factor. MM_s_x and MM_O_2 are between 0 (limiting) and 1 (non limiting). V_textmaxs_x, MM_s_x, and MM_O_2 are expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n V_textmaxs_x = alpha_s_x exp(frac-Ea_s_xRT_s)\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textsx = fracs_xkM_s_x+s_x\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textO_2 = fracO_2kM_O_2+O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where alpha_s_x is the pre-exponential factor, Ea_s_x is the activation energy of the reaction, R is the gas constant, and T_s is soil temperature. s_x is the concentration of all soluble substrate, and O_2 is the oxygen concentration. kM_s_x and kM_O_2 are the Michaelis constant for soil and oxygen, respectively.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, s_x is calculated as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n s_x = p_s_xtimesC_somtimes D_liqtimestheta^3\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where C_som is the total amount of soil organic carbon, and p_s_x is the fraction of C_som that is soluble. D_liq is the diffusion coefficient of the soluble carbon. theta is soil moisture.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of O_2 depends on the diffusion of gases within the soil, which is calculated as below:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n O_2 = D_Oatimes O_2a times porosity_air^43\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where D_Oa is the diffusion coefficient for O_2 in air, O_2a is the volume fraction of O_2 in air, and porosity_air is the air-filled porosity.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The air-filled porosity is calculated by subtracting the soil moisture from the total porosity (nu):","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n porosity_air = nu - theta\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"To sum up, the model has the following parameters:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Output Symbol Unit Range\nHeterotrophic respiration Rh mumol m^-2 s^-2 0–25","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Drivers Symbol Unit Range\nSoil temperature T_s C -20–50\nSoil moisture theta m^3 m^-3 0.0–1.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Parameters Symbol Unit Range\nSoil porosity nu m^3 m^-3 0.0–1.0\nPre-exponential factor alpha_s_x kg C m^-3 s^-1 100e3–300e3\nActivation energy Ea_s_x Jmol^-1 50e3–70e3\nMichaelis constant for soil kM_s_x kg C m^-3 1e-10–0.1\nMichaelis constant for O_2 kM_O_2 m^3 m^-3 1e-10–0.1\nVolumetric fraction of O_2 in the soil air content O_2_a - 0.005–0.5\nFraction of soil carbon that is considered soluble p_s_x - 0.005–0.5\nSoil organic C C_som kg C m^-3 1.0–10.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Constants Symbol Unit Value\nAir-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa) O_a100 - 0.1816\nDiffusivity of soil C substrate in liquid D_liq - 3.17\nDiffusion coefficient of oxygen in air D_Oa - 1.67","category":"page"},{"location":"Contributing/#Contributing","page":"Contribution guide","title":"Contributing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.","category":"page"},{"location":"Contributing/#Some-useful-tips","page":"Contribution guide","title":"Some useful tips","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"When you start working on a new feature branch, make sure you start from main by running: git checkout main.\nMake sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.\nWhen your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.","category":"page"},{"location":"Contributing/#Continuous-integration","page":"Contribution guide","title":"Continuous integration","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.","category":"page"},{"location":"Contributing/#Automated-testing","page":"Contribution guide","title":"Automated testing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Currently a number of checks are run per commit for a given PR.","category":"page"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.\nDocumentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.\nTests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Canopy-Energy-Model","page":"Canopy Energy","title":"Canopy Energy Model","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Methods","page":"Canopy Energy","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.canopy_temperature\nClimaLand.Canopy.root_energy_flux_per_ground_area!","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.canopy_temperature","page":"Canopy Energy","title":"ClimaLand.Canopy.canopy_temperature","text":"canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.\n\n\n\n\n\ncanopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.root_energy_flux_per_ground_area!","page":"Canopy Energy","title":"ClimaLand.Canopy.root_energy_flux_per_ground_area!","text":"root_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrognosticSoil{F},\n model::Canopy.AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT, F}\n\nA method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).\n\nNote that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\nroot_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.\n\nBackground information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#Types","page":"Canopy Energy","title":"Types","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.AbstractCanopyEnergyModel\nClimaLand.Canopy.PrescribedCanopyTempModel\nClimaLand.Canopy.BigLeafEnergyModel","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.PrescribedCanopyTempModel","page":"Canopy Energy","title":"ClimaLand.Canopy.PrescribedCanopyTempModel","text":"PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}\n\nA model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.\n\nNo equation for the energy of the canopy is solved.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.BigLeafEnergyModel","page":"Canopy Energy","title":"ClimaLand.Canopy.BigLeafEnergyModel","text":"BigLeafEnergyModel{FT} <: AbstractCanopyEnergyModel{FT}\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#SurfaceWater","page":"Surface Water Models","title":"SurfaceWater","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"CurrentModule = ClimaLand.Pond","category":"page"},{"location":"APIs/SurfaceWater/#Models","page":"Surface Water Models","title":"Models","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PondModel","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PondModel","page":"Surface Water Models","title":"ClimaLand.Pond.PondModel","text":"PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}\n\nA stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.\n\ndomain: The domain for the pond model\nrunoff: The runoff model for the pond model\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#Methods-and-Types","page":"Surface Water Models","title":"Methods and Types","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PrescribedRunoff\nClimaLand.Pond.surface_runoff","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PrescribedRunoff","page":"Surface Water Models","title":"ClimaLand.Pond.PrescribedRunoff","text":"PrescribedRunoff{F1 <: Function, F2 <: Function} <: AbstractSurfaceRunoff\n\nThe required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.surface_runoff","page":"Surface Water Models","title":"ClimaLand.Pond.surface_runoff","text":"function Pond.surface_runoff(\n runoff::PrognosticRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nExtension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#Autotrophic-Respiration","page":"Canopy Autotrophic Respiration","title":"Autotrophic Respiration","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#Parameters","page":"Canopy Autotrophic Respiration","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.AutotrophicRespirationParameters","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.AutotrophicRespirationParameters","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.AutotrophicRespirationParameters","text":"AutotrophicRespirationParameters{FT<:AbstractFloat}\n\nThe required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. \"The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics.\" Geoscientific Model Development 4.3 (2011): 701-722.\n\nne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)\nηsl: Live stem wood coefficient (kg C m-3)\nσl: Specific leaf density (kg C m-2 [leaf])\nμr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\nμs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1\nRel: Relative contribution or Rgrowth (-)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/AutotrophicRespiration/#Methods","page":"Canopy Autotrophic Respiration","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.nitrogen_content\nClimaLand.Canopy.plant_respiration_maintenance\nClimaLand.Canopy.plant_respiration_growth","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.nitrogen_content","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.nitrogen_content","text":"nitrogen_content(\n ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)\n Vcmax25::FT, #\n LAI::FT, # Leaf area index\n SAI::FT,\n RAI::FT,\n ηsl::FT, # live stem wood coefficient (kg C m-3) \n h::FT, # canopy height (m)\n σl::FT # Specific leaf density (kg C m-2 [leaf])\n μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\n μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 \n ) where {FT}\n\nComputes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_maintenance","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_maintenance","text":"plant_respiration_maintenance(\n Rd::FT, # Dark respiration\n β::FT, # Soil moisture factor\n Nl::FT, # Nitrogen content of leafs\n Nr::FT, # Nitrogen content of roots\n Ns::FT, # Nitrogen content of stems\n ) where {FT}\n\nComputes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_growth","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_growth","text":"plant_respiration_growth(\n Rel::FT, # Factor of relative contribution\n An::FT, # Net photosynthesis\n Rpm::FT # Plant maintenance respiration\n ) where {FT}\n\nComputes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.\n\n\n\n\n\n","category":"function"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Using-atmospheric-and-radiative-drivers","page":"Intro to forced site-level runs","title":"Using atmospheric and radiative drivers","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers\"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Types-of-forcing-data","page":"Intro to forced site-level runs","title":"Types of forcing data","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Creating-site-level-drivers-for-radiation","page":"Intro to forced site-level runs","title":"Creating site-level drivers for radiation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"using Dates\nusing Insolation # for computing zenith angle given lat, lon, time.\nusing ClimaLand\nimport ClimaLand.Parameters as LP\nimport ClimaParams","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the local_datetime array is read in from the data file.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Timezone (offset of local time from UTC in hrs)","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"time_offset = 7;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Site latitude and longitude","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"lat = 38.7441; # degree\nlong = -92.2000; # degree","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"ref_time = local_datetime[1] + Dates.Hour(time_offset);\ndata_dt = 3600.0;\nseconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));\nLW_d = 5.67 * 10^(-8) .* T .^ 4;\nSW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"LW_d = TimeVaryingInput(seconds, LW_d)\nSW_d = TimeVaryingInput(seconds, SW_d);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"earth_param_set = LP.LandParameters(Float64);\ninsol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation\nfunction zenith_angle(\n t,\n ref_time;\n latitude = lat,\n longitude = long,\n insol_params = insol_params,\n)\n current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC\n\n d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(\n current_datetime,\n ref_time,\n insol_params,\n ))\n\n\n return Insolation.instantaneous_zenith_angle(\n d,\n δ,\n η_UTC,\n longitude,\n latitude,\n )[1]\nend;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"radiation = ClimaLand.PrescribedRadiativeFluxes(\n Float64,\n SW_d,\n LW_d,\n ref_time;\n θs = zenith_angle,\n);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Updating-the-driver-variables-during-the-simulation","page":"Intro to forced site-level runs","title":"Updating the driver variables during the simulation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"In order to update them, we can make use of default update functions:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"update_radiation! = ClimaLand.make_update_drivers(radiation)\nt0 = seconds[1] # midnight local time\nupdate_radiation!(p, t0);\n@show(p.drivers);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])\n","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"During a simulation, the drivers are updated in place in p.drivers via a \"callback\", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"updateat = collect(seconds[1]:(3600 * 3):seconds[end]);\nupdatefunc = update_radiation!;\ncb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This callback must then be provided to the simulation solve function.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Photosynthesis","page":"Canopy Photosynthesis","title":"Photosynthesis","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Parameters","page":"Canopy Photosynthesis","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.SIFParameters\nClimaLand.Canopy.FarquharParameters\nClimaLand.Canopy.OptimalityFarquharParameters","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.SIFParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.SIFParameters","text":"SIFParameters{FT<:AbstractFloat}\n\nThe required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.\n\nkf: The rate coefficient for florescence, unitless\nkd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nmin_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkp: Rate coefficient for photochemical quenching\nkappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\nkappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.FarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.FarquharParameters","text":"FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}\n\nThe required parameters for the Farquhar photosynthesis model.\n\nVcmax25: Vcmax at 25 °C (mol CO2/m^2/s)\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercelluar O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]\npc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]\nmechanism: Photosynthesis mechanism: C3 or C4\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.OptimalityFarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.OptimalityFarquharParameters","text":"OptimalityFarquharParameters{FT<:AbstractFloat}\n\nThe required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.\n\nmechanism: Photosynthesis mechanism: C3 only\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercellular O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Fitting constant to compute the moisture stress factor (Pa^{-1})\npc: Fitting constant to compute the moisture stress factor (Pa)\nc: Constant describing cost of maintaining electron transport (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#Methods","page":"Canopy Photosynthesis","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.arrhenius_function\nClimaLand.Canopy.intercellular_co2\nClimaLand.Canopy.co2_compensation\nClimaLand.Canopy.rubisco_assimilation\nClimaLand.Canopy.light_assimilation\nClimaLand.Canopy.C3\nClimaLand.Canopy.C4\nClimaLand.Canopy.max_electron_transport\nClimaLand.Canopy.electron_transport\nClimaLand.Canopy.net_photosynthesis\nClimaLand.Canopy.optimality_max_photosynthetic_rates\nClimaLand.Canopy.moisture_stress\nClimaLand.Canopy.dark_respiration\nClimaLand.Canopy.compute_GPP\nClimaLand.Canopy.MM_Kc\nClimaLand.Canopy.MM_Ko\nClimaLand.Canopy.compute_Vcmax","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.arrhenius_function","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.arrhenius_function","text":"arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)\n\nComputes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.intercellular_co2","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.intercellular_co2","text":"intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}\n\nComputes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.co2_compensation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.co2_compensation","text":"co2_compensation(Γstar25::FT,\n ΔHΓstar::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.rubisco_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.rubisco_assimilation","text":"rubisco_assimilation(::C3,\n Vcmax::FT,\n ci::FT,\n Γstar::FT,\n Kc::FT,\n Ko::FT,\n oi::FT) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).\n\nThe empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nrubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.light_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.light_assimilation","text":"light_assimilation(::C3,\n J::FT,\n ci::FT,\n Γstar::FT) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nlight_assimilation(::C4, J::FT, _...) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C3","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C3","text":"C3 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C4","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C4","text":"C4 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.max_electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.max_electron_transport","text":"max_electron_transport(Vcmax::FT) where {FT}\n\nComputes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.electron_transport","text":"electron_transport(APAR::FT,\n Jmax::FT,\n θj::FT,\n ϕ::FT) where {FT}\n\nComputes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). \n\nSee Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.net_photosynthesis","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.net_photosynthesis","text":"net_photosynthesis(Ac::FT,\n Aj::FT,\n Rd::FT,\n β::FT) where {FT}\n\nComputes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). \n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.optimality_max_photosynthetic_rates","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.optimality_max_photosynthetic_rates","text":"optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)\n\nComputes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.\n\nSee Smith et al. 2019.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.moisture_stress","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.moisture_stress","text":"moisture_stress(pl::FT,\n sc::FT,\n pc::FT) where {FT}\n\nComputes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) . \n\nSee Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.dark_respiration","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.dark_respiration","text":"dark_respiration(Vcmax25::FT,\n β::FT,\n f::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_GPP","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_GPP","text":"compute_GPP(An::FT,\n K::FT,\n LAI::FT,\n Ω::FT) where {FT}\n\nComputes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Kc","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Kc","text":"MM_Kc(Kc25::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Ko","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Ko","text":"MM_Ko(Ko25::FT,\n ΔHko::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_Vcmax","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_Vcmax","text":"compute_Vcmax(Vcmax25::FT,\n T::FT,\n To::FT,\n R::FT,\n ep5::FT) where {FT}\n\nComputes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"getting_started/#Getting-Started","page":"Getting Started","title":"Getting Started","text":"","category":"section"},{"location":"getting_started/#For-Users","page":"Getting Started","title":"For Users","text":"","category":"section"},{"location":"getting_started/#Installation","page":"Getting Started","title":"Installation","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"julia> ] # Enter Package REPL mode\nPkg> add ClimaLand # Install ClimaLand\nPkg> # Go back to Julia REPL mode\nJulia> using ClimaLand","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.","category":"page"},{"location":"getting_started/#Parameterization","page":"Getting Started","title":"Parameterization","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Let's start with a basic example: compute canopy gross photosynthesis (GPP).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\n@doc ClimaLand.Canopy.compute_GPP","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"import ClimaLand.Canopy as canopy\ncanopy.compute_GPP(5.0, 0.5, 3.0, 0.7)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Et voilà!","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.","category":"page"},{"location":"getting_started/#ClimaLand-structure","page":"Getting Started","title":"ClimaLand structure","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ClimaLand contains multiple modules. They are listed below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using MethodAnalysis, ClimaLand\nchild_modules(ClimaLand)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To explore what modules, functions and types are exported in a particular module, you can use About.jl:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\nusing About\nabout(ClimaLand.Soil.Biogeochemistry)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/#Seasonal-Snow-Timeseries-Generation-with-a-Neural-Network","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"section"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The updates of the neural snow model follow the equation","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"fracdzdt = mathcalMleft(z SWE φ R v T_air P_snowright)","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"where","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"t is the time (s),\nz is the snow depth (m),\nmathcalM is the neural network,\nSWE is the Snow Water Equivalent, or the height of water if all the snow melted (m),\nφ is the relative humidity (0-1),\nR is the solar radiation (W/m²).\nv is the wind speed (W/m²).\nT_air is the air temperature (degrees C).\nP_snow is the water equivalent rate of snow precipitation (m/s).","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The model is a 1D model to permit utilization over any desired grid resolution and shape.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"┌ Warning: Package cuDNN not found in current path.\n│ - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia.\n│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.\n└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools\nModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\ncode_dir = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Snow\")\ninclude(joinpath(code_dir, \"display_tools.jl\"));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter n as outlined in the associated parameter, and the two loss function hyperparameters n_1, n_2.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"n = 4\nn1 = 2\nn2 = 4;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the fracdzdt column.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"pred_vars = [\n :z,\n :SWE,\n :rel_hum_avg,\n :sol_rad_avg,\n :wind_speed_avg,\n :air_temp_avg,\n :dprecipdt_snow,\n]\ntarget = :dzdt;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"nfeatures = length(pred_vars)\nz_idx = 1\np_idx = 7;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"training_data_download_link = \"https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv\"\ntesting_data_download_link = \"https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv\"\ndata_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)\nvaldata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)\nΔt = Second(86400)\nhole_thresh = 5;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"usedata = DataTools.prep_data(data_train);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of \"0\" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"out_scale = maximum(abs.(usedata[!, target]))\nin_scales = std.(eachcol(select(usedata, pred_vars)))\nx_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)\nps = ModelTools.get_model_ps(model);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)\nModelTools.setoutscale!(model, 1.0);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With that, training is as simple as calling the trainmodel! function:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"print(\"\\nTraining model!\\n\")\nModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"\nTraining model!\nEpoch: 10 | training loss: 0.0027635782\nEpoch: 20 | training loss: 0.0027531595\nEpoch: 30 | training loss: 0.002643681\nEpoch: 40 | training loss: 0.0026667549\nEpoch: 50 | training loss: 0.0027028923\nEpoch: 60 | training loss: 0.0026131957\nEpoch: 70 | training loss: 0.0026789021\nEpoch: 80 | training loss: 0.002583873\nEpoch: 90 | training loss: 0.0026229469\nEpoch: 100 | training loss: 0.0027189315\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.setoutscale!(model, out_scale)\nModelTools.settimescale!(model, Dates.value(Δt));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Note that gaps in the data are shown as shaded regions on the plotted timeseries.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = 1286\nsitedata = usedata[usedata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Slagamount Lakes, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot1.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = \"1070\" #string format for the testing ids is due to non-numerical testing site codes.\nsitedata = valdata[valdata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Anchorage Hillside, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot2.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This page was generated using Literate.jl.","category":"page"}] } diff --git a/dev/standalone/README/index.html b/dev/standalone/README/index.html index 05a1e79a7b..4c2fb296e0 100644 --- a/dev/standalone/README/index.html +++ b/dev/standalone/README/index.html @@ -1,2 +1,2 @@ -DynamicDocs · ClimaLand.jl
GitHub
+DynamicDocs · ClimaLand.jl
GitHub
diff --git a/dev/standalone/pages/snow/snow_model/index.html b/dev/standalone/pages/snow/snow_model/index.html index c6416910db..711d336df2 100644 --- a/dev/standalone/pages/snow/snow_model/index.html +++ b/dev/standalone/pages/snow/snow_model/index.html @@ -1,2 +1,2 @@ -Snow model · ClimaLand.jl
GitHub
+Snow model · ClimaLand.jl
GitHub
diff --git a/dev/standalone/pages/soil/biogeochemistry/DAMM_model/index.html b/dev/standalone/pages/soil/biogeochemistry/DAMM_model/index.html index 02ea7cd925..13f5abd8f2 100644 --- a/dev/standalone/pages/soil/biogeochemistry/DAMM_model/index.html +++ b/dev/standalone/pages/soil/biogeochemistry/DAMM_model/index.html @@ -15,4 +15,4 @@ [O_2] = D_{Oa}\times O_{2a} \times porosity_{air}^{4/3} \end{equation}\]

where $D_{Oa}$ is the diffusion coefficient for $O_2$ in air, $O_{2a}$ is the volume fraction of $O_2$ in air, and $porosity_{air}$ is the air-filled porosity.

The air-filled porosity is calculated by subtracting the soil moisture from the total porosity ($\nu$):

\[\begin{equation} porosity_{air} = \nu - \theta -\end{equation}\]

To sum up, the model has the following parameters:

OutputSymbolUnitRange
Heterotrophic respirationRh$\mu$mol $m^{-2}$ $s^{-2}$0–25
DriversSymbolUnitRange
Soil temperature$T_s$$°C$-20–50
Soil moisture$\theta$$m^3$ $m^{-3}$0.0–1.0
ParametersSymbolUnitRange
Soil porosity$\nu$$m^3$ $m^{-3}$0.0–1.0
Pre-exponential factor$\alpha_{s_x}$kg C $m^{-3}$ $s^{-1}$100e3–300e3
Activation energy$Ea_{s_x}$J$mol^{-1}$50e3–70e3
Michaelis constant for soil$kM_{s_x}$kg C $m^{-3}$1e-10–0.1
Michaelis constant for $O_2$$kM_{O_2}$$m^3$ $m^{-3}$1e-10–0.1
Volumetric fraction of $O_2$ in the soil air content$O_{2_a}$-0.005–0.5
Fraction of soil carbon that is considered soluble$p_{s_x}$-0.005–0.5
Soil organic C$C_{som}$kg C $m^{-3}$1.0–10.0
ConstantsSymbolUnitValue
Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)$O_{a100}$-0.1816
Diffusivity of soil C substrate in liquid$D_{liq}$-3.17
Diffusion coefficient of oxygen in air$D_{Oa}$-1.67
+\end{equation}\]

To sum up, the model has the following parameters:

OutputSymbolUnitRange
Heterotrophic respirationRh$\mu$mol $m^{-2}$ $s^{-2}$0–25
DriversSymbolUnitRange
Soil temperature$T_s$$°C$-20–50
Soil moisture$\theta$$m^3$ $m^{-3}$0.0–1.0
ParametersSymbolUnitRange
Soil porosity$\nu$$m^3$ $m^{-3}$0.0–1.0
Pre-exponential factor$\alpha_{s_x}$kg C $m^{-3}$ $s^{-1}$100e3–300e3
Activation energy$Ea_{s_x}$J$mol^{-1}$50e3–70e3
Michaelis constant for soil$kM_{s_x}$kg C $m^{-3}$1e-10–0.1
Michaelis constant for $O_2$$kM_{O_2}$$m^3$ $m^{-3}$1e-10–0.1
Volumetric fraction of $O_2$ in the soil air content$O_{2_a}$-0.005–0.5
Fraction of soil carbon that is considered soluble$p_{s_x}$-0.005–0.5
Soil organic C$C_{som}$kg C $m^{-3}$1.0–10.0
ConstantsSymbolUnitValue
Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)$O_{a100}$-0.1816
Diffusivity of soil C substrate in liquid$D_{liq}$-3.17
Diffusion coefficient of oxygen in air$D_{Oa}$-1.67
diff --git a/dev/standalone/pages/soil/energy/energy_model/index.html b/dev/standalone/pages/soil/energy/energy_model/index.html index b635f422b0..535854a98a 100644 --- a/dev/standalone/pages/soil/energy/energy_model/index.html +++ b/dev/standalone/pages/soil/energy/energy_model/index.html @@ -1,2 +1,2 @@ -Energy model · ClimaLand.jl
GitHub
+Energy model · ClimaLand.jl
GitHub
diff --git a/dev/standalone/pages/soil/hydrology/richards_model/index.html b/dev/standalone/pages/soil/hydrology/richards_model/index.html index 9aa5ff5071..7931632cd6 100644 --- a/dev/standalone/pages/soil/hydrology/richards_model/index.html +++ b/dev/standalone/pages/soil/hydrology/richards_model/index.html @@ -1,2 +1,2 @@ -Richards model · ClimaLand.jl
GitHub
+Richards model · ClimaLand.jl
GitHub
diff --git a/dev/standalone/pages/surface_water/surface_water_model/index.html b/dev/standalone/pages/surface_water/surface_water_model/index.html index bc99dcd1c2..83d78bb935 100644 --- a/dev/standalone/pages/surface_water/surface_water_model/index.html +++ b/dev/standalone/pages/surface_water/surface_water_model/index.html @@ -1,2 +1,2 @@ -Surface water model · ClimaLand.jl
GitHub
+Surface water model · ClimaLand.jl
GitHub
diff --git a/dev/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html b/dev/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html index 46ad0b0acf..4bf33fb0f5 100644 --- a/dev/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html +++ b/dev/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html @@ -33,4 +33,4 @@ \beta = \frac{1+ \exp{(s_c \psi_c)}}{1+ \exp{(s_c(\psi_c - \psi_l))}}, \end{align}\]

where $s_c = 4$MPa$^{-1}$, $\psi_c = -2$MPa, and $\psi_l$ is the leaf water potential computed by the plant hydraulics model.

GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:

\[\begin{align} GPP(T, PAR, c_a, VPD, \theta_s) = A_n (1 - \exp(-K LAI \Omega))/K. -\end{align}\]

This is not currently needed by other components, but is used for offline validation of the model.

We need to supply the following parameters and “drivers"

OutputSymbolUnitRange
Total net carbon assimilation$A_n$μmol CO$_2$ m$^{-2}$ s$^{-1}$0–25
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Temperature$T$°C0–50
ParametersSymbolUnitRange
Moisture stress$β$-0-1
Leaf Area IndexLAIm² m⁻²1–10
$CO_2$ concentration$c_a$ppm300e–500
Vapor pressure deficitVPDkPa1-10
ConstantsSymbolUnitValue
Zenith angle$θ_s$rad0.6
Leaf angle distribution$l_d$-0.5
Canopy reflectance$ρ_{leaf}$-0.1
Clumping index$Ω$-0.69
$CO_2$ compensation at 25°CΓ$^*_{25}$mol/mol4.275e-5
Energy of activation for $Γ^*$$ΔH_{Γ^*}$J/mol37830
Standard temperature$T_o$K298.15
Universal gas constant$R$J/mol8.314
The maximum rate of carboxylation of Rubisco$V_{cmax25}$mol CO$_2$ m$^{-2}$ s$^{-1}$5e-5
Energy of activation for $J_max$$ΔH_{J_max}$J/mol43540
Curvature parameter, a fitting constant to compute $J$$θ_j$-0.9
The quantum yied of photosystem II$\phi$-0.6
Energy of activation for $V_{cmax}$$ΔH_{V_{cmax}}$J/mol58520
Slope parameter for stomatal conductance models$g_1$-141
Michaelis Menten constant for $CO_2$ and at 25°C$K_{c25}$mol/mol4.049e-4
Energy of activation for $CO_2$$ΔH_{K_c}$J/mol79430
Michaelis Menten constant for $O_2$ at 25 °C$K_{o25}$mmol/mol0.2874
Energy of activation for $O_2$$ΔH_{K_o}$J/mol36380
Intercellular $O_2$ concentration$o_i$mol/mol0.209
Constant factor appearing the dark respiration term$f$-0.015
Energy of activation for $R_d$$ΔH_{R_d}$J/mol43390
+\end{align}\]

This is not currently needed by other components, but is used for offline validation of the model.

We need to supply the following parameters and “drivers"

OutputSymbolUnitRange
Total net carbon assimilation$A_n$μmol CO$_2$ m$^{-2}$ s$^{-1}$0–25
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Temperature$T$°C0–50
ParametersSymbolUnitRange
Moisture stress$β$-0-1
Leaf Area IndexLAIm² m⁻²1–10
$CO_2$ concentration$c_a$ppm300e–500
Vapor pressure deficitVPDkPa1-10
ConstantsSymbolUnitValue
Zenith angle$θ_s$rad0.6
Leaf angle distribution$l_d$-0.5
Canopy reflectance$ρ_{leaf}$-0.1
Clumping index$Ω$-0.69
$CO_2$ compensation at 25°CΓ$^*_{25}$mol/mol4.275e-5
Energy of activation for $Γ^*$$ΔH_{Γ^*}$J/mol37830
Standard temperature$T_o$K298.15
Universal gas constant$R$J/mol8.314
The maximum rate of carboxylation of Rubisco$V_{cmax25}$mol CO$_2$ m$^{-2}$ s$^{-1}$5e-5
Energy of activation for $J_max$$ΔH_{J_max}$J/mol43540
Curvature parameter, a fitting constant to compute $J$$θ_j$-0.9
The quantum yied of photosystem II$\phi$-0.6
Energy of activation for $V_{cmax}$$ΔH_{V_{cmax}}$J/mol58520
Slope parameter for stomatal conductance models$g_1$-141
Michaelis Menten constant for $CO_2$ and at 25°C$K_{c25}$mol/mol4.049e-4
Energy of activation for $CO_2$$ΔH_{K_c}$J/mol79430
Michaelis Menten constant for $O_2$ at 25 °C$K_{o25}$mmol/mol0.2874
Energy of activation for $O_2$$ΔH_{K_o}$J/mol36380
Intercellular $O_2$ concentration$o_i$mol/mol0.209
Constant factor appearing the dark respiration term$f$-0.015
Energy of activation for $R_d$$ΔH_{R_d}$J/mol43390
diff --git a/dev/standalone/pages/vegetation/photosynthesis/optimality_model/index.html b/dev/standalone/pages/vegetation/photosynthesis/optimality_model/index.html index fdc6ba6c2e..28b9418dc0 100644 --- a/dev/standalone/pages/vegetation/photosynthesis/optimality_model/index.html +++ b/dev/standalone/pages/vegetation/photosynthesis/optimality_model/index.html @@ -21,4 +21,4 @@ m_c = \frac{C'_i - \Gamma^*}{C'_i + K} \end{equation}\]

\[\Gamma^*\]

is the CO$_2$ compensation point in the absence of mitochondrial respiration

\[\begin{equation} \Gamma^* = \Gamma^*_0 f(T, \Delta H_a) p/p_0 -\end{equation}\]

where $\Gamma^*_0 = 4.332$ Pa, $p$ is the atmospheric pressure, $p_0 = 101325$ Pa, and $\Delta H_a = 37830$ J/mol.

The model has the following parameters:

For Smith et al. (2019) Vcmax model:

+\end{equation}\]

where $\Gamma^*_0 = 4.332$ Pa, $p$ is the atmospheric pressure, $p_0 = 101325$ Pa, and $\Delta H_a = 37830$ J/mol.

The model has the following parameters:

For Smith et al. (2019) Vcmax model:

diff --git a/dev/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html b/dev/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html index a819744d8e..fed0aaa042 100644 --- a/dev/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html +++ b/dev/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html @@ -20,4 +20,4 @@ q_{roots} = -\int_{z_{min}}^{z_{sfc}} \frac{dq_{roots}(z)}{dz}dz , \end{equation}\]

where $z_{min}$ is the minimum soil layer of the simulation.

The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with

\[\begin{equation} S(z) = -(RAI) dq_{roots}(z)/dz = (RAI) P(z) \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh. -\end{equation}\]

The sign change occurs in the expression for $S(z)$ because a positive value of $q_{roots}$ indicates flow from the soil to the plant. This is a sink term for the soil.

The model needs the following parameters:

DriversSymbolUnitRange
A function of simulation time t given the leaf area indexLAIm²/m²0–10
The constant stem area indexSAIm²/m²0–10
The constant root area indexRAIm²/m²0–10
+\end{equation}\]

The sign change occurs in the expression for $S(z)$ because a positive value of $q_{roots}$ indicates flow from the soil to the plant. This is a sink term for the soil.

The model needs the following parameters:

DriversSymbolUnitRange
A function of simulation time t given the leaf area indexLAIm²/m²0–10
The constant stem area indexSAIm²/m²0–10
The constant root area indexRAIm²/m²0–10
diff --git a/dev/standalone/pages/vegetation/radiative_transfer/beer_model/index.html b/dev/standalone/pages/vegetation/radiative_transfer/beer_model/index.html index 995771b478..6ab3ad62a1 100644 --- a/dev/standalone/pages/vegetation/radiative_transfer/beer_model/index.html +++ b/dev/standalone/pages/vegetation/radiative_transfer/beer_model/index.html @@ -1,4 +1,4 @@ Beer model · ClimaLand.jl
GitHub

Radiative transfer scheme

This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand.

Beer's law

Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.

The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:

\[APAR(PAR, \theta_s) = (PAR)(1 - \rho_{leaf})(1 - e^{(-K(\theta_s) LAI \Omega)})\]

where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, $ρ_{leaf}$ is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, $θ_s$ is the zenith angle, and $Ω$ is the clumping index following Braghiere (2021). $K$, $Ω$ and $ρ_{leaf}$ are all unitless. LAI is in m² m⁻². In order to compute $K$, we need $θ_s$ in radians and the leaf angle distribution $l_d$ (unitless). K is then defined as

\[K = l_d/\max{(\cos{(\theta_s)}, \epsilon)}\]

so that at night, when 3π/2 > $θ_s$ > π/2, $K$ is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.

The model has the following parameters:

OutputSymbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy reflectance$ρ_{leaf}$-0.0–1.0
Extinction coefficient$K$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5

Interactive APAR(PAR, LAI, $ρ_{leaf}$, $K$, $Ω$)

+ diff --git a/dev/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html b/dev/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html index c9f7279270..94ae507c36 100644 --- a/dev/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html +++ b/dev/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html @@ -2,4 +2,4 @@ Two-Stream model · ClimaLand.jl
GitHub

The Two-Stream Scheme

In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).

The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.

Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:

\[-\overline{\mu}(dI^{\uparrow})/dL + [1 - (1 - \beta)\omega]I^{\uparrow} - \omega \beta I^{\downarrow} = \omega \overline{\mu} K \beta_0 \exp{(-KL)},\\ -\overline{\mu}(dI^{\downarrow})/dL + [1 - (1 - \beta)\omega]I^{\downarrow} - \omega \beta I^{\uparrow} = \omega \overline{\mu} K (1-\beta_0) \exp{(-KL)}\]

where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to

\[\int_{0}^{1}[\mu^{\prime}/G(\mu^{\prime})]d\mu^{\prime}\]

μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)

These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:

\[I^{\uparrow} = \frac{h_1\exp{(-KL)}}{\sigma} + h_2\exp{(-hL)} + h_3\exp{(hL)},\\ I^{\downarrow} = \frac{h_4\exp{(-KL)}}{\sigma} + h_5\exp{(-hL)} + h_6\exp{(hL)}\]

For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:

\[I^{\uparrow} = h_7\exp{(-hL)} + h_8\exp{(hL)},\\ -I^{\downarrow} = h_9\exp{(-hL)} + h_{10}\exp{(hL)}\]

where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.

The model has the following parameters:

OutputSymmbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
Absorbed Near-Infrared RadiationANIRμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy PAR Reflectance$\alpha\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Reflectance$\alpha\_NIR\_{leaf}$-0.0–1.0
Canopy PAR Transmittance$\tau\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Transmittance$\tau\_NIR\_{leaf}$-0.0–1.0
Canopy Emissivity$ϵ\_canopy$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5
Typical wavelength per photon PAR$\lambda\_\gamma\_PAR$m5e-7
Typical wavelength per photon NIR$\lambda\_\gamma\_NIR$m1.65e-6
+I^{\downarrow} = h_9\exp{(-hL)} + h_{10}\exp{(hL)}\]

where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.

The model has the following parameters:

OutputSymmbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
Absorbed Near-Infrared RadiationANIRμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy PAR Reflectance$\alpha\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Reflectance$\alpha\_NIR\_{leaf}$-0.0–1.0
Canopy PAR Transmittance$\tau\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Transmittance$\tau\_NIR\_{leaf}$-0.0–1.0
Canopy Emissivity$ϵ\_canopy$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5
Typical wavelength per photon PAR$\lambda\_\gamma\_PAR$m5e-7
Typical wavelength per photon NIR$\lambda\_\gamma\_NIR$m1.65e-6
diff --git a/dev/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html b/dev/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html index e2fb26aac0..c546f32375 100644 --- a/dev/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html +++ b/dev/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html @@ -8,4 +8,4 @@ g_s &= \frac{g_{s,m}}{\rho_m} \end{align}\]

where $D_{rel} =1.6$ (unitless) is the relative diffusivity of water vapor with respect to CO$_2$, $\rho_m$ is the molar density of water, and $m$ is the Medlyn factor,

\[\begin{equation} m = \left( 1 + \frac{g_1}{\sqrt{VPD}} \right), -\end{equation}\]

where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A$_n$ as the biochemical demand for CO$_2$ calculated using the photosynthesis model (Farquhar, 1980; Equation \eqref{eq:an}; units of molar flux). The resulting units are $m/s$. g{0,m}$ is a minimum molar conductivity. (subscript $m$ indicates molar).

The model has the following parameters:

ConstantsSymbolUnitValue
Relative diffusivity of water vapor$D_{rel}$-1.6
Minimum stomatal conductance$g_0$mol/$m^2$/s1e-4
Slope parameter$g_1$$\sqrt{Pa}$790
+\end{equation}\]

where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A$_n$ as the biochemical demand for CO$_2$ calculated using the photosynthesis model (Farquhar, 1980; Equation \eqref{eq:an}; units of molar flux). The resulting units are $m/s$. g{0,m}$ is a minimum molar conductivity. (subscript $m$ indicates molar).

The model has the following parameters:

ConstantsSymbolUnitValue
Relative diffusivity of water vapor$D_{rel}$-1.6
Minimum stomatal conductance$g_0$mol/$m^2$/s1e-4
Slope parameter$g_1$$\sqrt{Pa}$790
diff --git a/previews/PR746/.documenter-siteinfo.json b/previews/PR746/.documenter-siteinfo.json new file mode 100644 index 0000000000..de21438f49 --- /dev/null +++ b/previews/PR746/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-03T16:01:37","documenter_version":"1.6.0"}} \ No newline at end of file diff --git a/previews/PR746/APIs/Bucket/index.html b/previews/PR746/APIs/Bucket/index.html new file mode 100644 index 0000000000..4c76754e73 --- /dev/null +++ b/previews/PR746/APIs/Bucket/index.html @@ -0,0 +1,16 @@ + +Bucket Model · ClimaLand.jl
GitHub

Bucket

Types

ClimaLand.Bucket.BucketModelParameters — Type
struct BucketModelParameters{
+    FT <: AbstractFloat,
+    PSE,
+}

Container for holding the parameters of the bucket model.

  • κ_soil: Conductivity of the soil (W/K/m); constant

  • ρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant

  • albedo: Albedo Model

  • σS_c: Critical σSWE amount (m) where surface transitions from to snow-covered

  • f_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)

  • W_f: Capacity of the land bucket (m)

  • f_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)

  • p: Exponent used in β decay (unitless)

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • τc: τc timescale on which snow melts

  • earth_param_set: Earth Parameter set; physical constants, etc

source
ClimaLand.Bucket.PrescribedBaregroundAlbedo — Type
PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel

An albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).

source
ClimaLand.Bucket.PrescribedSurfaceAlbedo — Type
PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}
+                   <: AbstractBucketAlbedoModel

An albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.

Note that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.

source
ClimaLand.Bucket.BucketModel — Type
struct BucketModel{
+     FT,
+     PS <: BucketModelParameters{FT},
+     ATM <: AbstractAtmosphericDrivers{FT},
+     RAD <: AbstractRadiativeDrivers{FT},
+     D,
+ } <: AbstractBucketModel{FT}

Concrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.

  • parameters: Parameters required by the bucket model

  • atmos: The atmospheric drivers: Prescribed or Coupled

  • radiation: The radiation drivers: Prescribed or Coupled

  • domain: The domain of the model

source

Misc Functions

ClimaLand.surface_albedo — Function
ClimaLand.surface_albedo(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+) where {FT}

Returns the surface albedo field of the EnergyHydrology soil model.

source
surface_albedo(model::SnowModel, Y, p)

A helper function which computes and returns the snow albedo.

source
surface_albedo(model::BucketModel, Y, p)

Returns the bulk surface albedo, which gets updated in update_aux via next_albedo.

source
surface_albedo(model::AbstractModel, Y, p)

A helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.Bucket.beta_factor — Function
beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}

Computes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:

β = (x/xc)^p x < xc 1 otherwise

where x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.

source
diff --git a/previews/PR746/APIs/ClimaLand/index.html b/previews/PR746/APIs/ClimaLand/index.html new file mode 100644 index 0000000000..12b9cdedc1 --- /dev/null +++ b/previews/PR746/APIs/ClimaLand/index.html @@ -0,0 +1,13 @@ + +ClimaLand · ClimaLand.jl
GitHub

ClimaLand

LSM Model Types and methods

Missing docstring.

Missing docstring for ClimaLand.SoilPlantHydrologyModel. Check Documenter's build log for details.

ClimaLand.LandSoilBiogeochemistry — Type
struct LandSoilBiogeochemistry{
+    FT,
+    SEH <: Soil.EnergyHydrology{FT},
+    SB <: Soil.Biogeochemistry.SoilCO2Model{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.

  • soil: The soil model

  • soilco2: The biochemistry model

source
ClimaLand.LandHydrology — Type
struct LandHydrology{
+    FT,
+    SM <: Soil.AbstractSoilModel{FT},
+    SW <: Pond.AbstractSurfaceWaterModel{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil and surface water component.

  • soil: The soil model

  • surface_water: The surface water model

source
Missing docstring.

Missing docstring for ClimaLand.make_interactions_update_aux. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.initialize_interactions. Check Documenter's build log for details.

ClimaLand.land_components — Function
land_components(land::AbstractLandModel)

Returns the component names of the land model, by calling propertynames(land).

source
ClimaLand.lsm_aux_vars — Function

lsmauxvars(m::AbstractLandModel)

Returns the additional aux variable symbols for the model in the form of a tuple.

source
lsm_aux_vars(m::SoilCanopyModel)

The names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_types — Function

lsmauxtypes(m::AbstractLandModel)

Returns the shared additional aux variable types for the model in the form of a tuple.

source
lsm_aux_types(m::SoilCanopyModel)

The types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_domain_names — Function

lsmauxdomain_names(m::AbstractLandModel)

Returns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.

This is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.

source
lsm_aux_domain_names(m::SoilCanopyModel)

The domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
Missing docstring.

Missing docstring for ClimaLand.domain_name. Check Documenter's build log for details.

Land Hydrology

ClimaLand.infiltration_capacity — Function
function infiltration_capacity(
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+)

Function which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.

Defined such that positive means into soil.

source
ClimaLand.infiltration_at_point — Function
infiltration_at_point(η::FT, i_c::FT, P::FT)

Returns the infiltration given pond height η, infiltration capacity, and precipitation.

This is defined such that positive means into soil.

source
ClimaLand.PrognosticRunoff — Type
PrognosticRunoff <: Pond.AbstractSurfaceRunoff

Concrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.

This is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source
ClimaLand.RunoffBC — Type
RunoffBC <: Soil.AbstractSoilBC

Concrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.

This is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source

SoilCanopyModel

ClimaLand.PrognosticSoil — Type
 PrognosticSoil{FT} <: AbstractSoilDriver

Concrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

source
ClimaLand.RootExtraction — Type
RootExtraction{FT} <: Soil.AbstractSoilSource{FT}

Concrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.

This is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.

source

LandSoilBiogeochemistry

Missing docstring.

Missing docstring for ClimaLand.PrognosticMet. Check Documenter's build log for details.

diff --git a/previews/PR746/APIs/Regridder/index.html b/previews/PR746/APIs/Regridder/index.html new file mode 100644 index 0000000000..31b124bb12 --- /dev/null +++ b/previews/PR746/APIs/Regridder/index.html @@ -0,0 +1,2 @@ + +Parameter Dataset Tools · ClimaLand.jl
GitHub

Bucket

Functions

Missing docstring.

Missing docstring for ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.Regridder.swap_space. Check Documenter's build log for details.

diff --git a/previews/PR746/APIs/Snow/index.html b/previews/PR746/APIs/Snow/index.html new file mode 100644 index 0000000000..d2f5a9bea8 --- /dev/null +++ b/previews/PR746/APIs/Snow/index.html @@ -0,0 +1,2 @@ + +Snow Model · ClimaLand.jl
GitHub

Snow Model

Snow Parameters

ClimaLand.Snow.SnowParameters — Type
SnowParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SnowModel.

Note that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.

  • ρ_snow: Density of snow (kg/m^3)

  • z_0m: Roughness length over snow for momentum (m)

  • z_0b: Roughness length over snow for scalars (m)

  • α_snow: Albedo of snow (unitless)

  • ϵ_snow: Emissivity of snow (unitless)

  • θ_r: Volumetric holding capacity of water in snow (unitless)

  • Ksat: Hydraulic conductivity of wet snow (m/s)

  • κ_ice: Thermal conductivity of ice (W/m/K)

  • Δt: Timestep of the model (s)

  • ρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)

  • earth_param_set: Clima-wide parameters

source

Snow Functions of State

```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe

diff --git a/previews/PR746/APIs/Soil/index.html b/previews/PR746/APIs/Soil/index.html new file mode 100644 index 0000000000..ae8fdb6bb3 --- /dev/null +++ b/previews/PR746/APIs/Soil/index.html @@ -0,0 +1,109 @@ + +Soil Energy and Hydrology · ClimaLand.jl
GitHub

Soil Models

Soil Models

ClimaLand.Soil.AbstractSoilModel — Type
AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}

The abstract type for all soil models.

Currently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.

source
ClimaLand.Soil.RichardsModel — Type
RichardsModel

A model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.

A variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).

If you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water

source
ClimaLand.Soil.EnergyHydrology — Type
EnergyHydrology <: AbstractSoilModel

A model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.

A variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.

  • parameters: The parameter sets

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat

source

Soil Parameter Structs

ClimaLand.Soil.RichardsParameters — Type
RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}

A struct for storing parameters of the RichardsModel.

  • ν: The porosity of the soil (m^3/m^3)

  • hydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

source
ClimaLand.Soil.EnergyHydrologyParameters — Type
EnergyHydrologyParameters{
+        FT <: AbstractFloat,
+        F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        C,
+        PSE,
+    }

A parameter structure for the integrated soil water and energy equation system.

Note that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:

  • Surface parameters: albedo in each wavelength band (SF)
  • Scalar parameters: emissivity, α, β, γ, γT_ref, Ω,

roughness lengths z0, dds ) (FT)

  • Parameters defined in the interior: all else (F)

  • κ_dry: The dry soil thermal conductivity, W/m/K

  • κ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K

  • κ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K

  • ρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)

  • ν: The porosity of the soil (m^3/m^3)

  • ν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)

  • ν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)

  • ν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)

  • α: The parameter α used in computing Kersten number, unitless

  • β: The parameter β used in computing Kersten number, unitless

  • hydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

  • Ω: Ice impedance factor for the hydraulic conductivity

  • γ: Coefficient of viscosity factor for the hydraulic conductivity

  • γT_ref: Reference temperature for the viscosity factor

  • PAR_albedo: Soil PAR Albedo

  • NIR_albedo: Soil NIR Albedo

  • emissivity: Soil Emissivity

  • z_0m: Roughness length for momentum

  • z_0b: Roughness length for scalars

  • d_ds: Maximum dry soil layer thickness under evaporation (m)

  • earth_param_set: Physical constants and clima-wide parameters

source

Soil Hydrology Parameterizations

ClimaLand.Soil.volumetric_liquid_fraction — Function
volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}

A pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.

source
ClimaLand.Soil.pressure_head — Function
pressure_head(
+    cm::vanGenuchten{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
pressure_head(
+    cm::BrooksCorey{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
ClimaLand.Soil.hydraulic_conductivity — Function
 hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.

source
 hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.

source
ClimaLand.Soil.impedance_factor — Function
impedance_factor(
+    f_i::FT,
+    Ω::FT
+) where {FT}

Returns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.viscosity_factor — Function
viscosity_factor(
+    T::FT,
+    γ::FT,
+    γT_ref::FT,
+) where {FT}

Returns the multiplicative factor which accounts for the temperature dependence of the conductivity.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.matric_potential — Function
 matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the van Genuchten formulation.

source
 matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the Brooks and Corey formulation.

source
ClimaLand.Soil.dψdϑ — Function

dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.

source

dψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.

source
ClimaLand.Soil.inverse_matric_potential — Function
 inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.

source
 inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.

source
ClimaLand.Soil.AbstractSoilHydrologyClosure — Type
AbstractSoilHydrologyClosure{FT <: AbstractFloat}

The abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.

To add a new parameterization, methods are required for:

  • matric_potential,
  • inversematricpotential,
  • pressure_head,
  • dψdϑ,
  • hydraulic_conductivity.
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ClimaLand.Soil.vanGenuchten — Type
vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}

The van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).

  • α: The inverse of the air entry potential (1/m)

  • n: The van Genuchten pore-size distribution index (unitless)

  • m: The van Genuchten parameter m = 1 - 1/n (unitless)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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ClimaLand.Soil.BrooksCorey — Type

BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}

The Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).

  • c: The pore-size distribution index (unitless)

  • ψb: The air entry matric potential, when S=1 (m)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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Soil Heat Parameterizations

ClimaLand.Soil.κ_solid — Function
κ_solid(ν_ss_om::FT,
+        ν_ss_quartz::FT,
+        κ_om::FT,
+        κ_quartz::FT,
+        κ_minerals::FT) where {FT}

Computes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.

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ClimaLand.Soil.κ_sat_frozen — Function
function κ_sat_frozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_ice::FT
+) where {FT}

Computes the thermal conductivity for saturated frozen soil.

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ClimaLand.Soil.κ_sat_unfrozen — Function
function κ_sat_unfrozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_l::FT
+) where {FT}

Computes the thermal conductivity for saturated unfrozen soil.

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ClimaLand.Soil.κ_sat — Function
κ_sat(
+    θ_l::FT,
+    θ_i::FT,
+    κ_sat_unfrozen::FT,
+    κ_sat_frozen::FT
+) where {FT}

Compute the expression for saturated thermal conductivity of soil matrix.

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ClimaLand.Soil.κ_dry — Function
function κ_dry(ρp::FT,
+               ν::FT,
+               κ_solid::FT,
+               κ_air::FT;
+               a = FT(0.053)) where {FT}

Computes the thermal conductivity of dry soil according to the model of Balland and Arp.

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ClimaLand.Soil.kersten_number — Function
kersten_number(
+    θ_i::FT,
+    S_r::FT,
+    α::FT,
+    β::FT,
+    ν_ss_om::FT,
+    ν_ss_quartz::FT,
+    ν_ss_gravel::FT,
+    ) where {FT}

Compute the expression for the Kersten number, using the Balland and Arp model.

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ClimaLand.Soil.relative_saturation — Function
relative_saturation(
+        θ_l::FT,
+        θ_i::FT,
+        ν::FT
+) where {FT}

Compute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.

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ClimaLand.Soil.volumetric_internal_energy — Function
volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,
+                             earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.

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ClimaLand.Soil.volumetric_internal_energy_liq — Function
volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.

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ClimaLand.Soil.temperature_from_ρe_int — Function
temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT
+                        earth_param_set::EP) where {FT, EP}

A pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.

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ClimaLand.Soil.phase_change_source — Function
phase_change_source(
+    θ_l::FT,
+    θ_i::FT,
+    T::FT,
+    τ::FT,
+    ν::FT,
+    θ_r::FT,
+    hydrology_cm::C,
+    earth_param_set::EP,
+) where {FT, EP, C}

Returns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.

Note that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.

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ClimaLand.Soil.thermal_time — Function
thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}

Returns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.

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Soil Surface Parameterizations

Missing docstring.

Missing docstring for ClimaLand.soil.soil_resistance. Check Documenter's build log for details.

ClimaLand.Soil.dry_soil_layer_thickness — Function
dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}

Returns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).

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ClimaLand.Soil.soil_tortuosity — Function
soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}

Computes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.

See Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.

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Soil Runoff Types and Methods

ClimaLand.Soil.Runoff.SurfaceRunoff — Type
SurfaceRunoff <: AbstractRunoffModel

A simple model for runoff appropriate for single column runs.

Only surface runoff is computed, using a combination of Dunne and Hortonian runoff.

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ClimaLand.Soil.Runoff.TOPMODELRunoff — Type
TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel

The TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.

The runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

  • f_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.

  • subsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.

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ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff — Type
TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}

The TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.

The runoff flux is given by Equation 12 of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • R_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

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ClimaLand.Soil.Runoff.update_runoff! — Function
update_runoff!(p, runoff::NoRunoff, _...)

Updates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.

source
update_runoff!(
+    p,
+    runoff::SurfaceRunoff,
+    Y,
+    t,
+    model::AbstractSoilModel,

)

The update_runoff! function for the SurfaceRunoff model.

Updates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration

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update_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)

Updates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration

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Soil BC Methods and Types

ClimaLand.Soil.MoistureStateBC — Type

MoistureStateBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.HeatFluxBC — Type

HeatFluxBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.WaterFluxBC — Type

WaterFluxBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.TemperatureStateBC — Type

TemperatureStateBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.FreeDrainage — Type
FreeDrainage <: AbstractWaterBC

A concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.

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ClimaLand.Soil.RichardsAtmosDrivenFluxBC — Type

RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC

A concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.

If you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.

  • precip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.

  • runoff: The runoff model. The default is no runoff.

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ClimaLand.Soil.AtmosDrivenFluxBC — Type
AtmosDrivenFluxBC{
+    A <: AbstractAtmosphericDrivers,
+    B <: AbstractRadiativeDrivers,
+    R <: AbstractRunoffModel
+} <: AbstractEnergyHydrologyBC

A concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.

This choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.

  • atmos: The atmospheric conditions driving the model

  • radiation: The radiative fluxes driving the model

  • runoff: The runoff model. The default is no runoff.

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ClimaLand.Soil.WaterHeatBC — Type
WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:
+   AbstractEnergyHydrologyBC

A general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.

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ClimaLand.Soil.soil_boundary_fluxes! — Function
soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)

updates the boundary fluxes for ϑl and ρeint.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere,
+        <:PrescribedRadiativeFluxes,
+    },
+    boundary::ClimaLand.TopBoundary,
+    model::EnergyHydrology,
+    Δz,
+    Y,
+    p,
+    t,
+)

Returns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.

If you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.

This function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},
+    boundary::ClimaLand.TopBoundary,
+    soil::EnergyHydrology{FT},
+    Δz,
+    Y,
+    p,
+    t,
+) where {FT}

A method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.

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Soil Source Types

ClimaLand.Soil.AbstractSoilSource — Type
AbstractSoilSource{FT} <:  ClimaLand.AbstractSource{FT}

An abstract type for types of source terms for the soil equations.

In standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).

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Missing docstring.

Missing docstring for ClimaLand.Soil.RootExtraction. Check Documenter's build log for details.

Soil Jacobian Structures

ClimaLand.ImplicitEquationJacobian — Type
ImplicitEquationJacobian{M, S}

A struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.

matrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.

Note that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.

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diff --git a/previews/PR746/APIs/SoilBiogeochemistry/index.html b/previews/PR746/APIs/SoilBiogeochemistry/index.html new file mode 100644 index 0000000000..e55043a8f6 --- /dev/null +++ b/previews/PR746/APIs/SoilBiogeochemistry/index.html @@ -0,0 +1,17 @@ + +Soil Biogeochemistry · ClimaLand.jl
GitHub

Soil Biogeochemistry

Model Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2Model — Type
SoilCO2Model

A model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type NamedTuple

  • sources: A tuple of sources, each of type AbstractSource

  • drivers: Drivers

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Parameter Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters — Type
SoilCO2ModelParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SoilCO2Model.

All of these parameters are currently treated as global constants.

  • D_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)

  • D_liq: Diffusivity of soil C substrate in liquid (unitless)

  • α_sx: Pre-exponential factor (kg C m-3 s-1)

  • Ea_sx: Activation energy (J mol-1)

  • kM_sx: Michaelis constant (kg C m-3)

  • kM_o2: Michaelis constant for O2 (m3 m-3)

  • O2_a: Volumetric fraction of O₂ in the soil air, dimensionless

  • D_oa: Diffusion coefficient of oxygen in air, dimensionless

  • p_sx: Fraction of soil carbon that is considered soluble, dimensionless

  • earth_param_set: Physical constants used Clima-wide

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Model-specific Types

ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver — Type
AbstractSoilDriver

An abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).

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ClimaLand.Soil.Biogeochemistry.SoilDrivers — Type
SoilDrivers

A container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).

  • met: Soil temperature and moisture drivers - Prescribed or Prognostic

  • soc: Soil SOM driver - Prescribed only

  • atmos: Prescribed atmospheric variables

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ClimaLand.Soil.Biogeochemistry.PrescribedMet — Type
PrescribedMet <: AbstractSoilDriver

A container which holds the prescribed functions for soil temperature and moisture.

This is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.

  • temperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat

  • volumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat

  • ν: Soil porosity (m³ m⁻³)

  • θ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)

  • b: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)

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Functions of State

ClimaLand.Soil.Biogeochemistry.co2_diffusivity — Function
co2_diffusivity(
+                T_soil::FT,
+                θ_w::FT,
+                P_sfc::FT,
+                θ_a100::FT,
+                b::FT,
+                ν::FT,
+                params::SoilCO2ModelParameters{FT},
+                ) where {FT}

Computes the diffusivity of CO₂ within the soil (D).

First, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.

This parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.

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ClimaLand.Soil.Biogeochemistry.microbe_source — Function
microbe_source(T_soil::FT,
+               θ_l::FT,
+               Csom::FT,
+               ν::FT,
+               params::SoilCO2ModelParameters{FT}
+               ) where {FT}

Computes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).

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Extendible Functions

ClimaLand.Soil.Biogeochemistry.soil_moisture — Function
soil_moisture(driver::PrognosticSoil, p, Y, t, z)

Returns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.

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soil_moisture(driver::PrescribedMet, p, Y, t, z)

Returns the soil moisture at location (z) and time (t) for the prescribed soil case.

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ClimaLand.Soil.Biogeochemistry.soil_temperature — Function
soil_temperature(driver::PrognosticSoil, p, Y, t, z)

Returns the prognostic soil temperature.

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soil_temperature(driver::PrescribedMet, p, Y, t, z)

Returns the soil temperature at location (z) and time (t) for the prescribed soil case.

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Missing docstring.

Missing docstring for ClimaLand.Soil.Biogeochemistry.soil_SOM_C. Check Documenter's build log for details.

diff --git a/previews/PR746/APIs/SurfaceWater/index.html b/previews/PR746/APIs/SurfaceWater/index.html new file mode 100644 index 0000000000..b9275df784 --- /dev/null +++ b/previews/PR746/APIs/SurfaceWater/index.html @@ -0,0 +1,7 @@ + +Surface Water Models · ClimaLand.jl
GitHub

SurfaceWater

Models

ClimaLand.Pond.PondModel — Type
PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}

A stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.

  • domain: The domain for the pond model

  • runoff: The runoff model for the pond model

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Methods and Types

ClimaLand.Pond.PrescribedRunoff — Type
PrescribedRunoff{F1 <: Function, F2 <: Function} <:  AbstractSurfaceRunoff

The required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.

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ClimaLand.Pond.surface_runoff — Function
function Pond.surface_runoff(
+    runoff::PrognosticRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

Extension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.

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diff --git a/previews/PR746/APIs/canopy/AutotrophicRespiration/index.html b/previews/PR746/APIs/canopy/AutotrophicRespiration/index.html new file mode 100644 index 0000000000..40ad3429ca --- /dev/null +++ b/previews/PR746/APIs/canopy/AutotrophicRespiration/index.html @@ -0,0 +1,23 @@ + +Canopy Autotrophic Respiration · ClimaLand.jl
GitHub

Autotrophic Respiration

Parameters

ClimaLand.Canopy.AutotrophicRespirationParameters — Type
AutotrophicRespirationParameters{FT<:AbstractFloat}

The required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. "The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics." Geoscientific Model Development 4.3 (2011): 701-722.

  • ne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)

  • ηsl: Live stem wood coefficient (kg C m-3)

  • σl: Specific leaf density (kg C m-2 [leaf])

  • μr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0

  • μs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1

  • Rel: Relative contribution or Rgrowth (-)

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Methods

ClimaLand.Canopy.nitrogen_content — Function
nitrogen_content(
+                 ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)
+                 Vcmax25::FT, #
+                 LAI::FT, # Leaf area index
+                 SAI::FT,
+                 RAI::FT,
+                 ηsl::FT, # live stem  wood coefficient (kg C m-3) 
+                 h::FT, # canopy height (m)
+                 σl::FT # Specific leaf density (kg C m-2 [leaf])
+                 μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0
+                 μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 
+                ) where {FT}

Computes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).

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ClimaLand.Canopy.plant_respiration_maintenance — Function
plant_respiration_maintenance(
+    Rd::FT, # Dark respiration
+    β::FT, # Soil moisture factor
+    Nl::FT, # Nitrogen content of leafs
+    Nr::FT, # Nitrogen content of roots
+    Ns::FT, # Nitrogen content of stems
+    ) where {FT}

Computes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).

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ClimaLand.Canopy.plant_respiration_growth — Function
plant_respiration_growth(
+    Rel::FT, # Factor of relative contribution
+    An::FT, # Net photosynthesis
+    Rpm::FT # Plant maintenance respiration
+    ) where {FT}

Computes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.

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diff --git a/previews/PR746/APIs/canopy/Canopy/index.html b/previews/PR746/APIs/canopy/Canopy/index.html new file mode 100644 index 0000000000..fa5c491c25 --- /dev/null +++ b/previews/PR746/APIs/canopy/Canopy/index.html @@ -0,0 +1,2 @@ + +Canopy Models · ClimaLand.jl
GitHub

Canopy

Canopy Model Structs

ClimaLand.Canopy.CanopyModel — Type
 CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}

The model struct for the canopy, which contains

  • the canopy model domain (a point for site-level simulations, or

an extended surface (plane/spherical surface) for regional or global simulations.

  • subcomponent model type for radiative transfer. This is of type

AbstractRadiationModel.

  • subcomponent model type for photosynthesis. This is of type

AbstractPhotosynthesisModel, and currently only the FarquharModel is supported.

  • subcomponent model type for stomatal conductance. This is of type

AbstractStomatalConductanceModel and currently only the MedlynModel is supported

  • subcomponent model type for plant hydraulics. This is of type

AbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.

  • subcomponent model type for canopy energy. This is of type

AbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.

  • subcomponent model type for canopy SIF. prognostically.
  • canopy model parameters, which include parameters that are shared

between canopy model components or those needed to compute boundary fluxes.

  • The atmospheric conditions, which are either prescribed

(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).

  • The radiative flux conditions, which are either prescribed

(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).

  • The soil conditions, which are either prescribed (of type PrecribedSoil, for

running the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)

Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.

  • autotrophic_respiration: Autotrophic respiration model, a canopy component model

  • radiative_transfer: Radiative transfer model, a canopy component model

  • photosynthesis: Photosynthesis model, a canopy component model

  • conductance: Stomatal conductance model, a canopy component model

  • hydraulics: Plant hydraulics model, a canopy component model

  • energy: Energy balance model, a canopy component model

  • sif: SIF model, a canopy component model

  • atmos: Atmospheric forcing: prescribed or coupled

  • radiation: Radiative forcing: prescribed or coupled

  • soil_driver: Soil pressure: prescribed or prognostic

  • parameters: Shared canopy parameters between component models

  • domain: Canopy model domain

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ClimaLand.Canopy.SharedCanopyParameters — Type
SharedCanopyParameters{FT <: AbstractFloat, PSE}

A place to store shared parameters that are required by multiple canopy components.

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • earth_param_set: Earth param set

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Canopy Model Fluxes

ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration — Type
DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}

A concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.

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Canopy Model Soil Drivers

ClimaLand.Canopy.PrescribedSoil — Type
 PrescribedSoil <: AbstractSoilDriver

A container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.

  • root_depths: The depth of the root tips, in meters

  • ψ: Prescribed soil potential (m) in the root zone a function of time

  • T: Prescribed soil surface temperature (K) as a function of time

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

  • ϵ: Soil emissivity

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diff --git a/previews/PR746/APIs/canopy/CanopyEnergy/index.html b/previews/PR746/APIs/canopy/CanopyEnergy/index.html new file mode 100644 index 0000000000..28c7c49ac4 --- /dev/null +++ b/previews/PR746/APIs/canopy/CanopyEnergy/index.html @@ -0,0 +1,16 @@ + +Canopy Energy · ClimaLand.jl
GitHub

Canopy Energy Model

Methods

ClimaLand.Canopy.canopy_temperature — Function
canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)

Returns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.

source
canopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)

Returns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.

source
ClimaLand.Canopy.root_energy_flux_per_ground_area! — Function
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrognosticSoil{F},
+    model::Canopy.AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT, F}

A method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).

Note that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.

Background information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source

Types

Missing docstring.

Missing docstring for ClimaLand.Canopy.AbstractCanopyEnergyModel. Check Documenter's build log for details.

ClimaLand.Canopy.PrescribedCanopyTempModel — Type
PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}

A model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.

No equation for the energy of the canopy is solved.

source
diff --git a/previews/PR746/APIs/canopy/Photosynthesis/index.html b/previews/PR746/APIs/canopy/Photosynthesis/index.html new file mode 100644 index 0000000000..27fc7de5e4 --- /dev/null +++ b/previews/PR746/APIs/canopy/Photosynthesis/index.html @@ -0,0 +1,44 @@ + +Canopy Photosynthesis · ClimaLand.jl
GitHub

Photosynthesis

Parameters

ClimaLand.Canopy.SIFParameters — Type
SIFParameters{FT<:AbstractFloat}

The required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.

  • kf: The rate coefficient for florescence, unitless

  • kd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • min_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kp: Rate coefficient for photochemical quenching

  • kappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

  • kappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

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ClimaLand.Canopy.FarquharParameters — Type
FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}

The required parameters for the Farquhar photosynthesis model.

  • Vcmax25: Vcmax at 25 °C (mol CO2/m^2/s)

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercelluar O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]

  • pc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]

  • mechanism: Photosynthesis mechanism: C3 or C4

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ClimaLand.Canopy.OptimalityFarquharParameters — Type
OptimalityFarquharParameters{FT<:AbstractFloat}

The required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.

  • mechanism: Photosynthesis mechanism: C3 only

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercellular O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Fitting constant to compute the moisture stress factor (Pa^{-1})

  • pc: Fitting constant to compute the moisture stress factor (Pa)

  • c: Constant describing cost of maintaining electron transport (unitless)

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Methods

ClimaLand.Canopy.arrhenius_function — Function
arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)

Computes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.intercellular_co2 — Function
intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}

Computes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).

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ClimaLand.Canopy.co2_compensation — Function
co2_compensation(Γstar25::FT,
+                 ΔHΓstar::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.rubisco_assimilation — Function
rubisco_assimilation(::C3,
+                     Vcmax::FT,
+                     ci::FT,
+                     Γstar::FT,
+                     Kc::FT,
+                     Ko::FT,
+                     oi::FT) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).

The empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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rubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).

source
ClimaLand.Canopy.light_assimilation — Function
light_assimilation(::C3,
+                   J::FT,
+                   ci::FT,
+                   Γstar::FT) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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light_assimilation(::C4, J::FT, _...) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).

source
ClimaLand.Canopy.C3 — Type
C3 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

source
ClimaLand.Canopy.C4 — Type
C4 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

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ClimaLand.Canopy.max_electron_transport — Function
max_electron_transport(Vcmax::FT) where {FT}

Computes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.electron_transport — Function
electron_transport(APAR::FT,
+                   Jmax::FT,
+                   θj::FT,
+                   ϕ::FT) where {FT}

Computes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ).

See Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.net_photosynthesis — Function
net_photosynthesis(Ac::FT,
+                   Aj::FT,
+                   Rd::FT,
+                   β::FT) where {FT}

Computes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.optimality_max_photosynthetic_rates — Function

optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)

Computes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.

See Smith et al. 2019.

source
ClimaLand.Canopy.moisture_stress — Function
moisture_stress(pl::FT,
+                sc::FT,
+                pc::FT) where {FT}

Computes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) .

See Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.dark_respiration — Function
dark_respiration(Vcmax25::FT,
+                 β::FT,
+                 f::FT,
+                 ΔHkc::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.compute_GPP — Function
compute_GPP(An::FT,
+         K::FT,
+         LAI::FT,
+         Ω::FT) where {FT}

Computes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).

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ClimaLand.Canopy.MM_Kc — Function
MM_Kc(Kc25::FT,
+      ΔHkc::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.MM_Ko — Function
MM_Ko(Ko25::FT,
+      ΔHko::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.compute_Vcmax — Function
compute_Vcmax(Vcmax25::FT,
+       T::FT,
+       To::FT,
+       R::FT,
+       ep5::FT) where {FT}

Computes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
diff --git a/previews/PR746/APIs/canopy/PlantHydraulics/index.html b/previews/PR746/APIs/canopy/PlantHydraulics/index.html new file mode 100644 index 0000000000..522110a749 --- /dev/null +++ b/previews/PR746/APIs/canopy/PlantHydraulics/index.html @@ -0,0 +1,35 @@ + +Plant Hydraulics · ClimaLand.jl
GitHub

PlantHydraulics

Models

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel — Type
PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}

Defines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.

This model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can "turn off" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:

  • n_leaf = 1
  • n_stem = 0
  • LAI = SAI = RAI = 0.

A plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.

Finally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.

  • n_stem: The number of stem compartments for the plant; can be zero

  • n_leaf: The number of leaf compartments for the plant; must be >=1

  • compartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters

  • compartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.

  • compartment_labels: The label (:stem or :leaf) of each compartment

  • parameters: Parameters required by the Plant Hydraulics model

  • transpiration: The transpiration model, of type AbstractTranspiration

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Plant Hydraulics Diagnostic Variables

ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction — Function
augmented_liquid_fraction(
+    ν::FT,
+    S_l::FT) where {FT}

Computes the augmented liquid fraction from porosity and effective saturation.

Augmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.

source
ClimaLand.Canopy.PlantHydraulics.water_retention_curve — Function
water_retention_curve(
+    S_l::FT,
+    b::FT,
+    ν::FT,
+    S_s::FT) where {FT}

Returns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.

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ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area! — Function
PlantHydraulics.root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrognosticSoil,
+    model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

An extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.

It is computed by summing the flux of water per ground area between roots and soil at each soil layer.

source
root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.

The returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.

source
ClimaLand.Canopy.PlantHydraulics.flux — Function
flux(
+    z1,
+    z2,
+    ψ1,
+    ψ2,
+    K1,
+    K2,
+) where {FT}

Computes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.

We currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.

source

Plant Hydraulics Parameters

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters — Type
PlantHydraulicsParameters

A struct for holding parameters of the PlantHydraulics Model.

  • ai_parameterization: The area index model for LAI, SAI, RAI

  • ν: porosity (m3/m3)

  • S_s: storativity (m3/m3)

  • conductivity_model: Conductivity model and parameters

  • retention_model: Water retention model and parameters

  • root_distribution: Root distribution function P(z)

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ClimaLand.Canopy.PlantHydraulics.Weibull — Type
Weibull{FT} <: AbstractConductivityModel{FT}

A concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.

Fields

  • K_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation

  • ψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).

  • c: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).

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ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve — Type
LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}

A concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.

When ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.

Fields

  • a: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.
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Plant Hydraulics Methods and Types

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.PrescribedSoilPressure. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.AbstractRootExtraction. Check Documenter's build log for details.

diff --git a/previews/PR746/APIs/canopy/RadiativeTransfer/index.html b/previews/PR746/APIs/canopy/RadiativeTransfer/index.html new file mode 100644 index 0000000000..248e5e947f --- /dev/null +++ b/previews/PR746/APIs/canopy/RadiativeTransfer/index.html @@ -0,0 +1,33 @@ + +Canopy RT · ClimaLand.jl
GitHub

Radiative Transfer

Parameters

ClimaLand.Canopy.BeerLambertParameters — Type
BeerLambertParameters{FT <: AbstractFloat}

The required parameters for the Beer-Lambert radiative transfer model.

  • α_PAR_leaf: PAR leaf reflectance (unitless)

  • α_NIR_leaf: NIR leaf reflectance

  • ϵ_canopy: Emissivity of the canopy

  • Ω: Clumping index following Braghiere (2021) (unitless)

  • λ_γ_PAR: Typical wavelength per PAR photon (m)

  • λ_γ_NIR: Typical wavelength per NIR photon (m)

  • G_Function: Leaf angle distribution function

source

Methods

Missing docstring.

Missing docstring for ClimaLand.Canopy.compute_absorbances. Check Documenter's build log for details.

ClimaLand.Canopy.plant_absorbed_pfd — Function
plant_absorbed_pfd(
+    RT::BeerLambertModel{FT},
+    SW_IN:FT,
+    α_leaf::FT,
+    LAI::FT,
+    K::FT,
+    α_soil::FT
+)

Computes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil).

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

source
plant_absorbed_pfd(
+    RT::TwoStreamModel{FT},
+    α_leaf,
+    SW_IN::FT,
+    LAI::FT,
+    K::FT,
+    τ_leaf,
+    θs::FT,
+    α_soil::FT,
+)

Computes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

source
ClimaLand.Canopy.extinction_coeff — Function
extinction_coeff(ld::FT,
+                 θs::FT) where {FT}

Computes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).

source
Missing docstring.

Missing docstring for ClimaLand.Canopy.extinction_coeff. Check Documenter's build log for details.

ClimaLand.Canopy.canopy_radiant_energy_fluxes! — Function
Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,
+                                     s::PrognosticSoil{F},
+                                     canopy,
+                                     radiation::PrescribedRadiativeFluxes,
+                                     earth_param_set::PSE,
+                                     Y::ClimaCore.Fields.FieldVector,
+                                     t,
+                                    ) where {FT, PSE}

In standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.

In integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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canopy_radiant_energy_fluxes!(p::NamedTuple,
+                              s::PrescribedSoil,
+                              canopy,
+                              radiation::PrescribedRadiativeFluxes,
+                              earth_param_set::PSE,
+                              Y::ClimaCore.Fields.FieldVector,
+                              t,
+                             ) where {PSE}

Computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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diff --git a/previews/PR746/APIs/canopy/StomatalConductance/index.html b/previews/PR746/APIs/canopy/StomatalConductance/index.html new file mode 100644 index 0000000000..2c4d1e3891 --- /dev/null +++ b/previews/PR746/APIs/canopy/StomatalConductance/index.html @@ -0,0 +1,17 @@ + +Canopy Stomatal Conductance · ClimaLand.jl
GitHub

Stomatal Conductance

Parameters

ClimaLand.Canopy.MedlynConductanceParameters — Type
MedlynConductanceParameters{FT <: AbstractFloat}

The required parameters for the Medlyn stomatal conductance model.

  • Drel: Relative diffusivity of water vapor (unitless)

  • g0: Minimum stomatal conductance mol/m^2/s

  • g1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})

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Methods

ClimaLand.Canopy.medlyn_term — Function
medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}

Computes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).

thermo_params is the Thermodynamics.jl parameter set.

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ClimaLand.Canopy.medlyn_conductance — Function
medlyn_conductance(g0::FT,
+                   Drel::FT,
+                   medlyn_term::FT,
+                   An::FT,
+                   ca::FT) where {FT}

Computes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).

This returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.

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ClimaLand.Canopy.upscale_leaf_conductance — Function
upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}

This currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.

TODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.

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ClimaLand.Canopy.penman_monteith — Function
penman_monteith(
+    Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1)  
+    Rn::FT, # Net irradiance (W m−2)
+    G::FT, # Ground heat flux (W m−2)
+    ρa::FT, # Dry air density (kg m−3)
+    cp::FT, # Specific heat capacity of air (J kg−1 K−1) 
+    VPD::FT, # vapor pressure deficit (Pa)
+    ga::FT, # atmospheric conductance (m s−1)
+    γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)
+    gs::FT, # surface or stomatal conductance (m s−1)
+    Lv::FT, # Volumetric latent heat of vaporization (J m-3)
+    ) where {FT}

Computes the evapotranspiration in m/s using the Penman-Monteith equation.

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diff --git a/previews/PR746/APIs/shared_utilities/index.html b/previews/PR746/APIs/shared_utilities/index.html new file mode 100644 index 0000000000..8137d6f305 --- /dev/null +++ b/previews/PR746/APIs/shared_utilities/index.html @@ -0,0 +1,249 @@ + +Shared Utilities · ClimaLand.jl
GitHub

Shared Utilities

Domains

ClimaLand.Domains.AbstractDomain — Type
AbstractDomain{FT <:AbstractFloat}

An abstract type for domains.

The domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.

Additionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.

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Missing docstring.

Missing docstring for ClimaLand.Domains.AbstractLSMDomain. Check Documenter's build log for details.

ClimaLand.Domains.SphericalShell — Type
struct SphericalShell{FT} <: AbstractDomain{FT}
+    radius::FT
+    depth::FT
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • radius: The radius of the shell

  • depth: The radial extent of the shell

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: The number of elements to be used in the non-radial and radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.SphericalSurface — Type
struct SphericalSurface{FT} <: AbstractDomain{FT}
+    radius::FT
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).

Fields

  • radius: The radius of the surface

  • nelements: The number of elements to be used in the non-radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space

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ClimaLand.Domains.HybridBox — Type
struct HybridBox{FT} <: AbstractDomain{FT}
+    xlim::Tuple{FT, FT}
+    ylim::Tuple{FT, FT}
+    zlim::Tuple{FT, FT}
+    longlat::Union{Nothing, Tuple{FT, FT}},
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int, Int}
+    npolynomial::Int
+    periodic::Tuple{Bool, Bool}
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.

When longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • zlim: Domain interval limits along z axis, in meters

  • longlat: When not nothing, a Tuple that contains the center long and lat.

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: Number of elements to discretize interval, (nx, ny,nz)

  • npolynomial: Polynomial order for the horizontal directions

  • periodic: Flag indicating periodic boundaries in horizontal

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Column — Type
Column{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • zlim: Domain interval limits, (zmin, zmax), in meters

  • nelements: Number of elements used to discretize the interval

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • boundary_names: Boundary face identifiers

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Plane — Type
Plane{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.

When longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.

longlat are in degrees, with longitude going from -180 to 180.

:warning: Only independent columns are supported! (No lateral flow).

space is a NamedTuple holding the surface space (in this case, the entire Plane space).

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • longlat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).

  • nelements: Number of elements to discretize interval, (nx, ny)

  • periodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.

  • npolynomial: Polynomial order for both x and y

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space

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ClimaLand.Domains.Point — Type
Point{FT} <: AbstractDomain{FT}

A domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.

space is a NamedTuple holding the surface space (in this case, the Point space).

Fields

  • z_sfc: Surface elevation relative to a reference (m)

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space

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ClimaLand.Domains.coordinates — Function
coordinates(domain::AbstractDomain)

Returns the coordinate fields for the domain as a NamedTuple.

The returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.

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Domains.coordinates(model::AbstractLandModel)

Returns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.

For example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.

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ClimaLand.Domains.obtain_face_space — Function
obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the face space, if applicable, for the center space cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the face space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_space — Function
obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the surface space, if applicable, for the center space cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_domain — Function
obtain_surface_domain(d::AbstractDomain) where {FT}

Default method throwing an error; any domain with a corresponding domain should define a new method of this function.

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obtain_surface_domain(c::Column{FT}) where {FT}

Returns the Point domain corresponding to the top face (surface) of the Column domain c.

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obtain_surface_domain(b::HybridBox{FT}) where {FT}

Returns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.

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obtain_surface_domain(s::SphericalShell{FT}) where {FT}

Returns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.

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ClimaLand.Domains.top_center_to_surface — Function
top_center_to_surface(center_field::ClimaCore.Fields.Field)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.

For example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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top_center_to_surface(val)

When val is a scalar (e.g. a single float or struct), returns val.

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ClimaLand.Domains.top_face_to_surface — Function
top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.

Given a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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ClimaLand.Domains.linear_interpolation_to_surface! — Function
linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)

Linearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.

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ClimaLand.Domains.get_Δz — Function
get_Δz(z::ClimaCore.Fields.Field)

A function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.

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Models

ClimaLand.AbstractImExModel — Type
AbstractImExModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.

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ClimaLand.AbstractExpModel — Type
AbstractExpModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.

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ClimaLand.make_exp_tendency — Function
make_exp_tendency(model::AbstractModel)

Returns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_imp_tendency — Function
make_imp_tendency(model::AbstractImExModel)

Returns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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make_imp_tendency(model::AbstractModel)

Returns an imp_tendency that does nothing. This model type is not stepped explicity.

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ClimaLand.make_compute_exp_tendency — Function
make_explicit_tendency(model::Soil.RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

Construct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.

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make_compute_exp_tendency(model::EnergyHydrology)

An extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.

This function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::BucketModel{FT}) where {FT}

Creates the computeexptendency! function for the bucket model.

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make_compute_exp_tendency(model::AbstractModel)

Return a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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 ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)

Creates the computeexptendency!(dY,Y,p,t) function for the canopy component.

Since component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.

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make_compute_exp_tendency(canopy::CanopyModel)

Creates and returns the computeexptendency! for the CanopyModel.

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make_compute_exp_tendency(model::SoilCO2Model)

An extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::PlantHydraulicsModel, _)

A function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.

Below, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).

Note that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.

To prevent dividing by zero, we change AI/(AI x dz)" to "AI/max(AI x dz, eps(FT))"

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ClimaLand.make_compute_imp_tendency — Function
make_compute_imp_tendency(model::RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

This function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.

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make_compute_imp_tendency(model::EnergyHydrology)

An extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.

This version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_imp_tendency(model::AbstractModel)

Return a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_update_aux — Function
make_update_aux(model::RichardsModel)

An extension of the function make_update_aux, for the Richardson- Richards equation.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::EnergyHydrology)

An extension of the function make_update_aux, for the integrated soil hydrology and energy model.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::BucketModel{FT}) where {FT}

Creates the update_aux! function for the BucketModel.

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make_update_aux(model::AbstractModel)

Return an update_aux! function that updates auxiliary parameters p.

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 ClimaLand.make_update_aux(canopy::CanopyModel{FT,
+                                              <:AutotrophicRespirationModel,
+                                              <:Union{BeerLambertModel, TwoStreamModel},
+                                              <:FarquharModel,
+                                              <:MedlynConductanceModel,
+                                              <:PlantHydraulicsModel,},
+                          ) where {FT}

Creates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.

Please note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.

The other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.

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make_update_aux(model::SoilCO2Model)

An extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.

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ClimaLand.make_update_boundary_fluxes — Function
make_update_boundary_fluxes(model::AbstractModel)

Return an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..

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make_update_boundary_fluxes(
+    land::LandHydrology{FT, SM, SW},
+) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}

A method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.

This function is called each ode function evaluation.

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make_update_boundary_fluxes(
+    land::SoilCanopyModel{FT, MM, SM, RM},
+) where {
+    FT,
+    MM <: Soil.Biogeochemistry.SoilCO2Model{FT},
+    SM <: Soil.RichardsModel{FT},
+    RM <: Canopy.CanopyModel{FT}
+    }

A method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models.

This function is called each ode function evaluation, prior to the tendency function evaluation.

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ClimaLand.make_set_initial_cache — Function
make_set_initial_cache(model::AbstractModel)

Returns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.

In principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.

Furthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.

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ClimaLand.make_update_drivers — Function
make_update_drivers(::AbstractClimaLandDrivers)

Creates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.

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make_update_drivers(driver_tuple)

Creates and returns a function which updates the forcing variables ("drivers"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.

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make_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.

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make_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.

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make_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.

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make_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.

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ClimaLand.prognostic_vars — Function
prognostic_vars(soil::RichardsModel)

A function which returns the names of the prognostic variables of RichardsModel.

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prognostic_vars(soil::EnergyHydrology)

A function which returns the names of the prognostic variables of EnergyHydrology.

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prognostic_vars(::SnowModel)

Returns the prognostic variable names of the snow model.

For this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.

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prognostic_vars(m::AbstractModel)

Returns the prognostic variable symbols for the model in the form of a tuple.

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_vars(::AbstractCanopyComponent)

Returns the prognostic vars of the canopy component passed in as an argument.

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prognostic_vars(canopy::CanopyModel)

Returns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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prognostic_vars(model::PlantHydraulicsModel)

A function which returns the names of the prognostic variables of the PlantHydraulicsModel.

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ClimaLand.prognostic_types — Function
prognostic_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the prognostic variables of EnergyHydrology.

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prognostic_types(::SnowModel{FT})

Returns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.

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prognostic_types(m::AbstractModel{FT}) where {FT}

Returns the prognostic variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

Here, the coordinate points are those returned by coordinates(model).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_types(::AbstractCanopyComponent)

Returns the prognostic types of the canopy component passed in as an argument.

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prognostic_types(canopy::CanopyModel)

Returns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the prognostic types for the PlantHydraulicsModel.

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ClimaLand.prognostic_domain_names — Function
prognostic_domain_names(::SnowModel)

Returns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is ":surface".

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prognosticdomainnames(m::AbstractModel)

Returns the domain names for the prognostic variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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prognosticdomainnames(m::AbstractCanopyComponent)

Returns the domain names for the prognostic variables in the form of a tuple.

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ClimaLand.auxiliary_vars — Function
auxiliary_vars(soil::RichardsModel)

A function which returns the names of the auxiliary variables of RichardsModel.

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auxiliary_vars(soil::EnergyHydrology)

A function which returns the names of the auxiliary variables of EnergyHydrology.

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auxiliary_vars(::SnowModel)

Returns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.

Since the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.

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auxiliary_vars(m::AbstractModel)

Returns the auxiliary variable symbols for the model in the form of a tuple.

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ClimaLand.auxiliary_vars(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_vars(canopy::CanopyModel)

Returns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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auxiliary_vars(model::PlantHydraulicsModel)

A function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.

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ClimaLand.auxiliary_types — Function
auxiliary_types(soil::RichardsModel)

A function which returns the names of the auxiliary types of RichardsModel.

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auxiliary_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the auxiliary variables of EnergyHydrology.

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auxiliary_types(m::AbstractModel{FT}) where {FT}

Returns the auxiliary variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

  • Note that Arrays, MVectors are not isbits and cannot be used.

Here, the coordinate points are those returned by coordinates(model).

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ClimaLand.auxiliary_types(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_types(canopy::CanopyModel)

Returns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the auxiliary types for the PlantHydraulicsModel.

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ClimaLand.auxiliary_domain_names — Function
auxiliary_domain_names(soil::RichardsModel)

A function which returns the names of the auxiliary domain names of RichardsModel.

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auxiliarydomainnames(m::AbstractModel)

Returns the domain names for the auxiliary variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

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auxiliarydomainnames(m::AbstractCanopyComponent)

Returns the domain names for the auxiliary variables in the form of a tuple.

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ClimaLand.initialize_prognostic — Function
initialize_prognostic(model::AbstractModel, state::NamedTuple)

Returns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.

If a model has no prognostic variables, the returned FieldVector contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.

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initialize_prognostic(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_prognostic(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input state is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.

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ClimaLand.initialize_auxiliary — Function
initialize_auxiliary(model::AbstractModel, state::NamedTuple)

Returns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.

If a model has no auxiliary variables, the returned NamedTuple contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.

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initialize_auxiliary(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_auxiliary(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input coords is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.

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ClimaLand.initialize — Function
initialize(model::AbstractModel)

Creates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.

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ClimaLand.name — Function
name(model::AbstractModel)

Returns a symbol of the model component name, e.g. :soil or :vegetation.

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ClimaLand.AbstractBC — Type
AbstractBC

An abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.

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ClimaLand.source! — Function
 source!(dY::ClimaCore.Fields.FieldVector,
+         src::PhaseChange{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Computes the source terms for phase change.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::SoilSublimation{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Updates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::AbstractSource,
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple
+         )::ClimaCore.Fields.Field

A stub function, which is extended by ClimaLand.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                 src::RootExtraction,
+                 Y::ClimaCore.Fields.FieldVector,
+                 p::NamedTuple
+                 model::EnergyHydrology)

An extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                      src::MicrobeProduction,
+                      Y::ClimaCore.Fields.FieldVector,
+                      p::NamedTuple,
+                      params)

A method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.

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ClimaLand.source!(
+    dY::ClimaCore.Fields.FieldVector,
+    src::TOPMODELSubsurfaceRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    model::AbstractSoilModel{FT},
+) where {FT}

Adjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.

The sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).

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ClimaLand.AbstractBoundary — Type
AbstractBoundary

An abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).

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ClimaLand.TopBoundary — Type
TopBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the top boundary.

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ClimaLand.BottomBoundary — Type
BottomBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the bottom boundary.

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ClimaLand.boundary_flux — Function
boundary_flux(bc::WaterFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(bc::RichardsAtmosDrivenFluxBC,
+                       boundary::ClimaLand.AbstractBoundary,
+                       model::RichardsModel{FT},
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field where {FT}

A method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.

If model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.

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boundary_flux(bc::FreeDrainage,
+                       boundary::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which enforces free drainage at the bottom of the domain.

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boundary_flux(bc::HeatFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.

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boundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field

A function which returns the correct boundary flux given any boundary condition (BC).

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function ClimaLand.boundary_flux(
+    bc::RunoffBC,
+    ::TopBoundary,
+    model::Soil.RichardsModel,
+    Δz::FT,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+    params,
+)::ClimaCore.Fields.Field

Extension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).

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ClimaLand.boundary_flux(
+    bc::SoilCO2FluxBC,
+    boundary::ClimaLand.AbstractBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.

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ClimaLand.boundary_flux(
+bc::SoilCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.

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ClimaLand.boundary_flux(
+    bc::SoilCO2StateBC,
+    boundary::ClimaLand.BottomBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.

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ClimaLand.boundary_flux(
+bc::AtmosCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.

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ClimaLand.diffusive_flux — Function
diffusive_flux(K, x_2, x_1, Δz)

Calculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.

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ClimaLand.boundary_vars — Function
boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.

These variables are updated in place in boundary_flux.

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boundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                <:AbstractRadiativeDrivers,
+                                <:AbstractRunoffModel,
+                                }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.

These variables are updated in place in soil_boundary_fluxes!.

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boundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for MoistureStateBC at the top boundary.

These variables are updated in place in boundary_flux.

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boundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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boundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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ClimaLand.boundary_var_domain_names — Function
boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                              <:AbstractRadiativeDrivers,
+                                              <:AbstractRunoffModel,
+                                              },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.

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boundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.

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boundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)

The list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names.

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ClimaLand.boundary_var_types — Function
boundary_var_types(::RichardsModel{FT},
+                    ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                                <: Runoff.AbstractRunoffModel,
+                                              },
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.

Because we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.

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boundary_var_types(
+    ::EnergyHydrology{FT},
+    ::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere{FT},
+        <:AbstractRadiativeDrivers{FT},
+        <:AbstractRunoffModel,
+    }, ::ClimaLand.TopBoundary,
+) where {FT}

An extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.

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boundary_var_types(::RichardsModel{FT},
+                    ::MoistureStateBC,
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for MoistureStateBC at the top boundary.

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boundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.

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ClimaLand.make_jacobian — Function

make_jacobian(model::AbstractModel)

Creates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.

The default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.

Note that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.

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ClimaLand.make_compute_jacobian — Function
ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}

Creates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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ClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}

Creates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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make_compute_jacobian(model::AbstractModel)

Creates and returns a function which computes the entries of the Jacobian matrix W in place.

If the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.

The default is that no updates are required, but this function must be extended for models that use implicit timestepping.

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ClimaLand.set_dfluxBCdY! — Function
ClimaLand.set_dfluxBCdY!(
+    model::RichardsModel,
+    ::MoistureStateBC,
+    boundary::ClimaLand.TopBoundary,
+    Δz,
+    Y,
+    p,
+    t,

)

Computes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.

For Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.

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set_dfluxBCdY!(::AbstractModel,
+              ::AbstractBC,
+              ::AbstractBoundary,
+              _...)::Union{ClimaCore.Fields.FieldVector, Nothing}

A function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.

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ClimaLand.get_drivers — Function
ClimaLand.get_drivers(model::RichardsModel)

Returns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.

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ClimaLand.get_drivers(model::SnowModel)

Returns the driver variable symbols for the SnowModel.

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get_drivers(model::AbstractModel)

Returns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned

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Drivers

ClimaLand.PrescribedAtmosphere — Type
PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.

The default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.

Since not all models require co2 concentration, the default for that is nothing.

  • liquid_precip: Precipitation (m/s) function of time: positive by definition

  • snow_precip: Snow precipitation (m/s) function of time: positive by definition

  • T: Prescribed atmospheric temperature (function of time) at the reference height (K)

  • u: Prescribed wind speed (function of time) at the reference height (m/s)

  • q: Prescribed specific humidity (function of time) at the reference height (_)

  • P: Prescribed air pressure (function of time) at the reference height (Pa)

  • c_co2: CO2 concentration in atmosphere (mol/mol)

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • h: Reference height (m), relative to surface elevation

  • gustiness: Minimum wind speed (gustiness; m/s)

  • thermo_params: Thermodynamic parameters

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ClimaLand.PrescribedPrecipitation — Type
PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).

  • liquid_precip: Precipitation (m/s) function of time: positive by definition
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ClimaLand.PrescribedRadiativeFluxes — Type
PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}

Container for the prescribed radiation functions needed to drive land models in standalone mode.

  • SW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface

  • LW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • θs: Sun zenith angle, in radians

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ClimaLand.turbulent_fluxes — Function
turbulent_fluxes(atmos::PrescribedAtmosphere,
+               model::AbstractModel,
+               Y::ClimaCore.Fields.FieldVector,
+               p::NamedTuple,
+               t
+               )

Computes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.

It solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.

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turbulent_fluxes(atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                t)

Computes the turbulent surface fluxes terms at the ground for a coupled simulation.

source
ClimaLand.turbulent_fluxes_at_a_point — Function
turbulent_fluxes_at_a_point(T_sfc::FT,
+                            q_sfc::FT,
+                            ρ_sfc::FT,
+                            β_sfc::FT,
+                            h_sfc::FT,
+                            r_sfc::FT,
+                            d_sfc::FT,
+                            ts_in,
+                            u::FT,
+                            h::FT,
+                            gustiness::FT,
+                            z_0m::FT,
+                            z_0b::FT,
+                            earth_param_set::EP,
+                           ) where {FT <: AbstractFloat, P}

Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc

This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.

source
Missing docstring.

Missing docstring for ClimaLand.radiative_fluxes_at_a_point. Check Documenter's build log for details.

ClimaLand.set_atmos_ts! — Function
set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)

Fill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.

source
ClimaLand.surface_air_density — Function
surface_air_density(
+                    atmos::PrescribedAtmosphere,
+                    model::AbstractModel,
+                    Y,
+                    p,
+                    t,
+                    T_sfc,
+                    )

A helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.

We additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.

Extending this function for your model is only necessary if you need to compute the air density in a different way.

source
ClimaLand.surface_air_density(
+                atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                _...,
+            )

Returns the air density at the surface in the case of a coupled simulation.

This requires the field ρ_sfc to be present in the cache p under the name of the model.

source
ClimaLand.surface_temperature — Function
ClimaLand.surface_temperature(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface temperature field of the EnergyHydrology soil model.

The assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.

source
ClimaLand.surface_temperature(model::SnowModel, Y, p)

a helper function which returns the surface temperature for the snow model, which is stored in the aux state.

source
ClimaLand.surface_temperature(model::BucketModel, Y, p)

a helper function which returns the surface temperature for the bucket model, which is stored in the aux state.

source
surface_temperature(model::AbstractModel, Y, p, t)

A helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_temperature(model::CanopyModel, Y, p, t)

A helper function which returns the temperature for the canopy model.

source
ClimaLand.surface_resistance — Function
ClimaLand.surface_resistance(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface resistance field of the EnergyHydrology soil model.

source
surface_resistance(model::AbstractModel, Y, p, t)

A helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

The default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.

source
ClimaLand.surface_resistance(
+    model::CanopyModel{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the stomatal resistance field of the CanopyModel canopy.

source
ClimaLand.surface_specific_humidity — Function
ClimaLand.surface_specific_humidity(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    T_sfc,
+    ρ_sfc
+) where {FT}

Returns the surface specific humidity field of the EnergyHydrology soil model.

This models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.

Over the soil ice, the specific humidity is the saturated value.

The total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

Computes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

a helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.

source
surface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)

A helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)

A helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.

source
Missing docstring.

Missing docstring for ClimaLand.make_update_drivers. Check Documenter's build log for details.

diff --git a/previews/PR746/Contributing/index.html b/previews/PR746/Contributing/index.html new file mode 100644 index 0000000000..e6a9d037e6 --- /dev/null +++ b/previews/PR746/Contributing/index.html @@ -0,0 +1,2 @@ + +Contribution guide · ClimaLand.jl
GitHub

Contributing

Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.

Some useful tips

  • When you start working on a new feature branch, make sure you start from main by running: git checkout main.
  • Make sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.
  • When your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.

Continuous integration

After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.

Automated testing

Currently a number of checks are run per commit for a given PR.

  • JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.
  • Documentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.
  • Tests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.
diff --git a/previews/PR746/assets/documenter.js b/previews/PR746/assets/documenter.js new file mode 100644 index 0000000000..2e7840cea0 --- /dev/null +++ b/previews/PR746/assets/documenter.js @@ -0,0 +1,1074 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'mathjax': 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/MathJax.js?config=TeX-AMS_HTML', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "mathjax": { + "exports": "MathJax" + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['mathjax'], function(MathJax) { +MathJax.Hub.Config({ + "jax": [ + "input/TeX", + "output/HTML-CSS", + "output/NativeMML" + ], + "TeX": { + "equationNumbers": { + "autoNumber": "AMS" + }, + "Macros": {} + }, + "tex2jax": { + "inlineMath": [ + [ + "$", + "$" + ], + [ + "\\(", + "\\)" + ] + ], + "processEscapes": true + }, + "config": [ + "MMLorHTML.js" + ], + "extensions": [ + "MathMenu.js", + "MathZoom.js", + "TeX/AMSmath.js", + "TeX/AMSsymbols.js", + "TeX/autobold.js", + "TeX/autoload-all.js" + ] +} +); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on( + "click", + ".docstring .docstring-article-toggle-button", + function () { + let articleToggleTitle = "Expand docstring"; + const parent = $(this).parent(); + + debounce(() => { + if (parent.siblings("section").is(":visible")) { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + parent + .children(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + parent.siblings("section").slideToggle(); + }); + } +); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * RegX escape function from MDN + * Refer: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ + function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + searchstring = escapeRegExp(querystring); + let textindex = new RegExp(`${searchstring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(searchstring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + combineWith: "AND", + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` + + `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/previews/PR746/assets/logo.svg b/previews/PR746/assets/logo.svg new file mode 100644 index 0000000000..3717cae0a7 --- /dev/null +++ b/previews/PR746/assets/logo.svg @@ -0,0 +1,55 @@ + + + + + + + + + + + + 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.docstring>section>a.button.is-inverted.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #8caaee #8caaee !important}html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined[disabled],html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined,fieldset[disabled] html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-frappe .button.is-primary.is-light,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.docs-sourcelink{background-color:#edf2fc;color:#153a8e}html.theme--catppuccin-frappe .button.is-primary.is-light:hover,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.docs-sourcelink:hover,html.theme--catppuccin-frappe .button.is-primary.is-light.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-hovered.docs-sourcelink{background-color:#e2eafb;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-primary.is-light:active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.docs-sourcelink:active,html.theme--catppuccin-frappe .button.is-primary.is-light.is-active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d7e1f9;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-link{background-color:#8caaee;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:hover,html.theme--catppuccin-frappe .button.is-link.is-hovered{background-color:#81a2ec;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:focus,html.theme--catppuccin-frappe 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.button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-outlined:focus,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-focused{background-color:#8caaee;border-color:#8caaee;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #8caaee #8caaee !important}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #8caaee #8caaee !important}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-light{background-color:#edf2fc;color:#153a8e}html.theme--catppuccin-frappe .button.is-link.is-light:hover,html.theme--catppuccin-frappe .button.is-link.is-light.is-hovered{background-color:#e2eafb;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-link.is-light:active,html.theme--catppuccin-frappe .button.is-link.is-light.is-active{background-color:#d7e1f9;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-info{background-color:#81c8be;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info:hover,html.theme--catppuccin-frappe .button.is-info.is-hovered{background-color:#78c4b9;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info:focus,html.theme--catppuccin-frappe .button.is-info.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info:focus:not(:active),html.theme--catppuccin-frappe .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(129,200,190,0.25)}html.theme--catppuccin-frappe .button.is-info:active,html.theme--catppuccin-frappe .button.is-info.is-active{background-color:#6fc0b5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info{background-color:#81c8be;border-color:#81c8be;box-shadow:none}html.theme--catppuccin-frappe .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);color:#81c8be}html.theme--catppuccin-frappe .button.is-info.is-inverted:hover,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#81c8be}html.theme--catppuccin-frappe .button.is-info.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-info.is-outlined{background-color:transparent;border-color:#81c8be;color:#81c8be}html.theme--catppuccin-frappe .button.is-info.is-outlined:hover,html.theme--catppuccin-frappe .button.is-info.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-info.is-outlined:focus,html.theme--catppuccin-frappe .button.is-info.is-outlined.is-focused{background-color:#81c8be;border-color:#81c8be;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #81c8be #81c8be !important}html.theme--catppuccin-frappe .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info.is-outlined{background-color:transparent;border-color:#81c8be;box-shadow:none;color:#81c8be}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#81c8be}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #81c8be #81c8be !important}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-light{background-color:#f1f9f8;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:hover,html.theme--catppuccin-frappe .button.is-info.is-light.is-hovered{background-color:#e8f5f3;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:active,html.theme--catppuccin-frappe .button.is-info.is-light.is-active{background-color:#dff1ef;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe .button.is-success{background-color:#a6d189;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success:hover,html.theme--catppuccin-frappe .button.is-success.is-hovered{background-color:#9fcd80;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success:focus,html.theme--catppuccin-frappe .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success:focus:not(:active),html.theme--catppuccin-frappe .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,209,137,0.25)}html.theme--catppuccin-frappe .button.is-success:active,html.theme--catppuccin-frappe .button.is-success.is-active{background-color:#98ca77;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-success{background-color:#a6d189;border-color:#a6d189;box-shadow:none}html.theme--catppuccin-frappe .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6d189}html.theme--catppuccin-frappe .button.is-success.is-inverted:hover,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6d189}html.theme--catppuccin-frappe .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-success.is-outlined{background-color:transparent;border-color:#a6d189;color:#a6d189}html.theme--catppuccin-frappe .button.is-success.is-outlined:hover,html.theme--catppuccin-frappe 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.button.is-success.is-outlined{background-color:transparent;border-color:#a6d189;box-shadow:none;color:#a6d189}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6d189}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6d189 #a6d189 !important}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success.is-light{background-color:#f4f9f0;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:hover,html.theme--catppuccin-frappe .button.is-success.is-light.is-hovered{background-color:#edf6e7;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:active,html.theme--catppuccin-frappe .button.is-success.is-light.is-active{background-color:#e6f2de;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:hover,html.theme--catppuccin-frappe .button.is-warning.is-hovered{background-color:#e3c386;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus,html.theme--catppuccin-frappe .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus:not(:active),html.theme--catppuccin-frappe .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(229,200,144,0.25)}html.theme--catppuccin-frappe .button.is-warning:active,html.theme--catppuccin-frappe .button.is-warning.is-active{background-color:#e0be7b;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:#e5c890;box-shadow:none}html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-focused{background-color:#e5c890;border-color:#e5c890;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-light{background-color:#fbf7ee;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:hover,html.theme--catppuccin-frappe .button.is-warning.is-light.is-hovered{background-color:#f9f2e4;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:active,html.theme--catppuccin-frappe .button.is-warning.is-light.is-active{background-color:#f6edda;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:hover,html.theme--catppuccin-frappe .button.is-danger.is-hovered{background-color:#e57779;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus,html.theme--catppuccin-frappe .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus:not(:active),html.theme--catppuccin-frappe .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(231,130,132,0.25)}html.theme--catppuccin-frappe .button.is-danger:active,html.theme--catppuccin-frappe .button.is-danger.is-active{background-color:#e36d6f;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:#e78284;box-shadow:none}html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-frappe .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-focused{background-color:#e78284;border-color:#e78284;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-light{background-color:#fceeee;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:hover,html.theme--catppuccin-frappe .button.is-danger.is-light.is-hovered{background-color:#fae3e4;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:active,html.theme--catppuccin-frappe .button.is-danger.is-light.is-active{background-color:#f8d8d9;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-frappe .button.is-small:not(.is-rounded),html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .button.is-normal{font-size:1rem}html.theme--catppuccin-frappe .button.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .button.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .button[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button{background-color:#737994;border-color:#626880;box-shadow:none;opacity:.5}html.theme--catppuccin-frappe .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-frappe .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-frappe .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-frappe .button.is-static{background-color:#292c3c;border-color:#626880;color:#838ba7;box-shadow:none;pointer-events:none}html.theme--catppuccin-frappe .button.is-rounded,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-frappe .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-frappe .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-frappe .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-frappe .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-frappe .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-frappe .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-frappe .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-frappe .buttons.has-addons .button:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-frappe .buttons.has-addons .button:focus,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused,html.theme--catppuccin-frappe .buttons.has-addons .button:active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-frappe .buttons.has-addons .button:focus:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-frappe .buttons.has-addons .button:active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-frappe .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .buttons.is-centered{justify-content:center}html.theme--catppuccin-frappe .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-frappe .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-frappe .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-frappe .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-frappe .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-frappe .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-frappe .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-frappe .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-frappe .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-frappe .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-frappe .content li+li{margin-top:0.25em}html.theme--catppuccin-frappe .content p:not(:last-child),html.theme--catppuccin-frappe .content dl:not(:last-child),html.theme--catppuccin-frappe .content ol:not(:last-child),html.theme--catppuccin-frappe .content ul:not(:last-child),html.theme--catppuccin-frappe .content blockquote:not(:last-child),html.theme--catppuccin-frappe .content pre:not(:last-child),html.theme--catppuccin-frappe .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-frappe .content h1,html.theme--catppuccin-frappe .content h2,html.theme--catppuccin-frappe .content h3,html.theme--catppuccin-frappe .content h4,html.theme--catppuccin-frappe .content h5,html.theme--catppuccin-frappe .content h6{color:#c6d0f5;font-weight:600;line-height:1.125}html.theme--catppuccin-frappe .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-frappe .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-frappe .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-frappe .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-frappe .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-frappe .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-frappe .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-frappe .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-frappe .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-frappe .content blockquote{background-color:#292c3c;border-left:5px solid #626880;padding:1.25em 1.5em}html.theme--catppuccin-frappe .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-frappe .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-frappe .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-frappe .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-frappe .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-frappe .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-frappe .content ul ul ul{list-style-type:square}html.theme--catppuccin-frappe .content dd{margin-left:2em}html.theme--catppuccin-frappe .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-frappe .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-frappe .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-frappe .content figure img{display:inline-block}html.theme--catppuccin-frappe .content figure figcaption{font-style:italic}html.theme--catppuccin-frappe .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-frappe .content sup,html.theme--catppuccin-frappe .content sub{font-size:75%}html.theme--catppuccin-frappe .content table{width:100%}html.theme--catppuccin-frappe .content table td,html.theme--catppuccin-frappe .content table th{border:1px solid #626880;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-frappe .content table th{color:#b0bef1}html.theme--catppuccin-frappe .content table th:not([align]){text-align:inherit}html.theme--catppuccin-frappe .content table thead td,html.theme--catppuccin-frappe .content table thead th{border-width:0 0 2px;color:#b0bef1}html.theme--catppuccin-frappe .content table tfoot td,html.theme--catppuccin-frappe .content table tfoot th{border-width:2px 0 0;color:#b0bef1}html.theme--catppuccin-frappe .content table tbody tr:last-child td,html.theme--catppuccin-frappe .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-frappe .content .tabs li+li{margin-top:0}html.theme--catppuccin-frappe .content.is-small,html.theme--catppuccin-frappe #documenter 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select:not([multiple]){padding-right:2.5em}html.theme--catppuccin-frappe .select select[multiple]{height:auto;padding:0}html.theme--catppuccin-frappe .select select[multiple] option{padding:0.5em 1em}html.theme--catppuccin-frappe .select:not(.is-multiple):not(.is-loading):hover::after{border-color:#99d1db}html.theme--catppuccin-frappe .select.is-white:not(:hover)::after{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select:hover,html.theme--catppuccin-frappe .select.is-white select.is-hovered{border-color:#f2f2f2}html.theme--catppuccin-frappe .select.is-white select:focus,html.theme--catppuccin-frappe .select.is-white select.is-focused,html.theme--catppuccin-frappe .select.is-white select:active,html.theme--catppuccin-frappe .select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-frappe 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select.is-focused,html.theme--catppuccin-frappe .select.is-dark select:active,html.theme--catppuccin-frappe .content kbd.select select:active,html.theme--catppuccin-frappe .select.is-dark select.is-active,html.theme--catppuccin-frappe .content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(65,69,89,0.25)}html.theme--catppuccin-frappe .select.is-primary:not(:hover)::after,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select:hover,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:hover,html.theme--catppuccin-frappe .select.is-primary select.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-primary select:focus,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:focus,html.theme--catppuccin-frappe .select.is-primary select.is-focused,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-focused,html.theme--catppuccin-frappe .select.is-primary select:active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:active,html.theme--catppuccin-frappe .select.is-primary select.is-active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-link:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select:hover,html.theme--catppuccin-frappe .select.is-link select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-link select:focus,html.theme--catppuccin-frappe .select.is-link select.is-focused,html.theme--catppuccin-frappe .select.is-link select:active,html.theme--catppuccin-frappe .select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-info:not(:hover)::after{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select:hover,html.theme--catppuccin-frappe .select.is-info select.is-hovered{border-color:#6fc0b5}html.theme--catppuccin-frappe .select.is-info select:focus,html.theme--catppuccin-frappe .select.is-info select.is-focused,html.theme--catppuccin-frappe .select.is-info select:active,html.theme--catppuccin-frappe .select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(129,200,190,0.25)}html.theme--catppuccin-frappe 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a.navbar-item:focus,html.theme--catppuccin-frappe a.navbar-item:focus-within,html.theme--catppuccin-frappe a.navbar-item:hover,html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link:focus,html.theme--catppuccin-frappe .navbar-link:focus-within,html.theme--catppuccin-frappe .navbar-link:hover,html.theme--catppuccin-frappe .navbar-link.is-active{background-color:rgba(0,0,0,0);color:#8caaee}html.theme--catppuccin-frappe .navbar-item{flex-grow:0;flex-shrink:0}html.theme--catppuccin-frappe .navbar-item img{max-height:1.75rem}html.theme--catppuccin-frappe .navbar-item.has-dropdown{padding:0}html.theme--catppuccin-frappe .navbar-item.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-item.is-tab{border-bottom:1px solid transparent;min-height:4rem;padding-bottom:calc(0.5rem - 1px)}html.theme--catppuccin-frappe .navbar-item.is-tab:focus,html.theme--catppuccin-frappe .navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee}html.theme--catppuccin-frappe .navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee;border-bottom-style:solid;border-bottom-width:3px;color:#8caaee;padding-bottom:calc(0.5rem - 3px)}html.theme--catppuccin-frappe .navbar-content{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless){padding-right:2.5em}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless)::after{border-color:#fff;margin-top:-0.375em;right:1.125em}html.theme--catppuccin-frappe .navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}html.theme--catppuccin-frappe .navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}html.theme--catppuccin-frappe .navbar-divider{background-color:rgba(0,0,0,0.2);border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){html.theme--catppuccin-frappe 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a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#838ba7}html.theme--catppuccin-frappe .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#8caaee}.navbar.is-spaced html.theme--catppuccin-frappe .navbar-dropdown,html.theme--catppuccin-frappe .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--catppuccin-frappe .navbar-dropdown.is-right{left:auto;right:0}html.theme--catppuccin-frappe .navbar-divider{display:block}html.theme--catppuccin-frappe .navbar>.container .navbar-brand,html.theme--catppuccin-frappe .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--catppuccin-frappe .navbar>.container .navbar-menu,html.theme--catppuccin-frappe .container>.navbar 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body.has-spaced-navbar-fixed-bottom{padding-bottom:6rem}html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link.is-active{color:#8caaee}html.theme--catppuccin-frappe a.navbar-item.is-active:not(:focus):not(:hover),html.theme--catppuccin-frappe .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--catppuccin-frappe .navbar-item.has-dropdown:focus .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown:hover .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--catppuccin-frappe .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--catppuccin-frappe .pagination{font-size:1rem;margin:-.25rem}html.theme--catppuccin-frappe .pagination.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}html.theme--catppuccin-frappe .pagination.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .pagination.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-previous,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--catppuccin-frappe .pagination.is-rounded .pagination-next,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:9999px}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-link,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:9999px}html.theme--catppuccin-frappe .pagination,html.theme--catppuccin-frappe .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link{border-color:#626880;color:#8caaee;min-width:2.5em}html.theme--catppuccin-frappe .pagination-previous:hover,html.theme--catppuccin-frappe .pagination-next:hover,html.theme--catppuccin-frappe .pagination-link:hover{border-color:#737994;color:#99d1db}html.theme--catppuccin-frappe .pagination-previous:focus,html.theme--catppuccin-frappe .pagination-next:focus,html.theme--catppuccin-frappe .pagination-link:focus{border-color:#737994}html.theme--catppuccin-frappe .pagination-previous:active,html.theme--catppuccin-frappe .pagination-next:active,html.theme--catppuccin-frappe .pagination-link:active{box-shadow:inset 0 1px 2px 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768px){html.theme--catppuccin-frappe .pagination{flex-wrap:wrap}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{html.theme--catppuccin-frappe .pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination-previous{order:2}html.theme--catppuccin-frappe .pagination-next{order:3}html.theme--catppuccin-frappe .pagination{justify-content:space-between;margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination.is-centered .pagination-previous{order:1}html.theme--catppuccin-frappe .pagination.is-centered 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rgba(10,10,10,0.25)}html.theme--catppuccin-latte .button.is-black:active,html.theme--catppuccin-latte .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-black[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:hover,html.theme--catppuccin-latte .button.is-light.is-hovered{background-color:#eee;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus,html.theme--catppuccin-latte .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus:not(:active),html.theme--catppuccin-latte .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}html.theme--catppuccin-latte .button.is-light:active,html.theme--catppuccin-latte .button.is-light.is-active{background-color:#e8e8e8;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none}html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-outlined.is-focused{background-color:#f5f5f5;border-color:#f5f5f5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark,html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:hover,html.theme--catppuccin-latte .content kbd.button:hover,html.theme--catppuccin-latte .button.is-dark.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-hovered{background-color:#c5c9d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus,html.theme--catppuccin-latte .content kbd.button:focus,html.theme--catppuccin-latte .button.is-dark.is-focused,html.theme--catppuccin-latte .content kbd.button.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus:not(:active),html.theme--catppuccin-latte .content kbd.button:focus:not(:active),html.theme--catppuccin-latte .button.is-dark.is-focused:not(:active),html.theme--catppuccin-latte .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(204,208,218,0.25)}html.theme--catppuccin-latte .button.is-dark:active,html.theme--catppuccin-latte .content kbd.button:active,html.theme--catppuccin-latte .button.is-dark.is-active,html.theme--catppuccin-latte .content kbd.button.is-active{background-color:#bdc2cf;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark[disabled],html.theme--catppuccin-latte .content kbd.button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:#ccd0da;box-shadow:none}html.theme--catppuccin-latte .button.is-dark.is-inverted,html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-focused{background-color:#ccd0da;border-color:#ccd0da;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-primary,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:hover,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-focused,html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--catppuccin-latte .button.is-primary.is-focused:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-primary:active,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:active,html.theme--catppuccin-latte .button.is-primary.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-active.docs-sourcelink{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary[disabled],html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:#1e66f5;box-shadow:none}html.theme--catppuccin-latte .button.is-primary.is-inverted,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-inverted:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-inverted.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-primary.is-inverted[disabled],html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-loading::after,html.theme--catppuccin-latte .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-primary.is-outlined,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#1e66f5;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-outlined:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--catppuccin-latte .button.is-primary.is-outlined:focus,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-focused,html.theme--catppuccin-latte 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.button.is-primary.is-light.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d3e1fd;border-color:transparent;color:#0a52e1}html.theme--catppuccin-latte .button.is-link{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:hover,html.theme--catppuccin-latte .button.is-link.is-hovered{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus,html.theme--catppuccin-latte .button.is-link.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus:not(:active),html.theme--catppuccin-latte .button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-link:active,html.theme--catppuccin-latte .button.is-link.is-active{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte 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.button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-outlined{background-color:transparent;border-color:#40a02b;box-shadow:none;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #40a02b #40a02b !important}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-success.is-light{background-color:#f1fbef;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:hover,html.theme--catppuccin-latte .button.is-success.is-light.is-hovered{background-color:#e8f8e5;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:active,html.theme--catppuccin-latte .button.is-success.is-light.is-active{background-color:#e0f5db;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:hover,html.theme--catppuccin-latte .button.is-warning.is-hovered{background-color:#d4871c;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus,html.theme--catppuccin-latte .button.is-warning.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus:not(:active),html.theme--catppuccin-latte .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(223,142,29,0.25)}html.theme--catppuccin-latte .button.is-warning:active,html.theme--catppuccin-latte .button.is-warning.is-active{background-color:#c8801a;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:#df8e1d;box-shadow:none}html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-focused{background-color:#df8e1d;border-color:#df8e1d;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-light{background-color:#fdf6ed;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:hover,html.theme--catppuccin-latte .button.is-warning.is-light.is-hovered{background-color:#fbf1e2;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:active,html.theme--catppuccin-latte .button.is-warning.is-light.is-active{background-color:#faebd6;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:hover,html.theme--catppuccin-latte .button.is-danger.is-hovered{background-color:#c60e36;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus,html.theme--catppuccin-latte .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus:not(:active),html.theme--catppuccin-latte .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(210,15,57,0.25)}html.theme--catppuccin-latte .button.is-danger:active,html.theme--catppuccin-latte .button.is-danger.is-active{background-color:#ba0d33;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:#d20f39;box-shadow:none}html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-focused{background-color:#d20f39;border-color:#d20f39;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-light{background-color:#feecf0;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:hover,html.theme--catppuccin-latte .button.is-danger.is-light.is-hovered{background-color:#fde0e6;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:active,html.theme--catppuccin-latte .button.is-danger.is-light.is-active{background-color:#fcd4dd;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-latte .button.is-small:not(.is-rounded),html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .button.is-normal{font-size:1rem}html.theme--catppuccin-latte .button.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .button.is-large{font-size:1.5rem}html.theme--catppuccin-latte .button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button{background-color:#9ca0b0;border-color:#acb0be;box-shadow:none;opacity:.5}html.theme--catppuccin-latte .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-latte .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-latte .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-latte .button.is-static{background-color:#e6e9ef;border-color:#acb0be;color:#8c8fa1;box-shadow:none;pointer-events:none}html.theme--catppuccin-latte .button.is-rounded,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-latte .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-latte .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-latte .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-latte .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-latte .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-latte .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-latte .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-latte .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-latte .buttons.has-addons .button:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-latte .buttons.has-addons .button:focus,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused,html.theme--catppuccin-latte .buttons.has-addons .button:active,html.theme--catppuccin-latte .buttons.has-addons .button.is-active,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-latte .buttons.has-addons .button:focus:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-latte .buttons.has-addons .button:active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-latte .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .buttons.is-centered{justify-content:center}html.theme--catppuccin-latte .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-latte .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-latte .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-latte .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-latte .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-latte .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-latte .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-latte .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-latte .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-latte .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-latte .content li+li{margin-top:0.25em}html.theme--catppuccin-latte .content p:not(:last-child),html.theme--catppuccin-latte .content dl:not(:last-child),html.theme--catppuccin-latte .content ol:not(:last-child),html.theme--catppuccin-latte .content ul:not(:last-child),html.theme--catppuccin-latte .content blockquote:not(:last-child),html.theme--catppuccin-latte .content pre:not(:last-child),html.theme--catppuccin-latte .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-latte .content h1,html.theme--catppuccin-latte .content h2,html.theme--catppuccin-latte .content h3,html.theme--catppuccin-latte .content h4,html.theme--catppuccin-latte .content h5,html.theme--catppuccin-latte .content h6{color:#4c4f69;font-weight:600;line-height:1.125}html.theme--catppuccin-latte .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-latte .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-latte .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-latte .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-latte .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-latte .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-latte .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-latte .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-latte .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-latte .content blockquote{background-color:#e6e9ef;border-left:5px solid #acb0be;padding:1.25em 1.5em}html.theme--catppuccin-latte .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-latte .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-latte .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-latte .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-latte .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-latte .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-latte .content ul ul ul{list-style-type:square}html.theme--catppuccin-latte .content dd{margin-left:2em}html.theme--catppuccin-latte .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-latte .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-latte .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-latte .content figure img{display:inline-block}html.theme--catppuccin-latte .content figure figcaption{font-style:italic}html.theme--catppuccin-latte .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-latte .content sup,html.theme--catppuccin-latte .content sub{font-size:75%}html.theme--catppuccin-latte .content table{width:100%}html.theme--catppuccin-latte .content table td,html.theme--catppuccin-latte .content table th{border:1px solid #acb0be;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-latte .content table th{color:#41445a}html.theme--catppuccin-latte .content table 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#documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}html.theme--catppuccin-latte .file.is-normal{font-size:1rem}html.theme--catppuccin-latte .file.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .file.is-medium .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-large{font-size:1.5rem}html.theme--catppuccin-latte .file.is-large .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .file.has-name.is-empty .file-cta{border-radius:.4em}html.theme--catppuccin-latte .file.has-name.is-empty .file-name{display:none}html.theme--catppuccin-latte .file.is-boxed .file-label{flex-direction:column}html.theme--catppuccin-latte .file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}html.theme--catppuccin-latte .file.is-boxed .file-name{border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-boxed .file-icon{height:1.5em;width:1.5em}html.theme--catppuccin-latte .file.is-boxed .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-boxed.is-small .file-icon .fa,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .file.is-boxed.is-medium .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.is-boxed.is-large .file-icon .fa{font-size:35px}html.theme--catppuccin-latte .file.is-boxed.has-name .file-cta{border-radius:.4em .4em 0 0}html.theme--catppuccin-latte .file.is-boxed.has-name .file-name{border-radius:0 0 .4em .4em;border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-centered{justify-content:center}html.theme--catppuccin-latte .file.is-fullwidth .file-label{width:100%}html.theme--catppuccin-latte .file.is-fullwidth .file-name{flex-grow:1;max-width:none}html.theme--catppuccin-latte .file.is-right{justify-content:flex-end}html.theme--catppuccin-latte .file.is-right .file-cta{border-radius:0 .4em .4em 0}html.theme--catppuccin-latte .file.is-right .file-name{border-radius:.4em 0 0 .4em;border-width:1px 0 1px 1px;order:-1}html.theme--catppuccin-latte .file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}html.theme--catppuccin-latte .file-label:hover .file-cta{background-color:#c5c9d5;color:#41445a}html.theme--catppuccin-latte .file-label:hover .file-name{border-color:#a5a9b8}html.theme--catppuccin-latte .file-label:active .file-cta{background-color:#bdc2cf;color:#41445a}html.theme--catppuccin-latte .file-label:active .file-name{border-color:#9ea2b3}html.theme--catppuccin-latte .file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}html.theme--catppuccin-latte .file-cta,html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-radius:.4em;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}html.theme--catppuccin-latte .file-cta{background-color:#ccd0da;color:#4c4f69}html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:inherit;text-overflow:ellipsis}html.theme--catppuccin-latte .file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:.5em;width:1em}html.theme--catppuccin-latte .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .label{color:#41445a;display:block;font-size:1rem;font-weight:700}html.theme--catppuccin-latte .label:not(:last-child){margin-bottom:0.5em}html.theme--catppuccin-latte .label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}html.theme--catppuccin-latte .label.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .label.is-large{font-size:1.5rem}html.theme--catppuccin-latte .help{display:block;font-size:.75rem;margin-top:0.25rem}html.theme--catppuccin-latte .help.is-white{color:#fff}html.theme--catppuccin-latte .help.is-black{color:#0a0a0a}html.theme--catppuccin-latte .help.is-light{color:#f5f5f5}html.theme--catppuccin-latte .help.is-dark,html.theme--catppuccin-latte .content kbd.help{color:#ccd0da}html.theme--catppuccin-latte .help.is-primary,html.theme--catppuccin-latte .docstring>section>a.help.docs-sourcelink{color:#1e66f5}html.theme--catppuccin-latte .help.is-link{color:#1e66f5}html.theme--catppuccin-latte .help.is-info{color:#179299}html.theme--catppuccin-latte .help.is-success{color:#40a02b}html.theme--catppuccin-latte .help.is-warning{color:#df8e1d}html.theme--catppuccin-latte .help.is-danger{color:#d20f39}html.theme--catppuccin-latte .field:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.has-addons{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.has-addons .control:not(:last-child){margin-right:-1px}html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .button,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]){z-index:3}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}html.theme--catppuccin-latte .field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.has-addons.has-addons-centered{justify-content:center}html.theme--catppuccin-latte .field.has-addons.has-addons-right{justify-content:flex-end}html.theme--catppuccin-latte .field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.is-grouped>.control{flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--catppuccin-latte .field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.is-grouped.is-grouped-centered{justify-content:center}html.theme--catppuccin-latte .field.is-grouped.is-grouped-right{justify-content:flex-end}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline{flex-wrap:wrap}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:last-child,html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field.is-horizontal{display:flex}}html.theme--catppuccin-latte .field-label .label{font-size:inherit}@media screen and (max-width: 768px){html.theme--catppuccin-latte .field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}html.theme--catppuccin-latte .field-label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-normal{padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-medium{font-size:1.25rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-large{font-size:1.5rem;padding-top:0.375em}}html.theme--catppuccin-latte .field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}html.theme--catppuccin-latte .field-body .field{margin-bottom:0}html.theme--catppuccin-latte .field-body>.field{flex-shrink:1}html.theme--catppuccin-latte .field-body>.field:not(.is-narrow){flex-grow:1}html.theme--catppuccin-latte .field-body>.field:not(:last-child){margin-right:.75rem}}html.theme--catppuccin-latte .control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:inherit}html.theme--catppuccin-latte .control.has-icons-left .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left .select:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .select:focus~.icon{color:#ccd0da}html.theme--catppuccin-latte .control.has-icons-left .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-small~.icon{font-size:.75rem}html.theme--catppuccin-latte .control.has-icons-left .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}html.theme--catppuccin-latte .control.has-icons-left 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kbd.button.is-outlined{background-color:transparent;border-color:#363a4f;box-shadow:none;color:#363a4f}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-macchiato .content 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.button.is-info.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-focused{background-color:#8bd5ca;border-color:#8bd5ca;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-outlined{background-color:transparent;border-color:#8bd5ca;box-shadow:none;color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-light{background-color:#f0faf8;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:hover,html.theme--catppuccin-macchiato .button.is-info.is-light.is-hovered{background-color:#e7f6f4;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:active,html.theme--catppuccin-macchiato .button.is-info.is-light.is-active{background-color:#ddf3f0;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:hover,html.theme--catppuccin-macchiato .button.is-success.is-hovered{background-color:#9ed78c;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus,html.theme--catppuccin-macchiato .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus:not(:active),html.theme--catppuccin-macchiato .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,218,149,0.25)}html.theme--catppuccin-macchiato .button.is-success:active,html.theme--catppuccin-macchiato .button.is-success.is-active{background-color:#96d382;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:#a6da95;box-shadow:none}html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-focused{background-color:#a6da95;border-color:#a6da95;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-light{background-color:#f2faf0;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:hover,html.theme--catppuccin-macchiato .button.is-success.is-light.is-hovered{background-color:#eaf6e6;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:active,html.theme--catppuccin-macchiato .button.is-success.is-light.is-active{background-color:#e2f3dd;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:hover,html.theme--catppuccin-macchiato .button.is-warning.is-hovered{background-color:#eccf94;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus,html.theme--catppuccin-macchiato .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus:not(:active),html.theme--catppuccin-macchiato .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(238,212,159,0.25)}html.theme--catppuccin-macchiato .button.is-warning:active,html.theme--catppuccin-macchiato .button.is-warning.is-active{background-color:#eaca89;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:#eed49f;box-shadow:none}html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-focused{background-color:#eed49f;border-color:#eed49f;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-light{background-color:#fcf7ee;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:hover,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-hovered{background-color:#faf2e3;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:active,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-active{background-color:#f8eed8;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:hover,html.theme--catppuccin-macchiato .button.is-danger.is-hovered{background-color:#eb7c8c;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus,html.theme--catppuccin-macchiato .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus:not(:active),html.theme--catppuccin-macchiato .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(237,135,150,0.25)}html.theme--catppuccin-macchiato .button.is-danger:active,html.theme--catppuccin-macchiato .button.is-danger.is-active{background-color:#ea7183;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:#ed8796;box-shadow:none}html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-macchiato .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-focused{background-color:#ed8796;border-color:#ed8796;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-light{background-color:#fcedef;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:hover,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-hovered{background-color:#fbe2e6;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:active,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-active{background-color:#f9d7dc;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-small,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-macchiato .button.is-small:not(.is-rounded),html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-macchiato .button.is-normal{font-size:1rem}html.theme--catppuccin-macchiato .button.is-medium{font-size:1.25rem}html.theme--catppuccin-macchiato .button.is-large{font-size:1.5rem}html.theme--catppuccin-macchiato .button[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button{background-color:#6e738d;border-color:#5b6078;box-shadow:none;opacity:.5}html.theme--catppuccin-macchiato .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-macchiato .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-macchiato .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-macchiato .button.is-static{background-color:#1e2030;border-color:#5b6078;color:#8087a2;box-shadow:none;pointer-events:none}html.theme--catppuccin-macchiato .button.is-rounded,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-macchiato .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-macchiato .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-macchiato .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-macchiato .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-macchiato .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-macchiato .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-macchiato .buttons.are-small 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.container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-macchiato .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-macchiato .content li+li{margin-top:0.25em}html.theme--catppuccin-macchiato .content p:not(:last-child),html.theme--catppuccin-macchiato .content dl:not(:last-child),html.theme--catppuccin-macchiato .content ol:not(:last-child),html.theme--catppuccin-macchiato .content ul:not(:last-child),html.theme--catppuccin-macchiato .content blockquote:not(:last-child),html.theme--catppuccin-macchiato .content pre:not(:last-child),html.theme--catppuccin-macchiato .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-macchiato .content h1,html.theme--catppuccin-macchiato .content h2,html.theme--catppuccin-macchiato .content h3,html.theme--catppuccin-macchiato .content h4,html.theme--catppuccin-macchiato .content h5,html.theme--catppuccin-macchiato .content 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ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-macchiato .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-macchiato .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-macchiato .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-macchiato .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-macchiato .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-macchiato .content ul ul ul{list-style-type:square}html.theme--catppuccin-macchiato .content dd{margin-left:2em}html.theme--catppuccin-macchiato .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-macchiato 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form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-macchiato .is-black.textarea,html.theme--catppuccin-macchiato .is-black.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}html.theme--catppuccin-macchiato .is-black.textarea:focus,html.theme--catppuccin-macchiato .is-black.input:focus,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:focus,html.theme--catppuccin-macchiato .is-black.is-focused.textarea,html.theme--catppuccin-macchiato .is-black.is-focused.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--catppuccin-macchiato .is-black.textarea:active,html.theme--catppuccin-macchiato .is-black.input:active,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:active,html.theme--catppuccin-macchiato 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html.theme--catppuccin-mocha .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-link.is-light{background-color:#ebf3fe;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:hover,html.theme--catppuccin-mocha .button.is-link.is-light.is-hovered{background-color:#dfebfe;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:active,html.theme--catppuccin-mocha .button.is-link.is-light.is-active{background-color:#d3e3fd;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:hover,html.theme--catppuccin-mocha .button.is-info.is-hovered{background-color:#8adfd1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus,html.theme--catppuccin-mocha .button.is-info.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus:not(:active),html.theme--catppuccin-mocha .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(148,226,213,0.25)}html.theme--catppuccin-mocha .button.is-info:active,html.theme--catppuccin-mocha .button.is-info.is-active{background-color:#80ddcd;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:#94e2d5;box-shadow:none}html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-focused{background-color:#94e2d5;border-color:#94e2d5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-light{background-color:#effbf9;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:hover,html.theme--catppuccin-mocha .button.is-info.is-light.is-hovered{background-color:#e5f8f5;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:active,html.theme--catppuccin-mocha .button.is-info.is-light.is-active{background-color:#dbf5f1;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:hover,html.theme--catppuccin-mocha .button.is-success.is-hovered{background-color:#9de097;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus,html.theme--catppuccin-mocha .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus:not(:active),html.theme--catppuccin-mocha .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,227,161,0.25)}html.theme--catppuccin-mocha .button.is-success:active,html.theme--catppuccin-mocha .button.is-success.is-active{background-color:#93dd8d;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:#a6e3a1;box-shadow:none}html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-focused{background-color:#a6e3a1;border-color:#a6e3a1;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-light{background-color:#f0faef;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:hover,html.theme--catppuccin-mocha .button.is-success.is-light.is-hovered{background-color:#e7f7e5;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:active,html.theme--catppuccin-mocha .button.is-success.is-light.is-active{background-color:#def4dc;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:hover,html.theme--catppuccin-mocha .button.is-warning.is-hovered{background-color:#f8dea3;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus,html.theme--catppuccin-mocha .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus:not(:active),html.theme--catppuccin-mocha .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(249,226,175,0.25)}html.theme--catppuccin-mocha .button.is-warning:active,html.theme--catppuccin-mocha .button.is-warning.is-active{background-color:#f7d997;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:#f9e2af;box-shadow:none}html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-focused{background-color:#f9e2af;border-color:#f9e2af;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-light{background-color:#fef8ec;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:hover,html.theme--catppuccin-mocha .button.is-warning.is-light.is-hovered{background-color:#fdf4e0;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:active,html.theme--catppuccin-mocha .button.is-warning.is-light.is-active{background-color:#fcf0d4;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:hover,html.theme--catppuccin-mocha .button.is-danger.is-hovered{background-color:#f27f9f;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus,html.theme--catppuccin-mocha .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus:not(:active),html.theme--catppuccin-mocha .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(243,139,168,0.25)}html.theme--catppuccin-mocha .button.is-danger:active,html.theme--catppuccin-mocha .button.is-danger.is-active{background-color:#f17497;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:#f38ba8;box-shadow:none}html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-mocha .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-focused{background-color:#f38ba8;border-color:#f38ba8;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-light{background-color:#fdedf1;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:hover,html.theme--catppuccin-mocha .button.is-danger.is-light.is-hovered{background-color:#fce1e8;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:active,html.theme--catppuccin-mocha .button.is-danger.is-light.is-active{background-color:#fbd5e0;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-mocha .button.is-small:not(.is-rounded),html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .button.is-normal{font-size:1rem}html.theme--catppuccin-mocha .button.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .button.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .button[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button{background-color:#6c7086;border-color:#585b70;box-shadow:none;opacity:.5}html.theme--catppuccin-mocha .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-mocha .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-mocha .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-mocha .button.is-static{background-color:#181825;border-color:#585b70;color:#7f849c;box-shadow:none;pointer-events:none}html.theme--catppuccin-mocha .button.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-mocha .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-mocha .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-mocha .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-mocha .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-mocha .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-mocha .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-mocha .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-mocha .buttons.has-addons .button:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-mocha .buttons.has-addons .button:focus,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused,html.theme--catppuccin-mocha .buttons.has-addons .button:active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-mocha .buttons.has-addons .button:focus:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-mocha .buttons.has-addons .button:active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-mocha .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-mocha .buttons.is-centered{justify-content:center}html.theme--catppuccin-mocha .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-mocha .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-mocha .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-mocha .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-mocha .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-mocha .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-mocha .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-mocha .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-mocha .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-mocha .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-mocha .content li+li{margin-top:0.25em}html.theme--catppuccin-mocha .content p:not(:last-child),html.theme--catppuccin-mocha .content dl:not(:last-child),html.theme--catppuccin-mocha .content ol:not(:last-child),html.theme--catppuccin-mocha .content ul:not(:last-child),html.theme--catppuccin-mocha .content blockquote:not(:last-child),html.theme--catppuccin-mocha .content pre:not(:last-child),html.theme--catppuccin-mocha .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-mocha .content h1,html.theme--catppuccin-mocha .content h2,html.theme--catppuccin-mocha .content h3,html.theme--catppuccin-mocha .content h4,html.theme--catppuccin-mocha .content h5,html.theme--catppuccin-mocha .content h6{color:#cdd6f4;font-weight:600;line-height:1.125}html.theme--catppuccin-mocha .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-mocha .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-mocha .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-mocha .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-mocha .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-mocha .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-mocha .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-mocha .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-mocha .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-mocha .content blockquote{background-color:#181825;border-left:5px solid #585b70;padding:1.25em 1.5em}html.theme--catppuccin-mocha .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-mocha .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-mocha .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-mocha .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-mocha .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-mocha .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-mocha .content ul ul ul{list-style-type:square}html.theme--catppuccin-mocha .content dd{margin-left:2em}html.theme--catppuccin-mocha .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-mocha .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-mocha .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-mocha .content figure img{display:inline-block}html.theme--catppuccin-mocha .content figure figcaption{font-style:italic}html.theme--catppuccin-mocha .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-mocha .content sup,html.theme--catppuccin-mocha .content sub{font-size:75%}html.theme--catppuccin-mocha .content table{width:100%}html.theme--catppuccin-mocha .content table td,html.theme--catppuccin-mocha .content table th{border:1px solid #585b70;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-mocha .content table th{color:#b8c5ef}html.theme--catppuccin-mocha .content table th:not([align]){text-align:inherit}html.theme--catppuccin-mocha .content table thead td,html.theme--catppuccin-mocha .content table thead th{border-width:0 0 2px;color:#b8c5ef}html.theme--catppuccin-mocha .content table tfoot td,html.theme--catppuccin-mocha .content table tfoot th{border-width:2px 0 0;color:#b8c5ef}html.theme--catppuccin-mocha .content table tbody tr:last-child td,html.theme--catppuccin-mocha .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-mocha .content .tabs li+li{margin-top:0}html.theme--catppuccin-mocha .content.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--catppuccin-mocha .content.is-normal{font-size:1rem}html.theme--catppuccin-mocha .content.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .content.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--catppuccin-mocha .icon.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--catppuccin-mocha .icon.is-medium{height:2rem;width:2rem}html.theme--catppuccin-mocha .icon.is-large{height:3rem;width:3rem}html.theme--catppuccin-mocha .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--catppuccin-mocha .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--catppuccin-mocha .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--catppuccin-mocha .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--catppuccin-mocha div.icon-text{display:flex}html.theme--catppuccin-mocha .image,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--catppuccin-mocha .image img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--catppuccin-mocha .image img.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--catppuccin-mocha .image.is-fullwidth,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--catppuccin-mocha .image.is-square img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--catppuccin-mocha .image.is-square .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--catppuccin-mocha .image.is-1by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar 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.has-ratio,html.theme--catppuccin-mocha .image.is-5by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--catppuccin-mocha .image.is-5by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--catppuccin-mocha .image.is-16by9 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--catppuccin-mocha .image.is-16by9 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--catppuccin-mocha .image.is-2by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--catppuccin-mocha .image.is-2by1 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--catppuccin-mocha .image.is-3by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--catppuccin-mocha 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img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--catppuccin-mocha .image.is-3by5 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--catppuccin-mocha .image.is-9by16 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--catppuccin-mocha .image.is-9by16 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--catppuccin-mocha .image.is-1by2 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--catppuccin-mocha .image.is-1by2 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--catppuccin-mocha .image.is-1by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--catppuccin-mocha .image.is-1by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}html.theme--catppuccin-mocha .image.is-square,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square,html.theme--catppuccin-mocha .image.is-1by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}html.theme--catppuccin-mocha .image.is-5by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}html.theme--catppuccin-mocha .image.is-4by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--catppuccin-mocha .image.is-3by2,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--catppuccin-mocha .image.is-5by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--catppuccin-mocha .image.is-16by9,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--catppuccin-mocha .image.is-2by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--catppuccin-mocha .image.is-3by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--catppuccin-mocha .image.is-4by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--catppuccin-mocha .image.is-3by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--catppuccin-mocha .image.is-2by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--catppuccin-mocha .image.is-3by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--catppuccin-mocha .image.is-9by16,html.theme--catppuccin-mocha #documenter 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.button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark 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.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark 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.button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-link.is-light{background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:hover,html.theme--documenter-dark .button.is-link.is-light.is-hovered{background-color:#e2fbf6;border-color:transparent;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:active,html.theme--documenter-dark 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.button.is-info.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#3c5dcd}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #3c5dcd #3c5dcd !important}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark 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.button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;color:#259a12}html.theme--documenter-dark .button.is-success.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-outlined.is-focused{background-color:#259a12;border-color:#259a12;color:#fff}html.theme--documenter-dark .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-success.is-light{background-color:#effded;color:#2ec016}html.theme--documenter-dark .button.is-success.is-light:hover,html.theme--documenter-dark 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rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#f4c72f;box-shadow:none;color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f4c72f #f4c72f !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fefaec;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fdf7e0;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fdf3d3;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#c13930;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(203,60,51,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#b7362e;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:#cb3c33;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#cb3c33;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark 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html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fbefef;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#f8e6e5;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#f6dcda;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 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.buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark 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img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 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.docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar 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+ Maintainer: @highlightjs/core-team + Website: https://highlightjs.org/ + License: see project LICENSE + Touched: 2021 +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/previews/PR746/assets/themeswap.js b/previews/PR746/assets/themeswap.js new file mode 100644 index 0000000000..9f5eebe6aa --- /dev/null +++ b/previews/PR746/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } + } + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/previews/PR746/assets/warner.js b/previews/PR746/assets/warner.js new file mode 100644 index 0000000000..3f6f5d0083 --- /dev/null +++ b/previews/PR746/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; + + document.getElementsByTagName("head")[0].appendChild(meta); + } + + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/previews/PR746/diagnostics/available_diagnostics/index.html b/previews/PR746/diagnostics/available_diagnostics/index.html new file mode 100644 index 0000000000..e5504d4a9a --- /dev/null +++ b/previews/PR746/diagnostics/available_diagnostics/index.html @@ -0,0 +1,281 @@ + +Available diagnostics · ClimaLand.jl
GitHub

Available diagnostic variables

Autogenerate table of available diagnostics:

include("make_diagnostic_table.jl")
┌────────────┬─────────────────┬──────────┬──────────────────────────────────┬─────────────────┐
+│ Short name │ Long name       │ Units    │ Comments                         │ Standard name   │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ airp       │ Air pressure    │ Pa       │ The air pressure.                │ air_pressure    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ alpha      │ Albedo          │          │ The fraction of incoming         │ albedo          │
+│            │                 │          │ radiation reflected by the land  │                 │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ an         │ Leaf Net        │ mol CO2  │ Net photosynthesis (carbon       │ leaf_net_photos │
+│            │ Photosynthesis  │ m^-2     │ assimilation) of a leaf,         │ ynthesis        │
+│            │                 │ s^-1     │ computed for example by the      │                 │
+│            │                 │          │ Farquhar model.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ anir       │ Absorbed Near   │ mol      │ The amount of near infrared      │ absorbed_near_i │
+│            │ Infrared        │ photons  │ radiation absorbed by the        │ nfrared_radiati │
+│            │ Radiation       │ m^-2     │ canopy.                          │ on              │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ apar       │ Absorbed        │ mol      │ The amount of                    │ absorbed_photos │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ ynthetically_ac │
+│            │ lly Active      │ m^-2     │ radiation absorbed by the leaf.  │ tive_radiation  │
+│            │ Radiation       │ s^-1     │ The rest if reflected or         │                 │
+│            │                 │          │ transmitted.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ clhf       │ Canopy Latent   │ W m^-2   │ The energy used for canopy       │ canopy_latent_h │
+│            │ Heat Flux       │          │ transpiration.                   │ eat_flux        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ crae       │ Canopy          │ s m^-1   │ The canopy aerodynamic           │ canopy_aerodyna │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ mic_resistance  │
+│            │ Resistance      │          │ resistance (r_a) is a measure    │                 │
+│            │                 │          │ of how much the air near the     │                 │
+│            │                 │          │ Earth's surface resists the      │                 │
+│            │                 │          │ movement of water vapor and      │                 │
+│            │                 │          │ heat from the surface into the   │                 │
+│            │                 │          │ air.                             │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ cshf       │ Canopy          │ W m^-2   │ The energy used for canopy       │ canopy_sensible │
+│            │ Sensible Heat   │          │ temperature change.              │ _heat_flux      │
+│            │ Flux            │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ct         │ Canopy          │ K        │ Canopy temperature.              │ canopy_temperat │
+│            │ Temperature     │          │                                  │ ure             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ far        │ Root flux per   │ m s^-1   │ Flux of water volume per m^2 of  │ root_flux_per_g │
+│            │ ground area     │          │ root per second, multiplied by   │ round_area      │
+│            │                 │          │ the area index (root             │                 │
+│            │                 │          │ area/ground area).               │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gpp        │ Gross Primary   │ mol CO2  │ Net photosynthesis (carbon       │ gross_primary_p │
+│            │ Productivity    │ m^-2     │ assimilation) of the canopy.     │ roductivity     │
+│            │                 │ s^-1     │ This is equivalent to leaf net   │                 │
+│            │                 │          │ assimilation scaled to the       │                 │
+│            │                 │          │ canopy level.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gs         │ Stomatal        │ mol H2O  │ The conductance of leaves. This  │ stomatal_conduc │
+│            │ Conductance     │ m^-2     │ depends on stomatal opening. It  │ tance           │
+│            │                 │ s^-1     │ varies with factors such as      │                 │
+│            │                 │          │ soil moisture or atmospheric     │                 │
+│            │                 │          │ water demand.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ hr         │ Heterotrophic   │ mol      │ The CO2 efflux at the soil       │ heterotrophic_r │
+│            │ Respiration     │ m^-2     │ surface due to microbial         │ espiration      │
+│            │                 │ s^-1     │ decomposition of soil organic    │                 │
+│            │                 │          │ matter. This is not necessarily  │                 │
+│            │                 │          │ equal to CO2 production by       │                 │
+│            │                 │          │ microbes, as co2 diffusion       │                 │
+│            │                 │          │ through the soil pores takes     │                 │
+│            │                 │          │ time.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ infil      │ Infiltration    │ m s^-1   │ The flux of liquid water volume  │ infiltration    │
+│            │                 │          │ into the soil (m^3 of water per  │                 │
+│            │                 │          │ m^2 of ground per second).       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lai        │ Leaf area Index │ m^2 m^-2 │ The area index of leaves,        │ leaf_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ leaves per surface area of       │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lhf        │ Latent Heat     │ W m^-2   │ Exchange of energy at the        │ latent_heat_flu │
+│            │ Flux            │          │ land-atmosphere interface due    │ x               │
+│            │                 │          │ to water evaporation or          │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwd        │ Down Longwave   │ W m^-2   │ The down (in) longwave           │ down_longwave_r │
+│            │ Radiation       │          │ radiation at the surface.        │ adiation        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwn        │ Net Longwave    │ W m^-2   │ The net (in minus out) longwave  │ net_longwave_ra │
+│            │ Radiation       │          │ radiation at the surface.        │ diation         │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ msf        │ Moisture        │          │ Sensitivity of plants            │ moisture_stress │
+│            │ Stress Factor   │          │ conductance to soil water        │ _factor         │
+│            │                 │          │ content. Unitless                │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ nir        │ Near Infrared   │ mol      │ The amount of near infrared      │ near_infrared_r │
+│            │ Radiation       │ photons  │ radiation reaching the canopy.   │ adiation        │
+│            │                 │ m^-2     │                                  │                 │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ par        │ Photosynthetica │ mol      │ The subset of total radiation    │ photosynthetica │
+│            │ lly Active      │ photons  │ that activates photosynthesis    │ lly_active_radi │
+│            │ Radiation       │ m^-2     │ reaching the canopy.             │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ qsfc       │ Surface         │          │ Ratio of water vapor mass to     │ surface_specifi │
+│            │ Specific        │          │ total moist air parcel mass.     │ c_humidity      │
+│            │ Humidity        │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ra         │ Autotrophic     │ mol CO2  │ The canopy autotrophic           │ autotrophic_res │
+│            │ Respiration     │ m^-2     │ respiration, the sum of leaves,  │ piration        │
+│            │                 │ s^-1     │ stems and roots respiration.     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rae        │ Aerodynamic     │ m s^-1   │ Effiency of turbulent transport  │ aerodynamic_res │
+│            │ Resistance      │          │ controlling the land-atmosphere  │ istance         │
+│            │                 │          │ exchange of sensible and latent  │                 │
+│            │                 │          │ heat.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rai        │ Root area Index │ m^2 m^-2 │ The area index of roots,         │ root_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ roots per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rain       │ Rainfall        │ m s^-1   │ Precipitation of liquid water    │ rainfall        │
+│            │                 │          │ volume (m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground per second).              │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rd         │ Leaf            │ mol CO2  │ Leaf respiration, called dark    │ leaf_dark_respi │
+│            │ Respiration     │ m^-2     │ respiration because usually      │ ration          │
+│            │                 │ s^-1     │ measured in the abscence of      │                 │
+│            │                 │          │ radiation.                       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rhosfc     │ Surface Air     │ kg m^−3  │ Density of air at the            │ surface_air_den │
+│            │ Density         │          │ land-atmosphere interface.       │ sity            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rn         │ Net Radiation   │ W m^-2   │ Difference between incoming and  │ net_radiation   │
+│            │                 │          │ outgoing shortwave and longwave  │                 │
+│            │                 │          │ radiation at the land surface.   │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rnir       │ Reflected Near  │ mol      │ The amount of near infrared      │ reflected_near_ │
+│            │ Infrared        │ photons  │ radiation reflected by the       │ infrared_radiat │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ion             │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rpar       │ Reflected       │ mol      │ The amount of                    │ reflected_photo │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ synthetically_a │
+│            │ lly Active      │ m^-2     │ radiation reflected by leaves.   │ ctive_radiation │
+│            │ Radiation       │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sai        │ Stem area Index │ m^2 m^-2 │ The area index of stems,         │ stem_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ stems per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scd        │ Soil CO2        │ m^2 s^-1 │ The diffusivity of CO2 in the    │ soil_co2_diffus │
+│            │ Diffusivity     │          │ porous phase of the soil.        │ ivity           │
+│            │                 │          │ Depends on soil texture,         │                 │
+│            │                 │          │ moisture, and temperature.       │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scms       │ Soil CO2        │ kg C     │ The production of CO2 by         │ soil_co2_microb │
+│            │ Microbial       │ m^-3     │ microbes in the soil. Vary by    │ ial_source      │
+│            │ Source          │ s^-1     │ layers of soil depth. (depth     │                 │
+│            │                 │          │ resolved)                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sco2       │ Soil CO2        │ kg C m^3 │ Concentration of CO2 in the      │ soil_co2        │
+│            │                 │          │ porous air space of the soil.    │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shc        │ Soil Hydraulic  │ m s^-1   │ Soil hydraulic conductivity.     │ soil_hydraulic_ │
+│            │ Conductivity    │          │ (depth resolved)                 │ conductivity    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shf        │ Sensible Heat   │ W m^-2   │ Exchange of energy at the        │ sensible_heat_f │
+│            │ Flux            │          │ land-atmosphere interface due    │ lux             │
+│            │                 │          │ to temperature difference.       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ si         │ Soil Ice        │ m^3 m^-3 │ The volume of soil ice per       │ soil_ice        │
+│            │                 │          │ volume of soil. (depth resolved) │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sie        │ Soil Internal   │ W m^-2   │ The energy per volume of soil.   │ soil_internal_e │
+│            │ Energy          │          │ (depth resolved)                 │ nergy           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sif        │ Solar Induced   │ W m^-2   │ The fluorescence of leaves       │ solar_induced_f │
+│            │ Fluorescence    │          │ induced by solar radiation at    │ luorescence     │
+│            │                 │          │ 755nm. This quantity is          │                 │
+│            │                 │          │ correlated with photosynthesis   │                 │
+│            │                 │          │ activity.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ snow       │ Snowfall        │ m s^-1   │ The precipitation of snow in     │ snowfall        │
+│            │                 │          │ liquid water volume (m^3 of      │                 │
+│            │                 │          │ water per m^2 of ground per      │                 │
+│            │                 │          │ second).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soc        │ Soil organic    │ kg C     │ Mass of organic carbon per       │ soil_organic_ca │
+│            │ carbon          │ m^-3     │ volume of soil. (depth resolved) │ rbon            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soillhf    │ Soil Latent     │ W m^-2   │ Soil latent heat flux, the       │ soil_Latent_Hea │
+│            │ Heat Flux       │          │ amount of liquid water           │ t_Flux          │
+│            │                 │          │ evaporated by the soil,          │                 │
+│            │                 │          │ expressed in energy units (W     │                 │
+│            │                 │          │ m^-2).                           │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrae    │ Soil            │ s m^-1   │ The soil aerodynamic             │ soil_aerodynami │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ c_resistance    │
+│            │ Resistance      │          │ resistance is a measure of how   │                 │
+│            │                 │          │ much the air near the Earth's    │                 │
+│            │                 │          │ surface resists the movement of  │                 │
+│            │                 │          │ water vapor and heat from the    │                 │
+│            │                 │          │ surface into the air.            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrn     │ Soil Net        │ W m^-2   │ Net radiation at the soil        │ soil_net_radiat │
+│            │ Radiation       │          │ surface.                         │ ion             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilshf    │ Soil Sensible   │ W m^-2   │ Soil sensible heat flux, the     │ soil_sensible_H │
+│            │ Heat Flux       │          │ amount of energy exchanged       │ eat_Flux        │
+│            │                 │          │ between the soil and atmosphere  │                 │
+│            │                 │          │ to change the temperature of     │                 │
+│            │                 │          │ the soil.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ssfc       │ Snow Water      │ m        │ Snow at the soil surface,        │ snow_water_equi │
+│            │ Equivalent      │          │ expressed in water equivalent.   │ valent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ stc        │ Soil Thermal    │ W m^-1   │ Soil thermal conductivity.       │ soil_thermal_co │
+│            │ Conductivity    │ K^-1     │ (depth resolved)                 │ nductivity      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swc        │ Soil Water      │ m^3 m^-3 │ The volume of soil water per     │ soil_water_cont │
+│            │ Content         │          │ volume of soil. (depth resolved) │ ent             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swd        │ Short Longwave  │ W m^-2   │ The short (in) longwave          │ short_longwave_ │
+│            │ Radiation       │          │ radiation at the surface.        │ radiation       │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swn        │ Net Shortwave   │ W m^-2   │ The net (in minus out)           │ net_shortwave_r │
+│            │ Radiation       │          │ shortwave radiation at the       │ adiation        │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swp        │ Soil Water      │ Pa       │ Soil water potential. (depth     │ soil_water_pote │
+│            │ Potential       │          │ resolved)                        │ ntial           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sza        │ Solar Zenith    │          │ Solar zenith angle.              │ solar_zenith_an │
+│            │ Angle           │          │                                  │ gle             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tnir       │ Transmitted     │ mol      │ The amount of near infrared      │ transmitted_nea │
+│            │ Near Infrared   │ photons  │ radiation transmitted by the     │ r_infrared_radi │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tpar       │ Transmitted     │ mol      │ The amount of                    │ transmitted_pho │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ tosynthetically │
+│            │ lly Active      │ m^-2     │ radiation transmitted by leaves. │ _active_radiati │
+│            │ Radiation       │ s^-1     │                                  │ on              │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ trans      │ Canopy          │ m s^-1   │ The water evaporated from the    │ canopy_transpir │
+│            │ Transpiration   │          │ canopy due to leaf               │ ation           │
+│            │                 │          │ transpiration (flux of water     │                 │
+│            │                 │          │ volume, m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsfc       │ Surface         │ K        │ Temperature of the land surface. │ surface_tempera │
+│            │ Temperature     │          │                                  │ ture            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsoil      │ Soil            │ K        │ Soil temperature at multiple     │ soil_temperatur │
+│            │ temperature     │          │ soil depth. (depth resolved)     │ e               │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vcmax25    │ Vcmax25         │ mol CO2  │ The parameter vcmax at 25        │ vcmax25         │
+│            │                 │ m^-2     │ degree celsius. Important for    │                 │
+│            │                 │ s^-1     │ the Farquhar model of leaf       │                 │
+│            │                 │          │ photosynthesis.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vflux      │ Liquid water    │ m s^-1   │ Flux of water from the land      │ vapor_flux      │
+│            │ evaporation     │          │ surface to the atmosphere.       │                 │
+│            │                 │          │ E.g., evaporation or             │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ws         │ Wind Speed      │ m s^-1   │ The average wind speed.          │ wind_speed      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsfc       │ Surface Water   │ m        │ Water at the soil surface.       │ surface_water_c │
+│            │ Content         │          │                                  │ ontent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsoil      │ subsurface      │ m        │ Soil water content.              │ subsurface_wate │
+│            │ Water Storage   │          │                                  │ r_storage       │
+└────────────┴─────────────────┴──────────┴──────────────────────────────────┴─────────────────┘
diff --git a/previews/PR746/diagnostics/developers_diagnostics/index.html b/previews/PR746/diagnostics/developers_diagnostics/index.html new file mode 100644 index 0000000000..608b0575ef --- /dev/null +++ b/previews/PR746/diagnostics/developers_diagnostics/index.html @@ -0,0 +1,44 @@ + +For developers · ClimaLand.jl
GitHub

ClimaLand Diagnostics: why and how

ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in "K" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.

Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,

  • Diagnostics.jl defines the module,
  • diagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function

add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.

  • define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.

add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).

  • compute methods are defined in a separate file, for example, bucket_compute_methods.jl.
  • standard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.
  • default_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).

will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.

The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.

Compute methods

Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:

@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.α_sfc)
+    else
+        out .= p.bucket.α_sfc
+    end
+end

It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:

@diagnostic_compute "albedo" BucketModel p.bucket.α\_sfc

The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.

Define diagnostics

Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:

add_diagnostic_variable!(
+        short_name = "alpha",
+        long_name = "Albedo",
+        standard_name = "albedo",
+        units = "",
+        compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),
+    )

Default diagnostics

For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,

function default_diagnostics(land_model::BucketModel, t_start; output_writer)
+
+    define_diagnostics!(land_model)
+
+    bucket_diagnostics = [
+        "alpha",
+        "rn",
+        "tsfc",
+        "qsfc",
+        "lhf",
+        "rae",
+        "shf",
+        "vflux",
+        "rhosfc",
+        "t",
+        "w",
+        "ws",
+        "sigmas",
+    ]
+
+    default_outputs =
+        hourly_averages(bucket_diagnostics...; output_writer, t_start)
+    return [default_outputs...]
+end

is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).

For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = ["gpp", "ct", "lai", "swc", "si"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.

Standard diagnostic frequencies

We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example

hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(
+    60 * 60 * one(t_start),
+    (+),
+    output_writer,
+    t_start,
+    short_names...;
+    pre_output_hook! = average_pre_output_hook!,
+)

will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.

diff --git a/previews/PR746/diagnostics/make_diagnostic_table.jl b/previews/PR746/diagnostics/make_diagnostic_table.jl new file mode 100644 index 0000000000..4839efd307 --- /dev/null +++ b/previews/PR746/diagnostics/make_diagnostic_table.jl @@ -0,0 +1,35 @@ +import ClimaLand as CL +using PrettyTables + +# Print all available diagnostics to an ASCII table + +CL.Diagnostics.define_diagnostics!(nothing) +short_names = [] +long_names = [] +units = [] +comments = [] +standard_names = [] +for d in values(CL.Diagnostics.ALL_DIAGNOSTICS) + push!(short_names, d.short_name) + push!(long_names, d.long_name) + push!(units, d.units) + push!(comments, d.comments) + push!(standard_names, d.standard_name) +end +i = sortperm(short_names) # indices of short_names sorted alphabetically +data = hcat( + short_names[i], + long_names[i], + units[i], + comments[i], + standard_names[i], +) +pretty_table( + data; + autowrap = true, + linebreaks = true, + columns_width = [10, 15, 8, 32, 15], # Width = 80 + body_hlines = collect(1:size(data)[1]), + header = ["Short name", "Long name", "Units", "Comments", "Standard name"], + alignment = :l, +) diff --git a/previews/PR746/diagnostics/users_diagnostics/index.html b/previews/PR746/diagnostics/users_diagnostics/index.html new file mode 100644 index 0000000000..35cdb1aa5d --- /dev/null +++ b/previews/PR746/diagnostics/users_diagnostics/index.html @@ -0,0 +1,58 @@ + +For users · ClimaLand.jl
GitHub

Using ClimaLand Diagnostics when running a simulation

When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.

This is where ClimaLand Diagnostics comes in for users.

In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.

Default Diagnostics

Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:

define an output folder

output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path("base_output_dir/")

define a space

Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:

space = bucket_domain.space.subsurface

define your writter

Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:

nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)

providing the space and output_dir defined in steps 1. and 2.

make your diagnostics on your model, using your writter, and define a callback

Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:

t0 = 0 # the starting time of your simulation
+
+reference_date = DateTime(2024) # reference_date is the DateTime of your starting time
+
+diags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)
+
+diagnostic_handler =
+    ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)
+
+diag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)

Your diagnostics have now been written in netcdf files in your output folder.

Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.

Custom Diagnostics

When defining a custom diagnostic, follow these steps:

Define how to compute your diagnostic variable from your model state and cache.

For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.

function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)
+    else
+        out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf
+    end
+end

Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,

@diagnostic_compute "latent_heat_flux" BucketModel p.bucket.turbulent_fluxes.lhf

Add that diagnostic(s) variable to your list of variables

 add_diagnostic_variable!(
+    short_name = "bor",
+    long_name = "Bowen ratio",
+    standard_name = "bowen_ratio",
+    units = "",
+    comments = "Ratio of sensible to latent heat flux.",
+    compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),
+)
+
+add_diagnostic_variable!(
+    short_name = "lhf",
+    long_name = "Latent Heat Flux",
+    standard_name = "latent_heat_flux",
+    units = "W m^-2",
+    comments = "Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.",
+    compute! = (out, Y, p, t) ->
+    compute_latent_heat_flux!(out, Y, p, t, land_model),
+)

Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.

seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(
+    90 * 24 * 60 * 60 * one(t_start),
+    max,
+    output_writer,
+    t_start,
+    short_names...,
+)

Define a function to return your ScheduledDiagnostics

Now, you can call your schedule with your variables.

my_custom_diagnostics = ["lhf", "bor"]
+
+diags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)

Analyze your simulation output

Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.

Here is an example of how to plot a variable:

import ClimaAnalysis
+
+import ClimaAnalysis.Visualize as viz
+
+import CairoMakie # the plotting package used by ClimaAnalysis
+
+simdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.
+
+var = get(simdir; "lhf") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.
+
+fig = CairoMakie.Figure() # creates an empty figure object
+
+viz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.
+
+CairoMakie.save(fig) # saves the figure in current working directory
diff --git a/previews/PR746/folderstructure/index.html b/previews/PR746/folderstructure/index.html new file mode 100644 index 0000000000..d80c0ff6c7 --- /dev/null +++ b/previews/PR746/folderstructure/index.html @@ -0,0 +1,2 @@ + +Repository structure · ClimaLand.jl
GitHub

ClimaLand folder structure

ClimaLand home directory has 5 main folders:

  • docs: contains files to generate the documentation website.
  • experiments: contains simple runs of ClimaLand models.
  • parameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc.
  • src: contains the code of ClimaLand models.
  • test: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.

and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push.

  • .buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR.
  • .dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main).
  • .github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand.

as well as 5 files:

  • .gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git.
  • LICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code.
  • Project.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file.
  • README.md: This markdown file contains the info that you can read on ClimaLand GitHub web page

/docs folder

Julia packages are recommended to have a \docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.

The folder /docs contains:

  • a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation "contribution guide" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file.
  • a make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch.

Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl.

/experiments folder

The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified.

For example, /experiments/LSM/ozark/ contains:

  • ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).
  • ozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.
  • ozark_parameters.jl: In this file, parameters values are defined.
  • ozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm.
  • ozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.

/src folder

The /src folder contains the source code of ClimaLand models. It contains 3 folders:

  • sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.
  • standalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder.
  • integrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.

As well as one file:

  • ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase.
diff --git a/previews/PR746/generated/integrated/ozark_canopy_flux_test.png b/previews/PR746/generated/integrated/ozark_canopy_flux_test.png new file mode 100644 index 0000000000..3545594673 Binary files /dev/null and b/previews/PR746/generated/integrated/ozark_canopy_flux_test.png differ diff --git a/previews/PR746/generated/integrated/ozark_soil_plant_flux.png b/previews/PR746/generated/integrated/ozark_soil_plant_flux.png new file mode 100644 index 0000000000..5df1f84d8b Binary files /dev/null and b/previews/PR746/generated/integrated/ozark_soil_plant_flux.png differ diff --git a/previews/PR746/generated/integrated/ozark_soil_test.png b/previews/PR746/generated/integrated/ozark_soil_test.png new file mode 100644 index 0000000000..6589f9d47b Binary files /dev/null and b/previews/PR746/generated/integrated/ozark_soil_test.png differ diff --git a/previews/PR746/generated/integrated/soil_canopy_tutorial/index.html b/previews/PR746/generated/integrated/soil_canopy_tutorial/index.html new file mode 100644 index 0000000000..3e2eeb59d1 --- /dev/null +++ b/previews/PR746/generated/integrated/soil_canopy_tutorial/index.html @@ -0,0 +1,338 @@ + +Coupled Canopy and Soil · ClimaLand.jl
GitHub

Coupling the CliMA Canopy and Soil Hydraulics Models

In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.

In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Soil.Biogeochemistry
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the domain for the model:

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4)
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Column(; zlim = (zmin, zmax), nelements = nelements);
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32) # m
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Setup the Coupled Canopy and Soil Physics Model

We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.

For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.

Define the parameters for the soil model and provide them to the model parameters struct:

Soil parameters

soil_ν = FT(0.5) # m3/m3
+soil_K_sat = FT(4e-7) # m/s
+soil_S_s = FT(1e-3) # 1/m
+soil_vg_n = FT(2.05) # unitless
+soil_vg_α = FT(0.04) # inverse meters
+θ_r = FT(0.067); # m3/m3

Soil heat transfer parameters

ν_ss_quartz = FT(0.1)
+ν_ss_om = FT(0.1)
+ν_ss_gravel = FT(0.0);
+z_0m_soil = FT(0.1)
+z_0b_soil = FT(0.1)
+soil_ϵ = FT(0.98)
+soil_α_PAR = FT(0.2)
+soil_α_NIR = FT(0.4)
+
+soil_domain = land_domain
+soil_ps = Soil.EnergyHydrologyParameters(
+    FT;
+    ν = soil_ν,
+    ν_ss_om = ν_ss_om,
+    ν_ss_quartz = ν_ss_quartz,
+    ν_ss_gravel = ν_ss_gravel,
+    hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),
+    K_sat = soil_K_sat,
+    S_s = soil_S_s,
+    θ_r = θ_r,
+    earth_param_set = earth_param_set,
+    z_0m = z_0m_soil,
+    z_0b = z_0b_soil,
+    emissivity = soil_ϵ,
+    PAR_albedo = soil_α_PAR,
+    NIR_albedo = soil_α_NIR,
+);
+
+soil_args = (domain = soil_domain, parameters = soil_ps)
+soil_model_type = Soil.EnergyHydrology{FT}
ClimaLand.Soil.EnergyHydrology{Float32}

For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.

soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}
+
+soilco2_ps = SoilCO2ModelParameters(FT);

soil microbes args

Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))
+
+soilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()
+soilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);
+soilco2_sources = (MicrobeProduction{FT}(),);
+
+soilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);
+
+soilco2_args = (;
+    boundary_conditions = soilco2_boundary_conditions,
+    sources = soilco2_sources,
+    domain = soil_domain,
+    parameters = soilco2_ps,
+);

Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.

Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.

canopy_component_types = (;
+    autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},
+    radiative_transfer = Canopy.TwoStreamModel{FT},
+    photosynthesis = Canopy.FarquharModel{FT},
+    conductance = Canopy.MedlynConductanceModel{FT},
+    hydraulics = Canopy.PlantHydraulicsModel{FT},
+);

Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.

autotrophic_respiration_args =
+    (; parameters = AutotrophicRespirationParameters(FT))
+
+radiative_transfer_args = (;
+    parameters = TwoStreamParameters(
+        FT;
+        G_Function = ConstantGFunction(FT(0.5)),
+        α_PAR_leaf = 0.1,
+        α_NIR_leaf = 0.45,
+        τ_PAR_leaf = 0.05,
+        τ_NIR_leaf = 0.25,
+        Ω = 0.69,
+    )
+)
+
+conductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))
+
+photosynthesis_args =
+    (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));
+
+K_sat_plant = FT(1.8e-8)
+RAI = (SAI + maxLAI) * f_root_to_shoot;

Note: LAIfunction was determined from data in the script we included above.

ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)
+function root_distribution(z::T; rooting_depth = FT(1.0)) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m
+end
+
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)
+
+plant_ν = FT(0.7)
+plant_S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = plant_ν,
+    S_s = plant_S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+)
+
+plant_hydraulics_args = (
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_midpoints = compartment_midpoints,
+    compartment_surfaces = compartment_surfaces,
+);

We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.

canopy_component_args = (;
+    autotrophic_respiration = autotrophic_respiration_args,
+    radiative_transfer = radiative_transfer_args,
+    photosynthesis = photosynthesis_args,
+    conductance = conductance_args,
+    hydraulics = plant_hydraulics_args,
+);

We also need to provide the shared parameter struct to the canopy.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+)
+canopy_domain = obtain_surface_domain(land_domain)
+canopy_model_args = (; parameters = shared_params, domain = canopy_domain);

We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.

land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)
+
+land = SoilCanopyModel{FT}(;
+    soilco2_type = soilco2_type,
+    soilco2_args = soilco2_args,
+    land_args = land_input,
+    soil_model_type = soil_model_type,
+    soil_args = soil_args,
+    canopy_component_types = canopy_component_types,
+    canopy_component_args = canopy_component_args,
+    canopy_model_args = canopy_model_args,
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.

Y, p, coords = initialize(land);
+exp_tendency! = make_exp_tendency(land);
+imp_tendency! = make_imp_tendency(land);
+jacobian! = make_jacobian(land);
+jac_kwargs =
+    (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);

We need to provide initial conditions for the soil and canopy hydraulics models:

Y.soil.ϑ_l = FT(0.4)
+Y.soil.θ_i = FT(0.0)
+T_0 = FT(288.7)
+ρc_s =
+    volumetric_heat_capacity.(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        land.soil.parameters.ρc_ds,
+        earth_param_set,
+    )
+Y.soil.ρe_int =
+    volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)
+
+Y.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air
+
+ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        plant_ν,
+        plant_S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .=
+        augmented_liquid_fraction.(plant_ν, S_l_ini[i])
+end;

Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.

t0 = Float64(150 * 3600 * 24)# start mid year
+N_days = 100
+tf = t0 + Float64(3600 * 24 * N_days)
+dt = Float64(30)
+n = 120
+saveat = Array(t0:(n * dt):tf)
+
+timestepper = CTS.ARS343()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Now set the initial values for the cache variables for the combined soil and plant model.

set_initial_cache! = make_set_initial_cache(land)
+set_initial_cache!(p, Y, t0);

Set the callbacks, which govern how often we save output, and how often we update the forcing data ("drivers")

sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+model_drivers = ClimaLand.get_drivers(land)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+updateat = Array(t0:1800:tf)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Carry out the simulation

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(
+    prob,
+    ode_algo;
+    dt = dt,
+    callback = cb,
+    adaptive = false,
+    saveat = saveat,
+);

Plotting

Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_canopy_flux_test.png");

Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.

plt1 = Plots.plot(size = (500, 700));
+ϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]
+plt1 = Plots.plot(
+    daily,
+    ϑ_l_10,
+    label = "10 cm",
+    xlabel = "Days",
+    ylabel = "SWC [m/m]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+    color = "blue",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],
+    label = "20cm",
+    color = "red",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],
+    label = "30cm",
+    color = "purple",
+);

Save the output:

savefig("ozark_soil_test.png");

And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:

root_stem_flux = [
+    sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for
+    k in 1:length(sol.t)
+]
+plt1 = Plots.plot(
+    daily,
+    root_stem_flux,
+    label = "soil-root-stem water flux",
+    ylabel = "Water flux[mm/day]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+);

And save the output

savefig("ozark_soil_plant_flux.png");

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/shared_utilities/driver_tutorial/index.html b/previews/PR746/generated/shared_utilities/driver_tutorial/index.html new file mode 100644 index 0000000000..083560eb74 --- /dev/null +++ b/previews/PR746/generated/shared_utilities/driver_tutorial/index.html @@ -0,0 +1,49 @@ + +Intro to forced site-level runs · ClimaLand.jl
GitHub

Using atmospheric and radiative drivers

The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.

Types of forcing data

We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.

The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).

The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.

Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.

Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.

Creating site-level drivers for radiation

First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.

using Dates
+using Insolation # for computing zenith angle given lat, lon, time.
+using ClimaLand
+import ClimaLand.Parameters as LP
+import ClimaParams

Assume the local_datetime array is read in from the data file.

local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data

Timezone (offset of local time from UTC in hrs)

time_offset = 7;

Site latitude and longitude

lat = 38.7441; # degree
+long = -92.2000; # degree

Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time

ref_time = local_datetime[1] + Dates.Hour(time_offset);
+data_dt = 3600.0;
+seconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);

Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:

T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));
+LW_d = 5.67 * 10^(-8) .* T .^ 4;
+SW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);

Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.

LW_d = TimeVaryingInput(seconds, LW_d)
+SW_d = TimeVaryingInput(seconds, SW_d);

Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:

earth_param_set = LP.LandParameters(Float64);
+insol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation
+function zenith_angle(
+    t,
+    ref_time;
+    latitude = lat,
+    longitude = long,
+    insol_params = insol_params,
+)
+    current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC
+
+    d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(
+        current_datetime,
+        ref_time,
+        insol_params,
+    ))
+
+
+    return Insolation.instantaneous_zenith_angle(
+        d,
+        δ,
+        η_UTC,
+        longitude,
+        latitude,
+    )[1]
+end;

Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.

radiation = ClimaLand.PrescribedRadiativeFluxes(
+    Float64,
+    SW_d,
+    LW_d,
+    ref_time;
+    θs = zenith_angle,
+);

Updating the driver variables during the simulation

The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:

p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));

In order to update them, we can make use of default update functions:

update_radiation! = ClimaLand.make_update_drivers(radiation)
+t0 = seconds[1] # midnight local time
+update_radiation!(p, t0);
+@show(p.drivers);
p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])
+

During a simulation, the drivers are updated in place in p.drivers via a "callback", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:

updateat = collect(seconds[1]:(3600 * 3):seconds[end]);
+updatefunc = update_radiation!;
+cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);

This callback must then be provided to the simulation solve function.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Bucket/bucket_tutorial/index.html b/previews/PR746/generated/standalone/Bucket/bucket_tutorial/index.html new file mode 100644 index 0000000000..a6655ba044 --- /dev/null +++ b/previews/PR746/generated/standalone/Bucket/bucket_tutorial/index.html @@ -0,0 +1,145 @@ + +Introduction to the Land Bucket Model · ClimaLand.jl
GitHub

Introduction to the Land Bucket Model

The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.

This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.

At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.

In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.

All equation variables are defined immediately below. We have:

$\frac{d W}{dt} = -I,$

$\frac{d Ws}{dt} = -(P_{liq} + σM + (1-σ) E_{soil} - I),$

$\frac{d σS}{dt} = -(P_{snow} + σE_{snow} - σM),$

$ρc \frac{\partial T}{\partial t} = κ_{soil} \frac{\partial T}{\partial z}$

$F_{bot} = 0.0 = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{bot}}$

$(1-σ) (R_n+ SHF + LHF)_{soil} + σG_{undersnow} = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{sfc}}$

$G_{undersnow} = (R_n+ SHF + LHF)_{snow} - F_{intosnow}$

$F_{intosnow} = -ρ_l L_{f,0} (P_{snow} + E_{snow}-M)$

$R_n = -(1-α)*SW↓ -LW↓ + σ_{SB} T_{sfc}^4$

where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.

For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.

Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.

Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.

As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have

$E_{soil} = β(W, W_f) E(q_{sat}(T_{sfc}, ρ_{sfc}; liquid),$

where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have

$E_{snow} = E(q_{sat}(T_{sfc}, ρ_{sfc}; ice).$

Simulating a standalone bucket model

First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.

import SciMLBase
+import ClimaTimeSteppers as CTS

We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.

using ClimaCore

We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.

import ClimaParams as CP

We also use Insolation to calculate solar zenith angle and solar insolation.

Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.

using ClimaLand.Bucket:
+    BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo
+using ClimaLand.Domains: coordinates, Column
+using ClimaLand:
+    initialize,
+    make_update_aux,
+    make_exp_tendency,
+    make_set_initial_cache,
+    PrescribedAtmosphere,
+    PrescribedRadiativeFluxes
+using ClimaUtilities.TimeVaryingInputs: TimeVaryingInput

We also want to plot the solution

using Plots

And we need to use the DateTime type to store reference times

using Dates
+
+FT = Float32;

As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:

import ClimaLand
+import ClimaLand.Parameters as LP
+earth_param_set = LP.LandParameters(FT);

Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.

soil_depth = FT(3.5);
+bucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);
+surface_space = bucket_domain.space.surface
ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}
+  Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])

Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.

α_bareground_func = (coordinate_point) -> 0.2;
+α_snow = FT(0.8);
+albedo =
+    PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);

The critical snow level setting the scale for when we interpolate between snow and surface albedo

σS_c = FT(0.2);

The field capacity of the soil

W_f = FT(0.15);

Roughness lengths (meters)

z_0m = FT(1e-2);
+z_0b = FT(1e-3);

Thermal parameters of soil

κ_soil = FT(0.7);
+ρc_soil = FT(2e6);

Snow melt timescale

τc = FT(3600);

Simulation start time, end time, and timestep

t0 = 0.0;
+tf = 7 * 86400;
+Δt = 3600.0;
+
+bucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);

The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.

ref_time = DateTime(2005);

To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Here we define the model drivers Prescribed atmospheric variables

Precipitation:

precip = (t) -> 0;
+snow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);

Diurnal temperature variations:

T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);

Constant otherwise:

u_atmos = (t) -> 3.0;
+q_atmos = (t) -> 0.005;
+h_atmos = FT(2);
+P_atmos = (t) -> 101325;

We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)

bucket_atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(snow_precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set,
+);

Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:

SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));
+LW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;
+bucket_rad = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:

model = BucketModel(
+    parameters = bucket_parameters,
+    domain = bucket_domain,
+    atmosphere = bucket_atmos,
+    radiation = bucket_rad,
+);

Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.

Like all ClimaLand models, we set up the state vector using initialize:

Y, p, coords = initialize(model);

We can inspect the prognostic and auxiliary variables of the model:

ClimaLand.prognostic_vars(model)
+Y.bucket |> propertynames
(:W, :T, :Ws, :σS)

The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.

ClimaLand.auxiliary_vars(model)
+p.bucket |> propertynames
(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)

Next is to set initial conditions.

Y.bucket.T .= FT(270);
+Y.bucket.W .= FT(0.05);
+Y.bucket.Ws .= FT(0.0);
+Y.bucket.σS .= FT(0.08);

We also set the initial values of the cache here:

set_initial_cache! = make_set_initial_cache(model);
+set_initial_cache!(p, Y, t0);

Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:

exp_tendency! = make_exp_tendency(model);

Now we choose our timestepping algorithm.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

Then we can set up the simulation and solve it:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)

saveat = collect(t0:Δt:tf);
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+saving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);
+updateat = copy(saveat)
+model_drivers = ClimaLand.get_drivers(model)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);

Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.

W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];
+Ws = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];
+σS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];
+T_sfc =
+    [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];
+evaporation = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]
+    for k in 1:length(sol.t)
+];
+R_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];

The turbulent energy flux is the sum of latent and sensible heat fluxes.

LHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for
+    k in 1:length(sol.t)
+];
+SHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for
+    k in 1:length(sol.t)
+];
+turbulent_energy_flux = SHF .+ LHF
+
+plot(
+    sol.t ./ 86400,
+    W,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "W (m)",
+    title = "Land water storage (m)",
+)
+savefig("w.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png"

plot(
+    sol.t ./ 86400,
+    σS,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "σS (m)",
+    title = "Area weighted SWE (m) ",
+)
+savefig("swe.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png"

plot(
+    sol.t ./ 86400,
+    snow_precip.(sol.t),
+    label = "Net precipitation",
+    xlabel = "time (days)",
+    ylabel = "Flux (m/s)",
+    title = "Surface water fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, evaporation, label = "Sublimation/Evaporation")
+savefig("water_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png"

plot(
+    sol.t ./ 86400,
+    T_sfc,
+    title = "Surface Temperatures",
+    label = "Ground temperature",
+    xlabel = "time (days)",
+    ylabel = "T_sfc (K)",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, T_atmos.(sol.t), label = "Atmospheric Temperature")
+savefig("t.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png"

plot(
+    sol.t ./ 86400,
+    R_n,
+    label = "Net radiative flux",
+    xlabel = "time (days)",
+    ylabel = "Flux (W/m^2)",
+    title = "Surface energy fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, turbulent_energy_flux, label = "Turbulent fluxes")
+plot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = "Net flux")
+savefig("energy_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png"

References

[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Bucket/coupled_bucket/index.html b/previews/PR746/generated/standalone/Bucket/coupled_bucket/index.html new file mode 100644 index 0000000000..4c9886ba28 --- /dev/null +++ b/previews/PR746/generated/standalone/Bucket/coupled_bucket/index.html @@ -0,0 +1,2 @@ + +Setting up a Coupled Simulation · ClimaLand.jl
GitHub

Setting up a Coupled Simulation

For more information about the bucket model, please see the bucket model tutorial.

This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.

Background

Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.

In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.

In our current setup, "passed back to the land model via the coupler" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.

In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.

Turbulent Surface Fluxes and Radiation

Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:

prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)

These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.

In the coupled case, we want different behavior. We have defined new $coupled$ types to use instead of the "prescribed" types:

struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end

Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end

similarily:

function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end

These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!

Surface air density

Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.

In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end

Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Bucket/energy_f.png b/previews/PR746/generated/standalone/Bucket/energy_f.png new file mode 100644 index 0000000000..2fe9f0d606 Binary files /dev/null and b/previews/PR746/generated/standalone/Bucket/energy_f.png differ diff --git a/previews/PR746/generated/standalone/Bucket/swe.png b/previews/PR746/generated/standalone/Bucket/swe.png new file mode 100644 index 0000000000..9201c070e6 Binary files /dev/null and b/previews/PR746/generated/standalone/Bucket/swe.png differ diff --git a/previews/PR746/generated/standalone/Bucket/t.png b/previews/PR746/generated/standalone/Bucket/t.png new file mode 100644 index 0000000000..387db53ad8 Binary files /dev/null and b/previews/PR746/generated/standalone/Bucket/t.png differ diff --git a/previews/PR746/generated/standalone/Bucket/w.png b/previews/PR746/generated/standalone/Bucket/w.png new file mode 100644 index 0000000000..b11d3a746a Binary files /dev/null and b/previews/PR746/generated/standalone/Bucket/w.png differ diff --git a/previews/PR746/generated/standalone/Bucket/water_f.png b/previews/PR746/generated/standalone/Bucket/water_f.png new file mode 100644 index 0000000000..a5ee08bbf4 Binary files /dev/null and b/previews/PR746/generated/standalone/Bucket/water_f.png differ diff --git a/previews/PR746/generated/standalone/Canopy/canopy_tutorial/index.html b/previews/PR746/generated/standalone/Canopy/canopy_tutorial/index.html new file mode 100644 index 0000000000..5382849611 --- /dev/null +++ b/previews/PR746/generated/standalone/Canopy/canopy_tutorial/index.html @@ -0,0 +1,202 @@ + +Standalone Canopy · ClimaLand.jl
GitHub

Introduction to the Canopy Model

This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.

The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.

ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.

The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.

The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using StaticArrays
+using ClimaLand
+using ClimaLand.Domains: Point
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the Canopy Model

We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.

First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4) # kg/m^2
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Point(; z_sfc = FT(0.0))
ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}
+  Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32)
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+);

For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:

ψ_soil0 = FT(0.0)
+
+soil_driver = PrescribedSoil(
+    FT;
+    root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),
+    ψ = t -> ψ_soil0,
+    α_PAR = FT(0.2),
+    α_NIR = FT(0.4),
+    T = t -> 298.0,
+    ϵ = FT(0.99),
+);

Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:

rt_params = TwoStreamParameters(
+    FT;
+    G_Function = ConstantGFunction(FT(0.5)),
+    α_PAR_leaf = FT(0.1),
+    α_NIR_leaf = FT(0.45),
+    τ_PAR_leaf = FT(0.05),
+    τ_NIR_leaf = FT(0.25),
+    Ω = FT(0.69),
+    λ_γ_PAR = FT(5e-7),
+    λ_γ_NIR = FT(1.65e-6),
+)
+
+rt_model = TwoStreamModel{FT}(rt_params);

Arguments for conductance model:

cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))
+
+stomatal_model = MedlynConductanceModel{FT}(cond_params);

Arguments for photosynthesis model:

photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))
+
+photosynthesis_model = FarquharModel{FT}(photo_params);

Arguments for autotrophic respiration model:

AR_params = AutotrophicRespirationParameters(FT)
+AR_model = AutotrophicRespirationModel{FT}(AR_params);

Arguments for plant hydraulics model are more complicated.

Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.

LAI = 4.2
+LAIfunction = (t) -> LAI
+SAI = FT(0.00242)
+f_root_to_shoot = FT(3.5)
+RAI = FT((SAI + LAI) * f_root_to_shoot)
+ai_parameterization =
+    PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)
+rooting_depth = FT(1.0);

Define the root distribution function p(z):

function root_distribution(z::T; rooting_depth = rooting_depth) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))
+end;

Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.

K_sat_plant = FT(1.8e-8)
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);

Use these values to populate the parameters of the PlantHydraulics model:

ν = FT(0.7)
+S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = ν,
+    S_s = S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+);

Define the remaining variables required for the plant hydraulics model.

plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_surfaces = compartment_surfaces,
+    compartment_midpoints = compartment_midpoints,
+);

Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.

canopy = ClimaLand.Canopy.CanopyModel{FT}(;
+    parameters = shared_params,
+    domain = land_domain,
+    autotrophic_respiration = AR_model,
+    radiative_transfer = rt_model,
+    photosynthesis = photosynthesis_model,
+    conductance = stomatal_model,
+    hydraulics = plant_hydraulics,
+    soil_driver = soil_driver,
+    atmos = atmos,
+    radiation = radiation,
+);
[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.

Y, p, coords = ClimaLand.initialize(canopy)
+exp_tendency! = make_exp_tendency(canopy);

Provide initial conditions for the canopy hydraulics model

ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        ν,
+        S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])
+end;

Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

t0 = 0.0
+N_days = 364
+tf = t0 + 3600 * 24 * N_days
+dt = 225.0;

Initialize the cache variables for the canopy using the initial conditions and initial time.

set_initial_cache! = make_set_initial_cache(canopy)
+set_initial_cache!(p, Y, t0);

Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.

n = 16
+saveat = Array(t0:(n * dt):tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);

Create the callback function which updates the forcing variables, or drivers.

updateat = Array(t0:1800:tf)
+model_drivers = ClimaLand.get_drivers(canopy)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Select a timestepping algorithm and setup the ODE problem.

timestepper = CTS.RK4();
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Create some plots

We can now plot the data produced in the simulation. For example, GPP:

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_standalone_canopy_test.png");

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Canopy/ozark_standalone_canopy_test.png b/previews/PR746/generated/standalone/Canopy/ozark_standalone_canopy_test.png new file mode 100644 index 0000000000..7a17facc9c Binary files /dev/null and b/previews/PR746/generated/standalone/Canopy/ozark_standalone_canopy_test.png differ diff --git a/previews/PR746/generated/standalone/Snow/base_tutorial/index.html b/previews/PR746/generated/standalone/Snow/base_tutorial/index.html new file mode 100644 index 0000000000..6ed5939dd3 --- /dev/null +++ b/previews/PR746/generated/standalone/Snow/base_tutorial/index.html @@ -0,0 +1,74 @@ + +Seasonal Snow Timeseries Generation with a Neural Network · ClimaLand.jl
GitHub

Seasonal Snow Timeseries Generation with a Neural Network

This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.

The updates of the neural snow model follow the equation

$\frac{dz}{dt} = \mathcal{M}\left(z, SWE, φ, R, v, T_{air}, P_{snow}\right),$

where

  • $t$ is the time (s),

  • $z$ is the snow depth (m),

  • $\mathcal{M}$ is the neural network,

  • $SWE$ is the Snow Water Equivalent, or the height of water if all the snow melted (m),

  • $φ$ is the relative humidity (0-1),

  • $R$ is the solar radiation (W/m²).

  • $v$ is the wind speed (W/m²).

  • $T_{air}$ is the air temperature (degrees C).

  • $P_{snow}$ is the water equivalent rate of snow precipitation (m/s).

The model is a 1D model to permit utilization over any desired grid resolution and shape.

We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.

We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN
┌ Warning: Package cuDNN not found in current path.
+│ - Run `import Pkg; Pkg.add("cuDNN")` to install the cuDNN package, then restart julia.
+│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.
+└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57
+

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools
+ModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;

and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.

using ClimaLand
+code_dir = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Snow")
+include(joinpath(code_dir, "display_tools.jl"));

Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter $n$ as outlined in the associated parameter, and the two loss function hyperparameters $n_1$, $n_2$.

n = 4
+n1 = 2
+n2 = 4;

We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the $\frac{dz}{dt}$ column.

pred_vars = [
+    :z,
+    :SWE,
+    :rel_hum_avg,
+    :sol_rad_avg,
+    :wind_speed_avg,
+    :air_temp_avg,
+    :dprecipdt_snow,
+]
+target = :dzdt;

Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.

nfeatures = length(pred_vars)
+z_idx = 1
+p_idx = 7;

We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.

training_data_download_link = "https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv"
+testing_data_download_link = "https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv"
+data_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)
+valdata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)
+Δt = Second(86400)
+hole_thresh = 5;

With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):

usedata = DataTools.prep_data(data_train);

After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of "0" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.

out_scale = maximum(abs.(usedata[!, target]))
+in_scales = std.(eachcol(select(usedata, pred_vars)))
+x_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);

We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.

model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)
+ps = ModelTools.get_model_ps(model);

As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.

ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)
+ModelTools.setoutscale!(model, 1.0);

With that, training is as simple as calling the trainmodel! function:

print("\nTraining model!\n")
+ModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);

+Training model!
+Epoch: 10 | training loss: 0.002783926
+Epoch: 20 | training loss: 0.002693796
+Epoch: 30 | training loss: 0.0026894645
+Epoch: 40 | training loss: 0.0026167487
+Epoch: 50 | training loss: 0.0026483187
+Epoch: 60 | training loss: 0.0025837263
+Epoch: 70 | training loss: 0.0026131733
+Epoch: 80 | training loss: 0.00276859
+Epoch: 90 | training loss: 0.002585475
+Epoch: 100 | training loss: 0.0026451899
+

To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.

ModelTools.setoutscale!(model, out_scale)
+ModelTools.settimescale!(model, Dates.value(Δt));

For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):

Note that gaps in the data are shown as shaded regions on the plotted timeseries.

site_id = 1286
+sitedata = usedata[usedata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Slagamount Lakes, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot1.png",
+    display_plot = false,
+);

Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:

site_id = "1070" #string format for the testing ids is due to non-numerical testing site codes.
+sitedata = valdata[valdata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Anchorage Hillside, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot2.png",
+    display_plot = false,
+);

Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Snow/base_tutorial_plot1.png b/previews/PR746/generated/standalone/Snow/base_tutorial_plot1.png new file mode 100644 index 0000000000..58e920ef1f Binary files /dev/null and b/previews/PR746/generated/standalone/Snow/base_tutorial_plot1.png differ diff --git a/previews/PR746/generated/standalone/Snow/base_tutorial_plot2.png b/previews/PR746/generated/standalone/Snow/base_tutorial_plot2.png new file mode 100644 index 0000000000..92f27fdc6e Binary files /dev/null and b/previews/PR746/generated/standalone/Snow/base_tutorial_plot2.png differ diff --git a/previews/PR746/generated/standalone/Snow/data_tutorial/index.html b/previews/PR746/generated/standalone/Snow/data_tutorial/index.html new file mode 100644 index 0000000000..394c6738f0 --- /dev/null +++ b/previews/PR746/generated/standalone/Snow/data_tutorial/index.html @@ -0,0 +1,132 @@ + +Scraping SNOTEL Data · ClimaLand.jl
GitHub

Scraping SNOTEL Data

This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.

We begin by importing all required packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;

We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.

metadata = DataTools.snotel_metadata();
+metacols = ["id", "name", "state", "elev", "lat", "lon"]
+DataFrames.rename!(metadata, Symbol.(metacols));

At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:

example_ID = 1030
+example_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]
+example_data = DataTools.scrape_site_paper(example_ID, example_state);

And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.

We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.

const inch2meter = 0.0254
+const kmphr2mps = 5.0 / 18.0
+
+filter_val = Dict{Symbol, Tuple{Real, Real}}(
+    :SWE => (0.0, 250.0),
+    :z => (0.0, 420.0),
+    :precip => (0.0, 250.0),
+    :rel_hum_avg => (10.0, 100.0),
+    :sol_rad_avg => (0.0, 1500.0),
+    :wind_speed_avg => (0.0, 216.0),
+    :air_temp_avg => (-40.0, 60.0),
+)
+
+scales = Dict{Symbol, Real}(
+    :SWE => inch2meter,
+    :z => inch2meter,
+    :precip => inch2meter,
+    :rel_hum_avg => 0.01,
+    :wind_speed_avg => kmphr2mps,
+);

We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. "start" refers to 1850-01-01 or the first available date, while "end" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:

  • * Indicates alternative handling of the rectify_daily_hourly() function.

  • ^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.

  • A Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.

  • T Requires a site that already has had the temperature bias correction at the portal level as of May 2024.

  • X Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.

good_stations = Dict{Int, Tuple{String, String}}(
+    #306 => ("start", "end"), #*
+    316 => ("start", "end"),
+    344 => ("start", "end"),
+    #=367 => ("start", "end"),
+    395 => ("start", "end"),
+    457 => ("start", "end"),
+    482 => ("start", "end"),
+    491 => ("start", "end"),
+    515 => ("start", "2023-06-02"), #X
+    532 => ("start", "end"),
+    551 => ("start", "end"),
+    571 => ("start", "end"),
+    599 => ("start", "end"),
+    608 => ("start", "end"),
+    613 => ("start", "end"),
+    641 => ("start", "end"), #A^
+    665 => ("start", "end"),
+    708 => ("start", "end"),
+    715 => ("start", "end"),
+    734 => ("start", "end"),
+    737 => ("start", "end"),
+    744 => ("start", "end"),
+    832 => ("start", "end"),
+    845 => ("start", "end"),
+    854 => ("start", "end"),
+    857 => ("start", "end"),
+    921 => ("start", "end"),
+    922 => ("start", "end"),
+    927 => ("start", "end"),
+    942 => ("start", "end"),
+    963 => ("start", "end"), #A^
+    969 => ("start", "end"),
+    974 => ("start", "end"),
+    978 => ("start", "end"), #*
+    1030 => ("start", "end"),
+    1035 => ("start", "end"), #A^
+    1053 => ("start", "end"),
+    1070 => ("start", "end"), #A^T
+    1083 => ("start", "end"),
+    1091 => ("start", "end"), #A^T
+    1092 => ("start", "end"), #A^T
+    1105 => ("start", "end"),
+    1122 => ("start", "end"), #*
+    1123 => ("start", "end"),
+    1159 => ("start", "end"),
+    1168 => ("start", "end"),
+    1170 => ("start", "end"),
+    1254 => ("start", "end"),
+    1286 => ("start", "end"),
+    2080 => ("start", "end"), #A^
+    2170 => ("start", "end"), #^
+    =#
+);

We then loop through each site to scrape and follow an automated data pipeline, consisting of:

  • Extracting the daily and hourly timeseries from the site
  • Applying the sensor bounds over each data timeseries (i.e. remove sensor error)
  • Converting the hourly dataset into a daily dataset
  • Coalescing the converted-hourly and daily data into one dataset
  • Scaling all data to the appropriate metric units
  • Restricting data to complete cases
  • Making the differential variables ( $\frac{dz}{dt}$, etc.)
  • Resetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip
  • Attaching appropriate metadata

A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.

allsites = Any[];
+for site in sort(collect(keys(good_stations)))
+    state = metadata[metadata[!, :id] .== site, :state][1]
+    start_date = good_stations[site][1]
+    end_date = good_stations[site][2]
+
+    hourly = DataTools.apply_bounds(
+        DataTools.sitedata_hourly(
+             site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+       filter_val,
+    )
+    hourly[!, :id] .= site
+    #hourly = DataTools.bcqc_hourly(hourly)
+    hourly_d = DataTools.hourly2daily(hourly)
+    #DataFrames.allowmissing!(hourly_d)
+    #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)
+   #hourly_d[sflags, :sol_rad_avg] .= missing
+
+    daily = DataTools.apply_bounds(
+        DataTools.sitedata_daily(
+            site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+        filter_val,
+    )
+    daily[!, :id] .= site
+    gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)
+    #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)
+    #gap_daily = DataTools.d_impute(gap_daily)
+    daily_scaled = DataTools.scale_cols(gap_daily, scales)
+    daily_clean = daily_scaled[completecases(daily_scaled), :]
+    daily_clean = DataTools.makediffs(daily_clean, Day(1))
+    good_vals = daily_clean[!, :dprecipdt] .>= 0.0
+    daily_clean[(!).(good_vals), :dprecipdt] .= 0.0
+    daily_clean = daily_clean[!, Not(:precip)]
+    #show(describe(daily_clean), allrows = true, allcols = true)
+    #print("\nSIZE: ", nrow(daily_clean), "\n")
+
+    daily_clean[!, :id] .= site
+    daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]
+    daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]
+    daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]
+
+    push!(allsites, daily_clean)
+end;

With the sites complete, we condense all sites into a single DataFrame,

totaldata = deepcopy(allsites[1])
+for site in allsites[2:end]
+    append!(totaldata, site)
+end

and a final CSV.write("data.csv", totaldata) call will save the file.

Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/boundary_conditions/index.html b/previews/PR746/generated/standalone/Soil/boundary_conditions/index.html new file mode 100644 index 0000000000..6827168d91 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/boundary_conditions/index.html @@ -0,0 +1,2 @@ + +Boundary conditions · ClimaLand.jl
GitHub

Boundary conditions for the soil model

In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.

Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.

Boundary conditions for Richards equation

  1. FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.

  2. WaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).

  3. MoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).

  4. RichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.

Boundary conditions for the soil heat equation

  1. HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).

  2. TemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).

Boundary conditions for the soil heat + water equations (EnergyHydrology model)

The full soil model requires boundary conditions for both Richards equation and the soil heat equation.

  1. WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).

  2. AtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/eq_moisture_plot.png b/previews/PR746/generated/standalone/Soil/eq_moisture_plot.png new file mode 100644 index 0000000000..df15e83379 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/eq_moisture_plot.png differ diff --git a/previews/PR746/generated/standalone/Soil/eq_temperature_plot.png b/previews/PR746/generated/standalone/Soil/eq_temperature_plot.png new file mode 100644 index 0000000000..f50685a410 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/eq_temperature_plot.png differ diff --git "a/previews/PR746/generated/standalone/Soil/equilibrium_test_\317\221_l.png" "b/previews/PR746/generated/standalone/Soil/equilibrium_test_\317\221_l.png" new file mode 100644 index 0000000000..e19d4c660e Binary files /dev/null and "b/previews/PR746/generated/standalone/Soil/equilibrium_test_\317\221_l.png" differ diff --git a/previews/PR746/generated/standalone/Soil/evaporation/index.html b/previews/PR746/generated/standalone/Soil/evaporation/index.html new file mode 100644 index 0000000000..b0a876d8d5 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/evaporation/index.html @@ -0,0 +1,229 @@ + +Coarse Sand Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.

Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.

ref_time = DateTime(2005) # required argument, but not used in this case
+SW_d = (t) -> 0
+LW_d = (t) -> 301.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 5
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)
+    Y.soil.θ_i .= 0
+    T = FT(296.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 13)
+dt = Float64(900.0)
900.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Define the tendency functions

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+evaporation_data =
+    ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();
+ref_soln_E = readdlm(evaporation_data, ',')
+ref_soln_E_350mm = ref_soln_E[2:end, 1:2]
+data_dates = ref_soln_E_350mm[:, 1]
+data_e = ref_soln_E_350mm[:, 2];
+
+fig = Figure(size = (800, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Evaporation rate (mm/d)",
+    title = "Bare soil evaporation",
+)
+CairoMakie.xlims!(minimum(data_dates), maximum(data_dates))
+CairoMakie.lines!(
+    ax,
+    FT.(data_dates),
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+ax = Axis(
+    fig[1, 2],
+    xlabel = "Mass (g)",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+A_col = π * (0.027)^2
+mass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col
+mass_loss =
+    [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]
+CairoMakie.lines!(
+    ax,
+    cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    mass_loss,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+save("evaporation_lehmann2008_fig8b.png", fig);

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/evaporation_gardner_fig1.png b/previews/PR746/generated/standalone/Soil/evaporation_gardner_fig1.png new file mode 100644 index 0000000000..4634fa4cf2 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/evaporation_gardner_fig1.png differ diff --git a/previews/PR746/generated/standalone/Soil/evaporation_gilat_loess/index.html b/previews/PR746/generated/standalone/Soil/evaporation_gilat_loess/index.html new file mode 100644 index 0000000000..a5d02223ea --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/evaporation_gilat_loess/index.html @@ -0,0 +1,374 @@ + +Gilat Loess Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.

For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations

The same experiment is carried out 3 times

  1. No evaporation (zero flux boundary conditions)
  2. With evaporation but no drainage (Ksat = 0)
  3. With evaporation and drainage
using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

Parameters

K_sat = FT(0.01 / 3600 / 24)
+vg_n = FT(1.45)
+vg_α = FT(1.5)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.4)
+θ_r = FT(0.04)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(0.3)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)# 10mm
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 294.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+)
PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}(Main.var"##317".var"#1#2"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}(Main.var"##317".var"#3#4"()), Dates.DateTime("2005-01-01T00:00:00"), nothing)

Atmos

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+)
PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}(Main.var"##317".var"#7#8"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}(Main.var"##317".var"#9#10"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}(Main.var"##317".var"#11#12"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}(Main.var"##317".var"#13#14"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}(ClimaLand.var"#21#24"()), Dates.DateTime("2005-01-01T00:00:00"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))

Simulation setup - no evaporation Boundary conditions

zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+no_flux_boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
+
+t0 = Float64(0)
+tf = Float64(24 * 3600 * 15)
+dt = Float64(900.0)
+Δz = 0.01
+zmax = FT(0)
+zmin = FT(-1.6)
+nelems = Int((zmax - zmin) / Δz)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_flux_boundary_fluxes,
+    sources = (),
+);

Initial conditions

Y, p, cds = initialize(soil)
+function estimated_ic(z)
+    0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= estimated_ic.(z)
+    Y.soil.θ_i .= 0
+    T = FT(294.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+cb = SciMLBase.CallbackSet(saving_cb);
+
+sol_no_evap =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Repeat with evaporation and drainage This requires different initial conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+evap_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+)
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = evap_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+soil_exp_tendency! = make_exp_tendency(soil)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];
+
+# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.

bottom_water_bc = MoistureStateBC((p, t) -> 0.35)
+no_drainage_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),
+)
+zmax = FT(0)
+zmin = FT(-0.16)
+nelems = Int((zmax - zmin) / Δz)
+dt = Float64(10.0)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_drainage_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z_no_evap, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol_no_drainage =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap_no_drainage = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];

Figures

savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (800, 400))
+ax = Axis(fig[1, 1], xlabel = "Day", ylabel = "Evaporation rate (mm/d)")
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "With drainage",
+    color = :red,
+)
+CairoMakie.lines!(
+    ax,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    evap_no_drainage .* (1000 * 3600 * 24),
+    label = "No drainage",
+    color = :blue,
+)
+
+CairoMakie.axislegend(ax)
+ax2 = Axis(fig[1, 2], xlabel = "Day", ylabel = "Cumulative evaporation (mm)")
+CairoMakie.lines!(
+    ax2,
+    sol.t ./ 3600 ./ 24,
+    cumsum(evap) .* (1000 * 3600),
+    color = :red,
+)
+CairoMakie.lines!(
+    ax2,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    cumsum(evap_no_drainage) .* (1000 * 3600),
+    color = :blue,
+)
+save("evaporation_lehmann2024_figS6.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Drainage only")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.4)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 10]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax1,
+        parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Evap+Drainage", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.3, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 1, 2, 5, 13]
+for i in 1:1:5
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Evap only", xlabel = "Volumetric Water Content")
+CairoMakie.ylims!(-0.15, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 2, 9, 14]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax3,
+        parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        label = "$(days[i]) days",
+        parent(z_no_evap)[:],
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("evaporation_gardner_fig1.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png b/previews/PR746/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png new file mode 100644 index 0000000000..95012f78db Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png differ diff --git a/previews/PR746/generated/standalone/Soil/evaporation_lehmann2024_figS6.png b/previews/PR746/generated/standalone/Soil/evaporation_lehmann2024_figS6.png new file mode 100644 index 0000000000..f6f7ed2cc8 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/evaporation_lehmann2024_figS6.png differ diff --git a/previews/PR746/generated/standalone/Soil/freezing_front/index.html b/previews/PR746/generated/standalone/Soil/freezing_front/index.html new file mode 100644 index 0000000000..3e58c77da5 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/freezing_front/index.html @@ -0,0 +1,177 @@ + +Phase Changes · ClimaLand.jl
GitHub

Modeling a freezing front in unsaturated soil

Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...) -\frac{F_T}{ρ_l}$

$\frac{ ∂ θ_i}{∂ t} = \frac{F_T}{ρ_i}$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity, and

$F_T$ is the freeze-thaw term.

To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.

We solve these equations in an effectively 1-d domain with $z ∈ [-0.2,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 28 W/m^2/K (T - 267.15K) ẑ$

$- κ ∇T(t, z= -0.2) = -3 W/m^2/K (T - 279.85K) ẑ$

$T(t = 0, z) = 279.85 K$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -0.2) = 0 ẑ$

$ϑ_l(t = 0, z) = 0.33$.

The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.

Import necessary modules

import SciMLBase
+import ClimaTimeSteppers as CTS
+using DelimitedFiles
+using CairoMakie
+
+using ClimaCore
+import ClimaParams as CP
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Preliminary set-up

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
Float32

Set the values of other parameters required by the model:

ν = FT(0.535)
+K_sat = FT(3.2e-6) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.48)
+vg_α = FT(1.11) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);

You could also try the Brooks and Corey model:

#ψb = FT(-0.6)
+#c = FT(0.43)
+#hcm = BrooksCorey(;ψb = ψb, c = c);
+θ_r = FT(0.05)
+ν_ss_om = FT(0.3)
+ν_ss_quartz = FT(0.7)
+ν_ss_gravel = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat,
+    S_s,
+    θ_r,
+);

Choose the domain and discretization:

zmax = FT(0)
+zmin = FT(-0.2)
+nelems = 20
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Set the boundary conditions:

zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)
+function top_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))
+    T_c = ClimaCore.Fields.level(p.soil.T, p_len)
+    return @. FT(28 * (T_c - 267.15))
+end
+function bottom_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    T_c = ClimaCore.Fields.level(p.soil.T, 1)
+    return @. FT(-3 * (T_c - 279.85))
+end
+top_heat_flux_bc = HeatFluxBC(top_heat_flux)
+bottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)
+boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),
+    bottom = WaterHeatBC(;
+        water = zero_water_flux_bc,
+        heat = bottom_heat_flux_bc,
+    ),
+);

Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.

sources = (PhaseChange{FT}(),);

Now we can package this up in the EnergyHydrology model struct:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Running a simulation

Once we have the model, we can initialize the state vectors and obtain the coordinates

Y, p, coords = initialize(soil);

After which, we can specify the initial condition function, and initialze the variables:

function init_soil!(Ysoil, z, params)
+    ν = params.ν
+    FT = eltype(Ysoil.soil.ϑ_l)
+    Ysoil.soil.ϑ_l .= FT(0.33)
+    Ysoil.soil.θ_i .= FT(0.0)
+    T = FT(279.85)
+    ρc_s = Soil.volumetric_heat_capacity(
+        FT(0.33),
+        FT(0.0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Ysoil.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            FT(0.0),
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 50);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Create the tendency function, and choose a timestep, and timestepper:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+dt = Float64(100)
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 3,
+        update_j = CTS.UpdateEvery(CTS.NewTimeStep),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);

Comparison to data

This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.

dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();
+ds = readdlm(dataset_path, ',')
+hours = ds[:, 1][2:end]
+vwc = ds[:, 2][2:end] ./ 100.0
+depth = ds[:, 3][2:end]
+mask_12h = hours .== 12
+mask_24h = hours .== 24
+mask_50h = hours .== 50;
+
+fig = Figure(size = (900, 300))
+ax1 = Axis(
+    fig[1, 1],
+    title = "12 hours",
+    xlabel = L"θ_l + θ_i",
+    ylabel = "Soil depth (m)",
+)
+limits!(ax1, 0.2, 0.5, -0.2, 0.0)
+ax2 = Axis(
+    fig[1, 2],
+    title = "24 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax2, 0.2, 0.5, -0.2, 0.0)
+ax3 = Axis(
+    fig[1, 3],
+    title = "50 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax3, 0.2, 0.5, -0.2, 0.0)
+
+
+z = parent(coords.subsurface.z)[:];
+
+scatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = "", color = "purple")
+lines!(
+    ax1,
+    parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+
+scatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = "", color = "purple")
+lines!(
+    ax2,
+    parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+scatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = "Data", color = "purple")
+lines!(
+    ax3,
+    parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],
+    z,
+    label = "Simulation",
+    color = :green,
+)
+axislegend(ax3, position = :rb)
+
+save("mizoguchi_data_comparison.png", fig);

Discussion and Model Explanation

To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies

$ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0$

Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing ($T < T_f$)

$\frac{dp_l}{ρ_l} = L_f \frac{dT}{T},$

or

$p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)$

where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).

What this implies is that above the freezing point, the pressure is equal to $p_{l,0}$, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure $p_{l,0}$ is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):

$p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)$

where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for

$θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.$

For freezing, the freeze thaw function F_T is equal to

$F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)$

which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).

Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).

This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time

$τ_{LTE}= c̃ Δz²/κ,$

which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).

References

  • Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.
  • Hansson et al., Vadose Zone Journal 3:693–704 (2004).
  • M. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).
  • Kurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)
  • Watanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58
  • Painter and Karra, Vadose Zone Journal (2014) 13 (4)

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/layered_soil/index.html b/previews/PR746/generated/standalone/Soil/layered_soil/index.html new file mode 100644 index 0000000000..329f883e53 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/layered_soil/index.html @@ -0,0 +1,105 @@ + +Layered Soil · ClimaLand.jl
GitHub

This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.

using Plots
+import ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using ClimaCore
+import ClimaParams as CP
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+FT = Float64;

Define simulation times

t0 = Float64(0)
+tf = Float64(60 * 60)
+dt = Float64(30);

Define the domain

zmax = FT(0)
+zmin = FT(-1.1)
+nelems = 75
+Δ = FT((zmax - zmin) / nelems / 2)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118

data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();
+parameter_data = readdlm(data_file, ',');

Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.

depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m
+ksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s
+vgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).
+vgn = reverse(parameter_data[5, :])
+residual_frac = reverse(parameter_data[2, :])
+porosity = reverse(parameter_data[3, :]);

Create fields corresponding to the parameter

ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)
+K_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)
+θ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);

The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:

S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;

The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:

hcm = SpaceVaryingInput(
+    depth,
+    (; α = vgα, n = vgn),
+    soil_domain.space.subsurface,
+    vanGenuchten{FT},
+);

The parameter struct:

params = ClimaLand.Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.

function top_flux_function(p, t)
+    return -0.0001033
+end
+top_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)
+bottom_bc = ClimaLand.Soil.FreeDrainage()
+boundary_fluxes = (; top = top_bc, bottom = bottom_bc)
+soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+);

Initial the state vectors, and set initial conditions

Y, p, cds = initialize(soil);

Initial conditions

Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.

We also set the initial conditions of the auxiliary state here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

stepper = CTS.ARS111()
+@assert FT in (Float32, Float64)
+err = (FT == Float64) ? 1e-8 : 1e-4
+convergence_cond = CTS.MaximumError(err)
+conv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 10,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+        convergence_checker = conv_checker,
+    ),
+)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = make_jacobian(soil)
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);
+
+z = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]
+plot(ϑ_l[1], z, label = "initial", color = "grey", aspect_ratio = 0.8)
+plot!(ϑ_l[2], z, label = "8min", color = "orange")
+plot!(ϑ_l[3], z, label = "16min", color = "red")
+plot!(ϑ_l[4], z, label = "24min", color = "teal")
+plot!(ϑ_l[5], z, label = "32min", color = "blue")
+plot!(ϑ_l[6], z, label = "40min", color = "purple")
+plot!(ϑ_l[7], z, label = "60min", color = "green")
+scatter!(porosity, depth, label = "Porosity")
+plot!(legend = :bottomright)
+
+plot!(xlim = [0, 0.7])
+
+plot!(
+    ylim = [-1.1, 0],
+    yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],
+)
+
+plot!(ylabel = "Depth (m)")
+
+plot!(xlabel = "Volumeteric Water Content")
+
+savefig("./sv62_alpha_2_inf_updated_data_climaland.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png"

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/mizoguchi_data_comparison.png b/previews/PR746/generated/standalone/Soil/mizoguchi_data_comparison.png new file mode 100644 index 0000000000..a7b91672fd Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/mizoguchi_data_comparison.png differ diff --git a/previews/PR746/generated/standalone/Soil/profiles.png b/previews/PR746/generated/standalone/Soil/profiles.png new file mode 100644 index 0000000000..0e27696c63 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/profiles.png differ diff --git a/previews/PR746/generated/standalone/Soil/richards_equation/index.html b/previews/PR746/generated/standalone/Soil/richards_equation/index.html new file mode 100644 index 0000000000..d5a003cd5f --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/richards_equation/index.html @@ -0,0 +1,90 @@ + +Richards Equation · ClimaLand.jl
GitHub

Hydrostatic Equilibrium test for Richards Equation

This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.

The equations are:

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (ϑ_l; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in a 1-d domain with $z ∈ [-5,0]$, and with the following boundary and initial conditions:

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -5) = 0 ẑ$

$ϑ(t = 0, z) = ν-0.001$

$θ_i(t = 0, z) = 0.0.$

where $\nu$ is the porosity.

When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.

Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).

Preliminary setup

  • Load external packages
import SciMLBase
+using Plots
  • Load CliMA packages and ClimaLand modules
using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP
  • Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:
const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Set up the soil model

We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.

First, we define the parameters: porosity \nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.

K_sat = FT(0.0443 / (3600 * 100))
+S_s = FT(1e-3)
+ν = FT(0.495)
+vg_α = FT(2.6)
+vg_n = FT(2)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0)
+params = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:

zmax = FT(0)
+zmin = FT(-5)
+nelems = 10
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:

surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+bottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+boundary_conditions = (; top = surface_flux, bottom = bottom_flux);

Lastly, in this case we don't have any sources, so we pass an empty tuple:

sources = ();

Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.

soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_conditions,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = ClimaLand.make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.

Y.soil.ϑ_l .= FT(0.494);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 24 * 36);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

dt = Float64(1e3);

Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.

stepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Here we set up the information used for our Jacobian.

jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);

And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);

Create some plots

We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).

t = sol.t ./ (60 * 60 * 24);
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+z = parent(coords.subsurface.z)
+plot(
+    ϑ_l[1],
+    z,
+    label = string("t = ", string(t[1]), "days"),
+    xlim = [0.47, 0.501],
+    ylabel = "z",
+    xlabel = "ϑ_l",
+    legend = :bottomleft,
+    title = "Equilibrium test",
+);
+plot!(ϑ_l[end], z, label = string("t = ", string(t[end]), "days"));
+function hydrostatic_equilibrium(z, z_interface)
+    ν = 0.495
+    S_s = 1e-3
+    α = 2.6
+    n = 2.0
+    m = 0.5
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+plot!(hydrostatic_equilibrium.(z, -0.56), z, label = "equilibrium solution");
+
+plot!(1e-3 .+ ϑ_l[1], z, label = "porosity");

Save the output:

savefig("equilibrium_test_ϑ_l.png");

References

  • Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/soil_energy_hydrology/index.html b/previews/PR746/generated/standalone/Soil/soil_energy_hydrology/index.html new file mode 100644 index 0000000000..c67f56d3f2 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/soil_energy_hydrology/index.html @@ -0,0 +1,125 @@ + +Energy and Hydrology · ClimaLand.jl
GitHub

Coupled heat and water equations tending towards equilibrium

The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.

Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.

The equations are:

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction, and

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in an effectively 1-d domain with $z ∈ [-1,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 0 ẑ$

$-κ ∇T(t, z = -1) = 0 ẑ$

$T(t = 0, z) = T_{min} + (T_{max}-T_{min}) e^{Cz}$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -1) = 0 ẑ$

$ϑ(t = 0, z) = ϑ_{min} + (ϑ_{max}-ϑ_{min}) e^{Cz},$

where $C, T_{min}, T_{max}, ϑ_{min},$ and $ϑ_{max}$ are constants.

If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy $∂h/∂z = 0$ and $∂T/∂z = 0$. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.

We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.

Import necessary modules

External (non - CliMA) modules

import SciMLBase
+using Statistics
+using Plots

CliMA packages and ClimaLand modules

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
+earth_param_set = LP.LandParameters(FT);

Create the model

Set the values of other parameters required by the model:

ν = FT(0.395)
0.395f0

Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).

ν_ss_quartz = FT(0.92)
+ν_ss_om = FT(0.0)
+ν_ss_gravel = FT(0.0)
0.0f0

Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.

Ksat = FT(4.42 / 3600 / 100) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.89)
+vg_α = FT(7.5) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+θ_r = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat = Ksat,
+    S_s,
+    θ_r,
+);

We also need to pick a domain on which to solve the equations:

zmax = FT(0)
+zmin = FT(-1.0)
+nelems = 50
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.

Water boundary conditions:

surface_water_flux = WaterFluxBC((p, t) -> 0.0)
+bottom_water_flux = WaterFluxBC((p, t) -> 0.0);

The boundary conditions for the heat equation:

surface_heat_flux = HeatFluxBC((p, t) -> 0.0)
+bottom_heat_flux = HeatFluxBC((p, t) -> 0.0);

We wrap up all of those in a WaterHeatBC struct:

boundary_fluxes = (;
+    top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),
+    bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),
+);

We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.

sources = ();

Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.

function init_soil!(Y, z, params)
+    ν = params.ν
+    θ_r = params.θ_r
+    FT = eltype(Y.soil.ϑ_l)
+    zmax = FT(0)
+    zmin = FT(-1)
+
+    theta_max = FT(ν * 0.5)
+    theta_min = FT(ν * 0.4)
+    T_max = FT(289.0)
+    T_min = FT(288.0)
+
+    c = FT(20.0)
+    @. Y.soil.ϑ_l =
+        theta_min +
+        (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)
+    Y.soil.θ_i .= FT(0.0)
+
+    T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))
+
+    θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)
+    ρc_s =
+        Soil.volumetric_heat_capacity.(
+            θ_l,
+            Y.soil.θ_i,
+            params.ρc_ds,
+            params.earth_param_set,
+        )
+    Y.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 72);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

We use ClimaTimesteppers.jl for carrying out the time integration.

Choose a timestepper and set up the ODE problem:

dt = Float64(1000.0);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:

saveat = collect(t0:FT(30000):tf)
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);

Extract output

z = parent(coords.subsurface.z)
+t = parent(sol.t)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+T = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];

Let's look at the initial and final times:

plot(ϑ_l[1], z, xlabel = "ϑ_l", ylabel = "z (m)", label = "t = 0d")
+plot!(ϑ_l[4], z, label = "t = 1.5d")
+plot!(ϑ_l[end], z, label = "t = 3d")
+savefig("eq_moisture_plot.png");

plot(T[1], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 0d")
+plot!(T[4], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 1.5d")
+plot!(T[end], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 3d")
+savefig("eq_temperature_plot.png");

Analytic Expectations

We can determine a priori what we expect the final temperature to be in equilibrium.

Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).

Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.

One could also solve the equation for ϑ_l specified by $∂ h/∂ z = 0$ to determine the functional form of the equilibrium profile of the liquid water.

References

  • Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.
  • Balland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/sublimation/index.html b/previews/PR746/generated/standalone/Soil/sublimation/index.html new file mode 100644 index 0000000000..49714d7ce4 --- /dev/null +++ b/previews/PR746/generated/standalone/Soil/sublimation/index.html @@ -0,0 +1,255 @@ + +Bare soil site · ClimaLand.jl
GitHub

Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 270.0^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(270.0)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 12
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

sources = (PhaseChange{FT}(),);
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)
+    Y.soil.θ_i .= 0
+    T = FT(275.0)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 4)
+dt = Float64(5)
5.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping functions:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = ClimaLand.make_jacobian(soil)
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+
+timestepper = CTS.ARS111()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+sub = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        sv.saveval[k].soil.ice_frac,
+    )[1] for k in 1:length(sol.t)
+]
+
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (400, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Rate (mm/d)",
+    title = "Vapor Fluxes",
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    sub .* (1000 * 3600 * 24),
+    label = "Sublimation",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Evaporation",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+save("water_fluxes.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Temperature")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(260, 280)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 3, 4]
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax1,
+        parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Ice", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Liquid Water", xlabel = "")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax3,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("profiles.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png b/previews/PR746/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png new file mode 100644 index 0000000000..1d25aedb18 Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png differ diff --git a/previews/PR746/generated/standalone/Soil/water_fluxes.png b/previews/PR746/generated/standalone/Soil/water_fluxes.png new file mode 100644 index 0000000000..9a54a6023d Binary files /dev/null and b/previews/PR746/generated/standalone/Soil/water_fluxes.png differ diff --git a/previews/PR746/generated/standalone/Usage/LSM_single_column_tutorial/index.html b/previews/PR746/generated/standalone/Usage/LSM_single_column_tutorial/index.html new file mode 100644 index 0000000000..c051d7a537 --- /dev/null +++ b/previews/PR746/generated/standalone/Usage/LSM_single_column_tutorial/index.html @@ -0,0 +1,82 @@ + +Intro to multi-component models · ClimaLand.jl
GitHub

The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.

The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.

We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.

First, let's load the required modules:

using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Pond
+
+FT = Float32;

The individual component models I - Soil Hydrology

The RR equation for the volumetric water content of soil is given by

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S(x,y,z, t)$

In order to solve this, one must specify:

  • boundary conditions,
  • relevant parameters (closure models for K and ψ),
  • a domain and a spatial discretization scheme,
  • additional source terms S, if applicable,
  • a time-stepping algorithm,
  • initial conditions.

We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).

Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.

For the RR equation, we can create this as follows. First, we specify parameters:

ν = FT(0.495);
+K_sat = FT(0.0443 / 3600 / 100); # m/s
+S_s = FT(1e-3); #inverse meters
+vg_n = FT(2.0);
+vg_α = FT(2.6); # inverse meters
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0);
+soil_ps = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, let's define the spatial domain and discretization:

zmax = FT(0);
+zmin = FT(-1);
+nelems = 20;
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

And boundary conditions and source terms (none currently):

top_flux_bc = WaterFluxBC((p, t) -> 0.0);
+bot_flux_bc = WaterFluxBC((p, t) -> 0.0);
+sources = ()
+boundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)
(top = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#1#2"}(Main.var"##494".var"#1#2"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#3#4"}(Main.var"##494".var"#3#4"()))

With this information, we can make our model:

soil = Soil.RichardsModel{FT}(;
+    parameters = soil_ps,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,

Y_soil, p_soil, coords_soil = initialize(soil);

and we can set up the tendency function using the default as well,

soil_ode! = make_exp_tendency(soil);

which computes, for the column domain,

$-\frac{∂ }{∂z} (-K\frac{∂(ψ+z)}{∂ z})$

for each value of ϑ on the mesh of our soil_domain.

Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:

function soil_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place
+         update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place
+         compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.
+end

It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.

Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.

Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.

The individual component models II - Surface Water

The pond model has a single variable, the pond height η, which satisfies the ODE:

$\frac{∂ η}{∂ t} = -(P - I) = R,$

where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.

To write down the pond equations, we need to specify

  • P
  • I

which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:

precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s
+
+infiltration(t) = -(1e-6) #m/s
+pond_model =
+    Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));

Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.

Again we can initialize the state vector and auxiliary vectors:

Y_pond, p_pond, coords_pond = initialize(pond_model);

We can make the tendency function in the same way, for stepping the state forward in time:

pond_ode! = make_exp_tendency(pond_model);

The pond_ode! function works in the same way as for the soil model:

function pond_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # falls back to default; does nothing
+         update_boundary_fluxes!(p,Y,t)  # p.surface_water.runoff in place
+         compute_exp_tendency!(dY, Y, p, t)
+end

An LSM with pond and soil:

The LSM model must contain everything needed to write down the joint system of equations

$\frac{\partial \eta}{\partial t} = -(P(t) - I(ϑ, η, P)) = R,$

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S$

$-K ∇(ψ+z)|_{z = zmax} ⋅ ẑ = I(ϑ, η, P)$

$-K ∇(ψ+z)|_{z = zmin} ⋅ ẑ = 0.0.$

These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.

As in the standalone cases, defining our model requires specifying

  • parameters,
  • domains, discretizations
  • precipitation,
  • boundary conditions,
  • sources in the soil equation, if any.

First, let's make our single column domain.

lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.

soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());
+surface_water_args = NamedTuple();

Atmospheric drivers don't "belong" to either component alone:

land_args = (precip = precipitation,);
+land = LandHydrology{FT}(;
+    land_args = land_args,
+    soil_model_type = Soil.RichardsModel{FT},
+    soil_args = soil_args,
+    surface_water_model_type = Pond.PondModel{FT},
+    surface_water_args = surface_water_args,
+);

Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).

Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?

Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.

To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:

function make_update_aux(land)
+         soil_update_aux! = make_update_aux(land.soil)
+         surface_update_aux! = make_update_aux(land.surface_water)
+         function update_aux!(p,Y,t)
+                  surface_update_aux!(p,Y,t) # does nothing to `p`
+                  soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ
+         end
+         return update_aux!
+end
function make_update_boundary_fluxes(land)
+         update_soil_bf! = make_update_boundary_fluxes(land.soil)
+         update_pond_bf! = make_update_boundary_fluxes(land.surface_water)
+         function update_boundary_fluxes!(p,Y,t)
+                  p.soil_infiltration = compute_infiltration(Y,p, t)
+                  update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration
+                  update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration
+         end
+         return update_boundary_fluxes!
+end

and similarily for the compute_exp_tendency! functions:

function make_compute_exp_tendency(land)
+         soil_compute_exp_tendency! = make_update_aux(land.soil)
+         surface_compute_exp_tendency! = make_update_aux(land.surface_water)
+         function compute_exp_tendency!(dY,Y,p,t)
+                  surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η
+                  soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ
+         end
+         return compute_exp_tendency!
+end

The exp_tendency! for the land model is then again just

function exp_tendency!(dY, Y, p, t)
+         update_aux!(p,Y,t)
+         update_boundary_fluxes!(p,Y,t)
+         compute_exp_tendency!(dY, Y, p, t)
+end

In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.

A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:

  • initialize(land.soil)
  • initialize(land.surface_water)
  • initializes additional auxiliary variables, like p.soil_infiltration
  • append these into Y, p, and coords:
Y, p, coords = initialize(land);

We have volumetric liquid water fraction:

propertynames(Y.soil)
(:ϑ_l,)

and surface height of the pond:

propertynames(Y.surface_water)
(:η,)

as well as auxiliary variables for the soil:

propertynames(p.soil)
(:K, :ψ, :top_bc, :bottom_bc)

and the runoff for surface water:

propertynames(p.surface_water)
(:runoff,)

and the additional variable required in the LSM is stored here as well:

propertynames(p)
(:soil_infiltration, :soil, :surface_water)

and finally, coordinates - useful for visualization of solutions:

coords.subsurface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]

and the coordinates of the surface variables:

coords.surface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[0.0]

And we can make the tendency function as before:

land_ode! = make_exp_tendency(land);

Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.

Advantages and disadvantages

Some advantages to our interface design are as follows:

  • a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.
  • likewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.
  • the exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).
  • although we wrote it here in a $hardwired$ fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.
  • the order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.
  • the code is also modular in terms of swapping out a simple component model for a more complex version.

Possible disadvantages to our interface design:

  • Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.
  • To accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.
  • standalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.
  • we need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.

This page was generated using Literate.jl.

diff --git a/previews/PR746/generated/standalone/Usage/domain_tutorial/index.html b/previews/PR746/generated/standalone/Usage/domain_tutorial/index.html new file mode 100644 index 0000000000..62dc0e7025 --- /dev/null +++ b/previews/PR746/generated/standalone/Usage/domain_tutorial/index.html @@ -0,0 +1,2 @@ + +Intro to ClimaLand Domains · ClimaLand.jl
GitHub

Domain Tutorial

Goals of the tutorial

The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.

Background

In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question "only" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).

For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.

In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.

ClimaLand Domains were designed with this in mind. The domains are defined so that

  1. the user can easily switch geometries, e.g. single column to global model,
  2. individual component models can be run by themselves, using a single domain,
  3. the same domains can be used to set up multi-component models (LSMs),
  4. different variables can exist on different parts of the domain.

What is a ClimaLand Domain?

A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.

Domain types

All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:

As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.

DomainSurface DomainSubsurface Domain
ColumnPointColumn
HybridBoxPlaneHybridBox
SphericalShellSphericalSurfaceSphericalShell

There is a single key method which take a ClimaLand domain as an argument.

the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.

It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.

How variable initialization depends on domains

Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.

When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.

They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.

The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].

How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.

This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.

Future work

Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into "vertical" and "horizontal" pieces.

We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.

This page was generated using Literate.jl.

  • 1finite differencing is used in the vertical, and spectral elements are used in the horizontal.
  • 2a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.
  • 3We also will support having an array-like type of variable.
diff --git a/previews/PR746/generated/standalone/Usage/model_tutorial/index.html b/previews/PR746/generated/standalone/Usage/model_tutorial/index.html new file mode 100644 index 0000000000..c873373357 --- /dev/null +++ b/previews/PR746/generated/standalone/Usage/model_tutorial/index.html @@ -0,0 +1,133 @@ + +Intro to standalone models · ClimaLand.jl
GitHub

The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.

This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.

General setup

We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.

Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.

Your model defines a system of equations of the following form:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in $\vec{Y}$. Generically, we will speak of the functions $\vec{f}$ as tendencies; these can be functions of the prognostic state, of space $\vec{x}$, and of time $t$, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions

The cache ("auxiliary variables")

There are typically quantities, which depend on the state vector $\vec{Y}$, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require "a lot" of memory to store (e.g., most variables in global runs). Allocating memory "on-the-fly" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.

Denoting the cache as $\vec{p}$, your equations may be rewritten as:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{p}, \vec{x}, t; \mbox{params} \ldots)$

$\vec{p}(\vec{x}, t) = \vec{g}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables $\vec{p}$ at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported ($\vec{Y}$ cannot be zero dimensional).

In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.

The Model

All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.

The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.

The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.

As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.

Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to "imex" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as "explicit" or "implicit" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.

Let's first import some needed packages.

import ClimaTimeSteppers as CTS
+using SciMLBase
+using Plots
+using ClimaCore
+using ClimaLand

Import the functions we are extending for our model:

import ClimaLand:
+    name,
+    make_exp_tendency,
+    make_compute_exp_tendency,
+    make_update_aux,
+    make_update_boundary_fluxes,
+    prognostic_vars,
+    prognostic_types,
+    prognostic_domain_names,
+    auxiliary_vars,
+    auxiliary_types,
+    auxiliary_domain_names

The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).

struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}
+    "van Genuchten model parameters"
+    vGmodel::ClimaLand.Soil.vanGenuchten{FT}
+    "Porosity [unitless]"
+    ν::FT
+    "Residual water fraction [unitless]"
+    θ_r::FT
+    "Saturated hydraulic conductiity [m/s]"
+    Ksat::FT
+    "Surface flux, used as boundary condition [m/s]"
+    F_sfc::FT
+    "Domain of the model"
+    domain::D
+end;

For reasons that will be clear momentarily, let's also define the name of the model:

ClimaLand.name(model::RichardsTutorialModel) = :soil;

Explicit tendency

Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:

$\frac{\partial ϑ_l}{\partial t} = - ∇⋅[-K(θ) ∇(ψ(θ)+z)],$

where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:

function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)
+    function compute_exp_tendency!(dY, Y, p, t)
+        gradc2f = ClimaCore.Operators.GradientC2F()
+        interpc2f = ClimaCore.Operators.InterpolateC2F()
+        FT = FTfromY(Y)
+        divf2c = ClimaCore.Operators.DivergenceF2C(
+            top = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(model.F_sfc),
+            ),
+            bottom = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(FT(0)),
+            ),
+        )
+
+        @. dY.soil.θ =
+            -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))
+    end
+    return compute_exp_tendency!
+end;

A couple of notes: the vector $\vec{dY}$ contains the evaluation of the tendency function for each variable in $\vec{Y}$. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.

The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.

The prognostic state vector $\vec{Y}$ and cache $\vec{p}$

We have given the state vector $\vec{Y}$ a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.

ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);
+ClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);
+ClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);

The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).

ClimaLand.auxiliary_vars(::RichardsTutorialModel) =
+    (:K, :ψ, :top_flux, :bottom_flux, :z)
+ClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =
+    (FT, FT, FT, FT, FT);
+ClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =
+    (:subsurface, :subsurface, :surface, :surface, :subsurface);

Updating the cache

We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.

function ClimaLand.make_update_aux(model::RichardsTutorialModel)
+    function update_aux!(p, Y, t)
+        p.soil.z .=
+            ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step
+        @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(
+            model.vGmodel,
+            model.Ksat,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+        @. p.soil.ψ = ClimaLand.Soil.matric_potential(
+            model.vGmodel,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+    end
+    return update_aux!
+end;
+
+function ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)
+    function update_boundary_fluxes!(p, Y, t)
+        FT = ClimaLand.FTfromY(Y)
+        p.soil.top_flux .= model.F_sfc
+        p.soil.bottom_flux .= FT(0)
+    end
+    return update_boundary_fluxes!
+end;

The default tendency function in ClimaLand for any AbstractModel carries out the following:

function make_exp_tendency(model::AbstractModel)
+    update_aux! = make_update_aux(model)
+    update_boundary_fluxes! = make_update_boundary_fluxes(model)
+    compute_exp_tendency! = make_compute_exp_tendency(model)
+    function exp_tendency!(dY,Y,p,t)
+        update_aux!(p,Y,t)
+        update_boundary_fluxes!(p,Y,t)
+        compute_exp_tendency!(dY,Y,p,t)
+    end
+    return exp_tendency!
+end;

Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.

Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone "component" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.

More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.

Running a simulation

Create a model instance.

FT = Float32
+vGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)
+Ksat = FT(4.0e-7)
+ν = 0.5f0
+θ_r = 0.0f0
+F_sfc = FT(-3.0e-8)
+domain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)
+soil = RichardsTutorialModel{Float32, typeof(domain)}(
+    vGmodel,
+    ν,
+    θ_r,
+    Ksat,
+    F_sfc,
+    domain,
+);

Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.

Y, p, cds = initialize(soil);

Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,

Y.soil
1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0

The same is true for p:

p.soil
(K = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:
+  Float32[0.0], bottom_flux = Float32-valued Field:
+  Float32[0.0], z = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

Here we now update Y in place with initial conditions of our choosing.

Y.soil.θ = 0.25f0;

Set initial cache variable values, and inspect values:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, 0.0);
+@show p.soil.K
+
+@show p.soil.ψ
+
+@show p.soil.top_flux
Float32-valued Field:
+  Float32[-3.0f-8]

Next up is to create the exp_tendency! function:

exp_tendency! = make_exp_tendency(soil);

Running the simulation

Set the initial and end times, timestep:

t0 = 0.0;
+tf = 7 * 24 * 3600.0;
+dt = 1800.0;

Select the timestepping algorithm we want to use from CTS.jl.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

SciMLBase problem statement using CTS.jl internals:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!),
+    Y,
+    (t0, tf),
+    p,
+);

Solve command:

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);

The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.

This page was generated using Literate.jl.

diff --git a/previews/PR746/getting_started/index.html b/previews/PR746/getting_started/index.html new file mode 100644 index 0000000000..2109745ebf --- /dev/null +++ b/previews/PR746/getting_started/index.html @@ -0,0 +1,32 @@ + +Getting Started · ClimaLand.jl
GitHub

Getting Started

For Users

Installation

First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:

julia> ] # Enter Package REPL mode
+Pkg> add ClimaLand # Install ClimaLand
+Pkg> # Go back to Julia REPL mode
+Julia> using ClimaLand

A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.

Parameterization

Let's start with a basic example: compute canopy gross photosynthesis (GPP).

julia> using ClimaLand
julia> @doc ClimaLand.Canopy.compute_GPP  compute_GPP(An::FT,
+           K::FT,
+           LAI::FT,
+           Ω::FT) where {FT}
+
+  Computes the total canopy photosynthesis (GPP) as a function of the total
+  net carbon assimilation (An), the extinction coefficient (K), leaf area
+  index (LAI) and the clumping index (Ω).

As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:

julia> import ClimaLand.Canopy as canopy
julia> canopy.compute_GPP(5.0, 0.5, 3.0, 0.7)9.28660358412635

Et voilà!

Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.

ClimaLand structure

ClimaLand contains multiple modules. They are listed below:

julia> using MethodAnalysis, ClimaLand
julia> child_modules(ClimaLand)13-element Vector{Module}:
+ ClimaLand
+ ClimaLand.Artifacts
+ ClimaLand.Bucket
+ ClimaLand.Canopy
+ ClimaLand.Canopy.PlantHydraulics
+ ClimaLand.Diagnostics
+ ClimaLand.Domains
+ ClimaLand.Parameters
+ ClimaLand.Pond
+ ClimaLand.Snow
+ ClimaLand.Soil
+ ClimaLand.Soil.Biogeochemistry
+ ClimaLand.Soil.Runoff

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia> using ClimaLand
julia> using About
julia> about(ClimaLand.Soil.Biogeochemistry)Module ClimaLand.Soil.Biogeochemistry
+
+Re-exports 13 names (from ClimaLand):
+• Biogeochemistry         • AtmosCO2StateBC    • SoilCO2ModelParameters
+• co2_diffusivity         • MicrobeProduction  • SoilCO2StateBC        
+• microbe_source          • PrescribedMet      • SoilDrivers           
+• volumetric_air_content  • SoilCO2FluxBC    
+• AbstractSoilDriver      • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

diff --git a/previews/PR746/index.html b/previews/PR746/index.html new file mode 100644 index 0000000000..4b52e72be9 --- /dev/null +++ b/previews/PR746/index.html @@ -0,0 +1,2 @@ + +Home · ClimaLand.jl
GitHub

ClimaLand.jl Documentation (v0.14.3)

Introduction

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
diff --git a/previews/PR746/objects.inv b/previews/PR746/objects.inv new file mode 100644 index 0000000000..101874932f Binary files /dev/null and b/previews/PR746/objects.inv differ diff --git a/previews/PR746/search_index.js b/previews/PR746/search_index.js new file mode 100644 index 0000000000..097a1e008a --- /dev/null +++ b/previews/PR746/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"APIs/canopy/RadiativeTransfer/#Radiative-Transfer","page":"Canopy RT","title":"Radiative Transfer","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#Parameters","page":"Canopy RT","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.BeerLambertParameters","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.BeerLambertParameters","page":"Canopy RT","title":"ClimaLand.Canopy.BeerLambertParameters","text":"BeerLambertParameters{FT <: AbstractFloat}\n\nThe required parameters for the Beer-Lambert radiative transfer model.\n\nα_PAR_leaf: PAR leaf reflectance (unitless)\nα_NIR_leaf: NIR leaf reflectance\nϵ_canopy: Emissivity of the canopy\nΩ: Clumping index following Braghiere (2021) (unitless)\nλ_γ_PAR: Typical wavelength per PAR photon (m)\nλ_γ_NIR: Typical wavelength per NIR photon (m)\nG_Function: Leaf angle distribution function\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/RadiativeTransfer/#Methods","page":"Canopy RT","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.compute_absorbances\nClimaLand.Canopy.plant_absorbed_pfd\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.canopy_radiant_energy_fluxes!","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.plant_absorbed_pfd","page":"Canopy RT","title":"ClimaLand.Canopy.plant_absorbed_pfd","text":"plant_absorbed_pfd(\n RT::BeerLambertModel{FT},\n SW_IN:FT,\n α_leaf::FT,\n LAI::FT,\n K::FT,\n α_soil::FT\n)\n\nComputes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.\n\nThis applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). \n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\nplant_absorbed_pfd(\n RT::TwoStreamModel{FT},\n α_leaf,\n SW_IN::FT,\n LAI::FT,\n K::FT,\n τ_leaf,\n θs::FT,\n α_soil::FT,\n)\n\nComputes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band. \n\nThis applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.\n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.extinction_coeff","page":"Canopy RT","title":"ClimaLand.Canopy.extinction_coeff","text":"extinction_coeff(ld::FT,\n θs::FT) where {FT}\n\nComputes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.canopy_radiant_energy_fluxes!","page":"Canopy RT","title":"ClimaLand.Canopy.canopy_radiant_energy_fluxes!","text":"Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrognosticSoil{F},\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {FT, PSE}\n\nIn standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.\n\nIn integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\ncanopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrescribedSoil,\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {PSE}\n\nComputes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/available_diagnostics/#Available-diagnostic-variables","page":"Available diagnostics","title":"Available diagnostic variables","text":"","category":"section"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"Autogenerate table of available diagnostics:","category":"page"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"include(\"make_diagnostic_table.jl\")","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"ref_time = DateTime(2005) # required argument, but not used in this case\nSW_d = (t) -> 0\nLW_d = (t) -> 301.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 5\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)\n Y.soil.θ_i .= 0\n T = FT(296.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 13)\ndt = Float64(900.0)","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"900.0","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the tendency functions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\nevaporation_data =\n ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();\nref_soln_E = readdlm(evaporation_data, ',')\nref_soln_E_350mm = ref_soln_E[2:end, 1:2]\ndata_dates = ref_soln_E_350mm[:, 1]\ndata_e = ref_soln_E_350mm[:, 2];\n\nfig = Figure(size = (800, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Evaporation rate (mm/d)\",\n title = \"Bare soil evaporation\",\n)\nCairoMakie.xlims!(minimum(data_dates), maximum(data_dates))\nCairoMakie.lines!(\n ax,\n FT.(data_dates),\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nax = Axis(\n fig[1, 2],\n xlabel = \"Mass (g)\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nA_col = π * (0.027)^2\nmass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col\nmass_loss =\n [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]\nCairoMakie.lines!(\n ax,\n cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n mass_loss,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nsave(\"evaporation_lehmann2008_fig8b.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#General-setup","page":"Intro to standalone models","title":"General setup","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Your model defines a system of equations of the following form:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in vecY. Generically, we will speak of the functions vecf as tendencies; these can be functions of the prognostic state, of space vecx, and of time t, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-cache-(\"auxiliary-variables\")","page":"Intro to standalone models","title":"The cache (\"auxiliary variables\")","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"There are typically quantities, which depend on the state vector vecY, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require \"a lot\" of memory to store (e.g., most variables in global runs). Allocating memory \"on-the-fly\" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Denoting the cache as vecp, your equations may be rewritten as:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecp vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"vecp(vecx t) = vecg(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables vecp at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported (vecY cannot be zero dimensional).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-Model","page":"Intro to standalone models","title":"The Model","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to \"imex\" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as \"explicit\" or \"implicit\" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Let's first import some needed packages.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaTimeSteppers as CTS\nusing SciMLBase\nusing Plots\nusing ClimaCore\nusing ClimaLand","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Import the functions we are extending for our model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaLand:\n name,\n make_exp_tendency,\n make_compute_exp_tendency,\n make_update_aux,\n make_update_boundary_fluxes,\n prognostic_vars,\n prognostic_types,\n prognostic_domain_names,\n auxiliary_vars,\n auxiliary_types,\n auxiliary_domain_names","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}\n \"van Genuchten model parameters\"\n vGmodel::ClimaLand.Soil.vanGenuchten{FT}\n \"Porosity [unitless]\"\n ν::FT\n \"Residual water fraction [unitless]\"\n θ_r::FT\n \"Saturated hydraulic conductiity [m/s]\"\n Ksat::FT\n \"Surface flux, used as boundary condition [m/s]\"\n F_sfc::FT\n \"Domain of the model\"\n domain::D\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"For reasons that will be clear momentarily, let's also define the name of the model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.name(model::RichardsTutorialModel) = :soil;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Explicit-tendency","page":"Intro to standalone models","title":"Explicit tendency","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracpartial ϑ_lpartial t = - -K(θ) (ψ(θ)+z)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)\n function compute_exp_tendency!(dY, Y, p, t)\n gradc2f = ClimaCore.Operators.GradientC2F()\n interpc2f = ClimaCore.Operators.InterpolateC2F()\n FT = FTfromY(Y)\n divf2c = ClimaCore.Operators.DivergenceF2C(\n top = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(model.F_sfc),\n ),\n bottom = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(FT(0)),\n ),\n )\n\n @. dY.soil.θ =\n -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))\n end\n return compute_exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"A couple of notes: the vector vecdY contains the evaluation of the tendency function for each variable in vecY. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-prognostic-state-vector-\\vec{Y}-and-cache-\\vec{p}","page":"Intro to standalone models","title":"The prognostic state vector vecY and cache vecp","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We have given the state vector vecY a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);\nClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);\nClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.auxiliary_vars(::RichardsTutorialModel) =\n (:K, :ψ, :top_flux, :bottom_flux, :z)\nClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =\n (FT, FT, FT, FT, FT);\nClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =\n (:subsurface, :subsurface, :surface, :surface, :subsurface);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Updating-the-cache","page":"Intro to standalone models","title":"Updating the cache","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_update_aux(model::RichardsTutorialModel)\n function update_aux!(p, Y, t)\n p.soil.z .=\n ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step\n @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(\n model.vGmodel,\n model.Ksat,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n @. p.soil.ψ = ClimaLand.Soil.matric_potential(\n model.vGmodel,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n end\n return update_aux!\nend;\n\nfunction ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)\n function update_boundary_fluxes!(p, Y, t)\n FT = ClimaLand.FTfromY(Y)\n p.soil.top_flux .= model.F_sfc\n p.soil.bottom_flux .= FT(0)\n end\n return update_boundary_fluxes!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The default tendency function in ClimaLand for any AbstractModel carries out the following:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function make_exp_tendency(model::AbstractModel)\n update_aux! = make_update_aux(model)\n update_boundary_fluxes! = make_update_boundary_fluxes(model)\n compute_exp_tendency! = make_compute_exp_tendency(model)\n function exp_tendency!(dY,Y,p,t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY,Y,p,t)\n end\n return exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone \"component\" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-a-simulation","page":"Intro to standalone models","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create a model instance.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"FT = Float32\nvGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)\nKsat = FT(4.0e-7)\nν = 0.5f0\nθ_r = 0.0f0\nF_sfc = FT(-3.0e-8)\ndomain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)\nsoil = RichardsTutorialModel{Float32, typeof(domain)}(\n vGmodel,\n ν,\n θ_r,\n Ksat,\n F_sfc,\n domain,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The same is true for p:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"p.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"(K = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:\n Float32[0.0], bottom_flux = Float32-valued Field:\n Float32[0.0], z = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here we now update Y in place with initial conditions of our choosing.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil.θ = 0.25f0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set initial cache variable values, and inspect values:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, 0.0);\n@show p.soil.K\n\n@show p.soil.ψ\n\n@show p.soil.top_flux","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Float32-valued Field:\n Float32[-3.0f-8]","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Next up is to create the exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"exp_tendency! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-the-simulation","page":"Intro to standalone models","title":"Running the simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set the initial and end times, timestep:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"t0 = 0.0;\ntf = 7 * 24 * 3600.0;\ndt = 1800.0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Select the timestepping algorithm we want to use from CTS.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"SciMLBase problem statement using CTS.jl internals:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Solve command:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand","page":"ClimaLand","title":"ClimaLand","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/ClimaLand/#LSM-Model-Types-and-methods","page":"ClimaLand","title":"LSM Model Types and methods","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.SoilPlantHydrologyModel\nClimaLand.LandSoilBiogeochemistry\nClimaLand.LandHydrology\nClimaLand.make_interactions_update_aux\nClimaLand.initialize_interactions\nClimaLand.land_components\nClimaLand.lsm_aux_vars\nClimaLand.lsm_aux_types\nClimaLand.lsm_aux_domain_names\nClimaLand.domain_name","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.LandSoilBiogeochemistry","page":"ClimaLand","title":"ClimaLand.LandSoilBiogeochemistry","text":"struct LandSoilBiogeochemistry{\n FT,\n SEH <: Soil.EnergyHydrology{FT},\n SB <: Soil.Biogeochemistry.SoilCO2Model{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.\n\nsoil: The soil model\nsoilco2: The biochemistry model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.LandHydrology","page":"ClimaLand","title":"ClimaLand.LandHydrology","text":"struct LandHydrology{\n FT,\n SM <: Soil.AbstractSoilModel{FT},\n SW <: Pond.AbstractSurfaceWaterModel{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil and surface water component.\n\nsoil: The soil model\nsurface_water: The surface water model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.land_components","page":"ClimaLand","title":"ClimaLand.land_components","text":"land_components(land::AbstractLandModel)\n\nReturns the component names of the land model, by calling propertynames(land).\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_vars","page":"ClimaLand","title":"ClimaLand.lsm_aux_vars","text":"lsmauxvars(m::AbstractLandModel)\n\nReturns the additional aux variable symbols for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_vars(m::SoilCanopyModel)\n\nThe names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_types","page":"ClimaLand","title":"ClimaLand.lsm_aux_types","text":"lsmauxtypes(m::AbstractLandModel)\n\nReturns the shared additional aux variable types for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_types(m::SoilCanopyModel)\n\nThe types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_domain_names","page":"ClimaLand","title":"ClimaLand.lsm_aux_domain_names","text":"lsmauxdomain_names(m::AbstractLandModel)\n\nReturns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.\n\nThis is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.\n\n\n\n\n\nlsm_aux_domain_names(m::SoilCanopyModel)\n\nThe domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#Land-Hydrology","page":"ClimaLand","title":"Land Hydrology","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.infiltration_capacity\nClimaLand.infiltration_at_point\nClimaLand.PrognosticRunoff\nClimaLand.RunoffBC","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_capacity","page":"ClimaLand","title":"ClimaLand.infiltration_capacity","text":"function infiltration_capacity(\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n)\n\nFunction which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.\n\nDefined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_at_point","page":"ClimaLand","title":"ClimaLand.infiltration_at_point","text":"infiltration_at_point(η::FT, i_c::FT, P::FT)\n\nReturns the infiltration given pond height η, infiltration capacity, and precipitation.\n\nThis is defined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticRunoff","page":"ClimaLand","title":"ClimaLand.PrognosticRunoff","text":"PrognosticRunoff <: Pond.AbstractSurfaceRunoff\n\nConcrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.\n\nThis is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RunoffBC","page":"ClimaLand","title":"ClimaLand.RunoffBC","text":"RunoffBC <: Soil.AbstractSoilBC\n\nConcrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.\n\nThis is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#SoilCanopyModel","page":"ClimaLand","title":"SoilCanopyModel","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticSoil\nClimaLand.RootExtraction","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticSoil","page":"ClimaLand","title":"ClimaLand.PrognosticSoil","text":" PrognosticSoil{FT} <: AbstractSoilDriver\n\nConcrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.\n\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RootExtraction","page":"ClimaLand","title":"ClimaLand.RootExtraction","text":"RootExtraction{FT} <: Soil.AbstractSoilSource{FT}\n\nConcrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.\n\nThis is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#LandSoilBiogeochemistry","page":"ClimaLand","title":"LandSoilBiogeochemistry","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticMet","category":"page"},{"location":"APIs/shared_utilities/#Shared-Utilities","page":"Shared Utilities","title":"Shared Utilities","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/shared_utilities/#Domains","page":"Shared Utilities","title":"Domains","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.Domains.AbstractDomain\nClimaLand.Domains.AbstractLSMDomain\nClimaLand.Domains.SphericalShell\nClimaLand.Domains.SphericalSurface\nClimaLand.Domains.HybridBox\nClimaLand.Domains.Column\nClimaLand.Domains.Plane\nClimaLand.Domains.Point\nClimaLand.Domains.coordinates\nClimaLand.Domains.obtain_face_space\nClimaLand.Domains.obtain_surface_space\nClimaLand.Domains.obtain_surface_domain\nClimaLand.Domains.top_center_to_surface\nClimaLand.Domains.top_face_to_surface\nClimaLand.Domains.linear_interpolation_to_surface!\nClimaLand.Domains.get_Δz\n","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.AbstractDomain","page":"Shared Utilities","title":"ClimaLand.Domains.AbstractDomain","text":"AbstractDomain{FT <:AbstractFloat}\n\nAn abstract type for domains.\n\nThe domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.\n\nAdditionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalShell","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalShell","text":"struct SphericalShell{FT} <: AbstractDomain{FT}\n radius::FT\n depth::FT\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nradius: The radius of the shell\ndepth: The radial extent of the shell\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: The number of elements to be used in the non-radial and radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalSurface","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalSurface","text":"struct SphericalSurface{FT} <: AbstractDomain{FT}\n radius::FT\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).\n\nFields\n\nradius: The radius of the surface\nnelements: The number of elements to be used in the non-radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.HybridBox","page":"Shared Utilities","title":"ClimaLand.Domains.HybridBox","text":"struct HybridBox{FT} <: AbstractDomain{FT}\n xlim::Tuple{FT, FT}\n ylim::Tuple{FT, FT}\n zlim::Tuple{FT, FT}\n longlat::Union{Nothing, Tuple{FT, FT}},\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int, Int}\n npolynomial::Int\n periodic::Tuple{Bool, Bool}\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.\n\nWhen longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nzlim: Domain interval limits along z axis, in meters\nlonglat: When not nothing, a Tuple that contains the center long and lat.\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: Number of elements to discretize interval, (nx, ny,nz)\nnpolynomial: Polynomial order for the horizontal directions\nperiodic: Flag indicating periodic boundaries in horizontal\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Column","page":"Shared Utilities","title":"ClimaLand.Domains.Column","text":"Column{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nzlim: Domain interval limits, (zmin, zmax), in meters\nnelements: Number of elements used to discretize the interval\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nboundary_names: Boundary face identifiers\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Plane","page":"Shared Utilities","title":"ClimaLand.Domains.Plane","text":"Plane{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.\n\nWhen longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.\n\nlonglat are in degrees, with longitude going from -180 to 180.\n\n:warning: Only independent columns are supported! (No lateral flow).\n\nspace is a NamedTuple holding the surface space (in this case, the entire Plane space).\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nlonglat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).\nnelements: Number of elements to discretize interval, (nx, ny)\nperiodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.\nnpolynomial: Polynomial order for both x and y\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Point","page":"Shared Utilities","title":"ClimaLand.Domains.Point","text":"Point{FT} <: AbstractDomain{FT}\n\nA domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.\n\nspace is a NamedTuple holding the surface space (in this case, the Point space).\n\nFields\n\nz_sfc: Surface elevation relative to a reference (m)\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.coordinates","page":"Shared Utilities","title":"ClimaLand.Domains.coordinates","text":"coordinates(domain::AbstractDomain)\n\nReturns the coordinate fields for the domain as a NamedTuple.\n\nThe returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.\n\n\n\n\n\nDomains.coordinates(model::AbstractLandModel)\n\nReturns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.\n\nFor example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_face_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_face_space","text":"obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the face space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the face space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_space","text":"obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the surface space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_domain","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_domain","text":"obtain_surface_domain(d::AbstractDomain) where {FT}\n\nDefault method throwing an error; any domain with a corresponding domain should define a new method of this function.\n\n\n\n\n\nobtain_surface_domain(c::Column{FT}) where {FT}\n\nReturns the Point domain corresponding to the top face (surface) of the Column domain c.\n\n\n\n\n\nobtain_surface_domain(b::HybridBox{FT}) where {FT}\n\nReturns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.\n\n\n\n\n\nobtain_surface_domain(s::SphericalShell{FT}) where {FT}\n\nReturns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_center_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_center_to_surface","text":"top_center_to_surface(center_field::ClimaCore.Fields.Field)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.\n\nFor example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\ntop_center_to_surface(val)\n\nWhen val is a scalar (e.g. a single float or struct), returns val.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_face_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_face_to_surface","text":"top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.\n\nGiven a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.linear_interpolation_to_surface!","page":"Shared Utilities","title":"ClimaLand.Domains.linear_interpolation_to_surface!","text":"linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)\n\nLinearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.get_Δz","page":"Shared Utilities","title":"ClimaLand.Domains.get_Δz","text":"get_Δz(z::ClimaCore.Fields.Field)\n\nA function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Models","page":"Shared Utilities","title":"Models","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.AbstractModel\nClimaLand.AbstractImExModel\nClimaLand.AbstractExpModel\nClimaLand.make_exp_tendency\nClimaLand.make_imp_tendency\nClimaLand.make_compute_exp_tendency\nClimaLand.make_compute_imp_tendency\nClimaLand.make_update_aux\nClimaLand.make_update_boundary_fluxes\nClimaLand.make_set_initial_cache\nClimaLand.make_update_drivers\nClimaLand.prognostic_vars\nClimaLand.prognostic_types\nClimaLand.prognostic_domain_names\nClimaLand.auxiliary_vars\nClimaLand.auxiliary_types\nClimaLand.auxiliary_domain_names\nClimaLand.initialize_prognostic\nClimaLand.initialize_auxiliary\nClimaLand.initialize\nClimaLand.name\nClimaLand.AbstractBC\nClimaLand.AbstractSource\nClimaLand.source!\nClimaLand.AbstractBoundary\nClimaLand.TopBoundary\nClimaLand.BottomBoundary\nClimaLand.boundary_flux\nClimaLand.diffusive_flux\nClimaLand.boundary_vars\nClimaLand.boundary_var_domain_names\nClimaLand.boundary_var_types\nClimaLand.make_jacobian\nClimaLand.make_compute_jacobian\nClimaLand.set_dfluxBCdY!\nClimaLand.get_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractModel","page":"Shared Utilities","title":"ClimaLand.AbstractModel","text":"abstract type AbstractModel{FT <: AbstractFloat}\n\nAn abstract type for all models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractImExModel","page":"Shared Utilities","title":"ClimaLand.AbstractImExModel","text":"AbstractImExModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractExpModel","page":"Shared Utilities","title":"ClimaLand.AbstractExpModel","text":"AbstractExpModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.make_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_exp_tendency","text":"make_exp_tendency(model::AbstractModel)\n\nReturns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_imp_tendency","text":"make_imp_tendency(model::AbstractImExModel)\n\nReturns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\nmake_imp_tendency(model::AbstractModel)\n\nReturns an imp_tendency that does nothing. This model type is not stepped explicity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_exp_tendency","text":"make_explicit_tendency(model::Soil.RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nConstruct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.\n\n\n\n\n\nmake_compute_exp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::BucketModel{FT}) where {FT}\n\nCreates the computeexptendency! function for the bucket model.\n\n\n\n\n\nmake_compute_exp_tendency(model::AbstractModel)\n\nReturn a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)\n\nCreates the computeexptendency!(dY,Y,p,t) function for the canopy component.\n\nSince component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.\n\n\n\n\n\nmake_compute_exp_tendency(canopy::CanopyModel)\n\nCreates and returns the computeexptendency! for the CanopyModel.\n\n\n\n\n\nmake_compute_exp_tendency(model::SoilCO2Model)\n\nAn extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::PlantHydraulicsModel, _)\n\nA function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.\n\nBelow, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).\n\nNote that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.\n\nTo prevent dividing by zero, we change AI/(AI x dz)\" to \"AI/max(AI x dz, eps(FT))\"\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_imp_tendency","text":"make_compute_imp_tendency(model::RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.\n\n\n\n\n\nmake_compute_imp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_imp_tendency(model::AbstractModel)\n\nReturn a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_aux","page":"Shared Utilities","title":"ClimaLand.make_update_aux","text":"make_update_aux(model::RichardsModel)\n\nAn extension of the function make_update_aux, for the Richardson- Richards equation.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::EnergyHydrology)\n\nAn extension of the function make_update_aux, for the integrated soil hydrology and energy model.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::BucketModel{FT}) where {FT}\n\nCreates the update_aux! function for the BucketModel.\n\n\n\n\n\nmake_update_aux(model::AbstractModel)\n\nReturn an update_aux! function that updates auxiliary parameters p.\n\n\n\n\n\n ClimaLand.make_update_aux(canopy::CanopyModel{FT,\n <:AutotrophicRespirationModel,\n <:Union{BeerLambertModel, TwoStreamModel},\n <:FarquharModel,\n <:MedlynConductanceModel,\n <:PlantHydraulicsModel,},\n ) where {FT}\n\nCreates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.\n\nPlease note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.\n\nThe other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.\n\n\n\n\n\nmake_update_aux(model::SoilCO2Model)\n\nAn extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_boundary_fluxes","page":"Shared Utilities","title":"ClimaLand.make_update_boundary_fluxes","text":"make_update_boundary_fluxes(model::AbstractModel)\n\nReturn an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::LandHydrology{FT, SM, SW},\n) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}\n\nA method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.\n\nThis function is called each ode function evaluation.\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::SoilCanopyModel{FT, MM, SM, RM},\n) where {\n FT,\n MM <: Soil.Biogeochemistry.SoilCO2Model{FT},\n SM <: Soil.RichardsModel{FT},\n RM <: Canopy.CanopyModel{FT}\n }\n\nA method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. \n\nThis function is called each ode function evaluation, prior to the tendency function evaluation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_set_initial_cache","page":"Shared Utilities","title":"ClimaLand.make_set_initial_cache","text":"make_set_initial_cache(model::AbstractModel)\n\nReturns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.\n\nIn principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.\n\nFurthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_drivers","page":"Shared Utilities","title":"ClimaLand.make_update_drivers","text":"make_update_drivers(::AbstractClimaLandDrivers)\n\nCreates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.\n\n\n\n\n\nmake_update_drivers(driver_tuple)\n\nCreates and returns a function which updates the forcing variables (\"drivers\"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.\n\n\n\n\n\nmake_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.\n\n\n\n\n\nmake_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.\n\n\n\n\n\nmake_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.\n\n\n\n\n\nmake_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_vars","page":"Shared Utilities","title":"ClimaLand.prognostic_vars","text":"prognostic_vars(soil::RichardsModel)\n\nA function which returns the names of the prognostic variables of RichardsModel.\n\n\n\n\n\nprognostic_vars(soil::EnergyHydrology)\n\nA function which returns the names of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_vars(::SnowModel)\n\nReturns the prognostic variable names of the snow model.\n\nFor this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.\n\n\n\n\n\nprognostic_vars(m::AbstractModel)\n\nReturns the prognostic variable symbols for the model in the form of a tuple.\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_vars(::AbstractCanopyComponent)\n\nReturns the prognostic vars of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_vars(canopy::CanopyModel)\n\nReturns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nprognostic_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the prognostic variables of the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_types","page":"Shared Utilities","title":"ClimaLand.prognostic_types","text":"prognostic_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_types(::SnowModel{FT})\n\nReturns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.\n\n\n\n\n\nprognostic_types(m::AbstractModel{FT}) where {FT}\n\nReturns the prognostic variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nHere, the coordinate points are those returned by coordinates(model).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_types(::AbstractCanopyComponent)\n\nReturns the prognostic types of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_types(canopy::CanopyModel)\n\nReturns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the prognostic types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_domain_names","page":"Shared Utilities","title":"ClimaLand.prognostic_domain_names","text":"prognostic_domain_names(::SnowModel)\n\nReturns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is \":surface\".\n\n\n\n\n\nprognosticdomainnames(m::AbstractModel)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nprognosticdomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_vars","page":"Shared Utilities","title":"ClimaLand.auxiliary_vars","text":"auxiliary_vars(soil::RichardsModel)\n\nA function which returns the names of the auxiliary variables of RichardsModel.\n\n\n\n\n\nauxiliary_vars(soil::EnergyHydrology)\n\nA function which returns the names of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_vars(::SnowModel)\n\nReturns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.\n\nSince the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.\n\n\n\n\n\nauxiliary_vars(m::AbstractModel)\n\nReturns the auxiliary variable symbols for the model in the form of a tuple.\n\n\n\n\n\nClimaLand.auxiliary_vars(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_vars(canopy::CanopyModel)\n\nReturns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nauxiliary_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_types","page":"Shared Utilities","title":"ClimaLand.auxiliary_types","text":"auxiliary_types(soil::RichardsModel)\n\nA function which returns the names of the auxiliary types of RichardsModel.\n\n\n\n\n\nauxiliary_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_types(m::AbstractModel{FT}) where {FT}\n\nReturns the auxiliary variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nNote that Arrays, MVectors are not isbits and cannot be used.\n\nHere, the coordinate points are those returned by coordinates(model).\n\n\n\n\n\nClimaLand.auxiliary_types(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_types(canopy::CanopyModel)\n\nReturns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the auxiliary types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_domain_names","page":"Shared Utilities","title":"ClimaLand.auxiliary_domain_names","text":"auxiliary_domain_names(soil::RichardsModel)\n\nA function which returns the names of the auxiliary domain names of RichardsModel.\n\n\n\n\n\nauxiliarydomainnames(m::AbstractModel)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\n\n\n\n\nauxiliarydomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_prognostic","page":"Shared Utilities","title":"ClimaLand.initialize_prognostic","text":"initialize_prognostic(model::AbstractModel, state::NamedTuple)\n\nReturns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.\n\nIf a model has no prognostic variables, the returned FieldVector contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_prognostic(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_prognostic(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input state is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_auxiliary","page":"Shared Utilities","title":"ClimaLand.initialize_auxiliary","text":"initialize_auxiliary(model::AbstractModel, state::NamedTuple)\n\nReturns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.\n\nIf a model has no auxiliary variables, the returned NamedTuple contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_auxiliary(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_auxiliary(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input coords is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize","page":"Shared Utilities","title":"ClimaLand.initialize","text":"initialize(model::AbstractModel)\n\nCreates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.name","page":"Shared Utilities","title":"ClimaLand.name","text":"name(model::AbstractModel)\n\nReturns a symbol of the model component name, e.g. :soil or :vegetation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBC","page":"Shared Utilities","title":"ClimaLand.AbstractBC","text":"AbstractBC\n\nAn abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractSource","page":"Shared Utilities","title":"ClimaLand.AbstractSource","text":"AbstractSource{FT <: AbstractFloat}\n\nAn abstract type for types of source terms.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.source!","page":"Shared Utilities","title":"ClimaLand.source!","text":" source!(dY::ClimaCore.Fields.FieldVector,\n src::PhaseChange{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nComputes the source terms for phase change.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::SoilSublimation{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nUpdates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::AbstractSource,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n )::ClimaCore.Fields.Field\n\nA stub function, which is extended by ClimaLand.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::RootExtraction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n model::EnergyHydrology)\n\nAn extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::MicrobeProduction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n params)\n\nA method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.\n\n\n\n\n\nClimaLand.source!(\n dY::ClimaCore.Fields.FieldVector,\n src::TOPMODELSubsurfaceRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model::AbstractSoilModel{FT},\n) where {FT}\n\nAdjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.\n\nThe sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBoundary","page":"Shared Utilities","title":"ClimaLand.AbstractBoundary","text":"AbstractBoundary\n\nAn abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.TopBoundary","page":"Shared Utilities","title":"ClimaLand.TopBoundary","text":"TopBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the top boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.BottomBoundary","page":"Shared Utilities","title":"ClimaLand.BottomBoundary","text":"BottomBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the bottom boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_flux","page":"Shared Utilities","title":"ClimaLand.boundary_flux","text":"boundary_flux(bc::WaterFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(bc::RichardsAtmosDrivenFluxBC,\n boundary::ClimaLand.AbstractBoundary,\n model::RichardsModel{FT},\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field where {FT}\n\nA method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.\n\nIf model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(bc::FreeDrainage,\n boundary::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which enforces free drainage at the bottom of the domain.\n\n\n\n\n\nboundary_flux(bc::HeatFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.BottomBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field\n\nA function which returns the correct boundary flux given any boundary condition (BC). \n\n\n\n\n\nfunction ClimaLand.boundary_flux(\n bc::RunoffBC,\n ::TopBoundary,\n model::Soil.RichardsModel,\n Δz::FT,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n params,\n)::ClimaCore.Fields.Field\n\nExtension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2FluxBC,\n boundary::ClimaLand.AbstractBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::SoilCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2StateBC,\n boundary::ClimaLand.BottomBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::AtmosCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.diffusive_flux","page":"Shared Utilities","title":"ClimaLand.diffusive_flux","text":"diffusive_flux(K, x_2, x_1, Δz)\n\nCalculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_vars","page":"Shared Utilities","title":"ClimaLand.boundary_vars","text":"boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.\n\nThese variables are updated in place in soil_boundary_fluxes!.\n\n\n\n\n\nboundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for MoistureStateBC at the top boundary.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\nboundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_domain_names","page":"Shared Utilities","title":"ClimaLand.boundary_var_domain_names","text":"boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.\n\n\n\n\n\nboundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)\n\nThe list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. \n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_types","page":"Shared Utilities","title":"ClimaLand.boundary_var_types","text":"boundary_var_types(::RichardsModel{FT},\n ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <: Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.\n\nBecause we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.\n\n\n\n\n\nboundary_var_types(\n ::EnergyHydrology{FT},\n ::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere{FT},\n <:AbstractRadiativeDrivers{FT},\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary,\n) where {FT}\n\nAn extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.\n\n\n\n\n\nboundary_var_types(::RichardsModel{FT},\n ::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_jacobian","page":"Shared Utilities","title":"ClimaLand.make_jacobian","text":"make_jacobian(model::AbstractModel)\n\nCreates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.\n\nThe default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.\n\nNote that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_jacobian","page":"Shared Utilities","title":"ClimaLand.make_compute_jacobian","text":"ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nmake_compute_jacobian(model::AbstractModel)\n\nCreates and returns a function which computes the entries of the Jacobian matrix W in place.\n\nIf the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.\n\nThe default is that no updates are required, but this function must be extended for models that use implicit timestepping.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_dfluxBCdY!","page":"Shared Utilities","title":"ClimaLand.set_dfluxBCdY!","text":"ClimaLand.set_dfluxBCdY!(\n model::RichardsModel,\n ::MoistureStateBC,\n boundary::ClimaLand.TopBoundary,\n Δz,\n Y,\n p,\n t,\n\n)\n\nComputes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.\n\nFor Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.\n\n\n\n\n\nset_dfluxBCdY!(::AbstractModel,\n ::AbstractBC,\n ::AbstractBoundary,\n _...)::Union{ClimaCore.Fields.FieldVector, Nothing}\n\nA function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.get_drivers","page":"Shared Utilities","title":"ClimaLand.get_drivers","text":"ClimaLand.get_drivers(model::RichardsModel)\n\nReturns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.\n\n\n\n\n\nClimaLand.get_drivers(model::SnowModel)\n\nReturns the driver variable symbols for the SnowModel.\n\n\n\n\n\nget_drivers(model::AbstractModel)\n\nReturns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Drivers","page":"Shared Utilities","title":"Drivers","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.PrescribedAtmosphere\nClimaLand.PrescribedPrecipitation\nClimaLand.PrescribedRadiativeFluxes\nClimaLand.PrescribedSoilOrganicCarbon\nClimaLand.CoupledAtmosphere\nClimaLand.CoupledRadiativeFluxes\nClimaLand.AbstractAtmosphericDrivers\nClimaLand.AbstractRadiativeDrivers\nClimaLand.turbulent_fluxes\nClimaLand.turbulent_fluxes_at_a_point\nClimaLand.radiative_fluxes_at_a_point\nClimaLand.set_atmos_ts!\nClimaLand.surface_air_density\nClimaLand.surface_temperature\nClimaLand.surface_resistance\nClimaLand.surface_specific_humidity\nClimaLand.make_update_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedAtmosphere","page":"Shared Utilities","title":"ClimaLand.PrescribedAtmosphere","text":"PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.\n\nThe default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.\n\nSince not all models require co2 concentration, the default for that is nothing.\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\nsnow_precip: Snow precipitation (m/s) function of time: positive by definition\nT: Prescribed atmospheric temperature (function of time) at the reference height (K)\nu: Prescribed wind speed (function of time) at the reference height (m/s)\nq: Prescribed specific humidity (function of time) at the reference height (_)\nP: Prescribed air pressure (function of time) at the reference height (Pa)\nc_co2: CO2 concentration in atmosphere (mol/mol)\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nh: Reference height (m), relative to surface elevation\ngustiness: Minimum wind speed (gustiness; m/s)\nthermo_params: Thermodynamic parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedPrecipitation","page":"Shared Utilities","title":"ClimaLand.PrescribedPrecipitation","text":"PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.PrescribedRadiativeFluxes","text":"PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}\n\nContainer for the prescribed radiation functions needed to drive land models in standalone mode.\n\nSW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface\nLW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nθs: Sun zenith angle, in radians\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedSoilOrganicCarbon","page":"Shared Utilities","title":"ClimaLand.PrescribedSoilOrganicCarbon","text":" PrescribedSoilOrganicCarbon{FT}\n\nA type for prescribing soil organic carbon.\n\nsoc: Soil organic carbon, function of time and space: kg C/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledAtmosphere","page":"Shared Utilities","title":"ClimaLand.CoupledAtmosphere","text":"CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.CoupledRadiativeFluxes","text":"CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractAtmosphericDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractAtmosphericDrivers","text":" AbstractAtmosphericDrivers{FT}\n\nAn abstract type of atmospheric drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractRadiativeDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractRadiativeDrivers","text":" AbstractRadiativeDrivers{FT}\n\nAn abstract type of radiative drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes","text":"turbulent_fluxes(atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t\n )\n\nComputes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.\n\nIt solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.\n\n\n\n\n\nturbulent_fluxes(atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t)\n\nComputes the turbulent surface fluxes terms at the ground for a coupled simulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes_at_a_point","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes_at_a_point","text":"turbulent_fluxes_at_a_point(T_sfc::FT,\n q_sfc::FT,\n ρ_sfc::FT,\n β_sfc::FT,\n h_sfc::FT,\n r_sfc::FT,\n d_sfc::FT,\n ts_in,\n u::FT,\n h::FT,\n gustiness::FT,\n z_0m::FT,\n z_0b::FT,\n earth_param_set::EP,\n ) where {FT <: AbstractFloat, P}\n\nComputes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc\n\nThis returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_atmos_ts!","page":"Shared Utilities","title":"ClimaLand.set_atmos_ts!","text":"set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)\n\nFill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_air_density","page":"Shared Utilities","title":"ClimaLand.surface_air_density","text":"surface_air_density(\n atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t,\n T_sfc,\n )\n\nA helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.\n\nWe additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.\n\nExtending this function for your model is only necessary if you need to compute the air density in a different way.\n\n\n\n\n\nClimaLand.surface_air_density(\n atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n _...,\n )\n\nReturns the air density at the surface in the case of a coupled simulation.\n\nThis requires the field ρ_sfc to be present in the cache p under the name of the model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_temperature","page":"Shared Utilities","title":"ClimaLand.surface_temperature","text":"ClimaLand.surface_temperature(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface temperature field of the EnergyHydrology soil model.\n\nThe assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.\n\n\n\n\n\nClimaLand.surface_temperature(model::SnowModel, Y, p)\n\na helper function which returns the surface temperature for the snow model, which is stored in the aux state.\n\n\n\n\n\nClimaLand.surface_temperature(model::BucketModel, Y, p)\n\na helper function which returns the surface temperature for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_temperature(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_temperature(model::CanopyModel, Y, p, t)\n\nA helper function which returns the temperature for the canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_resistance","page":"Shared Utilities","title":"ClimaLand.surface_resistance","text":"ClimaLand.surface_resistance(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface resistance field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_resistance(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\nThe default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.\n\n\n\n\n\nClimaLand.surface_resistance(\n model::CanopyModel{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the stomatal resistance field of the CanopyModel canopy.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_specific_humidity","page":"Shared Utilities","title":"ClimaLand.surface_specific_humidity","text":"ClimaLand.surface_specific_humidity(\n model::EnergyHydrology{FT},\n Y,\n p,\n T_sfc,\n ρ_sfc\n) where {FT}\n\nReturns the surface specific humidity field of the EnergyHydrology soil model.\n\nThis models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.\n\nOver the soil ice, the specific humidity is the saturated value.\n\nThe total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\nComputes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\na helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)\n\nA helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)\n\nA helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.\n\n\n\n\n\n","category":"function"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Farquhar-Model","page":"Farquhar model","title":"Farquhar Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO_2 into the leaf, the assimilation of CO_2 during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO_2 concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Rubisco-limited-rate","page":"Farquhar model","title":"Rubisco limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_1(T c_a VPD) =\nbegincases\n V_cmax(T) frac(c_i(T c_a VPD) - Gamma^*(T))(c_i(T c_a VPD) + K_c(T)*(1+o_iK_o(T))) textfor C3\n V_cmax(T) textfor C4\nendcases\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The dependence on the atmospheric CO_2 concentration c_a (mol/mol) and vapor pressure deficit VPD arise in the expression for c_i,","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n c_i(T c_a VPD) = max(c_a(1-1m(VPD)) Gamma^*(T))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where and m is the Medlyn factor (see Stomatal Conductance).","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We also have","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":" Gamma^*(T) = Gamma^*_25expleft(Delta H_Gamma^*fracT - T_oT_o R Tright)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where Delta H_Gamma^* is the activation energy per mol for Gamma^*.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Light-limited-rate","page":"Farquhar model","title":"Light limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_2 =\nbegincases\n J(T PAR) (c_i - Gamma^*)4(c_i + 2 Gamma^*) textfor C3\n J(T PAR) textfor C4\nendcases \nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where J is the rate of electron transport, which has units of mol photon per m^2 per s. It depends on PAR via APAR, as described below, and on T via the dependence on J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"J is given by the root of the equation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n theta_j J^2 - (I + J_max) J + I J_max = 0 nonumber \n I = fracphi2 (APAR) nonumber \n J_max(T) = V_cmax(T)times e expleft(Delta H_J_maxfracT - T_oT_o R Tright)nonumber \nJ(T PAR) = frac(I + J_max - sqrt(I + J_max)^2 - 4theta_j I times J_max2theta_j\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where phi = 06 and theta_j = 09 are the quantum yield of photosystem II and a curvature function (Bonan's book), and Delta H_J_max is the energy of activation of J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The total net carbon assimilation (A_n, mol CO_2 m^-2 s^-1) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nA_n(T PAR VPD c_a) = textmax(0 textmin(a_1 beta a_2) - R_d)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where beta is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R_d is the leaf dark respiration calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n R_d25(psi_l) = f V_cmax25beta(psi_l) nonumber \n R_d (T psi_l) = R_d25(psi_l)expleft(Delta H_R_dfracT - T_oT_o R Tright)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where f = 0015 is a constant, Delta H_R_d is the energy of activation for R_d, and finally Vcmax is calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\nV_cmax(T) = V_cmax25 expleft(Delta H_VcmaxfracT - T_oT_o R Tright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"with V_cmax25 is a parameter (Vcmax at the reference temperature 25 C), and Delta H_Vcmax = 65330 Jmol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The moisture stress factor is related to the leaf water potential psi_l as","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n beta = frac1+ exp(s_c psi_c)1+ exp(s_c(psi_c - psi_l))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where s_c = 4MPa^-1, psi_c = -2MPa, and psi_l is the leaf water potential computed by the plant hydraulics model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nGPP(T PAR c_a VPD theta_s) = A_n (1 - exp(-K LAI Omega))K\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This is not currently needed by other components, but is used for offline validation of the model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We need to supply the following parameters and “drivers\"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"K_c25 and K_o25, V_cmax 25, Gamma^*_25phi, theta_j, o_i, s_c, psi_c\npsi_l, to compute beta\nTemperature T, PAR, c_a, VPD, theta_s.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Output Symbol Unit Range\nTotal net carbon assimilation A_n μmol CO_2 m^-2 s^-1 0–25","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nTemperature T °C 0–50","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Parameters Symbol Unit Range\nMoisture stress β - 0-1\nLeaf Area Index LAI m² m⁻² 1–10\nCO_2 concentration c_a ppm 300e–500\nVapor pressure deficit VPD kPa 1-10","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Constants Symbol Unit Value\nZenith angle θ_s rad 0.6\nLeaf angle distribution l_d - 0.5\nCanopy reflectance ρ_leaf - 0.1\nClumping index Ω - 0.69\nCO_2 compensation at 25°C Γ^*_25 mol/mol 4.275e-5\nEnergy of activation for Γ^* ΔH_Γ^* J/mol 37830\nStandard temperature T_o K 298.15\nUniversal gas constant R J/mol 8.314\nThe maximum rate of carboxylation of Rubisco V_cmax25 mol CO_2 m^-2 s^-1 5e-5\nEnergy of activation for J_max ΔH_J_max J/mol 43540\nCurvature parameter, a fitting constant to compute J θ_j - 0.9\nThe quantum yied of photosystem II phi - 0.6\nEnergy of activation for V_cmax ΔH_V_cmax J/mol 58520\nSlope parameter for stomatal conductance models g_1 - 141\nMichaelis Menten constant for CO_2 and at 25\u000e°C K_c25 mol/mol 4.049e-4\nEnergy of activation for CO_2 ΔH_K_c J/mol 79430\nMichaelis Menten constant for O_2 at 25 °C\u000e K_o25 mmol/mol 0.2874\nEnergy of activation for O_2 ΔH_K_o J/mol 36380\nIntercellular O_2 concentration o_i mol/mol 0.209\nConstant factor appearing the dark respiration term\u000e f - 0.015\nEnergy of activation for R_d ΔH_R_d J/mol 43390","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Soil-Biogeochemistry","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"CurrentModule = ClimaLand.Soil.Biogeochemistry","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Model-Structure","page":"Soil Biogeochemistry","title":"Model Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2Model","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","text":"SoilCO2Model\n\nA model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type NamedTuple\nsources: A tuple of sources, each of type AbstractSource\ndrivers: Drivers\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Parameter-Structure","page":"Soil Biogeochemistry","title":"Parameter Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","text":"SoilCO2ModelParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SoilCO2Model.\n\nAll of these parameters are currently treated as global constants.\n\nD_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)\nD_liq: Diffusivity of soil C substrate in liquid (unitless)\nα_sx: Pre-exponential factor (kg C m-3 s-1)\nEa_sx: Activation energy (J mol-1)\nkM_sx: Michaelis constant (kg C m-3)\nkM_o2: Michaelis constant for O2 (m3 m-3)\nO2_a: Volumetric fraction of O₂ in the soil air, dimensionless\nD_oa: Diffusion coefficient of oxygen in air, dimensionless\np_sx: Fraction of soil carbon that is considered soluble, dimensionless\nearth_param_set: Physical constants used Clima-wide\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Model-specific-Types","page":"Soil Biogeochemistry","title":"Model-specific Types","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction\nClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC\nClimaLand.Soil.Biogeochemistry.SoilCO2StateBC\nClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC\nClimaLand.Soil.Biogeochemistry.AbstractSoilDriver\nClimaLand.Soil.Biogeochemistry.SoilDrivers\nClimaLand.Soil.Biogeochemistry.PrescribedMet","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.MicrobeProduction","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction","text":"MicrobeProduction{FT} <: AbstractCarbonSource{FT}\n\nStruct for the microbe production of CO2, appearing as a source term in the differential equation.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","text":"SoilCO2FluxBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 flux boundary condition, which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","text":"SoilCO2StateBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 state boundary condition (kg CO2 m−3), which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","text":"AtmosCO2StateBC <: ClimaLand.AbstractBC\n\nSet the CO2 concentration to the atmospheric one.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","text":"AbstractSoilDriver\n\nAn abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilDrivers","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilDrivers","text":"SoilDrivers\n\nA container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).\n\nmet: Soil temperature and moisture drivers - Prescribed or Prognostic\nsoc: Soil SOM driver - Prescribed only\natmos: Prescribed atmospheric variables\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.PrescribedMet","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.PrescribedMet","text":"PrescribedMet <: AbstractSoilDriver\n\nA container which holds the prescribed functions for soil temperature and moisture.\n\nThis is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.\n\ntemperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat\nvolumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat\nν: Soil porosity (m³ m⁻³)\nθ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)\nb: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Functions-of-State","page":"Soil Biogeochemistry","title":"Functions of State","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content\nClimaLand.Soil.Biogeochemistry.co2_diffusivity\nClimaLand.Soil.Biogeochemistry.microbe_source","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.volumetric_air_content","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content","text":"volumetric_air_content(θ_w::FT,\n ν::FT,\n ) where {FT}\n\nComputes the volumetric air content (θ_a) in the soil, which is related to the total soil porosity (ν) and volumetric soil water content (θ_w = θ_l+θ_i).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.co2_diffusivity","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.co2_diffusivity","text":"co2_diffusivity(\n T_soil::FT,\n θ_w::FT,\n P_sfc::FT,\n θ_a100::FT,\n b::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT},\n ) where {FT}\n\nComputes the diffusivity of CO₂ within the soil (D).\n\nFirst, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.\n\nThis parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.microbe_source","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.microbe_source","text":"microbe_source(T_soil::FT,\n θ_l::FT,\n Csom::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT}\n ) where {FT}\n\nComputes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#Extendible-Functions","page":"Soil Biogeochemistry","title":"Extendible Functions","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.soil_moisture\nClimaLand.Soil.Biogeochemistry.soil_temperature\nClimaLand.Soil.Biogeochemistry.soil_SOM_C","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_moisture","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_moisture","text":"soil_moisture(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.\n\n\n\n\n\nsoil_moisture(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil moisture at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_temperature","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_temperature","text":"soil_temperature(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the prognostic soil temperature.\n\n\n\n\n\nsoil_temperature(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil temperature at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/users_diagnostics/#Using-ClimaLand-Diagnostics-when-running-a-simulation","page":"For users","title":"Using ClimaLand Diagnostics when running a simulation","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"This is where ClimaLand Diagnostics comes in for users.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.","category":"page"},{"location":"diagnostics/users_diagnostics/#Default-Diagnostics","page":"For users","title":"Default Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#define-an-output-folder","page":"For users","title":"define an output folder","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path(\"base_output_dir/\")","category":"page"},{"location":"diagnostics/users_diagnostics/#define-a-space","page":"For users","title":"define a space","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"space = bucket_domain.space.subsurface","category":"page"},{"location":"diagnostics/users_diagnostics/#define-your-writter","page":"For users","title":"define your writter","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"providing the space and output_dir defined in steps 1. and 2.","category":"page"},{"location":"diagnostics/users_diagnostics/#make-your-diagnostics-on-your-model,-using-your-writter,-and-define-a-callback","page":"For users","title":"make your diagnostics on your model, using your writter, and define a callback","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"t0 = 0 # the starting time of your simulation\n\nreference_date = DateTime(2024) # reference_date is the DateTime of your starting time\n\ndiags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)\n\ndiagnostic_handler =\n ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)\n\ndiag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics have now been written in netcdf files in your output folder.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.","category":"page"},{"location":"diagnostics/users_diagnostics/#Custom-Diagnostics","page":"For users","title":"Custom Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When defining a custom diagnostic, follow these steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-compute-your-diagnostic-variable-from-your-model-state-and-cache.","page":"For users","title":"Define how to compute your diagnostic variable from your model state and cache.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)\n else\n out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf\n end\nend","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"@diagnostic_compute \"latent_heat_flux\" BucketModel p.bucket.turbulent_fluxes.lhf","category":"page"},{"location":"diagnostics/users_diagnostics/#Add-that-diagnostic(s)-variable-to-your-list-of-variables","page":"For users","title":"Add that diagnostic(s) variable to your list of variables","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":" add_diagnostic_variable!(\n short_name = \"bor\",\n long_name = \"Bowen ratio\",\n standard_name = \"bowen_ratio\",\n units = \"\",\n comments = \"Ratio of sensible to latent heat flux.\",\n compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),\n)\n\nadd_diagnostic_variable!(\n short_name = \"lhf\",\n long_name = \"Latent Heat Flux\",\n standard_name = \"latent_heat_flux\",\n units = \"W m^-2\",\n comments = \"Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.\",\n compute! = (out, Y, p, t) ->\n compute_latent_heat_flux!(out, Y, p, t, land_model),\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-schedule-your-variables.-For-example,-you-want-the-seasonal-maximum-of-your-variables,-where-season-is-defined-as-90-days.","page":"For users","title":"Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(\n 90 * 24 * 60 * 60 * one(t_start),\n max,\n output_writer,\n t_start,\n short_names...,\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-a-function-to-return-your-ScheduledDiagnostics","page":"For users","title":"Define a function to return your ScheduledDiagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now, you can call your schedule with your variables.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"my_custom_diagnostics = [\"lhf\", \"bor\"]\n\ndiags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)","category":"page"},{"location":"diagnostics/users_diagnostics/#Analyze-your-simulation-output","page":"For users","title":"Analyze your simulation output","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Here is an example of how to plot a variable:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"import ClimaAnalysis\n\nimport ClimaAnalysis.Visualize as viz\n\nimport CairoMakie # the plotting package used by ClimaAnalysis\n\nsimdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.\n\nvar = get(simdir; \"lhf\") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.\n\nfig = CairoMakie.Figure() # creates an empty figure object\n\nviz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.\n\nCairoMakie.save(fig) # saves the figure in current working directory","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/#Optimality-Model","page":"Optimality model","title":"Optimality Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation (V_textcmax). ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates V_textcmax and J_textmax as a function of environmental variables as follows:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n V_textcmax^* = varphi I left(fracmm_cright)left(fracoverlineomega^*8thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega^* = 1 + overlineomega - sqrt(1 + overlineomega)^2 - 4thetaoverlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega = -(1 - 2theta) + sqrt(1 - theta)left(frac1frac4cmleft(1 - thetafrac4cmright) - 4thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n c = fracm8thetaleft(1 - fracvarphi I + J_textmax - 2thetavarphi Isqrt(varphi I + J_textmax)^2 - 4thetavarphi I J_textmaxright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n J_textmax = varphi I overlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m = fracC_i - Gamma^*C_i + 2Gamma^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n C_i = Gamma^* + (C_a - Gamma^*)fracxixi + sqrtD_g\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n xi = sqrtbeta fracK + Gamma^*16eta^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n K = K_cleft(1 + fracO_iK_oright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m_c = fracC_i - Gamma^*C_i + K\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Gamma^*","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"is the CO_2 compensation point in the absence of mitochondrial respiration","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n Gamma^* = Gamma^*_0 f(T Delta H_a) pp_0\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where Gamma^*_0 = 4332 Pa, p is the atmospheric pressure, p_0 = 101325 Pa, and Delta H_a = 37830 J/mol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"varphi is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.\ntheta is the curvature of the light response curve (dimensionless). Estimated at 0.85.\nbeta is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"For Smith et al. (2019) Vcmax model:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Altitude\nD_g is the vapor pressure deficit (VPD) at altitude\nC_a is the CO_2 partial pressure\nI is the incident photosynthetically active photon flux (PAR)\nT is the temperature","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Introduction-to-the-Canopy-Model","page":"Standalone Canopy","title":"Introduction to the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Preliminary-Setup","page":"Standalone Canopy","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load External Packages:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing StaticArrays\nusing ClimaLand\nusing ClimaLand.Domains: Point\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Setup-the-Canopy-Model","page":"Standalone Canopy","title":"Setup the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4) # kg/m^2\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Point(; z_sfc = FT(0.0))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}\n Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"time_offset = 7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Site latitude and longitude","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"-92.2f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Height of the sensor at the site","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"atmos_h = FT(32)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"32.0f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_soil0 = FT(0.0)\n\nsoil_driver = PrescribedSoil(\n FT;\n root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),\n ψ = t -> ψ_soil0,\n α_PAR = FT(0.2),\n α_NIR = FT(0.4),\n T = t -> 298.0,\n ϵ = FT(0.99),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"rt_params = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = FT(0.1),\n α_NIR_leaf = FT(0.45),\n τ_PAR_leaf = FT(0.05),\n τ_NIR_leaf = FT(0.25),\n Ω = FT(0.69),\n λ_γ_PAR = FT(5e-7),\n λ_γ_NIR = FT(1.65e-6),\n)\n\nrt_model = TwoStreamModel{FT}(rt_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for conductance model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))\n\nstomatal_model = MedlynConductanceModel{FT}(cond_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for photosynthesis model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))\n\nphotosynthesis_model = FarquharModel{FT}(photo_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for autotrophic respiration model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"AR_params = AutotrophicRespirationParameters(FT)\nAR_model = AutotrophicRespirationModel{FT}(AR_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for plant hydraulics model are more complicated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"LAI = 4.2\nLAIfunction = (t) -> LAI\nSAI = FT(0.00242)\nf_root_to_shoot = FT(3.5)\nRAI = FT((SAI + LAI) * f_root_to_shoot)\nai_parameterization =\n PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)\nrooting_depth = FT(1.0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the root distribution function p(z):","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"function root_distribution(z::T; rooting_depth = rooting_depth) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"K_sat_plant = FT(1.8e-8)\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use these values to populate the parameters of the PlantHydraulics model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ν = FT(0.7)\nS_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = ν,\n S_s = S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the remaining variables required for the plant hydraulics model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_surfaces = compartment_surfaces,\n compartment_midpoints = compartment_midpoints,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"canopy = ClimaLand.Canopy.CanopyModel{FT}(;\n parameters = shared_params,\n domain = land_domain,\n autotrophic_respiration = AR_model,\n radiative_transfer = rt_model,\n photosynthesis = photosynthesis_model,\n conductance = stomatal_model,\n hydraulics = plant_hydraulics,\n soil_driver = soil_driver,\n atmos = atmos,\n radiation = radiation,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Y, p, coords = ClimaLand.initialize(canopy)\nexp_tendency! = make_exp_tendency(canopy);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide initial conditions for the canopy hydraulics model","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n ν,\n S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"t0 = 0.0\nN_days = 364\ntf = t0 + 3600 * 24 * N_days\ndt = 225.0;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the cache variables for the canopy using the initial conditions and initial time.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"set_initial_cache! = make_set_initial_cache(canopy)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"n = 16\nsaveat = Array(t0:(n * dt):tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the callback function which updates the forcing variables, or drivers.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"updateat = Array(t0:1800:tf)\nmodel_drivers = ClimaLand.get_drivers(canopy)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a timestepping algorithm and setup the ODE problem.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"timestepper = CTS.RK4();\node_algo = CTS.ExplicitAlgorithm(timestepper)\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Create-some-plots","page":"Standalone Canopy","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We can now plot the data produced in the simulation. For example, GPP:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Transpiration plot:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Show the two plots together:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Save the output:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"savefig(\"ozark_standalone_canopy_test.png\");","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"(Image: )","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Introduction-to-the-Land-Bucket-Model","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"All equation variables are defined immediately below. We have:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wdt = -I","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wsdt = -(P_liq + σM + (1-σ) E_soil - I)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd σSdt = -(P_snow + σE_snow - σM)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ρc fracpartial Tpartial t = κ_soil fracpartial Tpartial z","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_bot = 00 = -κ_soil fracpartial Tpartial z_z = z_bot","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(1-σ) (R_n+ SHF + LHF)_soil + σG_undersnow = -κ_soil fracpartial Tpartial z_z = z_sfc","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"G_undersnow = (R_n+ SHF + LHF)_snow - F_intosnow","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_intosnow = -ρ_l L_f0 (P_snow + E_snow-M)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"R_n = -(1-α)*SW -LW + σ_SB T_sfc^4","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_soil = β(W W_f) E(q_sat(T_sfc ρ_sfc liquid)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_snow = E(q_sat(T_sfc ρ_sfc ice)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Simulating-a-standalone-bucket-model","page":"Introduction to the Land Bucket Model","title":"Simulating a standalone bucket model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaCore","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaParams as CP","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use Insolation to calculate solar zenith angle and solar insolation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaLand.Bucket:\n BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo\nusing ClimaLand.Domains: coordinates, Column\nusing ClimaLand:\n initialize,\n make_update_aux,\n make_exp_tendency,\n make_set_initial_cache,\n PrescribedAtmosphere,\n PrescribedRadiativeFluxes\nusing ClimaUtilities.TimeVaryingInputs: TimeVaryingInput","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also want to plot the solution","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Plots","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"And we need to use the DateTime type to store reference times","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Dates\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaLand\nimport ClimaLand.Parameters as LP\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"soil_depth = FT(3.5);\nbucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);\nsurface_space = bucket_domain.space.surface","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}\n Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"α_bareground_func = (coordinate_point) -> 0.2;\nα_snow = FT(0.8);\nalbedo =\n PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The critical snow level setting the scale for when we interpolate between snow and surface albedo","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"σS_c = FT(0.2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The field capacity of the soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W_f = FT(0.15);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Roughness lengths (meters)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"z_0m = FT(1e-2);\nz_0b = FT(1e-3);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Thermal parameters of soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"κ_soil = FT(0.7);\nρc_soil = FT(2e6);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Snow melt timescale","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"τc = FT(3600);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Simulation start time, end time, and timestep","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"t0 = 0.0;\ntf = 7 * 86400;\nΔt = 3600.0;\n\nbucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ref_time = DateTime(2005);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Here we define the model drivers Prescribed atmospheric variables","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Precipitation:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"precip = (t) -> 0;\nsnow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Diurnal temperature variations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Constant otherwise:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"u_atmos = (t) -> 3.0;\nq_atmos = (t) -> 0.005;\nh_atmos = FT(2);\nP_atmos = (t) -> 101325;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"bucket_atmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(snow_precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));\nLW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;\nbucket_rad = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"model = BucketModel(\n parameters = bucket_parameters,\n domain = bucket_domain,\n atmosphere = bucket_atmos,\n radiation = bucket_rad,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Like all ClimaLand models, we set up the state vector using initialize:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y, p, coords = initialize(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We can inspect the prognostic and auxiliary variables of the model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.prognostic_vars(model)\nY.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:W, :T, :Ws, :σS)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.auxiliary_vars(model)\np.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Next is to set initial conditions.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y.bucket.T .= FT(270);\nY.bucket.W .= FT(0.05);\nY.bucket.Ws .= FT(0.0);\nY.bucket.σS .= FT(0.08);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also set the initial values of the cache here:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"set_initial_cache! = make_set_initial_cache(model);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"exp_tendency! = make_exp_tendency(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Now we choose our timestepping algorithm.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then we can set up the simulation and solve it:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"saveat = collect(t0:Δt:tf);\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\nsaving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);\nupdateat = copy(saveat)\nmodel_drivers = ClimaLand.get_drivers(model)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];\nWs = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];\nσS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];\nT_sfc =\n [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];\nevaporation = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]\n for k in 1:length(sol.t)\n];\nR_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The turbulent energy flux is the sum of latent and sensible heat fluxes.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"LHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for\n k in 1:length(sol.t)\n];\nSHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for\n k in 1:length(sol.t)\n];\nturbulent_energy_flux = SHF .+ LHF\n\nplot(\n sol.t ./ 86400,\n W,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"W (m)\",\n title = \"Land water storage (m)\",\n)\nsavefig(\"w.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n σS,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"σS (m)\",\n title = \"Area weighted SWE (m) \",\n)\nsavefig(\"swe.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n snow_precip.(sol.t),\n label = \"Net precipitation\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (m/s)\",\n title = \"Surface water fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, evaporation, label = \"Sublimation/Evaporation\")\nsavefig(\"water_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n T_sfc,\n title = \"Surface Temperatures\",\n label = \"Ground temperature\",\n xlabel = \"time (days)\",\n ylabel = \"T_sfc (K)\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, T_atmos.(sol.t), label = \"Atmospheric Temperature\")\nsavefig(\"t.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n R_n,\n label = \"Net radiative flux\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (W/m^2)\",\n title = \"Surface energy fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, turbulent_energy_flux, label = \"Turbulent fluxes\")\nplot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = \"Net flux\")\nsavefig(\"energy_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#References","page":"Introduction to the Land Bucket Model","title":"References","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/#The-Two-Stream-Scheme","page":"Two-Stream model","title":"The Two-Stream Scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"-overlinemu(dI^uparrow)dL + 1 - (1 - beta)omegaI^uparrow - omega beta I^downarrow = omega overlinemu K beta_0 exp(-KL)\n-overlinemu(dI^downarrow)dL + 1 - (1 - beta)omegaI^downarrow - omega beta I^uparrow = omega overlinemu K (1-beta_0) exp(-KL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"int_0^1mu^primeG(mu^prime)dmu^prime","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = frach_1exp(-KL)sigma + h_2exp(-hL) + h_3exp(hL)\nI^downarrow = frach_4exp(-KL)sigma + h_5exp(-hL) + h_6exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = h_7exp(-hL) + h_8exp(hL)\nI^downarrow = h_9exp(-hL) + h_10exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Output Symmbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500\nAbsorbed Near-Infrared Radiation ANIR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Parameters Symbol Unit Range\nCanopy PAR Reflectance alpha_PAR_leaf - 0.0–1.0\nCanopy NIR Reflectance alpha_NIR_leaf - 0.0–1.0\nCanopy PAR Transmittance tau_PAR_leaf - 0.0–1.0\nCanopy NIR Transmittance tau_NIR_leaf - 0.0–1.0\nCanopy Emissivity ϵ_canopy - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5\nTypical wavelength per photon PAR lambda_gamma_PAR m 5e-7\nTypical wavelength per photon NIR lambda_gamma_NIR m 1.65e-6","category":"page"},{"location":"APIs/Bucket/#Bucket","page":"Bucket Model","title":"Bucket","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"CurrentModule = ClimaLand.Bucket","category":"page"},{"location":"APIs/Bucket/#Types","page":"Bucket Model","title":"Types","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.BucketModelParameters\nClimaLand.Bucket.PrescribedBaregroundAlbedo\nClimaLand.Bucket.PrescribedSurfaceAlbedo\nClimaLand.Bucket.BucketModel","category":"page"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModelParameters","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModelParameters","text":"struct BucketModelParameters{\n FT <: AbstractFloat,\n PSE,\n}\n\nContainer for holding the parameters of the bucket model.\n\nκ_soil: Conductivity of the soil (W/K/m); constant\nρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant\nalbedo: Albedo Model\nσS_c: Critical σSWE amount (m) where surface transitions from to snow-covered\nf_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)\nW_f: Capacity of the land bucket (m)\nf_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)\np: Exponent used in β decay (unitless)\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nτc: τc timescale on which snow melts\nearth_param_set: Earth Parameter set; physical constants, etc\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedBaregroundAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedBaregroundAlbedo","text":"PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel\n\nAn albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedSurfaceAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedSurfaceAlbedo","text":"PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}\n <: AbstractBucketAlbedoModel\n\nAn albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.\n\nNote that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModel","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModel","text":"struct BucketModel{\n FT,\n PS <: BucketModelParameters{FT},\n ATM <: AbstractAtmosphericDrivers{FT},\n RAD <: AbstractRadiativeDrivers{FT},\n D,\n } <: AbstractBucketModel{FT}\n\nConcrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.\n\nparameters: Parameters required by the bucket model\natmos: The atmospheric drivers: Prescribed or Coupled\nradiation: The radiation drivers: Prescribed or Coupled\ndomain: The domain of the model\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#Misc-Functions","page":"Bucket Model","title":"Misc Functions","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.surface_albedo\nClimaLand.Bucket.beta_factor","category":"page"},{"location":"APIs/Bucket/#ClimaLand.surface_albedo","page":"Bucket Model","title":"ClimaLand.surface_albedo","text":"ClimaLand.surface_albedo(\n model::EnergyHydrology{FT},\n Y,\n p,\n) where {FT}\n\nReturns the surface albedo field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_albedo(model::SnowModel, Y, p)\n\nA helper function which computes and returns the snow albedo.\n\n\n\n\n\nsurface_albedo(model::BucketModel, Y, p)\n\nReturns the bulk surface albedo, which gets updated in update_aux via next_albedo.\n\n\n\n\n\nsurface_albedo(model::AbstractModel, Y, p)\n\nA helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Bucket/#ClimaLand.Bucket.beta_factor","page":"Bucket Model","title":"ClimaLand.Bucket.beta_factor","text":"beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}\n\nComputes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:\n\nβ = (x/xc)^p x < xc 1 otherwise\n\nwhere x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.\n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Coupled-heat-and-water-equations-tending-towards-equilibrium","page":"Energy and Hydrology","title":"Coupled heat and water equations tending towards equilibrium","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"θ_i is the volumetric ice fraction, and","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We will solve this equation in an effectively 1-d domain with z -10, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- κ T(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-κ T(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T(t = 0 z) = T_min + (T_max-T_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-K h(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ(t = 0 z) = ϑ_min + (ϑ_max-ϑ_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"where C T_min T_max ϑ_min and ϑ_max are constants.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy hz = 0 and Tz = 0. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Import-necessary-modules","page":"Energy and Hydrology","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"External (non - CliMA) modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"import SciMLBase\nusing Statistics\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"FT = Float32\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Create-the-model","page":"Energy and Hydrology","title":"Create the model","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν = FT(0.395)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.395f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν_ss_quartz = FT(0.92)\nν_ss_om = FT(0.0)\nν_ss_gravel = FT(0.0)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.0f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Ksat = FT(4.42 / 3600 / 100) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.89)\nvg_α = FT(7.5) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nθ_r = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat = Ksat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We also need to pick a domain on which to solve the equations:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"zmax = FT(0)\nzmin = FT(-1.0)\nnelems = 50\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Water boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_water_flux = WaterFluxBC((p, t) -> 0.0)\nbottom_water_flux = WaterFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary conditions for the heat equation:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_heat_flux = HeatFluxBC((p, t) -> 0.0)\nbottom_heat_flux = HeatFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We wrap up all of those in a WaterHeatBC struct:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"boundary_fluxes = (;\n top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),\n bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Set-up-the-simulation","page":"Energy and Hydrology","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"function init_soil!(Y, z, params)\n ν = params.ν\n θ_r = params.θ_r\n FT = eltype(Y.soil.ϑ_l)\n zmax = FT(0)\n zmin = FT(-1)\n\n theta_max = FT(ν * 0.5)\n theta_min = FT(ν * 0.4)\n T_max = FT(289.0)\n T_min = FT(288.0)\n\n c = FT(20.0)\n @. Y.soil.ϑ_l =\n theta_min +\n (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)\n Y.soil.θ_i .= FT(0.0)\n\n T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))\n\n θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)\n ρc_s =\n Soil.volumetric_heat_capacity.(\n θ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 72);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We use ClimaTimesteppers.jl for carrying out the time integration.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a timestepper and set up the ODE problem:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"dt = Float64(1000.0);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"saveat = collect(t0:FT(30000):tf)\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\ncb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Extract output","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z = parent(coords.subsurface.z)\nt = parent(sol.t)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nT = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Let's look at the initial and final times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(ϑ_l[1], z, xlabel = \"ϑ_l\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(ϑ_l[4], z, label = \"t = 1.5d\")\nplot!(ϑ_l[end], z, label = \"t = 3d\")\nsavefig(\"eq_moisture_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(T[1], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(T[4], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 1.5d\")\nplot!(T[end], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 3d\")\nsavefig(\"eq_temperature_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Analytic-Expectations","page":"Energy and Hydrology","title":"Analytic Expectations","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can determine a priori what we expect the final temperature to be in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"One could also solve the equation for ϑ_l specified by h z = 0 to determine the functional form of the equilibrium profile of the liquid water.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#References","page":"Energy and Hydrology","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.\nBalland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"using Plots\nimport ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing ClimaCore\nimport ClimaParams as CP\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nFT = Float64;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define simulation times","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"t0 = Float64(0)\ntf = Float64(60 * 60)\ndt = Float64(30);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define the domain","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"zmax = FT(0)\nzmin = FT(-1.1)\nnelems = 75\nΔ = FT((zmax - zmin) / nelems / 2)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();\nparameter_data = readdlm(data_file, ',');","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m\nksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s\nvgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).\nvgn = reverse(parameter_data[5, :])\nresidual_frac = reverse(parameter_data[2, :])\nporosity = reverse(parameter_data[3, :]);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Create fields corresponding to the parameter","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)\nK_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)\nθ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"hcm = SpaceVaryingInput(\n depth,\n (; α = vgα, n = vgn),\n soil_domain.space.subsurface,\n vanGenuchten{FT},\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The parameter struct:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"params = ClimaLand.Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"function top_flux_function(p, t)\n return -0.0001033\nend\ntop_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)\nbottom_bc = ClimaLand.Soil.FreeDrainage()\nboundary_fluxes = (; top = top_bc, bottom = bottom_bc)\nsoil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial the state vectors, and set initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"We also set the initial conditions of the auxiliary state here:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"stepper = CTS.ARS111()\n@assert FT in (Float32, Float64)\nerr = (FT == Float64) ? 1e-8 : 1e-4\nconvergence_cond = CTS.MaximumError(err)\nconv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 10,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n convergence_checker = conv_checker,\n ),\n)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = make_jacobian(soil)\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);\n\nz = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]\nplot(ϑ_l[1], z, label = \"initial\", color = \"grey\", aspect_ratio = 0.8)\nplot!(ϑ_l[2], z, label = \"8min\", color = \"orange\")\nplot!(ϑ_l[3], z, label = \"16min\", color = \"red\")\nplot!(ϑ_l[4], z, label = \"24min\", color = \"teal\")\nplot!(ϑ_l[5], z, label = \"32min\", color = \"blue\")\nplot!(ϑ_l[6], z, label = \"40min\", color = \"purple\")\nplot!(ϑ_l[7], z, label = \"60min\", color = \"green\")\nscatter!(porosity, depth, label = \"Porosity\")\nplot!(legend = :bottomright)\n\nplot!(xlim = [0, 0.7])\n\nplot!(\n ylim = [-1.1, 0],\n yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],\n)\n\nplot!(ylabel = \"Depth (m)\")\n\nplot!(xlabel = \"Volumeteric Water Content\")\n\nsavefig(\"./sv62_alpha_2_inf_updated_data_climaland.png\")","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy","page":"Canopy Models","title":"Canopy","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Structs","page":"Canopy Models","title":"Canopy Model Structs","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.CanopyModel\nClimaLand.Canopy.SharedCanopyParameters","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.CanopyModel","page":"Canopy Models","title":"ClimaLand.Canopy.CanopyModel","text":" CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}\n\nThe model struct for the canopy, which contains\n\nthe canopy model domain (a point for site-level simulations, or\n\nan extended surface (plane/spherical surface) for regional or global simulations.\n\nsubcomponent model type for radiative transfer. This is of type\n\nAbstractRadiationModel.\n\nsubcomponent model type for photosynthesis. This is of type\n\nAbstractPhotosynthesisModel, and currently only the FarquharModel is supported.\n\nsubcomponent model type for stomatal conductance. This is of type\n\nAbstractStomatalConductanceModel and currently only the MedlynModel is supported\n\nsubcomponent model type for plant hydraulics. This is of type\n\nAbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.\n\nsubcomponent model type for canopy energy. This is of type\n\nAbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.\n\nsubcomponent model type for canopy SIF. prognostically.\ncanopy model parameters, which include parameters that are shared\n\nbetween canopy model components or those needed to compute boundary fluxes.\n\nThe atmospheric conditions, which are either prescribed\n\n(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).\n\nThe radiative flux conditions, which are either prescribed\n\n(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).\n\nThe soil conditions, which are either prescribed (of type PrecribedSoil, for\n\nrunning the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)\n\nNote that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.\n\nautotrophic_respiration: Autotrophic respiration model, a canopy component model\nradiative_transfer: Radiative transfer model, a canopy component model\nphotosynthesis: Photosynthesis model, a canopy component model\nconductance: Stomatal conductance model, a canopy component model\nhydraulics: Plant hydraulics model, a canopy component model\nenergy: Energy balance model, a canopy component model\nsif: SIF model, a canopy component model\natmos: Atmospheric forcing: prescribed or coupled\nradiation: Radiative forcing: prescribed or coupled\nsoil_driver: Soil pressure: prescribed or prognostic\nparameters: Shared canopy parameters between component models\ndomain: Canopy model domain\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.SharedCanopyParameters","page":"Canopy Models","title":"ClimaLand.Canopy.SharedCanopyParameters","text":"SharedCanopyParameters{FT <: AbstractFloat, PSE}\n\nA place to store shared parameters that are required by multiple canopy components.\n\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nearth_param_set: Earth param set\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Fluxes","page":"Canopy Models","title":"Canopy Model Fluxes","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.DiagnosticTranspiration","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","page":"Canopy Models","title":"ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","text":"DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Soil-Drivers","page":"Canopy Models","title":"Canopy Model Soil Drivers","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.AbstractSoilDriver\nClimaLand.Canopy.PrescribedSoil","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.AbstractSoilDriver","page":"Canopy Models","title":"ClimaLand.Canopy.AbstractSoilDriver","text":"An abstract type of soil drivers of the canopy model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PrescribedSoil","page":"Canopy Models","title":"ClimaLand.Canopy.PrescribedSoil","text":" PrescribedSoil <: AbstractSoilDriver\n\nA container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.\n\nroot_depths: The depth of the root tips, in meters\nψ: Prescribed soil potential (m) in the root zone a function of time\nT: Prescribed soil surface temperature (K) as a function of time\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\nϵ: Soil emissivity\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-model","page":"Boundary conditions","title":"Boundary conditions for the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-Richards-equation","page":"Boundary conditions","title":"Boundary conditions for Richards equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.\nWaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).\nMoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).\nRichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-equation","page":"Boundary conditions","title":"Boundary conditions for the soil heat equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).\nTemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-water-equations-(EnergyHydrology-model)","page":"Boundary conditions","title":"Boundary conditions for the soil heat + water equations (EnergyHydrology model)","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"The full soil model requires boundary conditions for both Richards equation and the soil heat equation.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).\nAtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"This page was generated using Literate.jl.","category":"page"},{"location":"folderstructure/#ClimaLand-folder-structure","page":"Repository structure","title":"ClimaLand folder structure","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand home directory has 5 main folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"docs: contains files to generate the documentation website.\nexperiments: contains simple runs of ClimaLand models. \nparameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc. \nsrc: contains the code of ClimaLand models. \ntest: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR. \n.dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main). \n.github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"as well as 5 files:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git. \nLICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code. \nProject.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file. \nREADME.md: This markdown file contains the info that you can read on ClimaLand GitHub web page","category":"page"},{"location":"folderstructure/#/docs-folder","page":"Repository structure","title":"/docs folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Julia packages are recommended to have a \\docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The folder /docs contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation \"contribution guide\" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file. \na make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl. ","category":"page"},{"location":"folderstructure/#/experiments-folder","page":"Repository structure","title":"/experiments folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"For example, /experiments/LSM/ozark/ contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).\nozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.\nozark_parameters.jl: In this file, parameters values are defined. \nozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm. \nozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.","category":"page"},{"location":"folderstructure/#/src-folder","page":"Repository structure","title":"/src folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The /src folder contains the source code of ClimaLand models. It contains 3 folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.\nstandalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder. \nintegrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"As well as one file:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase. ","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Stomatal-Conductance","page":"Canopy Stomatal Conductance","title":"Stomatal Conductance","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Parameters","page":"Canopy Stomatal Conductance","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.MedlynConductanceParameters","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.MedlynConductanceParameters","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.MedlynConductanceParameters","text":"MedlynConductanceParameters{FT <: AbstractFloat}\n\nThe required parameters for the Medlyn stomatal conductance model.\n\nDrel: Relative diffusivity of water vapor (unitless)\ng0: Minimum stomatal conductance mol/m^2/s\ng1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/StomatalConductance/#Methods","page":"Canopy Stomatal Conductance","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.medlyn_term\nClimaLand.Canopy.medlyn_conductance\nClimaLand.Canopy.upscale_leaf_conductance\nClimaLand.Canopy.penman_monteith","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_term","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_term","text":"medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}\n\nComputes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).\n\nthermo_params is the Thermodynamics.jl parameter set.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_conductance","text":"medlyn_conductance(g0::FT,\n Drel::FT,\n medlyn_term::FT,\n An::FT,\n ca::FT) where {FT}\n\nComputes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).\n\nThis returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.upscale_leaf_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.upscale_leaf_conductance","text":"upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}\n\nThis currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.\n\nTODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.penman_monteith","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.penman_monteith","text":"penman_monteith(\n Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1) \n Rn::FT, # Net irradiance (W m−2)\n G::FT, # Ground heat flux (W m−2)\n ρa::FT, # Dry air density (kg m−3)\n cp::FT, # Specific heat capacity of air (J kg−1 K−1) \n VPD::FT, # vapor pressure deficit (Pa)\n ga::FT, # atmospheric conductance (m s−1)\n γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)\n gs::FT, # surface or stomatal conductance (m s−1)\n Lv::FT, # Volumetric latent heat of vaporization (J m-3)\n ) where {FT}\n\nComputes the evapotranspiration in m/s using the Penman-Monteith equation. \n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-Tutorial","page":"Intro to ClimaLand Domains","title":"Domain Tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/#Goals-of-the-tutorial","page":"Intro to ClimaLand Domains","title":"Goals of the tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Background","page":"Intro to ClimaLand Domains","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question \"only\" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"ClimaLand Domains were designed with this in mind. The domains are defined so that","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the user can easily switch geometries, e.g. single column to global model,\nindividual component models can be run by themselves, using a single domain,\nthe same domains can be used to set up multi-component models (LSMs),\ndifferent variables can exist on different parts of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#What-is-a-ClimaLand-Domain?","page":"Intro to ClimaLand Domains","title":"What is a ClimaLand Domain?","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-types","page":"Intro to ClimaLand Domains","title":"Domain types","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"0D: Domains.Point\n1D: Domains.Column\n2D: Domains.Plane, Domains.SphericalSurface\n3D: Domains.HybridBox, Domains.SphericalShell.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Domain Surface Domain Subsurface Domain\nColumn Point Column\nHybridBox Plane HybridBox\nSphericalShell SphericalSurface SphericalShell","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"There is a single key method which take a ClimaLand domain as an argument.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"coordinates(domain): under the hood, this function uses","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#How-variable-initialization-depends-on-domains","page":"Intro to ClimaLand Domains","title":"How variable initialization depends on domains","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Future-work","page":"Intro to ClimaLand Domains","title":"Future work","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into \"vertical\" and \"horizontal\" pieces.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[1]: finite differencing is used in the vertical, and spectral elements are used in the horizontal.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[2]: a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[3]: We also will support having an array-like type of variable.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Coupling-the-CliMA-Canopy-and-Soil-Hydraulics-Models","page":"Coupled Canopy and Soil","title":"Coupling the CliMA Canopy and Soil Hydraulics Models","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Preliminary-Setup","page":"Coupled Canopy and Soil","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load External Packages:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Soil.Biogeochemistry\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Setup the domain for the model:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4)\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Column(; zlim = (zmin, zmax), nelements = nelements);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"time_offset = 7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Site latitude and longitude","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"-92.2f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Height of the sensor at the site","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"atmos_h = FT(32) # m","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"32.0f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Setup-the-Coupled-Canopy-and-Soil-Physics-Model","page":"Coupled Canopy and Soil","title":"Setup the Coupled Canopy and Soil Physics Model","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the parameters for the soil model and provide them to the model parameters struct:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil_ν = FT(0.5) # m3/m3\nsoil_K_sat = FT(4e-7) # m/s\nsoil_S_s = FT(1e-3) # 1/m\nsoil_vg_n = FT(2.05) # unitless\nsoil_vg_α = FT(0.04) # inverse meters\nθ_r = FT(0.067); # m3/m3","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil heat transfer parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ν_ss_quartz = FT(0.1)\nν_ss_om = FT(0.1)\nν_ss_gravel = FT(0.0);\nz_0m_soil = FT(0.1)\nz_0b_soil = FT(0.1)\nsoil_ϵ = FT(0.98)\nsoil_α_PAR = FT(0.2)\nsoil_α_NIR = FT(0.4)\n\nsoil_domain = land_domain\nsoil_ps = Soil.EnergyHydrologyParameters(\n FT;\n ν = soil_ν,\n ν_ss_om = ν_ss_om,\n ν_ss_quartz = ν_ss_quartz,\n ν_ss_gravel = ν_ss_gravel,\n hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),\n K_sat = soil_K_sat,\n S_s = soil_S_s,\n θ_r = θ_r,\n earth_param_set = earth_param_set,\n z_0m = z_0m_soil,\n z_0b = z_0b_soil,\n emissivity = soil_ϵ,\n PAR_albedo = soil_α_PAR,\n NIR_albedo = soil_α_NIR,\n);\n\nsoil_args = (domain = soil_domain, parameters = soil_ps)\nsoil_model_type = Soil.EnergyHydrology{FT}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ClimaLand.Soil.EnergyHydrology{Float32}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}\n\nsoilco2_ps = SoilCO2ModelParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil microbes args","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))\n\nsoilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()\nsoilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);\nsoilco2_sources = (MicrobeProduction{FT}(),);\n\nsoilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);\n\nsoilco2_args = (;\n boundary_conditions = soilco2_boundary_conditions,\n sources = soilco2_sources,\n domain = soil_domain,\n parameters = soilco2_ps,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_types = (;\n autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},\n radiative_transfer = Canopy.TwoStreamModel{FT},\n photosynthesis = Canopy.FarquharModel{FT},\n conductance = Canopy.MedlynConductanceModel{FT},\n hydraulics = Canopy.PlantHydraulicsModel{FT},\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"autotrophic_respiration_args =\n (; parameters = AutotrophicRespirationParameters(FT))\n\nradiative_transfer_args = (;\n parameters = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = 0.1,\n α_NIR_leaf = 0.45,\n τ_PAR_leaf = 0.05,\n τ_NIR_leaf = 0.25,\n Ω = 0.69,\n )\n)\n\nconductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))\n\nphotosynthesis_args =\n (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));\n\nK_sat_plant = FT(1.8e-8)\nRAI = (SAI + maxLAI) * f_root_to_shoot;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Note: LAIfunction was determined from data in the script we included above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)\nfunction root_distribution(z::T; rooting_depth = FT(1.0)) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m\nend\n\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)\n\nplant_ν = FT(0.7)\nplant_S_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = plant_ν,\n S_s = plant_S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n)\n\nplant_hydraulics_args = (\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_midpoints = compartment_midpoints,\n compartment_surfaces = compartment_surfaces,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_args = (;\n autotrophic_respiration = autotrophic_respiration_args,\n radiative_transfer = radiative_transfer_args,\n photosynthesis = photosynthesis_args,\n conductance = conductance_args,\n hydraulics = plant_hydraulics_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We also need to provide the shared parameter struct to the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n)\ncanopy_domain = obtain_surface_domain(land_domain)\ncanopy_model_args = (; parameters = shared_params, domain = canopy_domain);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)\n\nland = SoilCanopyModel{FT}(;\n soilco2_type = soilco2_type,\n soilco2_args = soilco2_args,\n land_args = land_input,\n soil_model_type = soil_model_type,\n soil_args = soil_args,\n canopy_component_types = canopy_component_types,\n canopy_component_args = canopy_component_args,\n canopy_model_args = canopy_model_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y, p, coords = initialize(land);\nexp_tendency! = make_exp_tendency(land);\nimp_tendency! = make_imp_tendency(land);\njacobian! = make_jacobian(land);\njac_kwargs =\n (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We need to provide initial conditions for the soil and canopy hydraulics models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y.soil.ϑ_l = FT(0.4)\nY.soil.θ_i = FT(0.0)\nT_0 = FT(288.7)\nρc_s =\n volumetric_heat_capacity.(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n land.soil.parameters.ρc_ds,\n earth_param_set,\n )\nY.soil.ρe_int =\n volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)\n\nY.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air\n\nψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n plant_ν,\n plant_S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .=\n augmented_liquid_fraction.(plant_ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"t0 = Float64(150 * 3600 * 24)# start mid year\nN_days = 100\ntf = t0 + Float64(3600 * 24 * N_days)\ndt = Float64(30)\nn = 120\nsaveat = Array(t0:(n * dt):tf)\n\ntimestepper = CTS.ARS343()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now set the initial values for the cache variables for the combined soil and plant model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"set_initial_cache! = make_set_initial_cache(land)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Set the callbacks, which govern how often we save output, and how often we update the forcing data (\"drivers\")","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"sv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nmodel_drivers = ClimaLand.get_drivers(land)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\nupdateat = Array(t0:1800:tf)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Carry out the simulation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(\n prob,\n ode_algo;\n dt = dt,\n callback = cb,\n adaptive = false,\n saveat = saveat,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Plotting","page":"Coupled Canopy and Soil","title":"Plotting","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Transpiration plot:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Show the two plots together:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_canopy_flux_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"plt1 = Plots.plot(size = (500, 700));\nϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]\nplt1 = Plots.plot(\n daily,\n ϑ_l_10,\n label = \"10 cm\",\n xlabel = \"Days\",\n ylabel = \"SWC [m/m]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n color = \"blue\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],\n label = \"20cm\",\n color = \"red\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],\n label = \"30cm\",\n color = \"purple\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"root_stem_flux = [\n sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for\n k in 1:length(sol.t)\n]\nplt1 = Plots.plot(\n daily,\n root_stem_flux,\n label = \"soil-root-stem water flux\",\n ylabel = \"Water flux[mm/day]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And save the output","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_plant_flux.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"diagnostics/developers_diagnostics/#ClimaLand-Diagnostics:-why-and-how","page":"For developers","title":"ClimaLand Diagnostics: why and how","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in \"K\" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Diagnostics.jl defines the module,\ndiagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"compute methods are defined in a separate file, for example, bucket_compute_methods.jl.\nstandard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.\ndefault_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Compute-methods","page":"For developers","title":"Compute methods","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.α_sfc)\n else\n out .= p.bucket.α_sfc\n end\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@diagnostic_compute \"albedo\" BucketModel p.bucket.α\\_sfc","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Define-diagnostics","page":"For developers","title":"Define diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!(\n short_name = \"alpha\",\n long_name = \"Albedo\",\n standard_name = \"albedo\",\n units = \"\",\n compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),\n )","category":"page"},{"location":"diagnostics/developers_diagnostics/#Default-diagnostics","page":"For developers","title":"Default diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"function default_diagnostics(land_model::BucketModel, t_start; output_writer)\n\n define_diagnostics!(land_model)\n\n bucket_diagnostics = [\n \"alpha\",\n \"rn\",\n \"tsfc\",\n \"qsfc\",\n \"lhf\",\n \"rae\",\n \"shf\",\n \"vflux\",\n \"rhosfc\",\n \"t\",\n \"w\",\n \"ws\",\n \"sigmas\",\n ]\n\n default_outputs =\n hourly_averages(bucket_diagnostics...; output_writer, t_start)\n return [default_outputs...]\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = [\"gpp\", \"ct\", \"lai\", \"swc\", \"si\"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Standard-diagnostic-frequencies","page":"For developers","title":"Standard diagnostic frequencies","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(\n 60 * 60 * one(t_start),\n (+),\n output_writer,\n t_start,\n short_names...;\n pre_output_hook! = average_pre_output_hook!,\n)","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.","category":"page"},{"location":"","page":"Home","title":"Home","text":"using Pkg\nio = IOBuffer()\nv = Pkg.installed()[\"ClimaLand\"]\nprint(io, \"\"\"\n # ClimaLand.jl Documentation (v$(v))\n\n \"\"\")\nimport Markdown\nMarkdown.parse(String(take!(io)))","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand can be run coupled (or \"online\") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data (\"offline\").","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.","category":"page"},{"location":"#Important-Links","page":"Home","title":"Important Links","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CliMA Homepage\nCliMA GitHub Organisation\nClimaCoupler\nClimaAnalysis\nJulia Homepage","category":"page"},{"location":"#Documentation-for-Users-and-Developers","page":"Home","title":"Documentation for Users and Developers","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.","category":"page"},{"location":"#Physical-units","page":"Home","title":"Physical units","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Note that CliMA, in all its repositories, uses Standard Units, reminded below","category":"page"},{"location":"","page":"Home","title":"Home","text":"Quantity Unit Name SI Symbol SI Unit Equivalent\nLength Meter m 1 m\nMass Kilogram kg 1 kg\nTime Second s 1 s\nTemperature Kelvin K 1 K\nAmount of Substance Mole mol 1 mol\nEnergy Joule J 1 J = 1 N·m\nPower Watt W 1 W = 1 J/s\nPressure Pascal Pa 1 Pa = 1 N/m²\nFrequency Hertz Hz 1 Hz = 1 s⁻¹","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 270.0^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"T_air = FT(270.0)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 12\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sources = (PhaseChange{FT}(),);\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)\n Y.soil.θ_i .= 0\n T = FT(275.0)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 4)\ndt = Float64(5)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"5.0","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping functions:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = ClimaLand.make_jacobian(soil)\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\n\ntimestepper = CTS.ARS111()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsub = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n sv.saveval[k].soil.ice_frac,\n )[1] for k in 1:length(sol.t)\n]\n\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (400, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Rate (mm/d)\",\n title = \"Vapor Fluxes\",\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n sub .* (1000 * 3600 * 24),\n label = \"Sublimation\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Evaporation\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nsave(\"water_fluxes.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Temperature\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(260, 280)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 3, 4]\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax1,\n parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Ice\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax2,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Liquid Water\", xlabel = \"\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax3,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"profiles.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Soil/#Soil-Models","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"CurrentModule = ClimaLand.Soil","category":"page"},{"location":"APIs/Soil/#Soil-Models-2","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilModel\nClimaLand.Soil.RichardsModel\nClimaLand.Soil.EnergyHydrology","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilModel","text":"AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}\n\nThe abstract type for all soil models.\n\nCurrently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsModel","text":"RichardsModel\n\nA model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.\n\nA variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).\n\nIf you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrology","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrology","text":"EnergyHydrology <: AbstractSoilModel\n\nA model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.\n\nA variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.\n\nparameters: The parameter sets\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Parameter-Structs","page":"Soil Energy and Hydrology","title":"Soil Parameter Structs","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.RichardsParameters\nClimaLand.Soil.EnergyHydrologyParameters","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsParameters","text":"RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}\n\nA struct for storing parameters of the RichardsModel.\n\nν: The porosity of the soil (m^3/m^3)\nhydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrologyParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrologyParameters","text":"EnergyHydrologyParameters{\n FT <: AbstractFloat,\n F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n C,\n PSE,\n }\n\nA parameter structure for the integrated soil water and energy equation system.\n\nNote that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:\n\nSurface parameters: albedo in each wavelength band (SF)\nScalar parameters: emissivity, α, β, γ, γT_ref, Ω,\n\nroughness lengths z0, dds ) (FT)\n\nParameters defined in the interior: all else (F)\n\nκ_dry: The dry soil thermal conductivity, W/m/K\nκ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K\nκ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K\nρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)\nν: The porosity of the soil (m^3/m^3)\nν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)\nν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)\nν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)\nα: The parameter α used in computing Kersten number, unitless\nβ: The parameter β used in computing Kersten number, unitless\nhydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\nΩ: Ice impedance factor for the hydraulic conductivity\nγ: Coefficient of viscosity factor for the hydraulic conductivity\nγT_ref: Reference temperature for the viscosity factor\nPAR_albedo: Soil PAR Albedo\nNIR_albedo: Soil NIR Albedo\nemissivity: Soil Emissivity\nz_0m: Roughness length for momentum\nz_0b: Roughness length for scalars\nd_ds: Maximum dry soil layer thickness under evaporation (m)\nearth_param_set: Physical constants and clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Hydrology-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Hydrology Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_liquid_fraction\nClimaLand.Soil.pressure_head\nClimaLand.Soil.hydraulic_conductivity\nClimaLand.Soil.impedance_factor\nClimaLand.Soil.viscosity_factor\nClimaLand.Soil.effective_saturation\nClimaLand.Soil.matric_potential\nClimaLand.Soil.dψdϑ\nClimaLand.Soil.inverse_matric_potential\nClimaLand.Soil.AbstractSoilHydrologyClosure\nClimaLand.Soil.vanGenuchten\nClimaLand.Soil.BrooksCorey","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_liquid_fraction","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_liquid_fraction","text":"volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}\n\nA pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.pressure_head","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.pressure_head","text":"pressure_head(\n cm::vanGenuchten{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\npressure_head(\n cm::BrooksCorey{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.hydraulic_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.hydraulic_conductivity","text":" hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.\n\n\n\n\n\n hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.impedance_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.impedance_factor","text":"impedance_factor(\n f_i::FT,\n Ω::FT\n) where {FT}\n\nReturns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.viscosity_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.viscosity_factor","text":"viscosity_factor(\n T::FT,\n γ::FT,\n γT_ref::FT,\n) where {FT}\n\nReturns the multiplicative factor which accounts for the temperature dependence of the conductivity.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.effective_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.effective_saturation","text":"effective_saturation(porosity::FT, ϑ_l::FT, θr::FT) where {FT}\n\nA point-wise function computing the effective saturation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.matric_potential","text":" matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.dψdϑ","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dψdϑ","text":"dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.\n\n\n\n\n\ndψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.inverse_matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.inverse_matric_potential","text":" inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilHydrologyClosure","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilHydrologyClosure","text":"AbstractSoilHydrologyClosure{FT <: AbstractFloat}\n\nThe abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.\n\nTo add a new parameterization, methods are required for:\n\nmatric_potential,\ninversematricpotential,\npressure_head,\ndψdϑ,\nhydraulic_conductivity.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.vanGenuchten","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.vanGenuchten","text":"vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).\n\nα: The inverse of the air entry potential (1/m)\nn: The van Genuchten pore-size distribution index (unitless)\nm: The van Genuchten parameter m = 1 - 1/n (unitless)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.BrooksCorey","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.BrooksCorey","text":"BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).\n\nc: The pore-size distribution index (unitless)\nψb: The air entry matric potential, when S=1 (m)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Heat-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Heat Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_heat_capacity\nClimaLand.Soil.κ_solid\nClimaLand.Soil.κ_sat_frozen\nClimaLand.Soil.κ_sat_unfrozen\nClimaLand.Soil.κ_sat\nClimaLand.Soil.κ_dry\nClimaLand.Soil.kersten_number\nClimaLand.Soil.relative_saturation\nClimaLand.Soil.volumetric_internal_energy\nClimaLand.Soil.volumetric_internal_energy_liq\nClimaLand.Soil.temperature_from_ρe_int\nClimaLand.Soil.thermal_conductivity\nClimaLand.Soil.phase_change_source\nClimaLand.Soil.thermal_time","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_heat_capacity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_heat_capacity","text":"volumetric_heat_capacity(\n θ_l::FT,\n θ_i::FT,\n ρc_ds::FT,\n earth_param_set::EP,\n) where {FT,EP}\n\nCompute the expression for volumetric heat capacity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_solid","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_solid","text":"κ_solid(ν_ss_om::FT,\n ν_ss_quartz::FT,\n κ_om::FT,\n κ_quartz::FT,\n κ_minerals::FT) where {FT}\n\nComputes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_frozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_frozen","text":"function κ_sat_frozen(\n κ_solid::FT,\n ν::FT,\n κ_ice::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated frozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_unfrozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_unfrozen","text":"function κ_sat_unfrozen(\n κ_solid::FT,\n ν::FT,\n κ_l::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated unfrozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat","text":"κ_sat(\n θ_l::FT,\n θ_i::FT,\n κ_sat_unfrozen::FT,\n κ_sat_frozen::FT\n) where {FT}\n\nCompute the expression for saturated thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_dry","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_dry","text":"function κ_dry(ρp::FT,\n ν::FT,\n κ_solid::FT,\n κ_air::FT;\n a = FT(0.053)) where {FT}\n\nComputes the thermal conductivity of dry soil according to the model of Balland and Arp.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.kersten_number","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.kersten_number","text":"kersten_number(\n θ_i::FT,\n S_r::FT,\n α::FT,\n β::FT,\n ν_ss_om::FT,\n ν_ss_quartz::FT,\n ν_ss_gravel::FT,\n ) where {FT}\n\nCompute the expression for the Kersten number, using the Balland and Arp model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.relative_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.relative_saturation","text":"relative_saturation(\n θ_l::FT,\n θ_i::FT,\n ν::FT\n) where {FT}\n\nCompute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy","text":"volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy_liq","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy_liq","text":"volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.temperature_from_ρe_int","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.temperature_from_ρe_int","text":"temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_conductivity","text":"thermal_conductivity(\n κ_dry::FT,\n K_e::FT,\n κ_sat::FT\n) where {FT}\n\nCompute the expression for thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.phase_change_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.phase_change_source","text":"phase_change_source(\n θ_l::FT,\n θ_i::FT,\n T::FT,\n τ::FT,\n ν::FT,\n θ_r::FT,\n hydrology_cm::C,\n earth_param_set::EP,\n) where {FT, EP, C}\n\nReturns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.\n\nNote that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_time","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_time","text":"thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}\n\nReturns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Surface-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Surface Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.soil.soil_resistance\nClimaLand.Soil.dry_soil_layer_thickness\nClimaLand.Soil.soil_tortuosity","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.dry_soil_layer_thickness","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dry_soil_layer_thickness","text":"dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}\n\nReturns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_tortuosity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_tortuosity","text":"soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}\n\nComputes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.\n\nSee Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Runoff-Types-and-Methods","page":"Soil Energy and Hydrology","title":"Soil Runoff Types and Methods","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.NoRunoff\nClimaLand.Soil.SurfaceRunoff\nClimaLand.Soil.TOPMODELRunoff\nClimaLand.Soil.TOPMODELSubsurfaceRunoff\nClimaLand.Soil.subsurface_runoff_source\nClimaLand.Soil.update_runoff!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.NoRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.NoRunoff","text":"NoRunoff <: AbstractRunoffModel\n\nA concrete type of soil runoff model; the default choice, which does not include any runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.SurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.SurfaceRunoff","text":"SurfaceRunoff <: AbstractRunoffModel\n\nA simple model for runoff appropriate for single column runs.\n\nOnly surface runoff is computed, using a combination of Dunne and Hortonian runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELRunoff","text":"TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel\n\nThe TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.\n\nThe runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\nf_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.\nsubsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","text":"TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}\n\nThe TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.\n\nThe runoff flux is given by Equation 12 of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nR_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.subsurface_runoff_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.subsurface_runoff_source","text":"subsurface_runoff_source(runoff::AbstractRunoffModel)\n\nA helper function which returns the subsurface source of the runoff model runoff.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.update_runoff!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.update_runoff!","text":"update_runoff!(p, runoff::NoRunoff, _...)\n\nUpdates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.\n\n\n\n\n\nupdate_runoff!(\n p,\n runoff::SurfaceRunoff,\n Y,\n t,\n model::AbstractSoilModel,\n\n)\n\nThe update_runoff! function for the SurfaceRunoff model.\n\nUpdates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration\n\n\n\n\n\nupdate_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)\n\nUpdates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-BC-Methods-and-Types","page":"Soil Energy and Hydrology","title":"Soil BC Methods and Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.MoistureStateBC\nClimaLand.Soil.HeatFluxBC\nClimaLand.Soil.WaterFluxBC\nClimaLand.Soil.TemperatureStateBC\nClimaLand.Soil.FreeDrainage\nClimaLand.Soil.RichardsAtmosDrivenFluxBC\nClimaLand.Soil.AtmosDrivenFluxBC\nClimaLand.Soil.WaterHeatBC\nClimaLand.Soil.soil_boundary_fluxes!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.MoistureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.MoistureStateBC","text":"MoistureStateBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.HeatFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.HeatFluxBC","text":"HeatFluxBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterFluxBC","text":"WaterFluxBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.TemperatureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.TemperatureStateBC","text":"TemperatureStateBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.FreeDrainage","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.FreeDrainage","text":"FreeDrainage <: AbstractWaterBC\n\nA concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsAtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsAtmosDrivenFluxBC","text":"RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC\n\nA concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.\n\nIf you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.\n\nprecip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.AtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AtmosDrivenFluxBC","text":"AtmosDrivenFluxBC{\n A <: AbstractAtmosphericDrivers,\n B <: AbstractRadiativeDrivers,\n R <: AbstractRunoffModel\n} <: AbstractEnergyHydrologyBC\n\nA concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.\n\nThis choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.\n\natmos: The atmospheric conditions driving the model\nradiation: The radiative fluxes driving the model\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterHeatBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterHeatBC","text":"WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:\n AbstractEnergyHydrologyBC\n\nA general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_boundary_fluxes!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_boundary_fluxes!","text":"soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)\n\nupdates the boundary fluxes for ϑl and ρeint.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere,\n <:PrescribedRadiativeFluxes,\n },\n boundary::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz,\n Y,\n p,\n t,\n)\n\nReturns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.\n\nIf you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.\n\nThis function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},\n boundary::ClimaLand.TopBoundary,\n soil::EnergyHydrology{FT},\n Δz,\n Y,\n p,\n t,\n) where {FT}\n\nA method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Source-Types","page":"Soil Energy and Hydrology","title":"Soil Source Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilSource\nClimaLand.Soil.PhaseChange\nClimaLand.Soil.RootExtraction","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilSource","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilSource","text":"AbstractSoilSource{FT} <: ClimaLand.AbstractSource{FT}\n\nAn abstract type for types of source terms for the soil equations.\n\nIn standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.PhaseChange","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.PhaseChange","text":"PhaseChange{FT} <: AbstractSoilSource{FT}\n\nPhaseChange source type.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Jacobian-Structures","page":"Soil Energy and Hydrology","title":"Soil Jacobian Structures","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.ImplicitEquationJacobian","category":"page"},{"location":"APIs/Soil/#ClimaLand.ImplicitEquationJacobian","page":"Soil Energy and Hydrology","title":"ClimaLand.ImplicitEquationJacobian","text":"ImplicitEquationJacobian{M, S}\n\nA struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.\n\nmatrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.\n\nNote that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.\n\n\n\n\n\n","category":"type"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Radiative-transfer-scheme","page":"Beer model","title":"Radiative transfer scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Beer's-law","page":"Beer model","title":"Beer's law","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"APAR(PAR theta_s) = (PAR)(1 - rho_leaf)(1 - e^(-K(theta_s) LAI Omega))","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, ρ_leaf is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, θ_s is the zenith angle, and Ω is the clumping index following Braghiere (2021). K, Ω and ρ_leaf are all unitless. LAI is in m² m⁻². In order to compute K, we need θ_s in radians and the leaf angle distribution l_d (unitless). K is then defined as","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"K = l_dmax(cos(theta_s) epsilon)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"so that at night, when 3π/2 > θ_s > π/2, K is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Output Symbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Parameters Symbol Unit Range\nCanopy reflectance ρ_leaf - 0.0–1.0\nExtinction coefficient K - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Interactive-APAR(PAR,-LAI,-ρ_{leaf},-K,-Ω)","page":"Beer model","title":"Interactive APAR(PAR, LAI, ρ_leaf, K, Ω)","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Modeling-a-freezing-front-in-unsaturated-soil","page":"Phase Changes","title":"Modeling a freezing front in unsaturated soil","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν ) -fracF_Tρ_l","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac θ_i t = fracF_Tρ_i","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Here","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_i is the volumetric ice fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν denotes parameters relating to soil type, such as porosity, and","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T is the freeze-thaw term.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We solve these equations in an effectively 1-d domain with z -020, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z = 0) = 28 Wm^2K (T - 26715K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z= -02) = -3 Wm^2K (T - 27985K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T(t = 0 z) = 27985 K","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"-K h(t z = -02) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l(t = 0 z) = 033.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Import-necessary-modules","page":"Phase Changes","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS\nusing DelimitedFiles\nusing CairoMakie\n\nusing ClimaCore\nimport ClimaParams as CP\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Preliminary set-up","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"FT = Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν = FT(0.535)\nK_sat = FT(3.2e-6) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.48)\nvg_α = FT(1.11) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"You could also try the Brooks and Corey model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"#ψb = FT(-0.6)\n#c = FT(0.43)\n#hcm = BrooksCorey(;ψb = ψb, c = c);\nθ_r = FT(0.05)\nν_ss_om = FT(0.3)\nν_ss_quartz = FT(0.7)\nν_ss_gravel = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose the domain and discretization:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zmax = FT(0)\nzmin = FT(-0.2)\nnelems = 20\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)\nfunction top_heat_flux(p, t)\n FT = eltype(p.soil.T)\n p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))\n T_c = ClimaCore.Fields.level(p.soil.T, p_len)\n return @. FT(28 * (T_c - 267.15))\nend\nfunction bottom_heat_flux(p, t)\n FT = eltype(p.soil.T)\n T_c = ClimaCore.Fields.level(p.soil.T, 1)\n return @. FT(-3 * (T_c - 279.85))\nend\ntop_heat_flux_bc = HeatFluxBC(top_heat_flux)\nbottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)\nboundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),\n bottom = WaterHeatBC(;\n water = zero_water_flux_bc,\n heat = bottom_heat_flux_bc,\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sources = (PhaseChange{FT}(),);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can package this up in the EnergyHydrology model struct:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Running-a-simulation","page":"Phase Changes","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Once we have the model, we can initialize the state vectors and obtain the coordinates","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Y, p, coords = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"After which, we can specify the initial condition function, and initialze the variables:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"function init_soil!(Ysoil, z, params)\n ν = params.ν\n FT = eltype(Ysoil.soil.ϑ_l)\n Ysoil.soil.ϑ_l .= FT(0.33)\n Ysoil.soil.θ_i .= FT(0.0)\n T = FT(279.85)\n ρc_s = Soil.volumetric_heat_capacity(\n FT(0.33),\n FT(0.0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Ysoil.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n FT(0.0),\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 50);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the tendency function, and choose a timestep, and timestepper:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ndt = Float64(100)\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 3,\n update_j = CTS.UpdateEvery(CTS.NewTimeStep),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Comparison-to-data","page":"Phase Changes","title":"Comparison to data","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();\nds = readdlm(dataset_path, ',')\nhours = ds[:, 1][2:end]\nvwc = ds[:, 2][2:end] ./ 100.0\ndepth = ds[:, 3][2:end]\nmask_12h = hours .== 12\nmask_24h = hours .== 24\nmask_50h = hours .== 50;\n\nfig = Figure(size = (900, 300))\nax1 = Axis(\n fig[1, 1],\n title = \"12 hours\",\n xlabel = L\"θ_l + θ_i\",\n ylabel = \"Soil depth (m)\",\n)\nlimits!(ax1, 0.2, 0.5, -0.2, 0.0)\nax2 = Axis(\n fig[1, 2],\n title = \"24 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax2, 0.2, 0.5, -0.2, 0.0)\nax3 = Axis(\n fig[1, 3],\n title = \"50 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax3, 0.2, 0.5, -0.2, 0.0)\n\n\nz = parent(coords.subsurface.z)[:];\n\nscatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = \"\", color = \"purple\")\nlines!(\n ax1,\n parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\n\nscatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = \"\", color = \"purple\")\nlines!(\n ax2,\n parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\nscatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = \"Data\", color = \"purple\")\nlines!(\n ax3,\n parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],\n z,\n label = \"Simulation\",\n color = :green,\n)\naxislegend(ax3, position = :rb)\n\nsave(\"mizoguchi_data_comparison.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Discussion-and-Model-Explanation","page":"Phase Changes","title":"Discussion and Model Explanation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρ_l partial_tϑ_l + ρ_i partial_tθ_i = -F_T + F_T = 0","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing (T T_f)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"fracdp_lρ_l = L_f fracdTT","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"or","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l = p_l0 + L_f ρ_l fracT-T_fT_f mathcalH(T_f-T)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"What this implies is that above the freezing point, the pressure is equal to p_l0, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure p_l0 is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l0 = ρ_l g ψ(θ_l+ρ_iθ_iρ_l)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_l^* = (ν-θ_r) ψ^-1(p_l(ρ_l g)) + θ_r","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"For freezing, the freeze thaw function F_T is equal to","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T = frac1τ ρ_l (θ_l-θ_l^*) mathcalH(T_f-T) mathcalH(θ_l-θ_l^*)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"τ_LTE= c Δz²κ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#References","page":"Phase Changes","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.\nHansson et al., Vadose Zone Journal 3:693–704 (2004).\nM. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).\nKurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)\nWatanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58\nPainter and Karra, Vadose Zone Journal (2014) 13 (4)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/#Scraping-SNOTEL-Data","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"section"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We begin by importing all required packages:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"metadata = DataTools.snotel_metadata();\nmetacols = [\"id\", \"name\", \"state\", \"elev\", \"lat\", \"lon\"]\nDataFrames.rename!(metadata, Symbol.(metacols));","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"example_ID = 1030\nexample_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]\nexample_data = DataTools.scrape_site_paper(example_ID, example_state);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"const inch2meter = 0.0254\nconst kmphr2mps = 5.0 / 18.0\n\nfilter_val = Dict{Symbol, Tuple{Real, Real}}(\n :SWE => (0.0, 250.0),\n :z => (0.0, 420.0),\n :precip => (0.0, 250.0),\n :rel_hum_avg => (10.0, 100.0),\n :sol_rad_avg => (0.0, 1500.0),\n :wind_speed_avg => (0.0, 216.0),\n :air_temp_avg => (-40.0, 60.0),\n)\n\nscales = Dict{Symbol, Real}(\n :SWE => inch2meter,\n :z => inch2meter,\n :precip => inch2meter,\n :rel_hum_avg => 0.01,\n :wind_speed_avg => kmphr2mps,\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. \"start\" refers to 1850-01-01 or the first available date, while \"end\" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"* Indicates alternative handling of the rectify_daily_hourly() function.\n^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.\nA Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.\nT Requires a site that already has had the temperature bias correction at the portal level as of May 2024.\nX Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"good_stations = Dict{Int, Tuple{String, String}}(\n #306 => (\"start\", \"end\"), #*\n 316 => (\"start\", \"end\"),\n 344 => (\"start\", \"end\"),\n #=367 => (\"start\", \"end\"),\n 395 => (\"start\", \"end\"),\n 457 => (\"start\", \"end\"),\n 482 => (\"start\", \"end\"),\n 491 => (\"start\", \"end\"),\n 515 => (\"start\", \"2023-06-02\"), #X\n 532 => (\"start\", \"end\"),\n 551 => (\"start\", \"end\"),\n 571 => (\"start\", \"end\"),\n 599 => (\"start\", \"end\"),\n 608 => (\"start\", \"end\"),\n 613 => (\"start\", \"end\"),\n 641 => (\"start\", \"end\"), #A^\n 665 => (\"start\", \"end\"),\n 708 => (\"start\", \"end\"),\n 715 => (\"start\", \"end\"),\n 734 => (\"start\", \"end\"),\n 737 => (\"start\", \"end\"),\n 744 => (\"start\", \"end\"),\n 832 => (\"start\", \"end\"),\n 845 => (\"start\", \"end\"),\n 854 => (\"start\", \"end\"),\n 857 => (\"start\", \"end\"),\n 921 => (\"start\", \"end\"),\n 922 => (\"start\", \"end\"),\n 927 => (\"start\", \"end\"),\n 942 => (\"start\", \"end\"),\n 963 => (\"start\", \"end\"), #A^\n 969 => (\"start\", \"end\"),\n 974 => (\"start\", \"end\"),\n 978 => (\"start\", \"end\"), #*\n 1030 => (\"start\", \"end\"),\n 1035 => (\"start\", \"end\"), #A^\n 1053 => (\"start\", \"end\"),\n 1070 => (\"start\", \"end\"), #A^T\n 1083 => (\"start\", \"end\"),\n 1091 => (\"start\", \"end\"), #A^T\n 1092 => (\"start\", \"end\"), #A^T\n 1105 => (\"start\", \"end\"),\n 1122 => (\"start\", \"end\"), #*\n 1123 => (\"start\", \"end\"),\n 1159 => (\"start\", \"end\"),\n 1168 => (\"start\", \"end\"),\n 1170 => (\"start\", \"end\"),\n 1254 => (\"start\", \"end\"),\n 1286 => (\"start\", \"end\"),\n 2080 => (\"start\", \"end\"), #A^\n 2170 => (\"start\", \"end\"), #^\n =#\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We then loop through each site to scrape and follow an automated data pipeline, consisting of:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Extracting the daily and hourly timeseries from the site\nApplying the sensor bounds over each data timeseries (i.e. remove sensor error)\nConverting the hourly dataset into a daily dataset\nCoalescing the converted-hourly and daily data into one dataset\nScaling all data to the appropriate metric units\nRestricting data to complete cases\nMaking the differential variables ( fracdzdt, etc.)\nResetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip\nAttaching appropriate metadata","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"allsites = Any[];\nfor site in sort(collect(keys(good_stations)))\n state = metadata[metadata[!, :id] .== site, :state][1]\n start_date = good_stations[site][1]\n end_date = good_stations[site][2]\n\n hourly = DataTools.apply_bounds(\n DataTools.sitedata_hourly(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n hourly[!, :id] .= site\n #hourly = DataTools.bcqc_hourly(hourly)\n hourly_d = DataTools.hourly2daily(hourly)\n #DataFrames.allowmissing!(hourly_d)\n #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)\n #hourly_d[sflags, :sol_rad_avg] .= missing\n\n daily = DataTools.apply_bounds(\n DataTools.sitedata_daily(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n daily[!, :id] .= site\n gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)\n #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)\n #gap_daily = DataTools.d_impute(gap_daily)\n daily_scaled = DataTools.scale_cols(gap_daily, scales)\n daily_clean = daily_scaled[completecases(daily_scaled), :]\n daily_clean = DataTools.makediffs(daily_clean, Day(1))\n good_vals = daily_clean[!, :dprecipdt] .>= 0.0\n daily_clean[(!).(good_vals), :dprecipdt] .= 0.0\n daily_clean = daily_clean[!, Not(:precip)]\n #show(describe(daily_clean), allrows = true, allcols = true)\n #print(\"\\nSIZE: \", nrow(daily_clean), \"\\n\")\n\n daily_clean[!, :id] .= site\n daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]\n daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]\n daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]\n\n push!(allsites, daily_clean)\nend;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"With the sites complete, we condense all sites into a single DataFrame,","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"totaldata = deepcopy(allsites[1])\nfor site in allsites[2:end]\n append!(totaldata, site)\nend","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"and a final CSV.write(\"data.csv\", totaldata) call will save the file.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Setting-up-a-Coupled-Simulation","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"For more information about the bucket model, please see the bucket model tutorial.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Background","page":"Setting up a Coupled Simulation","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In our current setup, \"passed back to the land model via the coupler\" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Turbulent-Surface-Fluxes-and-Radiation","page":"Setting up a Coupled Simulation","title":"Turbulent Surface Fluxes and Radiation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we want different behavior. We have defined new coupled types to use instead of the \"prescribed\" types:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"similarily:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Surface-air-density","page":"Setting up a Coupled Simulation","title":"Surface air density","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Regridder/#Bucket","page":"Parameter Dataset Tools","title":"Bucket","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"CurrentModule = ClimaLand.Regridder","category":"page"},{"location":"APIs/Regridder/#Functions","page":"Parameter Dataset Tools","title":"Functions","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll\nClimaLand.Regridder.swap_space","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Hydrostatic-Equilibrium-test-for-Richards-Equation","page":"Richards Equation","title":"Hydrostatic Equilibrium test for Richards Equation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"frac ϑ_l t = K (ϑ_l ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We will solve this equation in a 1-d domain with z -50, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"-K h(t z = -5) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ(t = 0 z) = ν-0001","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"θ_i(t = 0 z) = 00","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"where nu is the porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Preliminary-setup","page":"Richards Equation","title":"Preliminary setup","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load external packages","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"import SciMLBase\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-soil-model","page":"Richards Equation","title":"Set up the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"First, we define the parameters: porosity \\nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K_sat = FT(0.0443 / (3600 * 100))\nS_s = FT(1e-3)\nν = FT(0.495)\nvg_α = FT(2.6)\nvg_n = FT(2)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0)\nparams = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"zmax = FT(0)\nzmin = FT(-5)\nnelems = 10\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nbottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nboundary_conditions = (; top = surface_flux, bottom = bottom_flux);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, in this case we don't have any sources, so we pass an empty tuple:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"soil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_conditions,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = ClimaLand.make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-simulation","page":"Richards Equation","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y.soil.ϑ_l .= FT(0.494);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 24 * 36);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"dt = Float64(1e3);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"stepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we set up the information used for our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Create-some-plots","page":"Richards Equation","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t = sol.t ./ (60 * 60 * 24);\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nz = parent(coords.subsurface.z)\nplot(\n ϑ_l[1],\n z,\n label = string(\"t = \", string(t[1]), \"days\"),\n xlim = [0.47, 0.501],\n ylabel = \"z\",\n xlabel = \"ϑ_l\",\n legend = :bottomleft,\n title = \"Equilibrium test\",\n);\nplot!(ϑ_l[end], z, label = string(\"t = \", string(t[end]), \"days\"));\nfunction hydrostatic_equilibrium(z, z_interface)\n ν = 0.495\n S_s = 1e-3\n α = 2.6\n n = 2.0\n m = 0.5\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nplot!(hydrostatic_equilibrium.(z, -0.56), z, label = \"equilibrium solution\");\n\nplot!(1e-3 .+ ϑ_l[1], z, label = \"porosity\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Save the output:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"savefig(\"equilibrium_test_ϑ_l.png\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#References","page":"Richards Equation","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#PlantHydraulics","page":"Plant Hydraulics","title":"PlantHydraulics","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"CurrentModule = ClimaLand.PlantHydraulics","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#Models","page":"Plant Hydraulics","title":"Models","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsModel","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","text":"PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}\n\nDefines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.\n\nThis model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can \"turn off\" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:\n\nn_leaf = 1\nn_stem = 0\nLAI = SAI = RAI = 0.\n\nA plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.\n\nFinally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.\n\nn_stem: The number of stem compartments for the plant; can be zero\nn_leaf: The number of leaf compartments for the plant; must be >=1\ncompartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters\ncompartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.\ncompartment_labels: The label (:stem or :leaf) of each compartment\nparameters: Parameters required by the Plant Hydraulics model\ntranspiration: The transpiration model, of type AbstractTranspiration\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Diagnostic-Variables","page":"Plant Hydraulics","title":"Plant Hydraulics Diagnostic Variables","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.effective_saturation\nClimaLand.PlantHydraulics.augmented_liquid_fraction\nClimaLand.PlantHydraulics.water_retention_curve\nClimaLand.PlantHydraulics.inverse_water_retention_curve\nClimaLand.PlantHydraulics.root_water_flux_per_ground_area!\nClimaLand.PlantHydraulics.flux\nClimaLand.PlantHydraulics.hydraulic_conductivity","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.effective_saturation","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.effective_saturation","text":"effective_saturation(\n ν::FT,\n ϑ_l::FT) where {FT}\n\nComputes the effective saturation given the augmented liquid fraction.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","text":"augmented_liquid_fraction(\n ν::FT,\n S_l::FT) where {FT}\n\nComputes the augmented liquid fraction from porosity and effective saturation.\n\nAugmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.water_retention_curve","text":"water_retention_curve(\n S_l::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","text":"inverse_water_retention_curve(\n ψ::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the effective saturation given the potential at a point, according to the linear retention curve model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","text":"PlantHydraulics.root_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrognosticSoil,\n model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nAn extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.\n\nIt is computed by summing the flux of water per ground area between roots and soil at each soil layer.\n\n\n\n\n\nroot_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.\n\nThe returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.flux","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.flux","text":"flux(\n z1,\n z2,\n ψ1,\n ψ2,\n K1,\n K2,\n) where {FT}\n\nComputes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.\n\nWe currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","text":"hydraulic_conductivity(conductivity_params::Weibull{FT}, ψ::FT) where {FT}\n\nComputes the hydraulic conductivity at a point, using the Weibull formulation, given the potential ψ.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Parameters","page":"Plant Hydraulics","title":"Plant Hydraulics Parameters","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsParameters\nClimaLand.PlantHydraulics.Weibull\nClimaLand.PlantHydraulics.LinearRetentionCurve","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","text":"PlantHydraulicsParameters\n\nA struct for holding parameters of the PlantHydraulics Model.\n\nai_parameterization: The area index model for LAI, SAI, RAI\nν: porosity (m3/m3)\nS_s: storativity (m3/m3)\nconductivity_model: Conductivity model and parameters\nretention_model: Water retention model and parameters\nroot_distribution: Root distribution function P(z)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.Weibull","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.Weibull","text":"Weibull{FT} <: AbstractConductivityModel{FT}\n\nA concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.\n\nFields\n\nK_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation\nψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).\nc: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","text":"LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}\n\nA concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.\n\nWhen ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.\n\nFields\n\na: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Methods-and-Types","page":"Plant Hydraulics","title":"Plant Hydraulics Methods and Types","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PrescribedSoilPressure\nClimaLand.PlantHydraulics.PrescribedTranspiration\nClimaLand.PlantHydraulics.AbstractRootExtraction","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","text":"PrescribedTranspiration{FT, F <: Function} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch when computing the transpiration from the leaves, in the case where transpiration is prescribed.\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's load the required modules:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"using ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Pond\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-I-Soil-Hydrology","page":"Intro to multi-component models","title":"The individual component models I - Soil Hydrology","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The RR equation for the volumetric water content of soil is given by","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S(xyz t)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In order to solve this, one must specify:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"boundary conditions,\nrelevant parameters (closure models for K and ψ),\na domain and a spatial discretization scheme,\nadditional source terms S, if applicable,\na time-stepping algorithm,\ninitial conditions.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"For the RR equation, we can create this as follows. First, we specify parameters:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ν = FT(0.495);\nK_sat = FT(0.0443 / 3600 / 100); # m/s\nS_s = FT(1e-3); #inverse meters\nvg_n = FT(2.0);\nvg_α = FT(2.6); # inverse meters\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0);\nsoil_ps = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next, let's define the spatial domain and discretization:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"zmax = FT(0);\nzmin = FT(-1);\nnelems = 20;\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And boundary conditions and source terms (none currently):","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"top_flux_bc = WaterFluxBC((p, t) -> 0.0);\nbot_flux_bc = WaterFluxBC((p, t) -> 0.0);\nsources = ()\nboundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(top = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#1#2\"}(Main.var\"##494\".var\"#1#2\"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#3#4\"}(Main.var\"##494\".var\"#3#4\"()))","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"With this information, we can make our model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil = Soil.RichardsModel{FT}(;\n parameters = soil_ps,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_soil, p_soil, coords_soil = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and we can set up the tendency function using the default as well,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_ode! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which computes, for the column domain,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-frac z (-Kfrac(ψ+z) z)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"for each value of ϑ on the mesh of our soil_domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function soil_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place\n update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place\n compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-II-Surface-Water","page":"Intro to multi-component models","title":"The individual component models II - Surface Water","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond model has a single variable, the pond height η, which satisfies the ODE:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"frac η t = -(P - I) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To write down the pond equations, we need to specify","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"P\nI","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s\n\ninfiltration(t) = -(1e-6) #m/s\npond_model =\n Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Again we can initialize the state vector and auxiliary vectors:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_pond, p_pond, coords_pond = initialize(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We can make the tendency function in the same way, for stepping the state forward in time:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"pond_ode! = make_exp_tendency(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond_ode! function works in the same way as for the soil model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function pond_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # falls back to default; does nothing\n update_boundary_fluxes!(p,Y,t) # p.surface_water.runoff in place\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#An-LSM-with-pond-and-soil:","page":"Intro to multi-component models","title":"An LSM with pond and soil:","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The LSM model must contain everything needed to write down the joint system of equations","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial etapartial t = -(P(t) - I(ϑ η P)) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmax z = I(ϑ η P)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmin z = 00","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"As in the standalone cases, defining our model requires specifying","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"parameters,\ndomains, discretizations\nprecipitation,\nboundary conditions,\nsources in the soil equation, if any.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's make our single column domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());\nsurface_water_args = NamedTuple();","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Atmospheric drivers don't \"belong\" to either component alone:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_args = (precip = precipitation,);\nland = LandHydrology{FT}(;\n land_args = land_args,\n soil_model_type = Soil.RichardsModel{FT},\n soil_args = soil_args,\n surface_water_model_type = Pond.PondModel{FT},\n surface_water_args = surface_water_args,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_aux(land)\n soil_update_aux! = make_update_aux(land.soil)\n surface_update_aux! = make_update_aux(land.surface_water)\n function update_aux!(p,Y,t)\n surface_update_aux!(p,Y,t) # does nothing to `p`\n soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ\n end\n return update_aux!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_boundary_fluxes(land)\n update_soil_bf! = make_update_boundary_fluxes(land.soil)\n update_pond_bf! = make_update_boundary_fluxes(land.surface_water)\n function update_boundary_fluxes!(p,Y,t)\n p.soil_infiltration = compute_infiltration(Y,p, t)\n update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration\n update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration\n end\n return update_boundary_fluxes!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and similarily for the compute_exp_tendency! functions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_compute_exp_tendency(land)\n soil_compute_exp_tendency! = make_update_aux(land.soil)\n surface_compute_exp_tendency! = make_update_aux(land.surface_water)\n function compute_exp_tendency!(dY,Y,p,t)\n surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η\n soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ\n end\n return compute_exp_tendency!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The exp_tendency! for the land model is then again just","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function exp_tendency!(dY, Y, p, t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"initialize(land.soil)\ninitialize(land.surface_water)\ninitializes additional auxiliary variables, like p.soil_infiltration\nappend these into Y, p, and coords:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y, p, coords = initialize(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We have volumetric liquid water fraction:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:ϑ_l,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and surface height of the pond:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:η,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"as well as auxiliary variables for the soil:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:K, :ψ, :top_bc, :bottom_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the runoff for surface water:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:runoff,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the additional variable required in the LSM is stored here as well:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:soil_infiltration, :soil, :surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and finally, coordinates - useful for visualization of solutions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.subsurface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the coordinates of the surface variables:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.surface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[0.0]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And we can make the tendency function as before:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_ode! = make_exp_tendency(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#Advantages-and-disadvantages","page":"Intro to multi-component models","title":"Advantages and disadvantages","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Some advantages to our interface design are as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.\nlikewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.\nthe exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).\nalthough we wrote it here in a hardwired fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.\nthe order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.\nthe code is also modular in terms of swapping out a simple component model for a more complex version.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Possible disadvantages to our interface design:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.\nTo accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.\nstandalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.\nwe need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"The same experiment is carried out 3 times","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"No evaporation (zero flux boundary conditions)\nWith evaporation but no drainage (Ksat = 0)\nWith evaporation and drainage","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Parameters","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"K_sat = FT(0.01 / 3600 / 24)\nvg_n = FT(1.45)\nvg_α = FT(1.5)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.4)\nθ_r = FT(0.04)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(0.3)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)# 10mm\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 294.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}(Main.var\"##317\".var\"#1#2\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}(Main.var\"##317\".var\"#3#4\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), nothing)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Atmos","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}(Main.var\"##317\".var\"#7#8\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}(Main.var\"##317\".var\"#9#10\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}(Main.var\"##317\".var\"#11#12\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}(Main.var\"##317\".var\"#13#14\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}(ClimaLand.var\"#21#24\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Simulation setup - no evaporation Boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"zero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nno_flux_boundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);\n\nt0 = Float64(0)\ntf = Float64(24 * 3600 * 15)\ndt = Float64(900.0)\nΔz = 0.01\nzmax = FT(0)\nzmin = FT(-1.6)\nnelems = Int((zmax - zmin) / Δz)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_flux_boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Y, p, cds = initialize(soil)\nfunction estimated_ic(z)\n 0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= estimated_ic.(z)\n Y.soil.θ_i .= 0\n T = FT(294.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\ncb = SciMLBase.CallbackSet(saving_cb);\n\nsol_no_evap =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Repeat with evaporation and drainage This requires different initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nevap_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n)\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = evap_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nsoil_exp_tendency! = make_exp_tendency(soil)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];\n\n# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"bottom_water_bc = MoistureStateBC((p, t) -> 0.35)\nno_drainage_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),\n)\nzmax = FT(0)\nzmin = FT(-0.16)\nnelems = Int((zmax - zmin) / Δz)\ndt = Float64(10.0)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_drainage_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z_no_evap, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol_no_drainage =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap_no_drainage = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"savepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (800, 400))\nax = Axis(fig[1, 1], xlabel = \"Day\", ylabel = \"Evaporation rate (mm/d)\")\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"With drainage\",\n color = :red,\n)\nCairoMakie.lines!(\n ax,\n sol_no_drainage.t ./ 3600 ./ 24,\n evap_no_drainage .* (1000 * 3600 * 24),\n label = \"No drainage\",\n color = :blue,\n)\n\nCairoMakie.axislegend(ax)\nax2 = Axis(fig[1, 2], xlabel = \"Day\", ylabel = \"Cumulative evaporation (mm)\")\nCairoMakie.lines!(\n ax2,\n sol.t ./ 3600 ./ 24,\n cumsum(evap) .* (1000 * 3600),\n color = :red,\n)\nCairoMakie.lines!(\n ax2,\n sol_no_drainage.t ./ 3600 ./ 24,\n cumsum(evap_no_drainage) .* (1000 * 3600),\n color = :blue,\n)\nsave(\"evaporation_lehmann2024_figS6.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Drainage only\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.4)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 10]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax1,\n parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Evap+Drainage\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.3, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 1, 2, 5, 13]\nfor i in 1:1:5\n CairoMakie.lines!(\n ax2,\n parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Evap only\", xlabel = \"Volumetric Water Content\")\nCairoMakie.ylims!(-0.15, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 2, 9, 14]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax3,\n parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n label = \"$(days[i]) days\",\n parent(z_no_evap)[:],\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"evaporation_gardner_fig1.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"Snow Model","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"CurrentModule = ClimaLand.Snow","category":"page"},{"location":"APIs/Snow/#Snow-Parameters","page":"Snow Model","title":"Snow Parameters","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"ClimaLand.Snow.SnowParameters","category":"page"},{"location":"APIs/Snow/#ClimaLand.Snow.SnowParameters","page":"Snow Model","title":"ClimaLand.Snow.SnowParameters","text":"SnowParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SnowModel.\n\nNote that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.\n\nρ_snow: Density of snow (kg/m^3)\nz_0m: Roughness length over snow for momentum (m)\nz_0b: Roughness length over snow for scalars (m)\nα_snow: Albedo of snow (unitless)\nϵ_snow: Emissivity of snow (unitless)\nθ_r: Volumetric holding capacity of water in snow (unitless)\nKsat: Hydraulic conductivity of wet snow (m/s)\nκ_ice: Thermal conductivity of ice (W/m/K)\nΔt: Timestep of the model (s)\nρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)\nearth_param_set: Clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Snow/#Snow-Functions-of-State","page":"Snow Model","title":"Snow Functions of State","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff\t ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Stomatal-conductance","page":"Medlyn model","title":"Stomatal conductance","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"This section describes multiple models of stomatal conductance implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Medlyn-Model","page":"Medlyn model","title":"Medlyn Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\nT = -rho_a g_mathrmeff leftq_a- q_v(T_mathrmleaf rho_mathrmsfc) right\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where T is the transpiration (mass flux of water vapor), q_a is the specific humidity at the lowest level of the atmosphere, q_v(T_mathrmT_leaf rho_mathrmsfc) is the saturated specific humidity over liquid water, given the temperature of the leave T_leaf and air density at the surface rho_sfc. We will approximate T_leaf = T_a and rho_mathrmsfc = rho_a.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"We also need the effective conductivity, given by","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n g_mathrmeff = frac1g_mathrmae^-1+g_mathrms^-1\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g_ae is the aerodynamic conductance, computed by the MOST solve, and g_s is the stomatal conductance to water vapor per unit ground area. The units of all conductances are ms.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginalign\ng_sm(PAR T VPD c_a) = g_0m + D_rel times m fracA_n(PAR T VPD c_a)c_anonumber \ng_s = fracg_smrho_m\nendalign","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where D_rel =16 (unitless) is the relative diffusivity of water vapor with respect to CO_2, rho_m is the molar density of water, and m is the Medlyn factor,","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n m = left( 1 + fracg_1sqrtVPD right) \nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A_n as the biochemical demand for CO_2 calculated using the photosynthesis model (Farquhar, 1980; Equation \\eqref{eq:an}; units of molar flux). The resulting units are ms. g{0,m}$ is a minimum molar conductivity. (subscript m indicates molar).","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Constants Symbol Unit Value\nRelative diffusivity of water vapor D_rel - 1.6\nMinimum stomatal conductance g_0 mol/m^2/s 1e-4\nSlope parameter g_1 sqrtPa 790","category":"page"},{"location":"standalone/README/#DynamicDocs","page":"DynamicDocs","title":"DynamicDocs","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Plant-Hydraulics","page":"Van Genuchten model","title":"Plant Hydraulics","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The plant hydraulics code solves for the volumetric water content in the stem and leaf (theta_stem and theta_leaf). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Van-Genuchten-Model","page":"Van Genuchten model","title":"Van Genuchten Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The volume flux of water q (m/s) between compartments with centers at two heights, z_1 and z_2, is given by Darcy's law as","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n q = -int_z_1^z_2 k(psi) dh\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where h = psi+z is the head (in meters), and k is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\\footnote{Double check this - the units of k in our code are ms.}","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\nq = -int_h_1^h_2 k(psi) dh approx -frack_1(psi_1) + k_2(psi_2)2 * (psi_2 - psi_1) + (z_2 - z_1)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"In order to close the set of equations, the user will have to specify k(psi) and a function psi(theta). In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of K_sat","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The change of water volume (m^3) V, in the compartments is then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd V_w stemdt = q_rootssigma_roots - q_stemsigma_stem nonumber \n fracd V_w leafdt = q_stemsigma_stem - tau sigma_leaf\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where tau is a transpiration volume flux per unit emitting area, and sigma is the total emitting/conducting area\\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals N. We can make use of the fact that NsigmaA, where A is the area of the ground those N individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Then we have:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd v_stemdt = q_rootsRAI - q_stemSAI nonumber \n fracd v_leafdt = q_stemSAI - tau LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where v now represents the volume of water in that compartment (of a bulk plant) per unit ground area.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We also need to convert from the variable v to psi, in order to compute root extraction with the soil. To do so, we can convert v to the volumetric water content, and from theta to psi using a van Genuchten relationship. To convert, let the volume of water per area of compartment be V_wstem, and H the typical ``length\" of the compartment. Then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n theta_stem=fracV_wstemA_ground times fracA_groundA_stem times frac1H_stem = fracv_stemH_stem times SAI \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Substituting in the volumetric water content, we have","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd theta_stemdt = fracq_rootsRAI - q_stemSAIH_stem SAI nonumber \n fracd theta_leafdt = fracq_stemSAI - tau LAIH_leaf LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction P(z), satisfying int P(z) dz = 1. Instead of having a single root at one discrete location, we can distribute the root system over different depths using P(z). The total flux from roots between z and z+dz is given by","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n dq_roots(z) = -P(z) dz int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"so that the net flux for the plant system would sum over this","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n q_roots = -int_z_min^z_sfc fracdq_roots(z)dzdz \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where z_min is the minimum soil layer of the simulation. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n S(z) = -(RAI) dq_roots(z)dz = (RAI) P(z) int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sign change occurs in the expression for S(z) because a positive value of q_roots indicates flow from the soil to the plant. This is a sink term for the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The model needs the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Drivers Symbol Unit Range\nA function of simulation time t given the leaf area index LAI m²/m² 0–10\nThe constant stem area index SAI m²/m² 0–10\nThe constant root area index RAI m²/m² 0–10","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Microbial-respiration","page":"DAMM model","title":"Microbial respiration","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Dual-Arrhenius-Michaelis-Menten","page":"DAMM model","title":"Dual Arrhenius Michaelis-Menten","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration (Rh) as a function of soil temperature (T_s) and soil moisture (theta).","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The rate of respiration, Rh, is expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n Rh = V_textmaxs_xMM_s_xMM_O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where V_textmaxs_x is the maximum potential rate of respiration, MM_s_x represents the availability of substrate, and MM_O_2 is used as the oxygen limitation factor. MM_s_x and MM_O_2 are between 0 (limiting) and 1 (non limiting). V_textmaxs_x, MM_s_x, and MM_O_2 are expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n V_textmaxs_x = alpha_s_x exp(frac-Ea_s_xRT_s)\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textsx = fracs_xkM_s_x+s_x\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textO_2 = fracO_2kM_O_2+O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where alpha_s_x is the pre-exponential factor, Ea_s_x is the activation energy of the reaction, R is the gas constant, and T_s is soil temperature. s_x is the concentration of all soluble substrate, and O_2 is the oxygen concentration. kM_s_x and kM_O_2 are the Michaelis constant for soil and oxygen, respectively.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, s_x is calculated as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n s_x = p_s_xtimesC_somtimes D_liqtimestheta^3\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where C_som is the total amount of soil organic carbon, and p_s_x is the fraction of C_som that is soluble. D_liq is the diffusion coefficient of the soluble carbon. theta is soil moisture.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of O_2 depends on the diffusion of gases within the soil, which is calculated as below:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n O_2 = D_Oatimes O_2a times porosity_air^43\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where D_Oa is the diffusion coefficient for O_2 in air, O_2a is the volume fraction of O_2 in air, and porosity_air is the air-filled porosity.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The air-filled porosity is calculated by subtracting the soil moisture from the total porosity (nu):","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n porosity_air = nu - theta\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"To sum up, the model has the following parameters:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Output Symbol Unit Range\nHeterotrophic respiration Rh mumol m^-2 s^-2 0–25","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Drivers Symbol Unit Range\nSoil temperature T_s C -20–50\nSoil moisture theta m^3 m^-3 0.0–1.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Parameters Symbol Unit Range\nSoil porosity nu m^3 m^-3 0.0–1.0\nPre-exponential factor alpha_s_x kg C m^-3 s^-1 100e3–300e3\nActivation energy Ea_s_x Jmol^-1 50e3–70e3\nMichaelis constant for soil kM_s_x kg C m^-3 1e-10–0.1\nMichaelis constant for O_2 kM_O_2 m^3 m^-3 1e-10–0.1\nVolumetric fraction of O_2 in the soil air content O_2_a - 0.005–0.5\nFraction of soil carbon that is considered soluble p_s_x - 0.005–0.5\nSoil organic C C_som kg C m^-3 1.0–10.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Constants Symbol Unit Value\nAir-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa) O_a100 - 0.1816\nDiffusivity of soil C substrate in liquid D_liq - 3.17\nDiffusion coefficient of oxygen in air D_Oa - 1.67","category":"page"},{"location":"Contributing/#Contributing","page":"Contribution guide","title":"Contributing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.","category":"page"},{"location":"Contributing/#Some-useful-tips","page":"Contribution guide","title":"Some useful tips","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"When you start working on a new feature branch, make sure you start from main by running: git checkout main.\nMake sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.\nWhen your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.","category":"page"},{"location":"Contributing/#Continuous-integration","page":"Contribution guide","title":"Continuous integration","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.","category":"page"},{"location":"Contributing/#Automated-testing","page":"Contribution guide","title":"Automated testing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Currently a number of checks are run per commit for a given PR.","category":"page"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.\nDocumentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.\nTests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Canopy-Energy-Model","page":"Canopy Energy","title":"Canopy Energy Model","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Methods","page":"Canopy Energy","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.canopy_temperature\nClimaLand.Canopy.root_energy_flux_per_ground_area!","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.canopy_temperature","page":"Canopy Energy","title":"ClimaLand.Canopy.canopy_temperature","text":"canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.\n\n\n\n\n\ncanopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.root_energy_flux_per_ground_area!","page":"Canopy Energy","title":"ClimaLand.Canopy.root_energy_flux_per_ground_area!","text":"root_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrognosticSoil{F},\n model::Canopy.AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT, F}\n\nA method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).\n\nNote that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\nroot_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.\n\nBackground information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#Types","page":"Canopy Energy","title":"Types","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.AbstractCanopyEnergyModel\nClimaLand.Canopy.PrescribedCanopyTempModel\nClimaLand.Canopy.BigLeafEnergyModel","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.PrescribedCanopyTempModel","page":"Canopy Energy","title":"ClimaLand.Canopy.PrescribedCanopyTempModel","text":"PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}\n\nA model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.\n\nNo equation for the energy of the canopy is solved.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.BigLeafEnergyModel","page":"Canopy Energy","title":"ClimaLand.Canopy.BigLeafEnergyModel","text":"BigLeafEnergyModel{FT} <: AbstractCanopyEnergyModel{FT}\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#SurfaceWater","page":"Surface Water Models","title":"SurfaceWater","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"CurrentModule = ClimaLand.Pond","category":"page"},{"location":"APIs/SurfaceWater/#Models","page":"Surface Water Models","title":"Models","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PondModel","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PondModel","page":"Surface Water Models","title":"ClimaLand.Pond.PondModel","text":"PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}\n\nA stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.\n\ndomain: The domain for the pond model\nrunoff: The runoff model for the pond model\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#Methods-and-Types","page":"Surface Water Models","title":"Methods and Types","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PrescribedRunoff\nClimaLand.Pond.surface_runoff","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PrescribedRunoff","page":"Surface Water Models","title":"ClimaLand.Pond.PrescribedRunoff","text":"PrescribedRunoff{F1 <: Function, F2 <: Function} <: AbstractSurfaceRunoff\n\nThe required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.surface_runoff","page":"Surface Water Models","title":"ClimaLand.Pond.surface_runoff","text":"function Pond.surface_runoff(\n runoff::PrognosticRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nExtension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#Autotrophic-Respiration","page":"Canopy Autotrophic Respiration","title":"Autotrophic Respiration","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#Parameters","page":"Canopy Autotrophic Respiration","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.AutotrophicRespirationParameters","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.AutotrophicRespirationParameters","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.AutotrophicRespirationParameters","text":"AutotrophicRespirationParameters{FT<:AbstractFloat}\n\nThe required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. \"The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics.\" Geoscientific Model Development 4.3 (2011): 701-722.\n\nne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)\nηsl: Live stem wood coefficient (kg C m-3)\nσl: Specific leaf density (kg C m-2 [leaf])\nμr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\nμs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1\nRel: Relative contribution or Rgrowth (-)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/AutotrophicRespiration/#Methods","page":"Canopy Autotrophic Respiration","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.nitrogen_content\nClimaLand.Canopy.plant_respiration_maintenance\nClimaLand.Canopy.plant_respiration_growth","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.nitrogen_content","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.nitrogen_content","text":"nitrogen_content(\n ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)\n Vcmax25::FT, #\n LAI::FT, # Leaf area index\n SAI::FT,\n RAI::FT,\n ηsl::FT, # live stem wood coefficient (kg C m-3) \n h::FT, # canopy height (m)\n σl::FT # Specific leaf density (kg C m-2 [leaf])\n μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\n μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 \n ) where {FT}\n\nComputes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_maintenance","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_maintenance","text":"plant_respiration_maintenance(\n Rd::FT, # Dark respiration\n β::FT, # Soil moisture factor\n Nl::FT, # Nitrogen content of leafs\n Nr::FT, # Nitrogen content of roots\n Ns::FT, # Nitrogen content of stems\n ) where {FT}\n\nComputes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_growth","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_growth","text":"plant_respiration_growth(\n Rel::FT, # Factor of relative contribution\n An::FT, # Net photosynthesis\n Rpm::FT # Plant maintenance respiration\n ) where {FT}\n\nComputes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.\n\n\n\n\n\n","category":"function"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Using-atmospheric-and-radiative-drivers","page":"Intro to forced site-level runs","title":"Using atmospheric and radiative drivers","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers\"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Types-of-forcing-data","page":"Intro to forced site-level runs","title":"Types of forcing data","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Creating-site-level-drivers-for-radiation","page":"Intro to forced site-level runs","title":"Creating site-level drivers for radiation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"using Dates\nusing Insolation # for computing zenith angle given lat, lon, time.\nusing ClimaLand\nimport ClimaLand.Parameters as LP\nimport ClimaParams","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the local_datetime array is read in from the data file.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Timezone (offset of local time from UTC in hrs)","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"time_offset = 7;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Site latitude and longitude","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"lat = 38.7441; # degree\nlong = -92.2000; # degree","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"ref_time = local_datetime[1] + Dates.Hour(time_offset);\ndata_dt = 3600.0;\nseconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));\nLW_d = 5.67 * 10^(-8) .* T .^ 4;\nSW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"LW_d = TimeVaryingInput(seconds, LW_d)\nSW_d = TimeVaryingInput(seconds, SW_d);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"earth_param_set = LP.LandParameters(Float64);\ninsol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation\nfunction zenith_angle(\n t,\n ref_time;\n latitude = lat,\n longitude = long,\n insol_params = insol_params,\n)\n current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC\n\n d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(\n current_datetime,\n ref_time,\n insol_params,\n ))\n\n\n return Insolation.instantaneous_zenith_angle(\n d,\n δ,\n η_UTC,\n longitude,\n latitude,\n )[1]\nend;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"radiation = ClimaLand.PrescribedRadiativeFluxes(\n Float64,\n SW_d,\n LW_d,\n ref_time;\n θs = zenith_angle,\n);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Updating-the-driver-variables-during-the-simulation","page":"Intro to forced site-level runs","title":"Updating the driver variables during the simulation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"In order to update them, we can make use of default update functions:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"update_radiation! = ClimaLand.make_update_drivers(radiation)\nt0 = seconds[1] # midnight local time\nupdate_radiation!(p, t0);\n@show(p.drivers);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])\n","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"During a simulation, the drivers are updated in place in p.drivers via a \"callback\", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"updateat = collect(seconds[1]:(3600 * 3):seconds[end]);\nupdatefunc = update_radiation!;\ncb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This callback must then be provided to the simulation solve function.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Photosynthesis","page":"Canopy Photosynthesis","title":"Photosynthesis","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Parameters","page":"Canopy Photosynthesis","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.SIFParameters\nClimaLand.Canopy.FarquharParameters\nClimaLand.Canopy.OptimalityFarquharParameters","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.SIFParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.SIFParameters","text":"SIFParameters{FT<:AbstractFloat}\n\nThe required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.\n\nkf: The rate coefficient for florescence, unitless\nkd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nmin_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkp: Rate coefficient for photochemical quenching\nkappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\nkappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.FarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.FarquharParameters","text":"FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}\n\nThe required parameters for the Farquhar photosynthesis model.\n\nVcmax25: Vcmax at 25 °C (mol CO2/m^2/s)\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercelluar O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]\npc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]\nmechanism: Photosynthesis mechanism: C3 or C4\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.OptimalityFarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.OptimalityFarquharParameters","text":"OptimalityFarquharParameters{FT<:AbstractFloat}\n\nThe required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.\n\nmechanism: Photosynthesis mechanism: C3 only\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercellular O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Fitting constant to compute the moisture stress factor (Pa^{-1})\npc: Fitting constant to compute the moisture stress factor (Pa)\nc: Constant describing cost of maintaining electron transport (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#Methods","page":"Canopy Photosynthesis","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.arrhenius_function\nClimaLand.Canopy.intercellular_co2\nClimaLand.Canopy.co2_compensation\nClimaLand.Canopy.rubisco_assimilation\nClimaLand.Canopy.light_assimilation\nClimaLand.Canopy.C3\nClimaLand.Canopy.C4\nClimaLand.Canopy.max_electron_transport\nClimaLand.Canopy.electron_transport\nClimaLand.Canopy.net_photosynthesis\nClimaLand.Canopy.optimality_max_photosynthetic_rates\nClimaLand.Canopy.moisture_stress\nClimaLand.Canopy.dark_respiration\nClimaLand.Canopy.compute_GPP\nClimaLand.Canopy.MM_Kc\nClimaLand.Canopy.MM_Ko\nClimaLand.Canopy.compute_Vcmax","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.arrhenius_function","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.arrhenius_function","text":"arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)\n\nComputes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.intercellular_co2","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.intercellular_co2","text":"intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}\n\nComputes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.co2_compensation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.co2_compensation","text":"co2_compensation(Γstar25::FT,\n ΔHΓstar::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.rubisco_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.rubisco_assimilation","text":"rubisco_assimilation(::C3,\n Vcmax::FT,\n ci::FT,\n Γstar::FT,\n Kc::FT,\n Ko::FT,\n oi::FT) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).\n\nThe empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nrubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.light_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.light_assimilation","text":"light_assimilation(::C3,\n J::FT,\n ci::FT,\n Γstar::FT) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nlight_assimilation(::C4, J::FT, _...) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C3","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C3","text":"C3 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C4","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C4","text":"C4 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.max_electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.max_electron_transport","text":"max_electron_transport(Vcmax::FT) where {FT}\n\nComputes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.electron_transport","text":"electron_transport(APAR::FT,\n Jmax::FT,\n θj::FT,\n ϕ::FT) where {FT}\n\nComputes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). \n\nSee Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.net_photosynthesis","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.net_photosynthesis","text":"net_photosynthesis(Ac::FT,\n Aj::FT,\n Rd::FT,\n β::FT) where {FT}\n\nComputes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). \n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.optimality_max_photosynthetic_rates","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.optimality_max_photosynthetic_rates","text":"optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)\n\nComputes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.\n\nSee Smith et al. 2019.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.moisture_stress","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.moisture_stress","text":"moisture_stress(pl::FT,\n sc::FT,\n pc::FT) where {FT}\n\nComputes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) . \n\nSee Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.dark_respiration","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.dark_respiration","text":"dark_respiration(Vcmax25::FT,\n β::FT,\n f::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_GPP","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_GPP","text":"compute_GPP(An::FT,\n K::FT,\n LAI::FT,\n Ω::FT) where {FT}\n\nComputes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Kc","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Kc","text":"MM_Kc(Kc25::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Ko","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Ko","text":"MM_Ko(Ko25::FT,\n ΔHko::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_Vcmax","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_Vcmax","text":"compute_Vcmax(Vcmax25::FT,\n T::FT,\n To::FT,\n R::FT,\n ep5::FT) where {FT}\n\nComputes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"getting_started/#Getting-Started","page":"Getting Started","title":"Getting Started","text":"","category":"section"},{"location":"getting_started/#For-Users","page":"Getting Started","title":"For Users","text":"","category":"section"},{"location":"getting_started/#Installation","page":"Getting Started","title":"Installation","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"julia> ] # Enter Package REPL mode\nPkg> add ClimaLand # Install ClimaLand\nPkg> # Go back to Julia REPL mode\nJulia> using ClimaLand","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.","category":"page"},{"location":"getting_started/#Parameterization","page":"Getting Started","title":"Parameterization","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Let's start with a basic example: compute canopy gross photosynthesis (GPP).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\n@doc ClimaLand.Canopy.compute_GPP","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"import ClimaLand.Canopy as canopy\ncanopy.compute_GPP(5.0, 0.5, 3.0, 0.7)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Et voilà!","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.","category":"page"},{"location":"getting_started/#ClimaLand-structure","page":"Getting Started","title":"ClimaLand structure","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ClimaLand contains multiple modules. They are listed below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using MethodAnalysis, ClimaLand\nchild_modules(ClimaLand)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To explore what modules, functions and types are exported in a particular module, you can use About.jl:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\nusing About\nabout(ClimaLand.Soil.Biogeochemistry)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/#Seasonal-Snow-Timeseries-Generation-with-a-Neural-Network","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"section"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The updates of the neural snow model follow the equation","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"fracdzdt = mathcalMleft(z SWE φ R v T_air P_snowright)","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"where","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"t is the time (s),\nz is the snow depth (m),\nmathcalM is the neural network,\nSWE is the Snow Water Equivalent, or the height of water if all the snow melted (m),\nφ is the relative humidity (0-1),\nR is the solar radiation (W/m²).\nv is the wind speed (W/m²).\nT_air is the air temperature (degrees C).\nP_snow is the water equivalent rate of snow precipitation (m/s).","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The model is a 1D model to permit utilization over any desired grid resolution and shape.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"┌ Warning: Package cuDNN not found in current path.\n│ - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia.\n│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.\n└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools\nModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\ncode_dir = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Snow\")\ninclude(joinpath(code_dir, \"display_tools.jl\"));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter n as outlined in the associated parameter, and the two loss function hyperparameters n_1, n_2.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"n = 4\nn1 = 2\nn2 = 4;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the fracdzdt column.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"pred_vars = [\n :z,\n :SWE,\n :rel_hum_avg,\n :sol_rad_avg,\n :wind_speed_avg,\n :air_temp_avg,\n :dprecipdt_snow,\n]\ntarget = :dzdt;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"nfeatures = length(pred_vars)\nz_idx = 1\np_idx = 7;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"training_data_download_link = \"https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv\"\ntesting_data_download_link = \"https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv\"\ndata_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)\nvaldata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)\nΔt = Second(86400)\nhole_thresh = 5;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"usedata = DataTools.prep_data(data_train);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of \"0\" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"out_scale = maximum(abs.(usedata[!, target]))\nin_scales = std.(eachcol(select(usedata, pred_vars)))\nx_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)\nps = ModelTools.get_model_ps(model);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)\nModelTools.setoutscale!(model, 1.0);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With that, training is as simple as calling the trainmodel! function:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"print(\"\\nTraining model!\\n\")\nModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"\nTraining model!\nEpoch: 10 | training loss: 0.002783926\nEpoch: 20 | training loss: 0.002693796\nEpoch: 30 | training loss: 0.0026894645\nEpoch: 40 | training loss: 0.0026167487\nEpoch: 50 | training loss: 0.0026483187\nEpoch: 60 | training loss: 0.0025837263\nEpoch: 70 | training loss: 0.0026131733\nEpoch: 80 | training loss: 0.00276859\nEpoch: 90 | training loss: 0.002585475\nEpoch: 100 | training loss: 0.0026451899\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.setoutscale!(model, out_scale)\nModelTools.settimescale!(model, Dates.value(Δt));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Note that gaps in the data are shown as shaded regions on the plotted timeseries.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = 1286\nsitedata = usedata[usedata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Slagamount Lakes, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot1.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = \"1070\" #string format for the testing ids is due to non-numerical testing site codes.\nsitedata = valdata[valdata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Anchorage Hillside, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot2.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This page was generated using Literate.jl.","category":"page"}] +} diff --git a/previews/PR746/siteinfo.js b/previews/PR746/siteinfo.js new file mode 100644 index 0000000000..ffa76c500d --- /dev/null +++ b/previews/PR746/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR746"; diff --git a/previews/PR746/standalone/Project.toml b/previews/PR746/standalone/Project.toml new file mode 100644 index 0000000000..e0874fbf1e --- /dev/null +++ b/previews/PR746/standalone/Project.toml @@ -0,0 +1,7 @@ +[deps] +Bonito = "824d6782-a2ef-11e9-3a09-e5662e0c26f8" +ClimaLand = "08f4d4ce-cf43-44bb-ad95-9d2d5f413532" +ClimaLandSimulations = "348a0bd3-1299-4261-8002-d2cd97df6055" +ClimaParams = "5c42b081-d73a-476f-9059-fd94b934656c" +ParamViz = "9706fb73-1de9-418d-83da-fef8cd99bb4a" +Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" diff --git a/previews/PR746/standalone/README/index.html b/previews/PR746/standalone/README/index.html new file mode 100644 index 0000000000..dcf72bc4ca --- /dev/null +++ b/previews/PR746/standalone/README/index.html @@ -0,0 +1,2 @@ + +DynamicDocs · ClimaLand.jl
GitHub
diff --git a/previews/PR746/standalone/apps.jl b/previews/PR746/standalone/apps.jl new file mode 100644 index 0000000000..cdb1002497 --- /dev/null +++ b/previews/PR746/standalone/apps.jl @@ -0,0 +1,47 @@ +# Load packages +using ParamViz +using Bonito +using ClimaLand +using ClimaLand.Canopy +using ClimaLand.Soil.Biogeochemistry +import ClimaParams as CP +import ClimaLand.Parameters as LP +using ClimaLandSimulations +FT = Float64 +earth_param_set = LP.LandParameters(FT) +RTparams = BeerLambertParameters(FT) + +# Create server +IPa = "127.0.0.1" +port = 9385 +server = Server( + IPa, + port; + proxy_url = "https://clima.westus3.cloudapp.azure.com/jsserve/", +) + +# Load and create apps +include("apps/leaf_an.jl"); +An_app = An_app_f(); +An_app = An_app_f(); # need to run twice for unicode character... (bug) +include("apps/beer.jl"); +beer_app = Beer_app_f(); +beer_app = Beer_app_f(); +include("apps/hetero_resp.jl"); +Rh_app = Rh_app_f(); +Rh_app = Rh_app_f(); +include("apps/leaf_an_ci.jl"); +An_ci_app = An_ci_app_f(); +An_ci_app = An_ci_app_f(); +# Fluxnet DashBoard +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); + +# Route apps +route!(server, "/leaf_An" => An_app) +route!(server, "/beer_APAR" => beer_app) +route!(server, "/leaf_An_ci" => An_ci_app) +route!(server, "/Rh" => Rh_app) +route!(server, "/fluxnet" => fluxnet_app) + +wait() diff --git a/previews/PR746/standalone/apps/beer.jl b/previews/PR746/standalone/apps/beer.jl new file mode 100644 index 0000000000..266f3b0da7 --- /dev/null +++ b/previews/PR746/standalone/apps/beer.jl @@ -0,0 +1,35 @@ +using Unitful: m, s, mol, μmol + +function ParamViz.parameterisation(PAR, LAI, ρ_leaf, K, Ω, a, b) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + return APAR +end + +function Beer_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "LAI (m² m⁻²)"), + (FT.([0, 1500 * 1e-6]), FT.([0, 10])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (m^2 * m^-2, m^2 * m^-2)), + ) + + parameters = Parameters( + ( + "canopy reflectance, ρ_leaf", + "extinction coefficient, K", + "clumping index, Ω", + ), + (FT.([0, 1]), FT.([0, 1]), FT.([0, 1])), + ((m, m), (m, m), (m, m)), # dummy units, no conversion + ) + + constants = Constants(("a", "b"), (FT(1), FT(2))) # dummy constants + inputs = Inputs(drivers, parameters, constants) + output = Output( + "APAR (μmol m⁻² s⁻¹)", + [0, 1500 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + beer_app = webapp(ParamViz.parameterisation, inputs, output) + return beer_app +end diff --git a/previews/PR746/standalone/apps/hetero_resp.jl b/previews/PR746/standalone/apps/hetero_resp.jl new file mode 100644 index 0000000000..0a6ef56fe6 --- /dev/null +++ b/previews/PR746/standalone/apps/hetero_resp.jl @@ -0,0 +1,92 @@ +using Unitful: K, °C, mol, μmol, m, s, kg, J + +function ParamViz.parameterisation( + Tₛ, + θ, # drivers + ν, + α_sx, + Ea_sx, + kM_sx, + kM_O₂, + O₂_a, + p_sx, + Csom, # parameters + θ_a100, + D_liq, + D_oa, +) # constants + + params = SoilCO2ModelParameters(FT; ν) + # θ has to be lower than porosity + if θ > ν + θ = ν + end + + Rh = microbe_source(Tₛ, θ, Csom, params) + return Rh +end + +function Rh_app_f() + drivers = Drivers( + ("Tₛ (°C)", "θ (m³ m⁻³)"), # drivers.names + (FT.([273, 323]), FT.([0.0, 1.0])), # drivers.ranges + ((K, °C), (m^3 * m^-3, m^3 * m^-3)), + ) + + parameters = Parameters( + (# names + "Soil porosity, ν (m³ m⁻³)", + "Pre-exponential factor, α_sx (kg C m⁻³ s⁻¹)", + "Activation energy, Ea_sx (J mol⁻¹)", + "Michaelis constant for soil, kM_sx (kg C m⁻³)", + "Michaelis constant for O₂, kM_O₂ (m³ m⁻³)", + "Volumetric fraction of O₂ in the soil air, O₂_a (dimensionless)", + "Fraction of soil carbon that is considered soluble, p_sx (dimensionless)", + "Soil organic C, Csom (kg C m⁻³)", + ), + (# ranges + FT.([0.0, 1.0]), # porosity + FT.([100e3, 300e3]), # α_sx + FT.([50e3, 70e3]), # Ea_sx + FT.([1e-10, 0.1]), # kM_sx + FT.([1e-10, 0.1]), # kM_o2 + FT.([0.005, 0.5]), # O2_a + FT.([0.005, 0.5]), # p_sx + FT.([1.0, 10.0]), # Csom + ), + ( + (m^3 * m^-3, m^3 * m^-3), # porosity + (kg * m^-3 * s^-1, kg * m^-3 * s^-1), # α_sx + (J * mol^-1, J * mol^-1), # Ea_sx + (kg * m^-3, kg * m^-3), # kM_sx + (m^3 * m^-3, m^3 * m^-3), # kM_O2 + (m, m), # O2_a + (m, m), # p_sx + (kg * m^-3, kg * m^-3), # Csom + ), + ) + + constants = Constants( + (# names + "Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)", + "Diffusivity of soil C substrate in liquid (unitless)", + "Diffusion coefficient of oxygen in air, dimensionless", + ), + (# values + FT(0.1816), # θ_a100 + FT(3.17), # D_liq + FT(1.67), # D_oa + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "Rh (mg C m⁻³ s⁻¹)", # name #NEED TO CONVERT THE UNIT + [0, 20 * 1e-6], # range + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), # unit from, unit to --> actually need to fix this, should be g to mg C m-3 s-1 + ) + + Rh_app = webapp(ParamViz.parameterisation, inputs, output) + return Rh_app +end diff --git a/previews/PR746/standalone/apps/leaf_an.jl b/previews/PR746/standalone/apps/leaf_an.jl new file mode 100644 index 0000000000..5960996a6c --- /dev/null +++ b/previews/PR746/standalone/apps/leaf_an.jl @@ -0,0 +1,137 @@ +using Unitful: K, °C, mol, μmol, m, s, Pa, kPa + +function ParamViz.parameterisation( + PAR, + T, + β, + LAI, + ca, + VPD, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + Vcmax25, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + g1, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + medlynt = 1 + g1 / sqrt(VPD) + ci = intercellular_co2(ca, Γstar, medlynt) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "T (°C)"), + (FT.([0, 1500 * 1e-6]), FT.([273, 323])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (K, °C)), + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "CO2 concentration, ca (ppm)", + "Vapor pressure deficit, VPD (Pa)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([300 * 1e-6, 500 * 1e-6]), # ca + FT.([500, 10000]), # VPD, Pa + ), + ( + (m, m), # dummy units, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol / mol, μmol / mol), + (Pa, kPa), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "Vcmax25", # the maximum rate of carboxylation of Rubisco + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "g1", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd", + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(5e-5), # Vcmax25 - ozark model + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(141), # g1 - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + + An_app = webapp(ParamViz.parameterisation, inputs, output) + return An_app +end diff --git a/previews/PR746/standalone/apps/leaf_an_ci.jl b/previews/PR746/standalone/apps/leaf_an_ci.jl new file mode 100644 index 0000000000..351338a131 --- /dev/null +++ b/previews/PR746/standalone/apps/leaf_an_ci.jl @@ -0,0 +1,127 @@ +using Unitful: K, °C, mol, μmol, m, s + +function ParamViz.parameterisation( + ci, + T, + β, + LAI, + PAR, + Vcmax25, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_ci_app_f() + drivers = Drivers( + ("ci (ppm)", "T (°C)"), # names + (FT.([0.0001, 0.001]), FT.([273, 323])), # ranges + ((mol / mol, μmol / mol), (K, °C)), # units from, unit to + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "Photosynthetic radiation, PAR (μmol m⁻² s⁻¹)", + "Vcmax25, (μmol m⁻² s⁻¹)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([0, 1500 * 1e-6]), # PAR + FT.([1e-5, 1e-4]), # Vcmax25 + ), + ( + (m, m), # dummy unit, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd",# a constant + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + An_ci_app = webapp(ParamViz.parameterisation, inputs, output) + return An_ci_app +end diff --git a/previews/PR746/standalone/pages/snow/snow_model/index.html b/previews/PR746/standalone/pages/snow/snow_model/index.html new file mode 100644 index 0000000000..7df7607500 --- /dev/null +++ b/previews/PR746/standalone/pages/snow/snow_model/index.html @@ -0,0 +1,2 @@ + +Snow model · ClimaLand.jl
GitHub
diff --git a/previews/PR746/standalone/pages/soil/biogeochemistry/DAMM_model/index.html b/previews/PR746/standalone/pages/soil/biogeochemistry/DAMM_model/index.html new file mode 100644 index 0000000000..a52c14e76c --- /dev/null +++ b/previews/PR746/standalone/pages/soil/biogeochemistry/DAMM_model/index.html @@ -0,0 +1,18 @@ + +DAMM model · ClimaLand.jl
GitHub

Microbial respiration

This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand.

Dual Arrhenius Michaelis-Menten

The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration ($Rh$) as a function of soil temperature ($T_s$) and soil moisture ($\theta$).

The rate of respiration, $Rh$, is expressed as:

\[\begin{equation} + Rh = V_\text{maxs_x}MM_{s_x}MM_{O_2} +\end{equation}\]

where $V_\text{max}{s_x}$ is the maximum potential rate of respiration, $MM_{s_x}$ represents the availability of substrate, and $MM_{O_2}$ is used as the oxygen limitation factor. $MM_{s_x}$ and $MM_{O_2}$ are between 0 (limiting) and 1 (non limiting). $V_\text{max}{s_x}$, $MM_{s_x}$, and $MM_{O_2}$ are expressed as:

\[\begin{equation} + V_\text{max}{s_x} = \alpha_{s_x} \exp(\frac{-Ea_{s_x}}{RT_s}) +\end{equation}\]

\[\begin{equation} + MM_\text{sx} = \frac{[s_x]}{kM_{s_x}+[s_x]} +\end{equation}\]

\[\begin{equation} + MM_\text{O_2} = \frac{[O_2]}{kM_{O_2}+[O_2]} +\end{equation}\]

where $\alpha_{s_x}$ is the pre-exponential factor, $Ea_{s_x}$ is the activation energy of the reaction, $R$ is the gas constant, and $T_s$ is soil temperature. $[s_x]$ is the concentration of all soluble substrate, and $[O_2]$ is the oxygen concentration. $kM_{s_x}$ and $kM_{O_2}$ are the Michaelis constant for soil and oxygen, respectively.

The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, $[s_x]$ is calculated as:

\[\begin{equation} + [s_x] = p_{s_x}\times[C_{som}]\times D_{liq}\times\theta^3 +\end{equation}\]

where $[C_{som}]$ is the total amount of soil organic carbon, and $p_{s_x}$ is the fraction of $[C_{som}]$ that is soluble. $D_{liq}$ is the diffusion coefficient of the soluble carbon. $\theta$ is soil moisture.

The concentration of $O_2$ depends on the diffusion of gases within the soil, which is calculated as below:

\[\begin{equation} + [O_2] = D_{Oa}\times O_{2a} \times porosity_{air}^{4/3} +\end{equation}\]

where $D_{Oa}$ is the diffusion coefficient for $O_2$ in air, $O_{2a}$ is the volume fraction of $O_2$ in air, and $porosity_{air}$ is the air-filled porosity.

The air-filled porosity is calculated by subtracting the soil moisture from the total porosity ($\nu$):

\[\begin{equation} + porosity_{air} = \nu - \theta +\end{equation}\]

To sum up, the model has the following parameters:

OutputSymbolUnitRange
Heterotrophic respirationRh$\mu$mol $m^{-2}$ $s^{-2}$0–25
DriversSymbolUnitRange
Soil temperature$T_s$$°C$-20–50
Soil moisture$\theta$$m^3$ $m^{-3}$0.0–1.0
ParametersSymbolUnitRange
Soil porosity$\nu$$m^3$ $m^{-3}$0.0–1.0
Pre-exponential factor$\alpha_{s_x}$kg C $m^{-3}$ $s^{-1}$100e3–300e3
Activation energy$Ea_{s_x}$J$mol^{-1}$50e3–70e3
Michaelis constant for soil$kM_{s_x}$kg C $m^{-3}$1e-10–0.1
Michaelis constant for $O_2$$kM_{O_2}$$m^3$ $m^{-3}$1e-10–0.1
Volumetric fraction of $O_2$ in the soil air content$O_{2_a}$-0.005–0.5
Fraction of soil carbon that is considered soluble$p_{s_x}$-0.005–0.5
Soil organic C$C_{som}$kg C $m^{-3}$1.0–10.0
ConstantsSymbolUnitValue
Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)$O_{a100}$-0.1816
Diffusivity of soil C substrate in liquid$D_{liq}$-3.17
Diffusion coefficient of oxygen in air$D_{Oa}$-1.67
diff --git a/previews/PR746/standalone/pages/soil/energy/energy_model/index.html b/previews/PR746/standalone/pages/soil/energy/energy_model/index.html new file mode 100644 index 0000000000..2c39b0270f --- /dev/null +++ b/previews/PR746/standalone/pages/soil/energy/energy_model/index.html @@ -0,0 +1,2 @@ + +Energy model · ClimaLand.jl
GitHub
diff --git a/previews/PR746/standalone/pages/soil/hydrology/richards_model/index.html b/previews/PR746/standalone/pages/soil/hydrology/richards_model/index.html new file mode 100644 index 0000000000..4563de03aa --- /dev/null +++ b/previews/PR746/standalone/pages/soil/hydrology/richards_model/index.html @@ -0,0 +1,2 @@ + +Richards model · ClimaLand.jl
GitHub
diff --git a/previews/PR746/standalone/pages/surface_water/surface_water_model/index.html b/previews/PR746/standalone/pages/surface_water/surface_water_model/index.html new file mode 100644 index 0000000000..ca84723963 --- /dev/null +++ b/previews/PR746/standalone/pages/surface_water/surface_water_model/index.html @@ -0,0 +1,2 @@ + +Surface water model · ClimaLand.jl
GitHub
diff --git a/previews/PR746/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html b/previews/PR746/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html new file mode 100644 index 0000000000..58803481f4 --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html @@ -0,0 +1,36 @@ + +Farquhar model · ClimaLand.jl
GitHub

Farquhar Model

This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.

The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO$_2$ into the leaf, the assimilation of CO$_2$ during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO$_2$ concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.

The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:

An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)

An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)

Rubisco limited rate

\[\begin{equation} +a_1(T, c_a, VPD) = +\begin{cases} + V_{cmax}(T) \frac{(c_i(T, c_a, VPD) - \Gamma^*(T))}{(c_i(T, c_a, VPD) + K_c(T)*(1+o_i/K_o(T)))} & \text{for C3}\\ + V_{cmax}(T) & \text{for C4} +\end{cases} +\end{equation}\]

The dependence on the atmospheric CO$_2$ concentration $c_a$ (mol/mol) and vapor pressure deficit $VPD$ arise in the expression for $c_i$,

\[\begin{align} + c_i(T, c_a, VPD) = \max{(c_a(1-1/m(VPD)), \Gamma^*(T)}), +\end{align}\]

where and $m$ is the Medlyn factor (see Stomatal Conductance).

We also have

\[ \Gamma^*(T) = \Gamma^*_{25}\exp\left(\Delta H_{\Gamma^*}\frac{T - T_o}{T_o R T}\right),\]

where $\Delta H_{\Gamma^*}$ is the activation energy per mol for $\Gamma^*$.

Light limited rate

\[\begin{equation} +a_2 = +\begin{cases} + J(T, PAR) (c_i - \Gamma^*)/4(c_i + 2 \Gamma^*) & \text{for C3}\\ + J(T, PAR) & \text{for C4} +\end{cases} +\end{equation}\]

where J is the rate of electron transport, which has units of mol photon per m$^2$ per s. It depends on $PAR$ via $APAR$, as described below, and on $T$ via the dependence on $J_{max}$.

J is given by the root of the equation

\[\begin{align} + \theta_j J^2 - (I + J_{max}) J + I J_{max} &= 0 \nonumber \\ + I &= \frac{\phi}{2} (APAR) \nonumber \\ + J_{max}(T) &= V_{cmax}(T)\times e \exp\left(\Delta H_{J_{max}}\frac{T - T_o}{T_o R T}\right),\nonumber \\ +J(T, PAR) &= \frac{(I + J_{max} - \sqrt{(I + J_{max})^2 - 4\theta_j I \times J_{max}}}{2\theta_j}, +\end{align}\]

where $\phi = 0.6$ and $\theta_j = 0.9$ are the quantum yield of photosystem II and a curvature function (Bonan's book), and $\Delta H_{J_{max}}$ is the energy of activation of $J_{max}$.

The total net carbon assimilation (A$_n$, mol CO$_2$ m$^{-2}$ s$^{-1}$) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:

\[\begin{align} +A_n(T, PAR, VPD, c_a) = \text{max}(0, \text{min}(a_1 \beta, a_2) - R_d) +\end{align}\]

where $\beta$ is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R$_d$ is the leaf dark respiration calculated as

\[\begin{align} + R_{d,25}(\psi_l) &= f V_{cmax,25}\beta(\psi_l), \nonumber \\ + R_d (T, \psi_l) & = R_{d,25}(\psi_l)\exp\left(\Delta H_{R_{d}}\frac{T - T_o}{T_o R T}\right), +\end{align}\]

where $f = 0.015$ is a constant, $\Delta H_{R_d}$ is the energy of activation for $R_d$, and finally Vcmax is calculated as

\[\begin{equation} +V_{cmax}(T) = V_{cmax,25} \exp\left(\Delta H_{Vcmax}\frac{T - T_o}{T_o R T}\right)\\ +\end{equation}\]

with $V_{cmax,25}$ is a parameter (Vcmax at the reference temperature 25 C), and $\Delta H_{Vcmax} = 65,330 J/mol$.

The moisture stress factor is related to the leaf water potential $\psi_l$ as

\[\begin{align} + \beta = \frac{1+ \exp{(s_c \psi_c)}}{1+ \exp{(s_c(\psi_c - \psi_l))}}, +\end{align}\]

where $s_c = 4$MPa$^{-1}$, $\psi_c = -2$MPa, and $\psi_l$ is the leaf water potential computed by the plant hydraulics model.

GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:

\[\begin{align} +GPP(T, PAR, c_a, VPD, \theta_s) = A_n (1 - \exp(-K LAI \Omega))/K. +\end{align}\]

This is not currently needed by other components, but is used for offline validation of the model.

We need to supply the following parameters and “drivers"

  • $K_{c,25}$ and $K_{o,25}$, $V_{cmax, 25}$, $\Gamma^*_{25},\phi$, $\theta_j$, $o_i$, $s_c$, $\psi_c$
  • $\psi_l$, to compute $\beta$
  • Temperature $T$, $PAR$, $c_a$, VPD, $\theta_s$.
OutputSymbolUnitRange
Total net carbon assimilation$A_n$μmol CO$_2$ m$^{-2}$ s$^{-1}$0–25
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Temperature$T$°C0–50
ParametersSymbolUnitRange
Moisture stress$β$-0-1
Leaf Area IndexLAIm² m⁻²1–10
$CO_2$ concentration$c_a$ppm300e–500
Vapor pressure deficitVPDkPa1-10
ConstantsSymbolUnitValue
Zenith angle$θ_s$rad0.6
Leaf angle distribution$l_d$-0.5
Canopy reflectance$ρ_{leaf}$-0.1
Clumping index$Ω$-0.69
$CO_2$ compensation at 25°CΓ$^*_{25}$mol/mol4.275e-5
Energy of activation for $Γ^*$$ΔH_{Γ^*}$J/mol37830
Standard temperature$T_o$K298.15
Universal gas constant$R$J/mol8.314
The maximum rate of carboxylation of Rubisco$V_{cmax25}$mol CO$_2$ m$^{-2}$ s$^{-1}$5e-5
Energy of activation for $J_max$$ΔH_{J_max}$J/mol43540
Curvature parameter, a fitting constant to compute $J$$θ_j$-0.9
The quantum yied of photosystem II$\phi$-0.6
Energy of activation for $V_{cmax}$$ΔH_{V_{cmax}}$J/mol58520
Slope parameter for stomatal conductance models$g_1$-141
Michaelis Menten constant for $CO_2$ and at 25°C$K_{c25}$mol/mol4.049e-4
Energy of activation for $CO_2$$ΔH_{K_c}$J/mol79430
Michaelis Menten constant for $O_2$ at 25 °C$K_{o25}$mmol/mol0.2874
Energy of activation for $O_2$$ΔH_{K_o}$J/mol36380
Intercellular $O_2$ concentration$o_i$mol/mol0.209
Constant factor appearing the dark respiration term$f$-0.015
Energy of activation for $R_d$$ΔH_{R_d}$J/mol43390
diff --git a/previews/PR746/standalone/pages/vegetation/photosynthesis/optimality_model/index.html b/previews/PR746/standalone/pages/vegetation/photosynthesis/optimality_model/index.html new file mode 100644 index 0000000000..5f5f6e4e76 --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/photosynthesis/optimality_model/index.html @@ -0,0 +1,24 @@ + +Optimality model · ClimaLand.jl
GitHub

Optimality Model

Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation ($V_\text{cmax}$).

In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates $V_\text{cmax}$ and $J_\text{max}$ as a function of environmental variables as follows:

\[\begin{equation} + V_\text{cmax}^* = \varphi I \left(\frac{m}{m_c}\right)\left(\frac{\overline{\omega}^*}{8\theta}\right), +\end{equation}\]

where

\[\begin{equation} + \overline{\omega}^* = 1 + \overline{\omega} - \sqrt{(1 + \overline{\omega})^2 - 4\theta\overline{\omega}}, +\end{equation}\]

and

\[\begin{equation} + \overline{\omega} = -(1 - 2\theta) + \sqrt{(1 - \theta)\left(\frac{1}{\frac{4c}{m}\left(1 - \theta\frac{4c}{m}\right)} - 4\theta\right)}, +\end{equation}\]

and

\[\begin{equation} + c = \frac{m}{8\theta}\left(1 - \frac{\varphi I + J_\text{max} - 2\theta\varphi I}{\sqrt{(\varphi I + J_\text{max})^2 - 4\theta\varphi I J_\text{max}}}\right) +\end{equation}\]

and

\[\begin{equation} + J_\text{max} = \varphi I \overline{\omega} +\end{equation}\]

\[\begin{equation} + m = \frac{C'_i - \Gamma^*}{C'_i + 2\Gamma^*} +\end{equation}\]

\[\begin{equation} + C'_i = \Gamma^* + (C_a - \Gamma^*)\frac{\xi}{\xi + \sqrt{D_g}} +\end{equation}\]

\[\begin{equation} + \xi = \sqrt{\beta \frac{K + \Gamma^*}{1.6\eta^*}} +\end{equation}\]

\[\begin{equation} + K = K_c\left(1 + \frac{O_i}{K_o}\right) +\end{equation}\]

\[\begin{equation} + m_c = \frac{C'_i - \Gamma^*}{C'_i + K} +\end{equation}\]

\[\Gamma^*\]

is the CO$_2$ compensation point in the absence of mitochondrial respiration

\[\begin{equation} + \Gamma^* = \Gamma^*_0 f(T, \Delta H_a) p/p_0 +\end{equation}\]

where $\Gamma^*_0 = 4.332$ Pa, $p$ is the atmospheric pressure, $p_0 = 101325$ Pa, and $\Delta H_a = 37830$ J/mol.

The model has the following parameters:

  • $\varphi$ is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.
  • $\theta$ is the curvature of the light response curve (dimensionless). Estimated at 0.85.
  • $\beta$ is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.

For Smith et al. (2019) Vcmax model:

  • Altitude
  • $D_g$ is the vapor pressure deficit (VPD) at altitude
  • $C_a$ is the CO$_2$ partial pressure
  • $I$ is the incident photosynthetically active photon flux (PAR)
  • $T$ is the temperature
diff --git a/previews/PR746/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html b/previews/PR746/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html new file mode 100644 index 0000000000..1a9f1b63cc --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html @@ -0,0 +1,23 @@ + +Van Genuchten model · ClimaLand.jl
GitHub

Plant Hydraulics

Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.

Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.

The plant hydraulics code solves for the volumetric water content in the stem and leaf ($\theta_{stem}$ and $\theta_{leaf}$). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment.

Van Genuchten Model

As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.

The volume flux of water $q$ (m/s) between compartments with centers at two heights, $z_1$ and $z_2$, is given by Darcy's law as

\[\begin{align} + q = -\int_{z_1}^{z_2} k(\psi) dh +\end{align}\]

where $h = \psi+z$ is the head (in meters), and $k$ is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\footnote{Double check this - the units of $k$ in our code are $m/s$.}

\[\begin{equation} +q = -\int_{h_1}^{h_2} k(\psi) dh \approx -\frac{k_1(\psi_1) + k_2(\psi_2)}{2} * [(\psi_2 - \psi_1) + (z_2 - z_1)]. +\end{equation}\]

In order to close the set of equations, the user will have to specify $k(\psi)$ and a function $\psi(\theta)$. In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of $K_{sat}.$

The change of water volume (m$^3), V$, in the compartments is then

\[\begin{align} + \frac{d V_{w, stem}}{dt} = q_{roots}\sigma_{roots} - q_{stem}\sigma_{stem} \nonumber \\ + \frac{d V_{w, leaf}}{dt} = q_{stem}\sigma_{stem} - \tau \sigma_{leaf}, +\end{align}\]

where $\tau$ is a transpiration volume flux per unit emitting area, and $\sigma$ is the total emitting/conducting area\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}.

This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals $N$. We can make use of the fact that $N\sigma/A$, where $A$ is the area of the ground those $N$ individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell.

Then we have:

\[\begin{align} + \frac{d v_{stem}}{dt} = q_{roots}RAI - q_{stem}SAI \nonumber \\ + \frac{d v_{leaf}}{dt} = q_{stem}SAI - \tau LAI, +\end{align}\]

where $v$ now represents the volume of water in that compartment (of a bulk plant) per unit ground area.

We also need to convert from the variable $v$ to $\psi$, in order to compute root extraction with the soil. To do so, we can convert $v$ to the volumetric water content, and from $\theta$ to $\psi$ using a van Genuchten relationship. To convert, let the volume of water per area of compartment be $V_{w,stem}$, and $H$ the typical ``length" of the compartment. Then

\[\begin{equation} + \theta_{stem}=\frac{V_{w,stem}}{A_{ground}} \times \frac{A_{ground}}{A_{stem}} \times \frac{1}{H_{stem}} = \frac{v_{stem}}{H_{stem} \times SAI }. +\end{equation}\]

Substituting in the volumetric water content, we have

\[\begin{align} + \frac{d \theta_{stem}}{dt} &= \frac{q_{roots}RAI - q_{stem}SAI}{H_{stem} SAI} \nonumber \\ + \frac{d \theta_{leaf}}{dt} &= \frac{q_{stem}SAI - \tau LAI}{H_{leaf} LAI}, +\end{align}\]

We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction $P(z)$, satisfying $\int P(z) dz = 1$. Instead of having a single root at one discrete location, we can distribute the root system over different depths using $P(z)$. The total flux from roots between $z$ and $z+dz$ is given by

\[\begin{equation} + dq_{roots}(z) = -P(z) dz \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh, +\end{equation}\]

so that the net flux for the plant system would sum over this

\[\begin{equation} + q_{roots} = -\int_{z_{min}}^{z_{sfc}} \frac{dq_{roots}(z)}{dz}dz , +\end{equation}\]

where $z_{min}$ is the minimum soil layer of the simulation.

The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with

\[\begin{equation} + S(z) = -(RAI) dq_{roots}(z)/dz = (RAI) P(z) \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh. +\end{equation}\]

The sign change occurs in the expression for $S(z)$ because a positive value of $q_{roots}$ indicates flow from the soil to the plant. This is a sink term for the soil.

The model needs the following parameters:

DriversSymbolUnitRange
A function of simulation time t given the leaf area indexLAIm²/m²0–10
The constant stem area indexSAIm²/m²0–10
The constant root area indexRAIm²/m²0–10
diff --git a/previews/PR746/standalone/pages/vegetation/radiative_transfer/beer_model/index.html b/previews/PR746/standalone/pages/vegetation/radiative_transfer/beer_model/index.html new file mode 100644 index 0000000000..f91185a99a --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/radiative_transfer/beer_model/index.html @@ -0,0 +1,4 @@ + +Beer model · ClimaLand.jl
GitHub

Radiative transfer scheme

This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand.

Beer's law

Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.

The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:

\[APAR(PAR, \theta_s) = (PAR)(1 - \rho_{leaf})(1 - e^{(-K(\theta_s) LAI \Omega)})\]

where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, $ρ_{leaf}$ is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, $θ_s$ is the zenith angle, and $Ω$ is the clumping index following Braghiere (2021). $K$, $Ω$ and $ρ_{leaf}$ are all unitless. LAI is in m² m⁻². In order to compute $K$, we need $θ_s$ in radians and the leaf angle distribution $l_d$ (unitless). K is then defined as

\[K = l_d/\max{(\cos{(\theta_s)}, \epsilon)}\]

so that at night, when 3π/2 > $θ_s$ > π/2, $K$ is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.

The model has the following parameters:

OutputSymbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy reflectance$ρ_{leaf}$-0.0–1.0
Extinction coefficient$K$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5

Interactive APAR(PAR, LAI, $ρ_{leaf}$, $K$, $Ω$)

diff --git a/previews/PR746/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html b/previews/PR746/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html new file mode 100644 index 0000000000..7d4cb81627 --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html @@ -0,0 +1,5 @@ + +Two-Stream model · ClimaLand.jl
GitHub

The Two-Stream Scheme

In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).

The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.

Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:

\[-\overline{\mu}(dI^{\uparrow})/dL + [1 - (1 - \beta)\omega]I^{\uparrow} - \omega \beta I^{\downarrow} = \omega \overline{\mu} K \beta_0 \exp{(-KL)},\\ +-\overline{\mu}(dI^{\downarrow})/dL + [1 - (1 - \beta)\omega]I^{\downarrow} - \omega \beta I^{\uparrow} = \omega \overline{\mu} K (1-\beta_0) \exp{(-KL)}\]

where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to

\[\int_{0}^{1}[\mu^{\prime}/G(\mu^{\prime})]d\mu^{\prime}\]

μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)

These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:

\[I^{\uparrow} = \frac{h_1\exp{(-KL)}}{\sigma} + h_2\exp{(-hL)} + h_3\exp{(hL)},\\ +I^{\downarrow} = \frac{h_4\exp{(-KL)}}{\sigma} + h_5\exp{(-hL)} + h_6\exp{(hL)}\]

For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:

\[I^{\uparrow} = h_7\exp{(-hL)} + h_8\exp{(hL)},\\ +I^{\downarrow} = h_9\exp{(-hL)} + h_{10}\exp{(hL)}\]

where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.

The model has the following parameters:

OutputSymmbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
Absorbed Near-Infrared RadiationANIRμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy PAR Reflectance$\alpha\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Reflectance$\alpha\_NIR\_{leaf}$-0.0–1.0
Canopy PAR Transmittance$\tau\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Transmittance$\tau\_NIR\_{leaf}$-0.0–1.0
Canopy Emissivity$ϵ\_canopy$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5
Typical wavelength per photon PAR$\lambda\_\gamma\_PAR$m5e-7
Typical wavelength per photon NIR$\lambda\_\gamma\_NIR$m1.65e-6
diff --git a/previews/PR746/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html b/previews/PR746/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html new file mode 100644 index 0000000000..1d2db31d13 --- /dev/null +++ b/previews/PR746/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html @@ -0,0 +1,11 @@ + +Medlyn model · ClimaLand.jl
GitHub

Stomatal conductance

Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.

This section describes multiple models of stomatal conductance implemented in ClimaLand.

Medlyn Model

The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.

Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.

\[\begin{equation} +T = -\rho_a g_{\mathrm{eff}} \left[q_{a}- q_v(T_\mathrm{leaf}, \rho_\mathrm{sfc}) \right], +\end{equation}\]

where $T$ is the transpiration (mass flux of water vapor), $q_{a}$ is the specific humidity at the lowest level of the atmosphere, $q_v(T_\mathrm{T_{leaf}}, \rho_\mathrm{sfc})$ is the saturated specific humidity over liquid water, given the temperature of the leave $T_{leaf}$ and air density at the surface $\rho_{sfc}$. We will approximate $T_{leaf} = T_{a}$ and $\rho_{\mathrm{sfc}} = \rho_a$.

We also need the effective conductivity, given by

\[\begin{equation} + g_{\mathrm{eff}} = \frac{1}{g_{\mathrm{ae}}^{-1}+g_{\mathrm{s}}^{-1}}, +\end{equation}\]

where $g_{ae}$ is the aerodynamic conductance, computed by the MOST solve, and $g_s$ is the stomatal conductance to water vapor per unit ground area. The units of all conductances are $m/s$.

The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:

\[\begin{align} +g_{s,m}(PAR, T, VPD, c_a) &= g_{0,m} + D_{rel} \times m \frac{A_n(PAR, T, VPD, c_a)}{c_a}\nonumber \\ +g_s &= \frac{g_{s,m}}{\rho_m} +\end{align}\]

where $D_{rel} =1.6$ (unitless) is the relative diffusivity of water vapor with respect to CO$_2$, $\rho_m$ is the molar density of water, and $m$ is the Medlyn factor,

\[\begin{equation} + m = \left( 1 + \frac{g_1}{\sqrt{VPD}} \right), +\end{equation}\]

where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A$_n$ as the biochemical demand for CO$_2$ calculated using the photosynthesis model (Farquhar, 1980; Equation \eqref{eq:an}; units of molar flux). The resulting units are $m/s$. g{0,m}$ is a minimum molar conductivity. (subscript $m$ indicates molar).

The model has the following parameters:

ConstantsSymbolUnitValue
Relative diffusivity of water vapor$D_{rel}$-1.6
Minimum stomatal conductance$g_0$mol/$m^2$/s1e-4
Slope parameter$g_1$$\sqrt{Pa}$790
diff --git a/previews/PR750/404.html b/previews/PR750/404.html index 6a5d7a9fe6..da360e5411 100644 --- a/previews/PR750/404.html +++ b/previews/PR750/404.html @@ -8,14 +8,14 @@ - +
- + \ No newline at end of file diff --git a/previews/PR750/API.html b/previews/PR750/API.html index 7ca9bc49fe..8375f56b61 100644 --- a/previews/PR750/API.html +++ b/previews/PR750/API.html @@ -8,9 +8,9 @@ - + - + @@ -200,7 +200,7 @@ z_0b::FT, earth_param_set::EP, ) where {FT <: AbstractFloat, P}

Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (T_sfc), specific humidity (q_sfc), and air density (ρ_sfc), (2) Other surface properties, such as the factor β_sfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance r_sfc (used in more complex land models), and the topographical height of the surface (h_sfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc

This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.

source


# ClimaLand.update_condition — Method.
julia
update_condition(updateat)

This function returns a function with the type signature expected by SciMLBase.DiscreteCallback, and determines whether affect! gets called in the callback. This implementation simply checks if the current time of the simulation is within the (inclusive) bounds of updateat.

source


# ClimaLand.vapor_pressure_deficit — Method.
julia
vapor_pressure_deficit(T_air, P_air, q_air, thermo_params)

Computes the vapor pressure deficit for air with temperature T_air, pressure P_air, and specific humidity q_air, using thermo_params, a Thermodynamics.jl param set.

source


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ct(Ve.Layout,null,{})},enhanceApp({app:n,router:e,siteData:t}){jr(n)}};export{Gr as R,Xs as c,V as u}; diff --git a/previews/PR750/assets/getting_started.md.Ak3mnkKX.js b/previews/PR750/assets/getting_started.md.KHW3Jdh1.js similarity index 64% rename from previews/PR750/assets/getting_started.md.Ak3mnkKX.js rename to previews/PR750/assets/getting_started.md.KHW3Jdh1.js index b765c8cb68..7a42fcfc5b 100644 --- a/previews/PR750/assets/getting_started.md.Ak3mnkKX.js +++ b/previews/PR750/assets/getting_started.md.KHW3Jdh1.js @@ -15,15 +15,31 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework._NduRQaH.js";const y julia> canopy.compute_GPP(5.0, 0.5, 3.0, 0.7) 9.28660358412635

Et voilà!

Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.

ClimaLand structure ​

ClimaLand contains multiple modules. They are listed below:

julia
julia> using MethodAnalysis, ClimaLand
-ERROR: ArgumentError: Package MethodAnalysis not found in current path.
-- Run \`import Pkg; Pkg.add("MethodAnalysis")\` to install the MethodAnalysis package.
 
 julia> child_modules(ClimaLand)
-ERROR: UndefVarError: \`child_modules\` not defined

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia
julia> using ClimaLand
+13-element Vector{Module}:
+ ClimaLand
+ ClimaLand.Artifacts
+ ClimaLand.Bucket
+ ClimaLand.Canopy
+ ClimaLand.Canopy.PlantHydraulics
+ ClimaLand.Diagnostics
+ ClimaLand.Domains
+ ClimaLand.Parameters
+ ClimaLand.Pond
+ ClimaLand.Snow
+ ClimaLand.Soil
+ ClimaLand.Soil.Biogeochemistry
+ ClimaLand.Soil.Runoff

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia
julia> using ClimaLand
 
 julia> using About
-ERROR: ArgumentError: Package About not found in current path.
-- Run \`import Pkg; Pkg.add("About")\` to install the About package.
 
 julia> about(ClimaLand.Soil.Biogeochemistry)
-ERROR: UndefVarError: \`about\` not defined

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

`,19),h=[l];function e(p,k,r,d,E,o){return a(),i("div",null,h)}const c=s(t,[["render",e]]);export{y as __pageData,c as default}; +Module ClimaLand.Soil.Biogeochemistry + +Re-exports 13 names (from ClimaLand): +• Biogeochemistry • AtmosCO2StateBC • SoilCO2ModelParameters +• co2_diffusivity • MicrobeProduction • SoilCO2StateBC +• microbe_source • PrescribedMet • SoilDrivers +• volumetric_air_content • SoilCO2FluxBC +• AbstractSoilDriver • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

`,19),h=[l];function e(p,k,E,r,d,o){return a(),i("div",null,h)}const c=s(t,[["render",e]]);export{y as __pageData,c as default}; diff --git a/previews/PR750/assets/getting_started.md.Ak3mnkKX.lean.js b/previews/PR750/assets/getting_started.md.KHW3Jdh1.lean.js similarity index 69% rename from previews/PR750/assets/getting_started.md.Ak3mnkKX.lean.js rename to previews/PR750/assets/getting_started.md.KHW3Jdh1.lean.js index c4f0496c5b..ba4995955f 100644 --- a/previews/PR750/assets/getting_started.md.Ak3mnkKX.lean.js +++ b/previews/PR750/assets/getting_started.md.KHW3Jdh1.lean.js @@ -1 +1 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework._NduRQaH.js";const y=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"getting_started.md","filePath":"getting_started.md","lastUpdated":null}'),t={name:"getting_started.md"},l=n("",19),h=[l];function e(p,k,r,d,E,o){return a(),i("div",null,h)}const c=s(t,[["render",e]]);export{y as __pageData,c as default}; +import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework._NduRQaH.js";const y=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"getting_started.md","filePath":"getting_started.md","lastUpdated":null}'),t={name:"getting_started.md"},l=n("",19),h=[l];function e(p,k,E,r,d,o){return a(),i("div",null,h)}const c=s(t,[["render",e]]);export{y as __pageData,c as default}; diff --git a/previews/PR750/getting_started.html b/previews/PR750/getting_started.html index 55e5180b97..6669d63179 100644 --- a/previews/PR750/getting_started.html +++ b/previews/PR750/getting_started.html @@ -8,11 +8,11 @@ - + - + - + @@ -34,19 +34,35 @@ julia> canopy.compute_GPP(5.0, 0.5, 3.0, 0.7) 9.28660358412635

Et voilà!

Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.

ClimaLand structure ​

ClimaLand contains multiple modules. They are listed below:

julia
julia> using MethodAnalysis, ClimaLand
-ERROR: ArgumentError: Package MethodAnalysis not found in current path.
-- Run `import Pkg; Pkg.add("MethodAnalysis")` to install the MethodAnalysis package.
 
 julia> child_modules(ClimaLand)
-ERROR: UndefVarError: `child_modules` not defined

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia
julia> using ClimaLand
+13-element Vector{Module}:
+ ClimaLand
+ ClimaLand.Artifacts
+ ClimaLand.Bucket
+ ClimaLand.Canopy
+ ClimaLand.Canopy.PlantHydraulics
+ ClimaLand.Diagnostics
+ ClimaLand.Domains
+ ClimaLand.Parameters
+ ClimaLand.Pond
+ ClimaLand.Snow
+ ClimaLand.Soil
+ ClimaLand.Soil.Biogeochemistry
+ ClimaLand.Soil.Runoff

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia
julia> using ClimaLand
 
 julia> using About
-ERROR: ArgumentError: Package About not found in current path.
-- Run `import Pkg; Pkg.add("About")` to install the About package.
 
 julia> about(ClimaLand.Soil.Biogeochemistry)
-ERROR: UndefVarError: `about` not defined

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

- +Module ClimaLand.Soil.Biogeochemistry + +Re-exports 13 names (from ClimaLand): +• Biogeochemistry • AtmosCO2StateBC • SoilCO2ModelParameters +• co2_diffusivity • MicrobeProduction • SoilCO2StateBC +• microbe_source • PrescribedMet • SoilDrivers +• volumetric_air_content • SoilCO2FluxBC +• AbstractSoilDriver • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

+ \ No newline at end of file diff --git a/previews/PR750/hashmap.json b/previews/PR750/hashmap.json index f6c9b14e1a..4e453d22fb 100644 --- a/previews/PR750/hashmap.json +++ b/previews/PR750/hashmap.json @@ -1 +1 @@ -{"api.md":"B4DBRyJ_","getting_started.md":"Ak3mnkKX","index.md":"DAE8JI5P"} +{"api.md":"B4DBRyJ_","getting_started.md":"KHW3Jdh1","index.md":"DAE8JI5P"} diff --git a/previews/PR750/index.html b/previews/PR750/index.html index be303647e8..cb522738f6 100644 --- a/previews/PR750/index.html +++ b/previews/PR750/index.html @@ -8,9 +8,9 @@ - + - + @@ -18,7 +18,7 @@
ClimaLand.jl

Appearance

ClimaLand.jl Documentation (v0.14.3) ​

Introduction ​

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers ​

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units ​

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
- + \ No newline at end of file diff --git a/previews/PR753/.documenter-siteinfo.json b/previews/PR753/.documenter-siteinfo.json new file mode 100644 index 0000000000..976f768d6b --- /dev/null +++ b/previews/PR753/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-02T18:45:00","documenter_version":"1.6.0"}} \ No newline at end of file diff --git a/previews/PR753/APIs/Bucket/index.html b/previews/PR753/APIs/Bucket/index.html new file mode 100644 index 0000000000..acededa4f7 --- /dev/null +++ b/previews/PR753/APIs/Bucket/index.html @@ -0,0 +1,16 @@ + +Bucket Model · ClimaLand.jl
GitHub

Bucket

Types

ClimaLand.Bucket.BucketModelParameters — Type
struct BucketModelParameters{
+    FT <: AbstractFloat,
+    PSE,
+}

Container for holding the parameters of the bucket model.

  • κ_soil: Conductivity of the soil (W/K/m); constant

  • ρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant

  • albedo: Albedo Model

  • σS_c: Critical σSWE amount (m) where surface transitions from to snow-covered

  • f_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)

  • W_f: Capacity of the land bucket (m)

  • f_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)

  • p: Exponent used in β decay (unitless)

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • τc: τc timescale on which snow melts

  • earth_param_set: Earth Parameter set; physical constants, etc

source
ClimaLand.Bucket.PrescribedBaregroundAlbedo — Type
PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel

An albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).

source
ClimaLand.Bucket.PrescribedSurfaceAlbedo — Type
PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}
+                   <: AbstractBucketAlbedoModel

An albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.

Note that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.

source
ClimaLand.Bucket.BucketModel — Type
struct BucketModel{
+     FT,
+     PS <: BucketModelParameters{FT},
+     ATM <: AbstractAtmosphericDrivers{FT},
+     RAD <: AbstractRadiativeDrivers{FT},
+     D,
+ } <: AbstractBucketModel{FT}

Concrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.

  • parameters: Parameters required by the bucket model

  • atmos: The atmospheric drivers: Prescribed or Coupled

  • radiation: The radiation drivers: Prescribed or Coupled

  • domain: The domain of the model

source

Misc Functions

ClimaLand.surface_albedo — Function
ClimaLand.surface_albedo(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+) where {FT}

Returns the surface albedo field of the EnergyHydrology soil model.

source
surface_albedo(model::SnowModel, Y, p)

A helper function which computes and returns the snow albedo.

source
surface_albedo(model::BucketModel, Y, p)

Returns the bulk surface albedo, which gets updated in update_aux via next_albedo.

source
surface_albedo(model::AbstractModel, Y, p)

A helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.Bucket.beta_factor — Function
beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}

Computes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:

β = (x/xc)^p x < xc 1 otherwise

where x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.

source
diff --git a/previews/PR753/APIs/ClimaLand/index.html b/previews/PR753/APIs/ClimaLand/index.html new file mode 100644 index 0000000000..406eeefc01 --- /dev/null +++ b/previews/PR753/APIs/ClimaLand/index.html @@ -0,0 +1,13 @@ + +ClimaLand · ClimaLand.jl
GitHub

ClimaLand

LSM Model Types and methods

Missing docstring.

Missing docstring for ClimaLand.SoilPlantHydrologyModel. Check Documenter's build log for details.

ClimaLand.LandSoilBiogeochemistry — Type
struct LandSoilBiogeochemistry{
+    FT,
+    SEH <: Soil.EnergyHydrology{FT},
+    SB <: Soil.Biogeochemistry.SoilCO2Model{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.

  • soil: The soil model

  • soilco2: The biochemistry model

source
ClimaLand.LandHydrology — Type
struct LandHydrology{
+    FT,
+    SM <: Soil.AbstractSoilModel{FT},
+    SW <: Pond.AbstractSurfaceWaterModel{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil and surface water component.

  • soil: The soil model

  • surface_water: The surface water model

source
Missing docstring.

Missing docstring for ClimaLand.make_interactions_update_aux. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.initialize_interactions. Check Documenter's build log for details.

ClimaLand.land_components — Function
land_components(land::AbstractLandModel)

Returns the component names of the land model, by calling propertynames(land).

source
ClimaLand.lsm_aux_vars — Function

lsmauxvars(m::AbstractLandModel)

Returns the additional aux variable symbols for the model in the form of a tuple.

source
lsm_aux_vars(m::SoilCanopyModel)

The names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_types — Function

lsmauxtypes(m::AbstractLandModel)

Returns the shared additional aux variable types for the model in the form of a tuple.

source
lsm_aux_types(m::SoilCanopyModel)

The types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_domain_names — Function

lsmauxdomain_names(m::AbstractLandModel)

Returns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.

This is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.

source
lsm_aux_domain_names(m::SoilCanopyModel)

The domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
Missing docstring.

Missing docstring for ClimaLand.domain_name. Check Documenter's build log for details.

Land Hydrology

ClimaLand.infiltration_capacity — Function
function infiltration_capacity(
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+)

Function which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.

Defined such that positive means into soil.

source
ClimaLand.infiltration_at_point — Function
infiltration_at_point(η::FT, i_c::FT, P::FT)

Returns the infiltration given pond height η, infiltration capacity, and precipitation.

This is defined such that positive means into soil.

source
ClimaLand.PrognosticRunoff — Type
PrognosticRunoff <: Pond.AbstractSurfaceRunoff

Concrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.

This is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source
ClimaLand.RunoffBC — Type
RunoffBC <: Soil.AbstractSoilBC

Concrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.

This is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source

SoilCanopyModel

ClimaLand.PrognosticSoil — Type
 PrognosticSoil{FT} <: AbstractSoilDriver

Concrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

source
ClimaLand.RootExtraction — Type
RootExtraction{FT} <: Soil.AbstractSoilSource{FT}

Concrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.

This is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.

source

LandSoilBiogeochemistry

Missing docstring.

Missing docstring for ClimaLand.PrognosticMet. Check Documenter's build log for details.

diff --git a/previews/PR753/APIs/Regridder/index.html b/previews/PR753/APIs/Regridder/index.html new file mode 100644 index 0000000000..ecee71612c --- /dev/null +++ b/previews/PR753/APIs/Regridder/index.html @@ -0,0 +1,2 @@ + +Parameter Dataset Tools · ClimaLand.jl
GitHub

Bucket

Functions

Missing docstring.

Missing docstring for ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.Regridder.swap_space. Check Documenter's build log for details.

diff --git a/previews/PR753/APIs/Snow/index.html b/previews/PR753/APIs/Snow/index.html new file mode 100644 index 0000000000..d36c9a7c63 --- /dev/null +++ b/previews/PR753/APIs/Snow/index.html @@ -0,0 +1,2 @@ + +Snow Model · ClimaLand.jl
GitHub

Snow Model

Snow Parameters

ClimaLand.Snow.SnowParameters — Type
SnowParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SnowModel.

Note that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.

  • ρ_snow: Density of snow (kg/m^3)

  • z_0m: Roughness length over snow for momentum (m)

  • z_0b: Roughness length over snow for scalars (m)

  • α_snow: Albedo of snow (unitless)

  • ϵ_snow: Emissivity of snow (unitless)

  • θ_r: Volumetric holding capacity of water in snow (unitless)

  • Ksat: Hydraulic conductivity of wet snow (m/s)

  • κ_ice: Thermal conductivity of ice (W/m/K)

  • Δt: Timestep of the model (s)

  • ρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)

  • earth_param_set: Clima-wide parameters

source

Snow Functions of State

```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe

diff --git a/previews/PR753/APIs/Soil/index.html b/previews/PR753/APIs/Soil/index.html new file mode 100644 index 0000000000..71d223dbb9 --- /dev/null +++ b/previews/PR753/APIs/Soil/index.html @@ -0,0 +1,109 @@ + +Soil Energy and Hydrology · ClimaLand.jl
GitHub

Soil Models

Soil Models

ClimaLand.Soil.AbstractSoilModel — Type
AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}

The abstract type for all soil models.

Currently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.

source
ClimaLand.Soil.RichardsModel — Type
RichardsModel

A model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.

A variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).

If you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water

source
ClimaLand.Soil.EnergyHydrology — Type
EnergyHydrology <: AbstractSoilModel

A model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.

A variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.

  • parameters: The parameter sets

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat

source

Soil Parameter Structs

ClimaLand.Soil.RichardsParameters — Type
RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}

A struct for storing parameters of the RichardsModel.

  • ν: The porosity of the soil (m^3/m^3)

  • hydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

source
ClimaLand.Soil.EnergyHydrologyParameters — Type
EnergyHydrologyParameters{
+        FT <: AbstractFloat,
+        F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        C,
+        PSE,
+    }

A parameter structure for the integrated soil water and energy equation system.

Note that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:

  • Surface parameters: albedo in each wavelength band (SF)
  • Scalar parameters: emissivity, α, β, γ, γT_ref, Ω,

roughness lengths z0, dds ) (FT)

  • Parameters defined in the interior: all else (F)

  • κ_dry: The dry soil thermal conductivity, W/m/K

  • κ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K

  • κ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K

  • ρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)

  • ν: The porosity of the soil (m^3/m^3)

  • ν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)

  • ν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)

  • ν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)

  • α: The parameter α used in computing Kersten number, unitless

  • β: The parameter β used in computing Kersten number, unitless

  • hydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

  • Ω: Ice impedance factor for the hydraulic conductivity

  • γ: Coefficient of viscosity factor for the hydraulic conductivity

  • γT_ref: Reference temperature for the viscosity factor

  • PAR_albedo: Soil PAR Albedo

  • NIR_albedo: Soil NIR Albedo

  • emissivity: Soil Emissivity

  • z_0m: Roughness length for momentum

  • z_0b: Roughness length for scalars

  • d_ds: Maximum dry soil layer thickness under evaporation (m)

  • earth_param_set: Physical constants and clima-wide parameters

source

Soil Hydrology Parameterizations

ClimaLand.Soil.volumetric_liquid_fraction — Function
volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}

A pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.

source
ClimaLand.Soil.pressure_head — Function
pressure_head(
+    cm::vanGenuchten{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
pressure_head(
+    cm::BrooksCorey{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
ClimaLand.Soil.hydraulic_conductivity — Function
 hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.

source
 hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.

source
ClimaLand.Soil.impedance_factor — Function
impedance_factor(
+    f_i::FT,
+    Ω::FT
+) where {FT}

Returns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.viscosity_factor — Function
viscosity_factor(
+    T::FT,
+    γ::FT,
+    γT_ref::FT,
+) where {FT}

Returns the multiplicative factor which accounts for the temperature dependence of the conductivity.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.matric_potential — Function
 matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the van Genuchten formulation.

source
 matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the Brooks and Corey formulation.

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ClimaLand.Soil.dψdϑ — Function

dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.

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dψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.

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ClimaLand.Soil.inverse_matric_potential — Function
 inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.

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 inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.

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ClimaLand.Soil.AbstractSoilHydrologyClosure — Type
AbstractSoilHydrologyClosure{FT <: AbstractFloat}

The abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.

To add a new parameterization, methods are required for:

  • matric_potential,
  • inversematricpotential,
  • pressure_head,
  • dψdϑ,
  • hydraulic_conductivity.
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ClimaLand.Soil.vanGenuchten — Type
vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}

The van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).

  • α: The inverse of the air entry potential (1/m)

  • n: The van Genuchten pore-size distribution index (unitless)

  • m: The van Genuchten parameter m = 1 - 1/n (unitless)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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ClimaLand.Soil.BrooksCorey — Type

BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}

The Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).

  • c: The pore-size distribution index (unitless)

  • ψb: The air entry matric potential, when S=1 (m)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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Soil Heat Parameterizations

ClimaLand.Soil.κ_solid — Function
κ_solid(ν_ss_om::FT,
+        ν_ss_quartz::FT,
+        κ_om::FT,
+        κ_quartz::FT,
+        κ_minerals::FT) where {FT}

Computes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.

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ClimaLand.Soil.κ_sat_frozen — Function
function κ_sat_frozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_ice::FT
+) where {FT}

Computes the thermal conductivity for saturated frozen soil.

source
ClimaLand.Soil.κ_sat_unfrozen — Function
function κ_sat_unfrozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_l::FT
+) where {FT}

Computes the thermal conductivity for saturated unfrozen soil.

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ClimaLand.Soil.κ_sat — Function
κ_sat(
+    θ_l::FT,
+    θ_i::FT,
+    κ_sat_unfrozen::FT,
+    κ_sat_frozen::FT
+) where {FT}

Compute the expression for saturated thermal conductivity of soil matrix.

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ClimaLand.Soil.κ_dry — Function
function κ_dry(ρp::FT,
+               ν::FT,
+               κ_solid::FT,
+               κ_air::FT;
+               a = FT(0.053)) where {FT}

Computes the thermal conductivity of dry soil according to the model of Balland and Arp.

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ClimaLand.Soil.kersten_number — Function
kersten_number(
+    θ_i::FT,
+    S_r::FT,
+    α::FT,
+    β::FT,
+    ν_ss_om::FT,
+    ν_ss_quartz::FT,
+    ν_ss_gravel::FT,
+    ) where {FT}

Compute the expression for the Kersten number, using the Balland and Arp model.

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ClimaLand.Soil.relative_saturation — Function
relative_saturation(
+        θ_l::FT,
+        θ_i::FT,
+        ν::FT
+) where {FT}

Compute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.

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ClimaLand.Soil.volumetric_internal_energy — Function
volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,
+                             earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.

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ClimaLand.Soil.volumetric_internal_energy_liq — Function
volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.

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ClimaLand.Soil.temperature_from_ρe_int — Function
temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT
+                        earth_param_set::EP) where {FT, EP}

A pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.

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ClimaLand.Soil.phase_change_source — Function
phase_change_source(
+    θ_l::FT,
+    θ_i::FT,
+    T::FT,
+    τ::FT,
+    ν::FT,
+    θ_r::FT,
+    hydrology_cm::C,
+    earth_param_set::EP,
+) where {FT, EP, C}

Returns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.

Note that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.

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ClimaLand.Soil.thermal_time — Function
thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}

Returns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.

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Soil Surface Parameterizations

Missing docstring.

Missing docstring for ClimaLand.soil.soil_resistance. Check Documenter's build log for details.

ClimaLand.Soil.dry_soil_layer_thickness — Function
dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}

Returns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).

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ClimaLand.Soil.soil_tortuosity — Function
soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}

Computes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.

See Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.

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Soil Runoff Types and Methods

ClimaLand.Soil.Runoff.SurfaceRunoff — Type
SurfaceRunoff <: AbstractRunoffModel

A simple model for runoff appropriate for single column runs.

Only surface runoff is computed, using a combination of Dunne and Hortonian runoff.

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ClimaLand.Soil.Runoff.TOPMODELRunoff — Type
TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel

The TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.

The runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

  • f_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.

  • subsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.

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ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff — Type
TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}

The TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.

The runoff flux is given by Equation 12 of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • R_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

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ClimaLand.Soil.Runoff.update_runoff! — Function
update_runoff!(p, runoff::NoRunoff, _...)

Updates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.

source
update_runoff!(
+    p,
+    runoff::SurfaceRunoff,
+    Y,
+    t,
+    model::AbstractSoilModel,

)

The update_runoff! function for the SurfaceRunoff model.

Updates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration

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update_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)

Updates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration

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Soil BC Methods and Types

ClimaLand.Soil.MoistureStateBC — Type

MoistureStateBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.HeatFluxBC — Type

HeatFluxBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.WaterFluxBC — Type

WaterFluxBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.TemperatureStateBC — Type

TemperatureStateBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.FreeDrainage — Type
FreeDrainage <: AbstractWaterBC

A concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.

source
ClimaLand.Soil.RichardsAtmosDrivenFluxBC — Type

RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC

A concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.

If you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.

  • precip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.

  • runoff: The runoff model. The default is no runoff.

source
ClimaLand.Soil.AtmosDrivenFluxBC — Type
AtmosDrivenFluxBC{
+    A <: AbstractAtmosphericDrivers,
+    B <: AbstractRadiativeDrivers,
+    R <: AbstractRunoffModel
+} <: AbstractEnergyHydrologyBC

A concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.

This choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.

  • atmos: The atmospheric conditions driving the model

  • radiation: The radiative fluxes driving the model

  • runoff: The runoff model. The default is no runoff.

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ClimaLand.Soil.WaterHeatBC — Type
WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:
+   AbstractEnergyHydrologyBC

A general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.

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ClimaLand.Soil.soil_boundary_fluxes! — Function
soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)

updates the boundary fluxes for ϑl and ρeint.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere,
+        <:PrescribedRadiativeFluxes,
+    },
+    boundary::ClimaLand.TopBoundary,
+    model::EnergyHydrology,
+    Δz,
+    Y,
+    p,
+    t,
+)

Returns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.

If you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.

This function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},
+    boundary::ClimaLand.TopBoundary,
+    soil::EnergyHydrology{FT},
+    Δz,
+    Y,
+    p,
+    t,
+) where {FT}

A method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.

source

Soil Source Types

ClimaLand.Soil.AbstractSoilSource — Type
AbstractSoilSource{FT} <:  ClimaLand.AbstractSource{FT}

An abstract type for types of source terms for the soil equations.

In standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).

source
Missing docstring.

Missing docstring for ClimaLand.Soil.RootExtraction. Check Documenter's build log for details.

Soil Jacobian Structures

ClimaLand.ImplicitEquationJacobian — Type
ImplicitEquationJacobian{M, S}

A struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.

matrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.

Note that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.

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diff --git a/previews/PR753/APIs/SoilBiogeochemistry/index.html b/previews/PR753/APIs/SoilBiogeochemistry/index.html new file mode 100644 index 0000000000..9c178fefd4 --- /dev/null +++ b/previews/PR753/APIs/SoilBiogeochemistry/index.html @@ -0,0 +1,17 @@ + +Soil Biogeochemistry · ClimaLand.jl
GitHub

Soil Biogeochemistry

Model Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2Model — Type
SoilCO2Model

A model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type NamedTuple

  • sources: A tuple of sources, each of type AbstractSource

  • drivers: Drivers

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Parameter Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters — Type
SoilCO2ModelParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SoilCO2Model.

All of these parameters are currently treated as global constants.

  • D_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)

  • D_liq: Diffusivity of soil C substrate in liquid (unitless)

  • α_sx: Pre-exponential factor (kg C m-3 s-1)

  • Ea_sx: Activation energy (J mol-1)

  • kM_sx: Michaelis constant (kg C m-3)

  • kM_o2: Michaelis constant for O2 (m3 m-3)

  • O2_a: Volumetric fraction of O₂ in the soil air, dimensionless

  • D_oa: Diffusion coefficient of oxygen in air, dimensionless

  • p_sx: Fraction of soil carbon that is considered soluble, dimensionless

  • earth_param_set: Physical constants used Clima-wide

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Model-specific Types

ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver — Type
AbstractSoilDriver

An abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).

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ClimaLand.Soil.Biogeochemistry.SoilDrivers — Type
SoilDrivers

A container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).

  • met: Soil temperature and moisture drivers - Prescribed or Prognostic

  • soc: Soil SOM driver - Prescribed only

  • atmos: Prescribed atmospheric variables

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ClimaLand.Soil.Biogeochemistry.PrescribedMet — Type
PrescribedMet <: AbstractSoilDriver

A container which holds the prescribed functions for soil temperature and moisture.

This is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.

  • temperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat

  • volumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat

  • ν: Soil porosity (m³ m⁻³)

  • θ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)

  • b: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)

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Functions of State

ClimaLand.Soil.Biogeochemistry.co2_diffusivity — Function
co2_diffusivity(
+                T_soil::FT,
+                θ_w::FT,
+                P_sfc::FT,
+                θ_a100::FT,
+                b::FT,
+                ν::FT,
+                params::SoilCO2ModelParameters{FT},
+                ) where {FT}

Computes the diffusivity of CO₂ within the soil (D).

First, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.

This parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.

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ClimaLand.Soil.Biogeochemistry.microbe_source — Function
microbe_source(T_soil::FT,
+               θ_l::FT,
+               Csom::FT,
+               ν::FT,
+               params::SoilCO2ModelParameters{FT}
+               ) where {FT}

Computes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).

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Extendible Functions

ClimaLand.Soil.Biogeochemistry.soil_moisture — Function
soil_moisture(driver::PrognosticSoil, p, Y, t, z)

Returns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.

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soil_moisture(driver::PrescribedMet, p, Y, t, z)

Returns the soil moisture at location (z) and time (t) for the prescribed soil case.

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ClimaLand.Soil.Biogeochemistry.soil_temperature — Function
soil_temperature(driver::PrognosticSoil, p, Y, t, z)

Returns the prognostic soil temperature.

source
soil_temperature(driver::PrescribedMet, p, Y, t, z)

Returns the soil temperature at location (z) and time (t) for the prescribed soil case.

source
Missing docstring.

Missing docstring for ClimaLand.Soil.Biogeochemistry.soil_SOM_C. Check Documenter's build log for details.

diff --git a/previews/PR753/APIs/SurfaceWater/index.html b/previews/PR753/APIs/SurfaceWater/index.html new file mode 100644 index 0000000000..58cf14fc2d --- /dev/null +++ b/previews/PR753/APIs/SurfaceWater/index.html @@ -0,0 +1,7 @@ + +Surface Water Models · ClimaLand.jl
GitHub

SurfaceWater

Models

ClimaLand.Pond.PondModel — Type
PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}

A stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.

  • domain: The domain for the pond model

  • runoff: The runoff model for the pond model

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Methods and Types

ClimaLand.Pond.PrescribedRunoff — Type
PrescribedRunoff{F1 <: Function, F2 <: Function} <:  AbstractSurfaceRunoff

The required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.

source
ClimaLand.Pond.surface_runoff — Function
function Pond.surface_runoff(
+    runoff::PrognosticRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

Extension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.

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diff --git a/previews/PR753/APIs/canopy/AutotrophicRespiration/index.html b/previews/PR753/APIs/canopy/AutotrophicRespiration/index.html new file mode 100644 index 0000000000..0dd063bdc9 --- /dev/null +++ b/previews/PR753/APIs/canopy/AutotrophicRespiration/index.html @@ -0,0 +1,23 @@ + +Canopy Autotrophic Respiration · ClimaLand.jl
GitHub

Autotrophic Respiration

Parameters

ClimaLand.Canopy.AutotrophicRespirationParameters — Type
AutotrophicRespirationParameters{FT<:AbstractFloat}

The required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. "The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics." Geoscientific Model Development 4.3 (2011): 701-722.

  • ne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)

  • ηsl: Live stem wood coefficient (kg C m-3)

  • σl: Specific leaf density (kg C m-2 [leaf])

  • μr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0

  • μs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1

  • Rel: Relative contribution or Rgrowth (-)

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Methods

ClimaLand.Canopy.nitrogen_content — Function
nitrogen_content(
+                 ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)
+                 Vcmax25::FT, #
+                 LAI::FT, # Leaf area index
+                 SAI::FT,
+                 RAI::FT,
+                 ηsl::FT, # live stem  wood coefficient (kg C m-3) 
+                 h::FT, # canopy height (m)
+                 σl::FT # Specific leaf density (kg C m-2 [leaf])
+                 μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0
+                 μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 
+                ) where {FT}

Computes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).

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ClimaLand.Canopy.plant_respiration_maintenance — Function
plant_respiration_maintenance(
+    Rd::FT, # Dark respiration
+    β::FT, # Soil moisture factor
+    Nl::FT, # Nitrogen content of leafs
+    Nr::FT, # Nitrogen content of roots
+    Ns::FT, # Nitrogen content of stems
+    ) where {FT}

Computes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).

source
ClimaLand.Canopy.plant_respiration_growth — Function
plant_respiration_growth(
+    Rel::FT, # Factor of relative contribution
+    An::FT, # Net photosynthesis
+    Rpm::FT # Plant maintenance respiration
+    ) where {FT}

Computes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.

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diff --git a/previews/PR753/APIs/canopy/Canopy/index.html b/previews/PR753/APIs/canopy/Canopy/index.html new file mode 100644 index 0000000000..d11c05f2ee --- /dev/null +++ b/previews/PR753/APIs/canopy/Canopy/index.html @@ -0,0 +1,2 @@ + +Canopy Models · ClimaLand.jl
GitHub

Canopy

Canopy Model Structs

ClimaLand.Canopy.CanopyModel — Type
 CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}

The model struct for the canopy, which contains

  • the canopy model domain (a point for site-level simulations, or

an extended surface (plane/spherical surface) for regional or global simulations.

  • subcomponent model type for radiative transfer. This is of type

AbstractRadiationModel.

  • subcomponent model type for photosynthesis. This is of type

AbstractPhotosynthesisModel, and currently only the FarquharModel is supported.

  • subcomponent model type for stomatal conductance. This is of type

AbstractStomatalConductanceModel and currently only the MedlynModel is supported

  • subcomponent model type for plant hydraulics. This is of type

AbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.

  • subcomponent model type for canopy energy. This is of type

AbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.

  • subcomponent model type for canopy SIF. prognostically.
  • canopy model parameters, which include parameters that are shared

between canopy model components or those needed to compute boundary fluxes.

  • The atmospheric conditions, which are either prescribed

(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).

  • The radiative flux conditions, which are either prescribed

(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).

  • The soil conditions, which are either prescribed (of type PrecribedSoil, for

running the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)

Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.

  • autotrophic_respiration: Autotrophic respiration model, a canopy component model

  • radiative_transfer: Radiative transfer model, a canopy component model

  • photosynthesis: Photosynthesis model, a canopy component model

  • conductance: Stomatal conductance model, a canopy component model

  • hydraulics: Plant hydraulics model, a canopy component model

  • energy: Energy balance model, a canopy component model

  • sif: SIF model, a canopy component model

  • atmos: Atmospheric forcing: prescribed or coupled

  • radiation: Radiative forcing: prescribed or coupled

  • soil_driver: Soil pressure: prescribed or prognostic

  • parameters: Shared canopy parameters between component models

  • domain: Canopy model domain

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ClimaLand.Canopy.SharedCanopyParameters — Type
SharedCanopyParameters{FT <: AbstractFloat, PSE}

A place to store shared parameters that are required by multiple canopy components.

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • earth_param_set: Earth param set

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Canopy Model Fluxes

ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration — Type
DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}

A concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.

source

Canopy Model Soil Drivers

ClimaLand.Canopy.PrescribedSoil — Type
 PrescribedSoil <: AbstractSoilDriver

A container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.

  • root_depths: The depth of the root tips, in meters

  • ψ: Prescribed soil potential (m) in the root zone a function of time

  • T: Prescribed soil surface temperature (K) as a function of time

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

  • ϵ: Soil emissivity

source
diff --git a/previews/PR753/APIs/canopy/CanopyEnergy/index.html b/previews/PR753/APIs/canopy/CanopyEnergy/index.html new file mode 100644 index 0000000000..08f158138a --- /dev/null +++ b/previews/PR753/APIs/canopy/CanopyEnergy/index.html @@ -0,0 +1,16 @@ + +Canopy Energy · ClimaLand.jl
GitHub

Canopy Energy Model

Methods

ClimaLand.Canopy.canopy_temperature — Function
canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)

Returns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.

source
canopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)

Returns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.

source
ClimaLand.Canopy.root_energy_flux_per_ground_area! — Function
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrognosticSoil{F},
+    model::Canopy.AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT, F}

A method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).

Note that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.

Background information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source

Types

Missing docstring.

Missing docstring for ClimaLand.Canopy.AbstractCanopyEnergyModel. Check Documenter's build log for details.

ClimaLand.Canopy.PrescribedCanopyTempModel — Type
PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}

A model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.

No equation for the energy of the canopy is solved.

source
diff --git a/previews/PR753/APIs/canopy/Photosynthesis/index.html b/previews/PR753/APIs/canopy/Photosynthesis/index.html new file mode 100644 index 0000000000..a96680335b --- /dev/null +++ b/previews/PR753/APIs/canopy/Photosynthesis/index.html @@ -0,0 +1,44 @@ + +Canopy Photosynthesis · ClimaLand.jl
GitHub

Photosynthesis

Parameters

ClimaLand.Canopy.SIFParameters — Type
SIFParameters{FT<:AbstractFloat}

The required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.

  • kf: The rate coefficient for florescence, unitless

  • kd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • min_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kp: Rate coefficient for photochemical quenching

  • kappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

  • kappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

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ClimaLand.Canopy.FarquharParameters — Type
FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}

The required parameters for the Farquhar photosynthesis model.

  • Vcmax25: Vcmax at 25 °C (mol CO2/m^2/s)

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercelluar O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]

  • pc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]

  • mechanism: Photosynthesis mechanism: C3 or C4

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ClimaLand.Canopy.OptimalityFarquharParameters — Type
OptimalityFarquharParameters{FT<:AbstractFloat}

The required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.

  • mechanism: Photosynthesis mechanism: C3 only

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercellular O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Fitting constant to compute the moisture stress factor (Pa^{-1})

  • pc: Fitting constant to compute the moisture stress factor (Pa)

  • c: Constant describing cost of maintaining electron transport (unitless)

source

Methods

ClimaLand.Canopy.arrhenius_function — Function
arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)

Computes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.intercellular_co2 — Function
intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}

Computes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).

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ClimaLand.Canopy.co2_compensation — Function
co2_compensation(Γstar25::FT,
+                 ΔHΓstar::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.rubisco_assimilation — Function
rubisco_assimilation(::C3,
+                     Vcmax::FT,
+                     ci::FT,
+                     Γstar::FT,
+                     Kc::FT,
+                     Ko::FT,
+                     oi::FT) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).

The empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
rubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).

source
ClimaLand.Canopy.light_assimilation — Function
light_assimilation(::C3,
+                   J::FT,
+                   ci::FT,
+                   Γstar::FT) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
light_assimilation(::C4, J::FT, _...) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).

source
ClimaLand.Canopy.C3 — Type
C3 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

source
ClimaLand.Canopy.C4 — Type
C4 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

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ClimaLand.Canopy.max_electron_transport — Function
max_electron_transport(Vcmax::FT) where {FT}

Computes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.electron_transport — Function
electron_transport(APAR::FT,
+                   Jmax::FT,
+                   θj::FT,
+                   ϕ::FT) where {FT}

Computes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ).

See Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.net_photosynthesis — Function
net_photosynthesis(Ac::FT,
+                   Aj::FT,
+                   Rd::FT,
+                   β::FT) where {FT}

Computes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.optimality_max_photosynthetic_rates — Function

optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)

Computes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.

See Smith et al. 2019.

source
ClimaLand.Canopy.moisture_stress — Function
moisture_stress(pl::FT,
+                sc::FT,
+                pc::FT) where {FT}

Computes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) .

See Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.dark_respiration — Function
dark_respiration(Vcmax25::FT,
+                 β::FT,
+                 f::FT,
+                 ΔHkc::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.compute_GPP — Function
compute_GPP(An::FT,
+         K::FT,
+         LAI::FT,
+         Ω::FT) where {FT}

Computes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).

source
ClimaLand.Canopy.MM_Kc — Function
MM_Kc(Kc25::FT,
+      ΔHkc::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.MM_Ko — Function
MM_Ko(Ko25::FT,
+      ΔHko::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.compute_Vcmax — Function
compute_Vcmax(Vcmax25::FT,
+       T::FT,
+       To::FT,
+       R::FT,
+       ep5::FT) where {FT}

Computes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
diff --git a/previews/PR753/APIs/canopy/PlantHydraulics/index.html b/previews/PR753/APIs/canopy/PlantHydraulics/index.html new file mode 100644 index 0000000000..f1417ff426 --- /dev/null +++ b/previews/PR753/APIs/canopy/PlantHydraulics/index.html @@ -0,0 +1,35 @@ + +Plant Hydraulics · ClimaLand.jl
GitHub

PlantHydraulics

Models

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel — Type
PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}

Defines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.

This model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can "turn off" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:

  • n_leaf = 1
  • n_stem = 0
  • LAI = SAI = RAI = 0.

A plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.

Finally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.

  • n_stem: The number of stem compartments for the plant; can be zero

  • n_leaf: The number of leaf compartments for the plant; must be >=1

  • compartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters

  • compartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.

  • compartment_labels: The label (:stem or :leaf) of each compartment

  • parameters: Parameters required by the Plant Hydraulics model

  • transpiration: The transpiration model, of type AbstractTranspiration

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Plant Hydraulics Diagnostic Variables

ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction — Function
augmented_liquid_fraction(
+    ν::FT,
+    S_l::FT) where {FT}

Computes the augmented liquid fraction from porosity and effective saturation.

Augmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.

source
ClimaLand.Canopy.PlantHydraulics.water_retention_curve — Function
water_retention_curve(
+    S_l::FT,
+    b::FT,
+    ν::FT,
+    S_s::FT) where {FT}

Returns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.

source
ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area! — Function
PlantHydraulics.root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrognosticSoil,
+    model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

An extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.

It is computed by summing the flux of water per ground area between roots and soil at each soil layer.

source
root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.

The returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.

source
ClimaLand.Canopy.PlantHydraulics.flux — Function
flux(
+    z1,
+    z2,
+    ψ1,
+    ψ2,
+    K1,
+    K2,
+) where {FT}

Computes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.

We currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.

source

Plant Hydraulics Parameters

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters — Type
PlantHydraulicsParameters

A struct for holding parameters of the PlantHydraulics Model.

  • ai_parameterization: The area index model for LAI, SAI, RAI

  • ν: porosity (m3/m3)

  • S_s: storativity (m3/m3)

  • conductivity_model: Conductivity model and parameters

  • retention_model: Water retention model and parameters

  • root_distribution: Root distribution function P(z)

source
ClimaLand.Canopy.PlantHydraulics.Weibull — Type
Weibull{FT} <: AbstractConductivityModel{FT}

A concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.

Fields

  • K_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation

  • ψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).

  • c: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).

source
ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve — Type
LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}

A concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.

When ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.

Fields

  • a: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.
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Plant Hydraulics Methods and Types

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.PrescribedSoilPressure. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.AbstractRootExtraction. Check Documenter's build log for details.

diff --git a/previews/PR753/APIs/canopy/RadiativeTransfer/index.html b/previews/PR753/APIs/canopy/RadiativeTransfer/index.html new file mode 100644 index 0000000000..9e7514cd73 --- /dev/null +++ b/previews/PR753/APIs/canopy/RadiativeTransfer/index.html @@ -0,0 +1,33 @@ + +Canopy RT · ClimaLand.jl
GitHub

Radiative Transfer

Parameters

ClimaLand.Canopy.BeerLambertParameters — Type
BeerLambertParameters{FT <: AbstractFloat}

The required parameters for the Beer-Lambert radiative transfer model.

  • α_PAR_leaf: PAR leaf reflectance (unitless)

  • α_NIR_leaf: NIR leaf reflectance

  • ϵ_canopy: Emissivity of the canopy

  • Ω: Clumping index following Braghiere (2021) (unitless)

  • λ_γ_PAR: Typical wavelength per PAR photon (m)

  • λ_γ_NIR: Typical wavelength per NIR photon (m)

  • G_Function: Leaf angle distribution function

source

Methods

Missing docstring.

Missing docstring for ClimaLand.Canopy.compute_absorbances. Check Documenter's build log for details.

ClimaLand.Canopy.plant_absorbed_pfd — Function
plant_absorbed_pfd(
+    RT::BeerLambertModel{FT},
+    SW_IN:FT,
+    α_leaf::FT,
+    LAI::FT,
+    K::FT,
+    α_soil::FT
+)

Computes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil).

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

source
plant_absorbed_pfd(
+    RT::TwoStreamModel{FT},
+    α_leaf,
+    SW_IN::FT,
+    LAI::FT,
+    K::FT,
+    τ_leaf,
+    θs::FT,
+    α_soil::FT,
+)

Computes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

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ClimaLand.Canopy.extinction_coeff — Function
extinction_coeff(ld::FT,
+                 θs::FT) where {FT}

Computes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).

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Missing docstring.

Missing docstring for ClimaLand.Canopy.extinction_coeff. Check Documenter's build log for details.

ClimaLand.Canopy.canopy_radiant_energy_fluxes! — Function
Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,
+                                     s::PrognosticSoil{F},
+                                     canopy,
+                                     radiation::PrescribedRadiativeFluxes,
+                                     earth_param_set::PSE,
+                                     Y::ClimaCore.Fields.FieldVector,
+                                     t,
+                                    ) where {FT, PSE}

In standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.

In integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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canopy_radiant_energy_fluxes!(p::NamedTuple,
+                              s::PrescribedSoil,
+                              canopy,
+                              radiation::PrescribedRadiativeFluxes,
+                              earth_param_set::PSE,
+                              Y::ClimaCore.Fields.FieldVector,
+                              t,
+                             ) where {PSE}

Computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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diff --git a/previews/PR753/APIs/canopy/StomatalConductance/index.html b/previews/PR753/APIs/canopy/StomatalConductance/index.html new file mode 100644 index 0000000000..a2ec9d38a5 --- /dev/null +++ b/previews/PR753/APIs/canopy/StomatalConductance/index.html @@ -0,0 +1,17 @@ + +Canopy Stomatal Conductance · ClimaLand.jl
GitHub

Stomatal Conductance

Parameters

ClimaLand.Canopy.MedlynConductanceParameters — Type
MedlynConductanceParameters{FT <: AbstractFloat}

The required parameters for the Medlyn stomatal conductance model.

  • Drel: Relative diffusivity of water vapor (unitless)

  • g0: Minimum stomatal conductance mol/m^2/s

  • g1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})

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Methods

ClimaLand.Canopy.medlyn_term — Function
medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}

Computes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).

thermo_params is the Thermodynamics.jl parameter set.

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ClimaLand.Canopy.medlyn_conductance — Function
medlyn_conductance(g0::FT,
+                   Drel::FT,
+                   medlyn_term::FT,
+                   An::FT,
+                   ca::FT) where {FT}

Computes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).

This returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.

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ClimaLand.Canopy.upscale_leaf_conductance — Function
upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}

This currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.

TODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.

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ClimaLand.Canopy.penman_monteith — Function
penman_monteith(
+    Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1)  
+    Rn::FT, # Net irradiance (W m−2)
+    G::FT, # Ground heat flux (W m−2)
+    ρa::FT, # Dry air density (kg m−3)
+    cp::FT, # Specific heat capacity of air (J kg−1 K−1) 
+    VPD::FT, # vapor pressure deficit (Pa)
+    ga::FT, # atmospheric conductance (m s−1)
+    γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)
+    gs::FT, # surface or stomatal conductance (m s−1)
+    Lv::FT, # Volumetric latent heat of vaporization (J m-3)
+    ) where {FT}

Computes the evapotranspiration in m/s using the Penman-Monteith equation.

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diff --git a/previews/PR753/APIs/shared_utilities/index.html b/previews/PR753/APIs/shared_utilities/index.html new file mode 100644 index 0000000000..eb3908ca62 --- /dev/null +++ b/previews/PR753/APIs/shared_utilities/index.html @@ -0,0 +1,249 @@ + +Shared Utilities · ClimaLand.jl
GitHub

Shared Utilities

Domains

ClimaLand.Domains.AbstractDomain — Type
AbstractDomain{FT <:AbstractFloat}

An abstract type for domains.

The domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.

Additionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.

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Missing docstring.

Missing docstring for ClimaLand.Domains.AbstractLSMDomain. Check Documenter's build log for details.

ClimaLand.Domains.SphericalShell — Type
struct SphericalShell{FT} <: AbstractDomain{FT}
+    radius::FT
+    depth::FT
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • radius: The radius of the shell

  • depth: The radial extent of the shell

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: The number of elements to be used in the non-radial and radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.SphericalSurface — Type
struct SphericalSurface{FT} <: AbstractDomain{FT}
+    radius::FT
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).

Fields

  • radius: The radius of the surface

  • nelements: The number of elements to be used in the non-radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space

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ClimaLand.Domains.HybridBox — Type
struct HybridBox{FT} <: AbstractDomain{FT}
+    xlim::Tuple{FT, FT}
+    ylim::Tuple{FT, FT}
+    zlim::Tuple{FT, FT}
+    longlat::Union{Nothing, Tuple{FT, FT}},
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int, Int}
+    npolynomial::Int
+    periodic::Tuple{Bool, Bool}
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.

When longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • zlim: Domain interval limits along z axis, in meters

  • longlat: When not nothing, a Tuple that contains the center long and lat.

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: Number of elements to discretize interval, (nx, ny,nz)

  • npolynomial: Polynomial order for the horizontal directions

  • periodic: Flag indicating periodic boundaries in horizontal

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Column — Type
Column{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • zlim: Domain interval limits, (zmin, zmax), in meters

  • nelements: Number of elements used to discretize the interval

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • boundary_names: Boundary face identifiers

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Plane — Type
Plane{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.

When longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.

longlat are in degrees, with longitude going from -180 to 180.

:warning: Only independent columns are supported! (No lateral flow).

space is a NamedTuple holding the surface space (in this case, the entire Plane space).

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • longlat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).

  • nelements: Number of elements to discretize interval, (nx, ny)

  • periodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.

  • npolynomial: Polynomial order for both x and y

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space

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ClimaLand.Domains.Point — Type
Point{FT} <: AbstractDomain{FT}

A domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.

space is a NamedTuple holding the surface space (in this case, the Point space).

Fields

  • z_sfc: Surface elevation relative to a reference (m)

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space

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ClimaLand.Domains.coordinates — Function
coordinates(domain::AbstractDomain)

Returns the coordinate fields for the domain as a NamedTuple.

The returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.

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Domains.coordinates(model::AbstractLandModel)

Returns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.

For example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.

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ClimaLand.Domains.obtain_face_space — Function
obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the face space, if applicable, for the center space cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the face space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_space — Function
obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the surface space, if applicable, for the center space cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_domain — Function
obtain_surface_domain(d::AbstractDomain) where {FT}

Default method throwing an error; any domain with a corresponding domain should define a new method of this function.

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obtain_surface_domain(c::Column{FT}) where {FT}

Returns the Point domain corresponding to the top face (surface) of the Column domain c.

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obtain_surface_domain(b::HybridBox{FT}) where {FT}

Returns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.

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obtain_surface_domain(s::SphericalShell{FT}) where {FT}

Returns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.

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ClimaLand.Domains.top_center_to_surface — Function
top_center_to_surface(center_field::ClimaCore.Fields.Field)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.

For example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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top_center_to_surface(val)

When val is a scalar (e.g. a single float or struct), returns val.

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ClimaLand.Domains.top_face_to_surface — Function
top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.

Given a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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ClimaLand.Domains.linear_interpolation_to_surface! — Function
linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)

Linearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.

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ClimaLand.Domains.get_Δz — Function
get_Δz(z::ClimaCore.Fields.Field)

A function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.

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Models

ClimaLand.AbstractImExModel — Type
AbstractImExModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.

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ClimaLand.AbstractExpModel — Type
AbstractExpModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.

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ClimaLand.make_exp_tendency — Function
make_exp_tendency(model::AbstractModel)

Returns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_imp_tendency — Function
make_imp_tendency(model::AbstractImExModel)

Returns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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make_imp_tendency(model::AbstractModel)

Returns an imp_tendency that does nothing. This model type is not stepped explicity.

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ClimaLand.make_compute_exp_tendency — Function
make_explicit_tendency(model::Soil.RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

Construct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.

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make_compute_exp_tendency(model::EnergyHydrology)

An extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.

This function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::BucketModel{FT}) where {FT}

Creates the computeexptendency! function for the bucket model.

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make_compute_exp_tendency(model::AbstractModel)

Return a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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 ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)

Creates the computeexptendency!(dY,Y,p,t) function for the canopy component.

Since component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.

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make_compute_exp_tendency(canopy::CanopyModel)

Creates and returns the computeexptendency! for the CanopyModel.

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make_compute_exp_tendency(model::SoilCO2Model)

An extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::PlantHydraulicsModel, _)

A function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.

Below, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).

Note that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.

To prevent dividing by zero, we change AI/(AI x dz)" to "AI/max(AI x dz, eps(FT))"

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ClimaLand.make_compute_imp_tendency — Function
make_compute_imp_tendency(model::RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

This function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.

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make_compute_imp_tendency(model::EnergyHydrology)

An extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.

This version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_imp_tendency(model::AbstractModel)

Return a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_update_aux — Function
make_update_aux(model::RichardsModel)

An extension of the function make_update_aux, for the Richardson- Richards equation.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::EnergyHydrology)

An extension of the function make_update_aux, for the integrated soil hydrology and energy model.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::BucketModel{FT}) where {FT}

Creates the update_aux! function for the BucketModel.

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make_update_aux(model::AbstractModel)

Return an update_aux! function that updates auxiliary parameters p.

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 ClimaLand.make_update_aux(canopy::CanopyModel{FT,
+                                              <:AutotrophicRespirationModel,
+                                              <:Union{BeerLambertModel, TwoStreamModel},
+                                              <:FarquharModel,
+                                              <:MedlynConductanceModel,
+                                              <:PlantHydraulicsModel,},
+                          ) where {FT}

Creates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.

Please note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.

The other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.

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make_update_aux(model::SoilCO2Model)

An extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.

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ClimaLand.make_update_boundary_fluxes — Function
make_update_boundary_fluxes(model::AbstractModel)

Return an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..

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make_update_boundary_fluxes(
+    land::LandHydrology{FT, SM, SW},
+) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}

A method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.

This function is called each ode function evaluation.

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make_update_boundary_fluxes(
+    land::SoilCanopyModel{FT, MM, SM, RM},
+) where {
+    FT,
+    MM <: Soil.Biogeochemistry.SoilCO2Model{FT},
+    SM <: Soil.RichardsModel{FT},
+    RM <: Canopy.CanopyModel{FT}
+    }

A method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models.

This function is called each ode function evaluation, prior to the tendency function evaluation.

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ClimaLand.make_set_initial_cache — Function
make_set_initial_cache(model::AbstractModel)

Returns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.

In principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.

Furthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.

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ClimaLand.make_update_drivers — Function
make_update_drivers(::AbstractClimaLandDrivers)

Creates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.

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make_update_drivers(driver_tuple)

Creates and returns a function which updates the forcing variables ("drivers"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.

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make_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.

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make_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.

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make_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.

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make_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.

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ClimaLand.prognostic_vars — Function
prognostic_vars(soil::RichardsModel)

A function which returns the names of the prognostic variables of RichardsModel.

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prognostic_vars(soil::EnergyHydrology)

A function which returns the names of the prognostic variables of EnergyHydrology.

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prognostic_vars(::SnowModel)

Returns the prognostic variable names of the snow model.

For this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.

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prognostic_vars(m::AbstractModel)

Returns the prognostic variable symbols for the model in the form of a tuple.

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_vars(::AbstractCanopyComponent)

Returns the prognostic vars of the canopy component passed in as an argument.

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prognostic_vars(canopy::CanopyModel)

Returns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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prognostic_vars(model::PlantHydraulicsModel)

A function which returns the names of the prognostic variables of the PlantHydraulicsModel.

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ClimaLand.prognostic_types — Function
prognostic_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the prognostic variables of EnergyHydrology.

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prognostic_types(::SnowModel{FT})

Returns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.

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prognostic_types(m::AbstractModel{FT}) where {FT}

Returns the prognostic variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

Here, the coordinate points are those returned by coordinates(model).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_types(::AbstractCanopyComponent)

Returns the prognostic types of the canopy component passed in as an argument.

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prognostic_types(canopy::CanopyModel)

Returns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the prognostic types for the PlantHydraulicsModel.

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ClimaLand.prognostic_domain_names — Function
prognostic_domain_names(::SnowModel)

Returns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is ":surface".

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prognosticdomainnames(m::AbstractModel)

Returns the domain names for the prognostic variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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prognosticdomainnames(m::AbstractCanopyComponent)

Returns the domain names for the prognostic variables in the form of a tuple.

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ClimaLand.auxiliary_vars — Function
auxiliary_vars(soil::RichardsModel)

A function which returns the names of the auxiliary variables of RichardsModel.

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auxiliary_vars(soil::EnergyHydrology)

A function which returns the names of the auxiliary variables of EnergyHydrology.

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auxiliary_vars(::SnowModel)

Returns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.

Since the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.

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auxiliary_vars(m::AbstractModel)

Returns the auxiliary variable symbols for the model in the form of a tuple.

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ClimaLand.auxiliary_vars(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_vars(canopy::CanopyModel)

Returns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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auxiliary_vars(model::PlantHydraulicsModel)

A function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.

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ClimaLand.auxiliary_types — Function
auxiliary_types(soil::RichardsModel)

A function which returns the names of the auxiliary types of RichardsModel.

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auxiliary_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the auxiliary variables of EnergyHydrology.

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auxiliary_types(m::AbstractModel{FT}) where {FT}

Returns the auxiliary variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

  • Note that Arrays, MVectors are not isbits and cannot be used.

Here, the coordinate points are those returned by coordinates(model).

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ClimaLand.auxiliary_types(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_types(canopy::CanopyModel)

Returns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the auxiliary types for the PlantHydraulicsModel.

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ClimaLand.auxiliary_domain_names — Function
auxiliary_domain_names(soil::RichardsModel)

A function which returns the names of the auxiliary domain names of RichardsModel.

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auxiliarydomainnames(m::AbstractModel)

Returns the domain names for the auxiliary variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

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auxiliarydomainnames(m::AbstractCanopyComponent)

Returns the domain names for the auxiliary variables in the form of a tuple.

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ClimaLand.initialize_prognostic — Function
initialize_prognostic(model::AbstractModel, state::NamedTuple)

Returns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.

If a model has no prognostic variables, the returned FieldVector contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.

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initialize_prognostic(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_prognostic(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input state is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.

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ClimaLand.initialize_auxiliary — Function
initialize_auxiliary(model::AbstractModel, state::NamedTuple)

Returns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.

If a model has no auxiliary variables, the returned NamedTuple contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.

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initialize_auxiliary(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_auxiliary(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input coords is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.

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ClimaLand.initialize — Function
initialize(model::AbstractModel)

Creates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.

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ClimaLand.name — Function
name(model::AbstractModel)

Returns a symbol of the model component name, e.g. :soil or :vegetation.

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ClimaLand.AbstractBC — Type
AbstractBC

An abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.

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ClimaLand.source! — Function
 source!(dY::ClimaCore.Fields.FieldVector,
+         src::PhaseChange{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Computes the source terms for phase change.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::SoilSublimation{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Updates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::AbstractSource,
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple
+         )::ClimaCore.Fields.Field

A stub function, which is extended by ClimaLand.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                 src::RootExtraction,
+                 Y::ClimaCore.Fields.FieldVector,
+                 p::NamedTuple
+                 model::EnergyHydrology)

An extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                      src::MicrobeProduction,
+                      Y::ClimaCore.Fields.FieldVector,
+                      p::NamedTuple,
+                      params)

A method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.

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ClimaLand.source!(
+    dY::ClimaCore.Fields.FieldVector,
+    src::TOPMODELSubsurfaceRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    model::AbstractSoilModel{FT},
+) where {FT}

Adjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.

The sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).

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ClimaLand.AbstractBoundary — Type
AbstractBoundary

An abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).

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ClimaLand.TopBoundary — Type
TopBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the top boundary.

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ClimaLand.BottomBoundary — Type
BottomBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the bottom boundary.

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ClimaLand.boundary_flux — Function
boundary_flux(bc::WaterFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(bc::RichardsAtmosDrivenFluxBC,
+                       boundary::ClimaLand.AbstractBoundary,
+                       model::RichardsModel{FT},
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field where {FT}

A method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.

If model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.

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boundary_flux(bc::FreeDrainage,
+                       boundary::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which enforces free drainage at the bottom of the domain.

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boundary_flux(bc::HeatFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.

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boundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field

A function which returns the correct boundary flux given any boundary condition (BC).

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function ClimaLand.boundary_flux(
+    bc::RunoffBC,
+    ::TopBoundary,
+    model::Soil.RichardsModel,
+    Δz::FT,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+    params,
+)::ClimaCore.Fields.Field

Extension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).

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ClimaLand.boundary_flux(
+    bc::SoilCO2FluxBC,
+    boundary::ClimaLand.AbstractBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.

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ClimaLand.boundary_flux(
+bc::SoilCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.

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ClimaLand.boundary_flux(
+    bc::SoilCO2StateBC,
+    boundary::ClimaLand.BottomBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.

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ClimaLand.boundary_flux(
+bc::AtmosCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.

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ClimaLand.diffusive_flux — Function
diffusive_flux(K, x_2, x_1, Δz)

Calculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.

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ClimaLand.boundary_vars — Function
boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.

These variables are updated in place in boundary_flux.

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boundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                <:AbstractRadiativeDrivers,
+                                <:AbstractRunoffModel,
+                                }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.

These variables are updated in place in soil_boundary_fluxes!.

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boundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for MoistureStateBC at the top boundary.

These variables are updated in place in boundary_flux.

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boundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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boundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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ClimaLand.boundary_var_domain_names — Function
boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                              <:AbstractRadiativeDrivers,
+                                              <:AbstractRunoffModel,
+                                              },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.

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boundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.

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boundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)

The list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names.

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ClimaLand.boundary_var_types — Function
boundary_var_types(::RichardsModel{FT},
+                    ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                                <: Runoff.AbstractRunoffModel,
+                                              },
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.

Because we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.

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boundary_var_types(
+    ::EnergyHydrology{FT},
+    ::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere{FT},
+        <:AbstractRadiativeDrivers{FT},
+        <:AbstractRunoffModel,
+    }, ::ClimaLand.TopBoundary,
+) where {FT}

An extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.

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boundary_var_types(::RichardsModel{FT},
+                    ::MoistureStateBC,
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for MoistureStateBC at the top boundary.

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boundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.

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ClimaLand.make_jacobian — Function

make_jacobian(model::AbstractModel)

Creates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.

The default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.

Note that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.

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ClimaLand.make_compute_jacobian — Function
ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}

Creates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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ClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}

Creates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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make_compute_jacobian(model::AbstractModel)

Creates and returns a function which computes the entries of the Jacobian matrix W in place.

If the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.

The default is that no updates are required, but this function must be extended for models that use implicit timestepping.

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ClimaLand.set_dfluxBCdY! — Function
ClimaLand.set_dfluxBCdY!(
+    model::RichardsModel,
+    ::MoistureStateBC,
+    boundary::ClimaLand.TopBoundary,
+    Δz,
+    Y,
+    p,
+    t,

)

Computes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.

For Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.

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set_dfluxBCdY!(::AbstractModel,
+              ::AbstractBC,
+              ::AbstractBoundary,
+              _...)::Union{ClimaCore.Fields.FieldVector, Nothing}

A function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.

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ClimaLand.get_drivers — Function
ClimaLand.get_drivers(model::RichardsModel)

Returns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.

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ClimaLand.get_drivers(model::SnowModel)

Returns the driver variable symbols for the SnowModel.

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get_drivers(model::AbstractModel)

Returns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned

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Drivers

ClimaLand.PrescribedAtmosphere — Type
PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.

The default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.

Since not all models require co2 concentration, the default for that is nothing.

  • liquid_precip: Precipitation (m/s) function of time: positive by definition

  • snow_precip: Snow precipitation (m/s) function of time: positive by definition

  • T: Prescribed atmospheric temperature (function of time) at the reference height (K)

  • u: Prescribed wind speed (function of time) at the reference height (m/s)

  • q: Prescribed specific humidity (function of time) at the reference height (_)

  • P: Prescribed air pressure (function of time) at the reference height (Pa)

  • c_co2: CO2 concentration in atmosphere (mol/mol)

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • h: Reference height (m), relative to surface elevation

  • gustiness: Minimum wind speed (gustiness; m/s)

  • thermo_params: Thermodynamic parameters

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ClimaLand.PrescribedPrecipitation — Type
PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).

  • liquid_precip: Precipitation (m/s) function of time: positive by definition
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ClimaLand.PrescribedRadiativeFluxes — Type
PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}

Container for the prescribed radiation functions needed to drive land models in standalone mode.

  • SW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface

  • LW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • θs: Sun zenith angle, in radians

source
ClimaLand.turbulent_fluxes — Function
turbulent_fluxes(atmos::PrescribedAtmosphere,
+               model::AbstractModel,
+               Y::ClimaCore.Fields.FieldVector,
+               p::NamedTuple,
+               t
+               )

Computes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.

It solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.

source
turbulent_fluxes(atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                t)

Computes the turbulent surface fluxes terms at the ground for a coupled simulation.

source
ClimaLand.turbulent_fluxes_at_a_point — Function
turbulent_fluxes_at_a_point(T_sfc::FT,
+                            q_sfc::FT,
+                            ρ_sfc::FT,
+                            β_sfc::FT,
+                            h_sfc::FT,
+                            r_sfc::FT,
+                            d_sfc::FT,
+                            ts_in,
+                            u::FT,
+                            h::FT,
+                            gustiness::FT,
+                            z_0m::FT,
+                            z_0b::FT,
+                            earth_param_set::EP,
+                           ) where {FT <: AbstractFloat, P}

Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc

This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.

source
Missing docstring.

Missing docstring for ClimaLand.radiative_fluxes_at_a_point. Check Documenter's build log for details.

ClimaLand.set_atmos_ts! — Function
set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)

Fill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.

source
ClimaLand.surface_air_density — Function
surface_air_density(
+                    atmos::PrescribedAtmosphere,
+                    model::AbstractModel,
+                    Y,
+                    p,
+                    t,
+                    T_sfc,
+                    )

A helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.

We additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.

Extending this function for your model is only necessary if you need to compute the air density in a different way.

source
ClimaLand.surface_air_density(
+                atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                _...,
+            )

Returns the air density at the surface in the case of a coupled simulation.

This requires the field ρ_sfc to be present in the cache p under the name of the model.

source
ClimaLand.surface_temperature — Function
ClimaLand.surface_temperature(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface temperature field of the EnergyHydrology soil model.

The assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.

source
ClimaLand.surface_temperature(model::SnowModel, Y, p)

a helper function which returns the surface temperature for the snow model, which is stored in the aux state.

source
ClimaLand.surface_temperature(model::BucketModel, Y, p)

a helper function which returns the surface temperature for the bucket model, which is stored in the aux state.

source
surface_temperature(model::AbstractModel, Y, p, t)

A helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_temperature(model::CanopyModel, Y, p, t)

A helper function which returns the temperature for the canopy model.

source
ClimaLand.surface_resistance — Function
ClimaLand.surface_resistance(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface resistance field of the EnergyHydrology soil model.

source
surface_resistance(model::AbstractModel, Y, p, t)

A helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

The default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.

source
ClimaLand.surface_resistance(
+    model::CanopyModel{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the stomatal resistance field of the CanopyModel canopy.

source
ClimaLand.surface_specific_humidity — Function
ClimaLand.surface_specific_humidity(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    T_sfc,
+    ρ_sfc
+) where {FT}

Returns the surface specific humidity field of the EnergyHydrology soil model.

This models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.

Over the soil ice, the specific humidity is the saturated value.

The total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

Computes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

a helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.

source
surface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)

A helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)

A helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.

source
Missing docstring.

Missing docstring for ClimaLand.make_update_drivers. Check Documenter's build log for details.

diff --git a/previews/PR753/Contributing/index.html b/previews/PR753/Contributing/index.html new file mode 100644 index 0000000000..41bf89b223 --- /dev/null +++ b/previews/PR753/Contributing/index.html @@ -0,0 +1,2 @@ + +Contribution guide · ClimaLand.jl
GitHub

Contributing

Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.

Some useful tips

  • When you start working on a new feature branch, make sure you start from main by running: git checkout main.
  • Make sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.
  • When your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.

Continuous integration

After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.

Automated testing

Currently a number of checks are run per commit for a given PR.

  • JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.
  • Documentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.
  • Tests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.
diff --git a/previews/PR753/assets/documenter.js b/previews/PR753/assets/documenter.js new file mode 100644 index 0000000000..2e7840cea0 --- /dev/null +++ b/previews/PR753/assets/documenter.js @@ -0,0 +1,1074 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'mathjax': 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/MathJax.js?config=TeX-AMS_HTML', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "mathjax": { + "exports": "MathJax" + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['mathjax'], function(MathJax) { +MathJax.Hub.Config({ + "jax": [ + "input/TeX", + "output/HTML-CSS", + "output/NativeMML" + ], + "TeX": { + "equationNumbers": { + "autoNumber": "AMS" + }, + "Macros": {} + }, + "tex2jax": { + "inlineMath": [ + [ + "$", + "$" + ], + [ + "\\(", + "\\)" + ] + ], + "processEscapes": true + }, + "config": [ + "MMLorHTML.js" + ], + "extensions": [ + "MathMenu.js", + "MathZoom.js", + "TeX/AMSmath.js", + "TeX/AMSsymbols.js", + "TeX/autobold.js", + "TeX/autoload-all.js" + ] +} +); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on( + "click", + ".docstring .docstring-article-toggle-button", + function () { + let articleToggleTitle = "Expand docstring"; + const parent = $(this).parent(); + + debounce(() => { + if (parent.siblings("section").is(":visible")) { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + parent + .children(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + parent.siblings("section").slideToggle(); + }); + } +); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * RegX escape function from MDN + * Refer: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ + function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + searchstring = escapeRegExp(querystring); + let textindex = new RegExp(`${searchstring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(searchstring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + combineWith: "AND", + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` +
+ + Ctrl + + / to search + + esc to close +
+ `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/previews/PR753/assets/logo.svg b/previews/PR753/assets/logo.svg new file mode 100644 index 0000000000..3717cae0a7 --- /dev/null +++ b/previews/PR753/assets/logo.svg @@ -0,0 +1,55 @@ + + + + + + + + + + + + diff --git a/previews/PR753/assets/themes/catppuccin-frappe.css b/previews/PR753/assets/themes/catppuccin-frappe.css new file mode 100644 index 0000000000..54a5b77ea0 --- /dev/null +++ b/previews/PR753/assets/themes/catppuccin-frappe.css @@ -0,0 +1 @@ +html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis,html.theme--catppuccin-frappe .file-cta,html.theme--catppuccin-frappe .file-name,html.theme--catppuccin-frappe .select select,html.theme--catppuccin-frappe .textarea,html.theme--catppuccin-frappe .input,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-frappe .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid 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.is-focused.pagination-next,html.theme--catppuccin-frappe .is-focused.pagination-link,html.theme--catppuccin-frappe .is-focused.pagination-ellipsis,html.theme--catppuccin-frappe .is-focused.file-cta,html.theme--catppuccin-frappe .is-focused.file-name,html.theme--catppuccin-frappe .select select.is-focused,html.theme--catppuccin-frappe .is-focused.textarea,html.theme--catppuccin-frappe .is-focused.input,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--catppuccin-frappe .is-focused.button,html.theme--catppuccin-frappe .pagination-previous:active,html.theme--catppuccin-frappe .pagination-next:active,html.theme--catppuccin-frappe .pagination-link:active,html.theme--catppuccin-frappe .pagination-ellipsis:active,html.theme--catppuccin-frappe .file-cta:active,html.theme--catppuccin-frappe .file-name:active,html.theme--catppuccin-frappe .select select:active,html.theme--catppuccin-frappe .textarea:active,html.theme--catppuccin-frappe 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html.theme--catppuccin-frappe .content kbd.button.is-outlined{background-color:transparent;border-color:#414559;box-shadow:none;color:#414559}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#414559}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #414559 #414559 !important}html.theme--catppuccin-frappe 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.button.is-primary.is-inverted.is-outlined.is-focused,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#8caaee}html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-frappe 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.button.is-primary.is-light.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-hovered.docs-sourcelink{background-color:#e2eafb;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-primary.is-light:active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.docs-sourcelink:active,html.theme--catppuccin-frappe .button.is-primary.is-light.is-active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d7e1f9;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-link{background-color:#8caaee;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:hover,html.theme--catppuccin-frappe .button.is-link.is-hovered{background-color:#81a2ec;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:focus,html.theme--catppuccin-frappe 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.button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-outlined:focus,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-focused{background-color:#8caaee;border-color:#8caaee;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #8caaee #8caaee !important}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe 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.button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #81c8be #81c8be !important}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-light{background-color:#f1f9f8;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:hover,html.theme--catppuccin-frappe .button.is-info.is-light.is-hovered{background-color:#e8f5f3;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:active,html.theme--catppuccin-frappe .button.is-info.is-light.is-active{background-color:#dff1ef;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe 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.button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6d189 #a6d189 !important}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success.is-light{background-color:#f4f9f0;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:hover,html.theme--catppuccin-frappe .button.is-success.is-light.is-hovered{background-color:#edf6e7;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:active,html.theme--catppuccin-frappe .button.is-success.is-light.is-active{background-color:#e6f2de;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:hover,html.theme--catppuccin-frappe .button.is-warning.is-hovered{background-color:#e3c386;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus,html.theme--catppuccin-frappe .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus:not(:active),html.theme--catppuccin-frappe .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(229,200,144,0.25)}html.theme--catppuccin-frappe .button.is-warning:active,html.theme--catppuccin-frappe .button.is-warning.is-active{background-color:#e0be7b;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:#e5c890;box-shadow:none}html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-focused{background-color:#e5c890;border-color:#e5c890;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-light{background-color:#fbf7ee;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:hover,html.theme--catppuccin-frappe .button.is-warning.is-light.is-hovered{background-color:#f9f2e4;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:active,html.theme--catppuccin-frappe .button.is-warning.is-light.is-active{background-color:#f6edda;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:hover,html.theme--catppuccin-frappe .button.is-danger.is-hovered{background-color:#e57779;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus,html.theme--catppuccin-frappe .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus:not(:active),html.theme--catppuccin-frappe .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(231,130,132,0.25)}html.theme--catppuccin-frappe .button.is-danger:active,html.theme--catppuccin-frappe .button.is-danger.is-active{background-color:#e36d6f;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:#e78284;box-shadow:none}html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-frappe .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-focused{background-color:#e78284;border-color:#e78284;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-light{background-color:#fceeee;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:hover,html.theme--catppuccin-frappe .button.is-danger.is-light.is-hovered{background-color:#fae3e4;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:active,html.theme--catppuccin-frappe .button.is-danger.is-light.is-active{background-color:#f8d8d9;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-frappe .button.is-small:not(.is-rounded),html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .button.is-normal{font-size:1rem}html.theme--catppuccin-frappe .button.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .button.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .button[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button{background-color:#737994;border-color:#626880;box-shadow:none;opacity:.5}html.theme--catppuccin-frappe .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-frappe .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-frappe .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-frappe .button.is-static{background-color:#292c3c;border-color:#626880;color:#838ba7;box-shadow:none;pointer-events:none}html.theme--catppuccin-frappe .button.is-rounded,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-frappe .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-frappe .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-frappe .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-frappe .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-frappe .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-frappe .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-frappe .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-frappe .buttons.has-addons .button:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-frappe .buttons.has-addons .button:focus,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused,html.theme--catppuccin-frappe .buttons.has-addons .button:active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-frappe .buttons.has-addons .button:focus:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-frappe .buttons.has-addons .button:active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-frappe .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .buttons.is-centered{justify-content:center}html.theme--catppuccin-frappe .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-frappe .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-frappe .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-frappe .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-frappe .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-frappe .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-frappe .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-frappe .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-frappe .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-frappe .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-frappe .content li+li{margin-top:0.25em}html.theme--catppuccin-frappe .content p:not(:last-child),html.theme--catppuccin-frappe .content dl:not(:last-child),html.theme--catppuccin-frappe .content ol:not(:last-child),html.theme--catppuccin-frappe .content ul:not(:last-child),html.theme--catppuccin-frappe .content blockquote:not(:last-child),html.theme--catppuccin-frappe .content pre:not(:last-child),html.theme--catppuccin-frappe .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-frappe .content h1,html.theme--catppuccin-frappe .content h2,html.theme--catppuccin-frappe .content h3,html.theme--catppuccin-frappe .content h4,html.theme--catppuccin-frappe .content h5,html.theme--catppuccin-frappe .content h6{color:#c6d0f5;font-weight:600;line-height:1.125}html.theme--catppuccin-frappe .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-frappe .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-frappe .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-frappe .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-frappe .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-frappe .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-frappe .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-frappe .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-frappe .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-frappe .content blockquote{background-color:#292c3c;border-left:5px solid #626880;padding:1.25em 1.5em}html.theme--catppuccin-frappe .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-frappe .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-frappe .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-frappe .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-frappe .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-frappe .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-frappe .content ul ul ul{list-style-type:square}html.theme--catppuccin-frappe .content dd{margin-left:2em}html.theme--catppuccin-frappe .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-frappe .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-frappe .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-frappe .content figure img{display:inline-block}html.theme--catppuccin-frappe .content figure figcaption{font-style:italic}html.theme--catppuccin-frappe .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-frappe .content sup,html.theme--catppuccin-frappe .content sub{font-size:75%}html.theme--catppuccin-frappe .content table{width:100%}html.theme--catppuccin-frappe .content table td,html.theme--catppuccin-frappe .content table th{border:1px solid #626880;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-frappe .content table th{color:#b0bef1}html.theme--catppuccin-frappe .content table th:not([align]){text-align:inherit}html.theme--catppuccin-frappe .content table thead td,html.theme--catppuccin-frappe .content table thead th{border-width:0 0 2px;color:#b0bef1}html.theme--catppuccin-frappe .content table tfoot td,html.theme--catppuccin-frappe .content table tfoot th{border-width:2px 0 0;color:#b0bef1}html.theme--catppuccin-frappe .content table tbody tr:last-child td,html.theme--catppuccin-frappe .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-frappe .content .tabs li+li{margin-top:0}html.theme--catppuccin-frappe .content.is-small,html.theme--catppuccin-frappe #documenter 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select:not([multiple]){padding-right:2.5em}html.theme--catppuccin-frappe .select select[multiple]{height:auto;padding:0}html.theme--catppuccin-frappe .select select[multiple] option{padding:0.5em 1em}html.theme--catppuccin-frappe .select:not(.is-multiple):not(.is-loading):hover::after{border-color:#99d1db}html.theme--catppuccin-frappe .select.is-white:not(:hover)::after{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select:hover,html.theme--catppuccin-frappe .select.is-white select.is-hovered{border-color:#f2f2f2}html.theme--catppuccin-frappe .select.is-white select:focus,html.theme--catppuccin-frappe .select.is-white select.is-focused,html.theme--catppuccin-frappe .select.is-white select:active,html.theme--catppuccin-frappe .select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-frappe 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select.is-focused,html.theme--catppuccin-frappe .select.is-dark select:active,html.theme--catppuccin-frappe .content kbd.select select:active,html.theme--catppuccin-frappe .select.is-dark select.is-active,html.theme--catppuccin-frappe .content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(65,69,89,0.25)}html.theme--catppuccin-frappe .select.is-primary:not(:hover)::after,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select:hover,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:hover,html.theme--catppuccin-frappe .select.is-primary select.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-primary select:focus,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:focus,html.theme--catppuccin-frappe .select.is-primary select.is-focused,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-focused,html.theme--catppuccin-frappe .select.is-primary select:active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:active,html.theme--catppuccin-frappe .select.is-primary select.is-active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-link:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select:hover,html.theme--catppuccin-frappe .select.is-link select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-link select:focus,html.theme--catppuccin-frappe .select.is-link select.is-focused,html.theme--catppuccin-frappe .select.is-link select:active,html.theme--catppuccin-frappe .select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-info:not(:hover)::after{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select:hover,html.theme--catppuccin-frappe .select.is-info select.is-hovered{border-color:#6fc0b5}html.theme--catppuccin-frappe .select.is-info select:focus,html.theme--catppuccin-frappe .select.is-info select.is-focused,html.theme--catppuccin-frappe .select.is-info select:active,html.theme--catppuccin-frappe .select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(129,200,190,0.25)}html.theme--catppuccin-frappe 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a.navbar-item:focus,html.theme--catppuccin-frappe a.navbar-item:focus-within,html.theme--catppuccin-frappe a.navbar-item:hover,html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link:focus,html.theme--catppuccin-frappe .navbar-link:focus-within,html.theme--catppuccin-frappe .navbar-link:hover,html.theme--catppuccin-frappe .navbar-link.is-active{background-color:rgba(0,0,0,0);color:#8caaee}html.theme--catppuccin-frappe .navbar-item{flex-grow:0;flex-shrink:0}html.theme--catppuccin-frappe .navbar-item img{max-height:1.75rem}html.theme--catppuccin-frappe .navbar-item.has-dropdown{padding:0}html.theme--catppuccin-frappe .navbar-item.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-item.is-tab{border-bottom:1px solid transparent;min-height:4rem;padding-bottom:calc(0.5rem - 1px)}html.theme--catppuccin-frappe .navbar-item.is-tab:focus,html.theme--catppuccin-frappe .navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee}html.theme--catppuccin-frappe .navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee;border-bottom-style:solid;border-bottom-width:3px;color:#8caaee;padding-bottom:calc(0.5rem - 3px)}html.theme--catppuccin-frappe .navbar-content{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless){padding-right:2.5em}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless)::after{border-color:#fff;margin-top:-0.375em;right:1.125em}html.theme--catppuccin-frappe .navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}html.theme--catppuccin-frappe .navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}html.theme--catppuccin-frappe .navbar-divider{background-color:rgba(0,0,0,0.2);border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){html.theme--catppuccin-frappe 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a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#838ba7}html.theme--catppuccin-frappe .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#8caaee}.navbar.is-spaced html.theme--catppuccin-frappe .navbar-dropdown,html.theme--catppuccin-frappe .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--catppuccin-frappe .navbar-dropdown.is-right{left:auto;right:0}html.theme--catppuccin-frappe .navbar-divider{display:block}html.theme--catppuccin-frappe .navbar>.container .navbar-brand,html.theme--catppuccin-frappe .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--catppuccin-frappe .navbar>.container .navbar-menu,html.theme--catppuccin-frappe .container>.navbar 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body.has-spaced-navbar-fixed-bottom{padding-bottom:6rem}html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link.is-active{color:#8caaee}html.theme--catppuccin-frappe a.navbar-item.is-active:not(:focus):not(:hover),html.theme--catppuccin-frappe .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--catppuccin-frappe .navbar-item.has-dropdown:focus .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown:hover .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--catppuccin-frappe .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--catppuccin-frappe .pagination{font-size:1rem;margin:-.25rem}html.theme--catppuccin-frappe .pagination.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}html.theme--catppuccin-frappe .pagination.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .pagination.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-previous,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--catppuccin-frappe .pagination.is-rounded .pagination-next,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:9999px}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-link,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:9999px}html.theme--catppuccin-frappe .pagination,html.theme--catppuccin-frappe .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link{border-color:#626880;color:#8caaee;min-width:2.5em}html.theme--catppuccin-frappe .pagination-previous:hover,html.theme--catppuccin-frappe .pagination-next:hover,html.theme--catppuccin-frappe .pagination-link:hover{border-color:#737994;color:#99d1db}html.theme--catppuccin-frappe .pagination-previous:focus,html.theme--catppuccin-frappe .pagination-next:focus,html.theme--catppuccin-frappe .pagination-link:focus{border-color:#737994}html.theme--catppuccin-frappe .pagination-previous:active,html.theme--catppuccin-frappe .pagination-next:active,html.theme--catppuccin-frappe .pagination-link:active{box-shadow:inset 0 1px 2px 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768px){html.theme--catppuccin-frappe .pagination{flex-wrap:wrap}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{html.theme--catppuccin-frappe .pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination-previous{order:2}html.theme--catppuccin-frappe .pagination-next{order:3}html.theme--catppuccin-frappe .pagination{justify-content:space-between;margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination.is-centered .pagination-previous{order:1}html.theme--catppuccin-frappe .pagination.is-centered 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rgba(10,10,10,0.25)}html.theme--catppuccin-latte .button.is-black:active,html.theme--catppuccin-latte .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-black[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:hover,html.theme--catppuccin-latte .button.is-light.is-hovered{background-color:#eee;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus,html.theme--catppuccin-latte .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus:not(:active),html.theme--catppuccin-latte .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}html.theme--catppuccin-latte .button.is-light:active,html.theme--catppuccin-latte .button.is-light.is-active{background-color:#e8e8e8;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none}html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-outlined.is-focused{background-color:#f5f5f5;border-color:#f5f5f5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark,html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:hover,html.theme--catppuccin-latte .content kbd.button:hover,html.theme--catppuccin-latte .button.is-dark.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-hovered{background-color:#c5c9d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus,html.theme--catppuccin-latte .content kbd.button:focus,html.theme--catppuccin-latte .button.is-dark.is-focused,html.theme--catppuccin-latte .content kbd.button.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus:not(:active),html.theme--catppuccin-latte .content kbd.button:focus:not(:active),html.theme--catppuccin-latte .button.is-dark.is-focused:not(:active),html.theme--catppuccin-latte .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(204,208,218,0.25)}html.theme--catppuccin-latte .button.is-dark:active,html.theme--catppuccin-latte .content kbd.button:active,html.theme--catppuccin-latte .button.is-dark.is-active,html.theme--catppuccin-latte .content kbd.button.is-active{background-color:#bdc2cf;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark[disabled],html.theme--catppuccin-latte .content kbd.button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:#ccd0da;box-shadow:none}html.theme--catppuccin-latte .button.is-dark.is-inverted,html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-focused{background-color:#ccd0da;border-color:#ccd0da;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-primary,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:hover,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-focused,html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--catppuccin-latte .button.is-primary.is-focused:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-primary:active,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:active,html.theme--catppuccin-latte .button.is-primary.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-active.docs-sourcelink{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary[disabled],html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:#1e66f5;box-shadow:none}html.theme--catppuccin-latte .button.is-primary.is-inverted,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-inverted:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-inverted.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-primary.is-inverted[disabled],html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-loading::after,html.theme--catppuccin-latte .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-primary.is-outlined,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#1e66f5;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-outlined:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--catppuccin-latte .button.is-primary.is-outlined:focus,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-focused,html.theme--catppuccin-latte 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.button.is-primary.is-light.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d3e1fd;border-color:transparent;color:#0a52e1}html.theme--catppuccin-latte .button.is-link{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:hover,html.theme--catppuccin-latte .button.is-link.is-hovered{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus,html.theme--catppuccin-latte .button.is-link.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus:not(:active),html.theme--catppuccin-latte .button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-link:active,html.theme--catppuccin-latte .button.is-link.is-active{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte 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.button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-outlined{background-color:transparent;border-color:#40a02b;box-shadow:none;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #40a02b #40a02b !important}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-success.is-light{background-color:#f1fbef;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:hover,html.theme--catppuccin-latte .button.is-success.is-light.is-hovered{background-color:#e8f8e5;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:active,html.theme--catppuccin-latte .button.is-success.is-light.is-active{background-color:#e0f5db;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:hover,html.theme--catppuccin-latte .button.is-warning.is-hovered{background-color:#d4871c;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus,html.theme--catppuccin-latte .button.is-warning.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus:not(:active),html.theme--catppuccin-latte .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(223,142,29,0.25)}html.theme--catppuccin-latte .button.is-warning:active,html.theme--catppuccin-latte .button.is-warning.is-active{background-color:#c8801a;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:#df8e1d;box-shadow:none}html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-focused{background-color:#df8e1d;border-color:#df8e1d;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-light{background-color:#fdf6ed;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:hover,html.theme--catppuccin-latte .button.is-warning.is-light.is-hovered{background-color:#fbf1e2;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:active,html.theme--catppuccin-latte .button.is-warning.is-light.is-active{background-color:#faebd6;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:hover,html.theme--catppuccin-latte .button.is-danger.is-hovered{background-color:#c60e36;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus,html.theme--catppuccin-latte .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus:not(:active),html.theme--catppuccin-latte .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(210,15,57,0.25)}html.theme--catppuccin-latte .button.is-danger:active,html.theme--catppuccin-latte .button.is-danger.is-active{background-color:#ba0d33;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:#d20f39;box-shadow:none}html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-focused{background-color:#d20f39;border-color:#d20f39;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-light{background-color:#feecf0;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:hover,html.theme--catppuccin-latte .button.is-danger.is-light.is-hovered{background-color:#fde0e6;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:active,html.theme--catppuccin-latte .button.is-danger.is-light.is-active{background-color:#fcd4dd;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-latte .button.is-small:not(.is-rounded),html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .button.is-normal{font-size:1rem}html.theme--catppuccin-latte .button.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .button.is-large{font-size:1.5rem}html.theme--catppuccin-latte .button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button{background-color:#9ca0b0;border-color:#acb0be;box-shadow:none;opacity:.5}html.theme--catppuccin-latte .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-latte .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-latte .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-latte .button.is-static{background-color:#e6e9ef;border-color:#acb0be;color:#8c8fa1;box-shadow:none;pointer-events:none}html.theme--catppuccin-latte .button.is-rounded,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-latte .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-latte .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-latte .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-latte .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-latte .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-latte .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-latte .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-latte .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-latte .buttons.has-addons .button:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-latte .buttons.has-addons .button:focus,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused,html.theme--catppuccin-latte .buttons.has-addons .button:active,html.theme--catppuccin-latte .buttons.has-addons .button.is-active,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-latte .buttons.has-addons .button:focus:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-latte .buttons.has-addons .button:active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-latte .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .buttons.is-centered{justify-content:center}html.theme--catppuccin-latte .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-latte .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-latte .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-latte .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-latte .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-latte .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-latte .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-latte .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-latte .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-latte .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-latte .content li+li{margin-top:0.25em}html.theme--catppuccin-latte .content p:not(:last-child),html.theme--catppuccin-latte .content dl:not(:last-child),html.theme--catppuccin-latte .content ol:not(:last-child),html.theme--catppuccin-latte .content ul:not(:last-child),html.theme--catppuccin-latte .content blockquote:not(:last-child),html.theme--catppuccin-latte .content pre:not(:last-child),html.theme--catppuccin-latte .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-latte .content h1,html.theme--catppuccin-latte .content h2,html.theme--catppuccin-latte .content h3,html.theme--catppuccin-latte .content h4,html.theme--catppuccin-latte .content h5,html.theme--catppuccin-latte .content h6{color:#4c4f69;font-weight:600;line-height:1.125}html.theme--catppuccin-latte .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-latte .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-latte .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-latte .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-latte .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-latte .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-latte .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-latte .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-latte .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-latte .content blockquote{background-color:#e6e9ef;border-left:5px solid #acb0be;padding:1.25em 1.5em}html.theme--catppuccin-latte .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-latte .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-latte .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-latte .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-latte .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-latte .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-latte .content ul ul ul{list-style-type:square}html.theme--catppuccin-latte .content dd{margin-left:2em}html.theme--catppuccin-latte .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-latte .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-latte .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-latte .content figure img{display:inline-block}html.theme--catppuccin-latte .content figure figcaption{font-style:italic}html.theme--catppuccin-latte .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-latte .content sup,html.theme--catppuccin-latte .content sub{font-size:75%}html.theme--catppuccin-latte .content table{width:100%}html.theme--catppuccin-latte .content table td,html.theme--catppuccin-latte .content table th{border:1px solid #acb0be;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-latte .content table th{color:#41445a}html.theme--catppuccin-latte .content table 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#documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}html.theme--catppuccin-latte .file.is-normal{font-size:1rem}html.theme--catppuccin-latte .file.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .file.is-medium .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-large{font-size:1.5rem}html.theme--catppuccin-latte .file.is-large .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .file.has-name.is-empty .file-cta{border-radius:.4em}html.theme--catppuccin-latte .file.has-name.is-empty .file-name{display:none}html.theme--catppuccin-latte .file.is-boxed .file-label{flex-direction:column}html.theme--catppuccin-latte .file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}html.theme--catppuccin-latte .file.is-boxed .file-name{border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-boxed .file-icon{height:1.5em;width:1.5em}html.theme--catppuccin-latte .file.is-boxed .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-boxed.is-small .file-icon .fa,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .file.is-boxed.is-medium .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.is-boxed.is-large .file-icon .fa{font-size:35px}html.theme--catppuccin-latte .file.is-boxed.has-name .file-cta{border-radius:.4em .4em 0 0}html.theme--catppuccin-latte .file.is-boxed.has-name .file-name{border-radius:0 0 .4em .4em;border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-centered{justify-content:center}html.theme--catppuccin-latte .file.is-fullwidth .file-label{width:100%}html.theme--catppuccin-latte .file.is-fullwidth .file-name{flex-grow:1;max-width:none}html.theme--catppuccin-latte .file.is-right{justify-content:flex-end}html.theme--catppuccin-latte .file.is-right .file-cta{border-radius:0 .4em .4em 0}html.theme--catppuccin-latte .file.is-right .file-name{border-radius:.4em 0 0 .4em;border-width:1px 0 1px 1px;order:-1}html.theme--catppuccin-latte .file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}html.theme--catppuccin-latte .file-label:hover .file-cta{background-color:#c5c9d5;color:#41445a}html.theme--catppuccin-latte .file-label:hover .file-name{border-color:#a5a9b8}html.theme--catppuccin-latte .file-label:active .file-cta{background-color:#bdc2cf;color:#41445a}html.theme--catppuccin-latte .file-label:active .file-name{border-color:#9ea2b3}html.theme--catppuccin-latte .file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}html.theme--catppuccin-latte .file-cta,html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-radius:.4em;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}html.theme--catppuccin-latte .file-cta{background-color:#ccd0da;color:#4c4f69}html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:inherit;text-overflow:ellipsis}html.theme--catppuccin-latte .file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:.5em;width:1em}html.theme--catppuccin-latte .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .label{color:#41445a;display:block;font-size:1rem;font-weight:700}html.theme--catppuccin-latte .label:not(:last-child){margin-bottom:0.5em}html.theme--catppuccin-latte .label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}html.theme--catppuccin-latte .label.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .label.is-large{font-size:1.5rem}html.theme--catppuccin-latte .help{display:block;font-size:.75rem;margin-top:0.25rem}html.theme--catppuccin-latte .help.is-white{color:#fff}html.theme--catppuccin-latte .help.is-black{color:#0a0a0a}html.theme--catppuccin-latte .help.is-light{color:#f5f5f5}html.theme--catppuccin-latte .help.is-dark,html.theme--catppuccin-latte .content kbd.help{color:#ccd0da}html.theme--catppuccin-latte .help.is-primary,html.theme--catppuccin-latte .docstring>section>a.help.docs-sourcelink{color:#1e66f5}html.theme--catppuccin-latte .help.is-link{color:#1e66f5}html.theme--catppuccin-latte .help.is-info{color:#179299}html.theme--catppuccin-latte .help.is-success{color:#40a02b}html.theme--catppuccin-latte .help.is-warning{color:#df8e1d}html.theme--catppuccin-latte .help.is-danger{color:#d20f39}html.theme--catppuccin-latte .field:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.has-addons{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.has-addons .control:not(:last-child){margin-right:-1px}html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .button,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]){z-index:3}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}html.theme--catppuccin-latte .field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.has-addons.has-addons-centered{justify-content:center}html.theme--catppuccin-latte .field.has-addons.has-addons-right{justify-content:flex-end}html.theme--catppuccin-latte .field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.is-grouped>.control{flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--catppuccin-latte .field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.is-grouped.is-grouped-centered{justify-content:center}html.theme--catppuccin-latte .field.is-grouped.is-grouped-right{justify-content:flex-end}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline{flex-wrap:wrap}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:last-child,html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field.is-horizontal{display:flex}}html.theme--catppuccin-latte .field-label .label{font-size:inherit}@media screen and (max-width: 768px){html.theme--catppuccin-latte .field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}html.theme--catppuccin-latte .field-label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-normal{padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-medium{font-size:1.25rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-large{font-size:1.5rem;padding-top:0.375em}}html.theme--catppuccin-latte .field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}html.theme--catppuccin-latte .field-body .field{margin-bottom:0}html.theme--catppuccin-latte .field-body>.field{flex-shrink:1}html.theme--catppuccin-latte .field-body>.field:not(.is-narrow){flex-grow:1}html.theme--catppuccin-latte .field-body>.field:not(:last-child){margin-right:.75rem}}html.theme--catppuccin-latte .control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:inherit}html.theme--catppuccin-latte .control.has-icons-left .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left .select:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .select:focus~.icon{color:#ccd0da}html.theme--catppuccin-latte .control.has-icons-left .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-small~.icon{font-size:.75rem}html.theme--catppuccin-latte .control.has-icons-left .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}html.theme--catppuccin-latte .control.has-icons-left 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kbd.button.is-outlined{background-color:transparent;border-color:#363a4f;box-shadow:none;color:#363a4f}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-macchiato .content 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.button.is-info.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-focused{background-color:#8bd5ca;border-color:#8bd5ca;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-outlined{background-color:transparent;border-color:#8bd5ca;box-shadow:none;color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-light{background-color:#f0faf8;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:hover,html.theme--catppuccin-macchiato .button.is-info.is-light.is-hovered{background-color:#e7f6f4;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:active,html.theme--catppuccin-macchiato .button.is-info.is-light.is-active{background-color:#ddf3f0;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:hover,html.theme--catppuccin-macchiato .button.is-success.is-hovered{background-color:#9ed78c;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus,html.theme--catppuccin-macchiato .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus:not(:active),html.theme--catppuccin-macchiato .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,218,149,0.25)}html.theme--catppuccin-macchiato .button.is-success:active,html.theme--catppuccin-macchiato .button.is-success.is-active{background-color:#96d382;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:#a6da95;box-shadow:none}html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-focused{background-color:#a6da95;border-color:#a6da95;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-light{background-color:#f2faf0;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:hover,html.theme--catppuccin-macchiato .button.is-success.is-light.is-hovered{background-color:#eaf6e6;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:active,html.theme--catppuccin-macchiato .button.is-success.is-light.is-active{background-color:#e2f3dd;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:hover,html.theme--catppuccin-macchiato .button.is-warning.is-hovered{background-color:#eccf94;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus,html.theme--catppuccin-macchiato .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus:not(:active),html.theme--catppuccin-macchiato .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(238,212,159,0.25)}html.theme--catppuccin-macchiato .button.is-warning:active,html.theme--catppuccin-macchiato .button.is-warning.is-active{background-color:#eaca89;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:#eed49f;box-shadow:none}html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-focused{background-color:#eed49f;border-color:#eed49f;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-light{background-color:#fcf7ee;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:hover,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-hovered{background-color:#faf2e3;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:active,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-active{background-color:#f8eed8;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:hover,html.theme--catppuccin-macchiato .button.is-danger.is-hovered{background-color:#eb7c8c;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus,html.theme--catppuccin-macchiato .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus:not(:active),html.theme--catppuccin-macchiato .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(237,135,150,0.25)}html.theme--catppuccin-macchiato .button.is-danger:active,html.theme--catppuccin-macchiato .button.is-danger.is-active{background-color:#ea7183;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:#ed8796;box-shadow:none}html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-macchiato .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-focused{background-color:#ed8796;border-color:#ed8796;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-light{background-color:#fcedef;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:hover,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-hovered{background-color:#fbe2e6;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:active,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-active{background-color:#f9d7dc;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-small,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-macchiato .button.is-small:not(.is-rounded),html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-macchiato .button.is-normal{font-size:1rem}html.theme--catppuccin-macchiato .button.is-medium{font-size:1.25rem}html.theme--catppuccin-macchiato .button.is-large{font-size:1.5rem}html.theme--catppuccin-macchiato .button[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button{background-color:#6e738d;border-color:#5b6078;box-shadow:none;opacity:.5}html.theme--catppuccin-macchiato .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-macchiato .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-macchiato .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-macchiato .button.is-static{background-color:#1e2030;border-color:#5b6078;color:#8087a2;box-shadow:none;pointer-events:none}html.theme--catppuccin-macchiato .button.is-rounded,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-macchiato .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-macchiato .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-macchiato .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-macchiato .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-macchiato .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-macchiato .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-macchiato .buttons.are-small 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.container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-macchiato .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-macchiato .content li+li{margin-top:0.25em}html.theme--catppuccin-macchiato .content p:not(:last-child),html.theme--catppuccin-macchiato .content dl:not(:last-child),html.theme--catppuccin-macchiato .content ol:not(:last-child),html.theme--catppuccin-macchiato .content ul:not(:last-child),html.theme--catppuccin-macchiato .content blockquote:not(:last-child),html.theme--catppuccin-macchiato .content pre:not(:last-child),html.theme--catppuccin-macchiato .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-macchiato .content h1,html.theme--catppuccin-macchiato .content h2,html.theme--catppuccin-macchiato .content h3,html.theme--catppuccin-macchiato .content h4,html.theme--catppuccin-macchiato .content h5,html.theme--catppuccin-macchiato .content 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ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-macchiato .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-macchiato .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-macchiato .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-macchiato .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-macchiato .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-macchiato .content ul ul ul{list-style-type:square}html.theme--catppuccin-macchiato .content dd{margin-left:2em}html.theme--catppuccin-macchiato .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-macchiato 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form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-macchiato .is-black.textarea,html.theme--catppuccin-macchiato .is-black.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}html.theme--catppuccin-macchiato .is-black.textarea:focus,html.theme--catppuccin-macchiato .is-black.input:focus,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:focus,html.theme--catppuccin-macchiato .is-black.is-focused.textarea,html.theme--catppuccin-macchiato .is-black.is-focused.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--catppuccin-macchiato .is-black.textarea:active,html.theme--catppuccin-macchiato .is-black.input:active,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:active,html.theme--catppuccin-macchiato 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html.theme--catppuccin-mocha .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-link.is-light{background-color:#ebf3fe;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:hover,html.theme--catppuccin-mocha .button.is-link.is-light.is-hovered{background-color:#dfebfe;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:active,html.theme--catppuccin-mocha .button.is-link.is-light.is-active{background-color:#d3e3fd;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:hover,html.theme--catppuccin-mocha .button.is-info.is-hovered{background-color:#8adfd1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus,html.theme--catppuccin-mocha .button.is-info.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus:not(:active),html.theme--catppuccin-mocha .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(148,226,213,0.25)}html.theme--catppuccin-mocha .button.is-info:active,html.theme--catppuccin-mocha .button.is-info.is-active{background-color:#80ddcd;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:#94e2d5;box-shadow:none}html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-focused{background-color:#94e2d5;border-color:#94e2d5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-light{background-color:#effbf9;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:hover,html.theme--catppuccin-mocha .button.is-info.is-light.is-hovered{background-color:#e5f8f5;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:active,html.theme--catppuccin-mocha .button.is-info.is-light.is-active{background-color:#dbf5f1;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:hover,html.theme--catppuccin-mocha .button.is-success.is-hovered{background-color:#9de097;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus,html.theme--catppuccin-mocha .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus:not(:active),html.theme--catppuccin-mocha .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,227,161,0.25)}html.theme--catppuccin-mocha .button.is-success:active,html.theme--catppuccin-mocha .button.is-success.is-active{background-color:#93dd8d;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:#a6e3a1;box-shadow:none}html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-focused{background-color:#a6e3a1;border-color:#a6e3a1;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-light{background-color:#f0faef;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:hover,html.theme--catppuccin-mocha .button.is-success.is-light.is-hovered{background-color:#e7f7e5;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:active,html.theme--catppuccin-mocha .button.is-success.is-light.is-active{background-color:#def4dc;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:hover,html.theme--catppuccin-mocha .button.is-warning.is-hovered{background-color:#f8dea3;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus,html.theme--catppuccin-mocha .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus:not(:active),html.theme--catppuccin-mocha .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(249,226,175,0.25)}html.theme--catppuccin-mocha .button.is-warning:active,html.theme--catppuccin-mocha .button.is-warning.is-active{background-color:#f7d997;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:#f9e2af;box-shadow:none}html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-focused{background-color:#f9e2af;border-color:#f9e2af;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-light{background-color:#fef8ec;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:hover,html.theme--catppuccin-mocha .button.is-warning.is-light.is-hovered{background-color:#fdf4e0;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:active,html.theme--catppuccin-mocha .button.is-warning.is-light.is-active{background-color:#fcf0d4;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:hover,html.theme--catppuccin-mocha .button.is-danger.is-hovered{background-color:#f27f9f;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus,html.theme--catppuccin-mocha .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus:not(:active),html.theme--catppuccin-mocha .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(243,139,168,0.25)}html.theme--catppuccin-mocha .button.is-danger:active,html.theme--catppuccin-mocha .button.is-danger.is-active{background-color:#f17497;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:#f38ba8;box-shadow:none}html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-mocha .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-focused{background-color:#f38ba8;border-color:#f38ba8;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-light{background-color:#fdedf1;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:hover,html.theme--catppuccin-mocha .button.is-danger.is-light.is-hovered{background-color:#fce1e8;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:active,html.theme--catppuccin-mocha .button.is-danger.is-light.is-active{background-color:#fbd5e0;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-mocha .button.is-small:not(.is-rounded),html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .button.is-normal{font-size:1rem}html.theme--catppuccin-mocha .button.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .button.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .button[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button{background-color:#6c7086;border-color:#585b70;box-shadow:none;opacity:.5}html.theme--catppuccin-mocha .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-mocha .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-mocha .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-mocha .button.is-static{background-color:#181825;border-color:#585b70;color:#7f849c;box-shadow:none;pointer-events:none}html.theme--catppuccin-mocha .button.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-mocha .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-mocha .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-mocha .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-mocha .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-mocha .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-mocha .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-mocha .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-mocha .buttons.has-addons .button:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-mocha .buttons.has-addons .button:focus,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused,html.theme--catppuccin-mocha .buttons.has-addons .button:active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-mocha .buttons.has-addons .button:focus:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-mocha .buttons.has-addons .button:active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-mocha .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-mocha .buttons.is-centered{justify-content:center}html.theme--catppuccin-mocha .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-mocha .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-mocha .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-mocha .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-mocha .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-mocha .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-mocha .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-mocha .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-mocha .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-mocha .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-mocha .content li+li{margin-top:0.25em}html.theme--catppuccin-mocha .content p:not(:last-child),html.theme--catppuccin-mocha .content dl:not(:last-child),html.theme--catppuccin-mocha .content ol:not(:last-child),html.theme--catppuccin-mocha .content ul:not(:last-child),html.theme--catppuccin-mocha .content blockquote:not(:last-child),html.theme--catppuccin-mocha .content pre:not(:last-child),html.theme--catppuccin-mocha .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-mocha .content h1,html.theme--catppuccin-mocha .content h2,html.theme--catppuccin-mocha .content h3,html.theme--catppuccin-mocha .content h4,html.theme--catppuccin-mocha .content h5,html.theme--catppuccin-mocha .content h6{color:#cdd6f4;font-weight:600;line-height:1.125}html.theme--catppuccin-mocha .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-mocha .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-mocha .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-mocha .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-mocha .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-mocha .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-mocha .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-mocha .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-mocha .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-mocha .content blockquote{background-color:#181825;border-left:5px solid #585b70;padding:1.25em 1.5em}html.theme--catppuccin-mocha .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-mocha .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-mocha .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-mocha .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-mocha .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-mocha .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-mocha .content ul ul ul{list-style-type:square}html.theme--catppuccin-mocha .content dd{margin-left:2em}html.theme--catppuccin-mocha .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-mocha .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-mocha .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-mocha .content figure img{display:inline-block}html.theme--catppuccin-mocha .content figure figcaption{font-style:italic}html.theme--catppuccin-mocha .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-mocha .content sup,html.theme--catppuccin-mocha .content sub{font-size:75%}html.theme--catppuccin-mocha .content table{width:100%}html.theme--catppuccin-mocha .content table td,html.theme--catppuccin-mocha .content table th{border:1px solid #585b70;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-mocha .content table th{color:#b8c5ef}html.theme--catppuccin-mocha .content table th:not([align]){text-align:inherit}html.theme--catppuccin-mocha .content table thead td,html.theme--catppuccin-mocha .content table thead th{border-width:0 0 2px;color:#b8c5ef}html.theme--catppuccin-mocha .content table tfoot td,html.theme--catppuccin-mocha .content table tfoot th{border-width:2px 0 0;color:#b8c5ef}html.theme--catppuccin-mocha .content table tbody tr:last-child td,html.theme--catppuccin-mocha .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-mocha .content .tabs li+li{margin-top:0}html.theme--catppuccin-mocha .content.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--catppuccin-mocha .content.is-normal{font-size:1rem}html.theme--catppuccin-mocha .content.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .content.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--catppuccin-mocha .icon.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--catppuccin-mocha .icon.is-medium{height:2rem;width:2rem}html.theme--catppuccin-mocha .icon.is-large{height:3rem;width:3rem}html.theme--catppuccin-mocha .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--catppuccin-mocha .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--catppuccin-mocha .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--catppuccin-mocha .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--catppuccin-mocha div.icon-text{display:flex}html.theme--catppuccin-mocha .image,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--catppuccin-mocha .image img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--catppuccin-mocha .image img.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--catppuccin-mocha .image.is-fullwidth,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--catppuccin-mocha .image.is-square img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--catppuccin-mocha .image.is-square .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--catppuccin-mocha .image.is-1by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar 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.has-ratio,html.theme--catppuccin-mocha .image.is-5by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--catppuccin-mocha .image.is-5by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--catppuccin-mocha .image.is-16by9 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--catppuccin-mocha .image.is-16by9 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--catppuccin-mocha .image.is-2by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--catppuccin-mocha .image.is-2by1 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--catppuccin-mocha .image.is-3by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--catppuccin-mocha 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img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--catppuccin-mocha .image.is-3by5 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--catppuccin-mocha .image.is-9by16 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--catppuccin-mocha .image.is-9by16 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--catppuccin-mocha .image.is-1by2 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--catppuccin-mocha .image.is-1by2 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--catppuccin-mocha .image.is-1by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--catppuccin-mocha .image.is-1by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}html.theme--catppuccin-mocha .image.is-square,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square,html.theme--catppuccin-mocha .image.is-1by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}html.theme--catppuccin-mocha .image.is-5by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}html.theme--catppuccin-mocha .image.is-4by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--catppuccin-mocha .image.is-3by2,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--catppuccin-mocha .image.is-5by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--catppuccin-mocha .image.is-16by9,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--catppuccin-mocha .image.is-2by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--catppuccin-mocha .image.is-3by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--catppuccin-mocha .image.is-4by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--catppuccin-mocha .image.is-3by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--catppuccin-mocha .image.is-2by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--catppuccin-mocha .image.is-3by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--catppuccin-mocha .image.is-9by16,html.theme--catppuccin-mocha #documenter 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.button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark 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.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark 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.button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-link.is-light{background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:hover,html.theme--documenter-dark .button.is-link.is-light.is-hovered{background-color:#e2fbf6;border-color:transparent;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:active,html.theme--documenter-dark 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.button.is-info.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#3c5dcd}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #3c5dcd #3c5dcd !important}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark 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.button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;color:#259a12}html.theme--documenter-dark .button.is-success.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-outlined.is-focused{background-color:#259a12;border-color:#259a12;color:#fff}html.theme--documenter-dark .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-success.is-light{background-color:#effded;color:#2ec016}html.theme--documenter-dark .button.is-success.is-light:hover,html.theme--documenter-dark 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rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#f4c72f;box-shadow:none;color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f4c72f #f4c72f !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fefaec;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fdf7e0;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fdf3d3;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#c13930;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(203,60,51,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#b7362e;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:#cb3c33;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#cb3c33;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark 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html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fbefef;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#f8e6e5;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#f6dcda;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 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.buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark 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img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 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.docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar 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.hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/previews/PR753/assets/themeswap.js b/previews/PR753/assets/themeswap.js new file mode 100644 index 0000000000..9f5eebe6aa --- /dev/null +++ b/previews/PR753/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. 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The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/previews/PR753/assets/warner.js b/previews/PR753/assets/warner.js new file mode 100644 index 0000000000..3f6f5d0083 --- /dev/null +++ b/previews/PR753/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. 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Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/previews/PR753/diagnostics/available_diagnostics/index.html b/previews/PR753/diagnostics/available_diagnostics/index.html new file mode 100644 index 0000000000..a73c27bd93 --- /dev/null +++ b/previews/PR753/diagnostics/available_diagnostics/index.html @@ -0,0 +1,281 @@ + +Available diagnostics · ClimaLand.jl
GitHub

Available diagnostic variables

Autogenerate table of available diagnostics:

include("make_diagnostic_table.jl")
┌────────────┬─────────────────┬──────────┬──────────────────────────────────┬─────────────────┐
+│ Short name │ Long name       │ Units    │ Comments                         │ Standard name   │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ airp       │ Air pressure    │ Pa       │ The air pressure.                │ air_pressure    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ alpha      │ Albedo          │          │ The fraction of incoming         │ albedo          │
+│            │                 │          │ radiation reflected by the land  │                 │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ an         │ Leaf Net        │ mol CO2  │ Net photosynthesis (carbon       │ leaf_net_photos │
+│            │ Photosynthesis  │ m^-2     │ assimilation) of a leaf,         │ ynthesis        │
+│            │                 │ s^-1     │ computed for example by the      │                 │
+│            │                 │          │ Farquhar model.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ anir       │ Absorbed Near   │ mol      │ The amount of near infrared      │ absorbed_near_i │
+│            │ Infrared        │ photons  │ radiation absorbed by the        │ nfrared_radiati │
+│            │ Radiation       │ m^-2     │ canopy.                          │ on              │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ apar       │ Absorbed        │ mol      │ The amount of                    │ absorbed_photos │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ ynthetically_ac │
+│            │ lly Active      │ m^-2     │ radiation absorbed by the leaf.  │ tive_radiation  │
+│            │ Radiation       │ s^-1     │ The rest if reflected or         │                 │
+│            │                 │          │ transmitted.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ clhf       │ Canopy Latent   │ W m^-2   │ The energy used for canopy       │ canopy_latent_h │
+│            │ Heat Flux       │          │ transpiration.                   │ eat_flux        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ crae       │ Canopy          │ s m^-1   │ The canopy aerodynamic           │ canopy_aerodyna │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ mic_resistance  │
+│            │ Resistance      │          │ resistance (r_a) is a measure    │                 │
+│            │                 │          │ of how much the air near the     │                 │
+│            │                 │          │ Earth's surface resists the      │                 │
+│            │                 │          │ movement of water vapor and      │                 │
+│            │                 │          │ heat from the surface into the   │                 │
+│            │                 │          │ air.                             │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ cshf       │ Canopy          │ W m^-2   │ The energy used for canopy       │ canopy_sensible │
+│            │ Sensible Heat   │          │ temperature change.              │ _heat_flux      │
+│            │ Flux            │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ct         │ Canopy          │ K        │ Canopy temperature.              │ canopy_temperat │
+│            │ Temperature     │          │                                  │ ure             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ far        │ Root flux per   │ m s^-1   │ Flux of water volume per m^2 of  │ root_flux_per_g │
+│            │ ground area     │          │ root per second, multiplied by   │ round_area      │
+│            │                 │          │ the area index (root             │                 │
+│            │                 │          │ area/ground area).               │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gpp        │ Gross Primary   │ mol CO2  │ Net photosynthesis (carbon       │ gross_primary_p │
+│            │ Productivity    │ m^-2     │ assimilation) of the canopy.     │ roductivity     │
+│            │                 │ s^-1     │ This is equivalent to leaf net   │                 │
+│            │                 │          │ assimilation scaled to the       │                 │
+│            │                 │          │ canopy level.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gs         │ Stomatal        │ mol H2O  │ The conductance of leaves. This  │ stomatal_conduc │
+│            │ Conductance     │ m^-2     │ depends on stomatal opening. It  │ tance           │
+│            │                 │ s^-1     │ varies with factors such as      │                 │
+│            │                 │          │ soil moisture or atmospheric     │                 │
+│            │                 │          │ water demand.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ hr         │ Heterotrophic   │ mol      │ The CO2 efflux at the soil       │ heterotrophic_r │
+│            │ Respiration     │ m^-2     │ surface due to microbial         │ espiration      │
+│            │                 │ s^-1     │ decomposition of soil organic    │                 │
+│            │                 │          │ matter. This is not necessarily  │                 │
+│            │                 │          │ equal to CO2 production by       │                 │
+│            │                 │          │ microbes, as co2 diffusion       │                 │
+│            │                 │          │ through the soil pores takes     │                 │
+│            │                 │          │ time.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ infil      │ Infiltration    │ m s^-1   │ The flux of liquid water volume  │ infiltration    │
+│            │                 │          │ into the soil (m^3 of water per  │                 │
+│            │                 │          │ m^2 of ground per second).       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lai        │ Leaf area Index │ m^2 m^-2 │ The area index of leaves,        │ leaf_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ leaves per surface area of       │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lhf        │ Latent Heat     │ W m^-2   │ Exchange of energy at the        │ latent_heat_flu │
+│            │ Flux            │          │ land-atmosphere interface due    │ x               │
+│            │                 │          │ to water evaporation or          │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwd        │ Down Longwave   │ W m^-2   │ The down (in) longwave           │ down_longwave_r │
+│            │ Radiation       │          │ radiation at the surface.        │ adiation        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwn        │ Net Longwave    │ W m^-2   │ The net (in minus out) longwave  │ net_longwave_ra │
+│            │ Radiation       │          │ radiation at the surface.        │ diation         │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ msf        │ Moisture        │          │ Sensitivity of plants            │ moisture_stress │
+│            │ Stress Factor   │          │ conductance to soil water        │ _factor         │
+│            │                 │          │ content. Unitless                │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ nir        │ Near Infrared   │ mol      │ The amount of near infrared      │ near_infrared_r │
+│            │ Radiation       │ photons  │ radiation reaching the canopy.   │ adiation        │
+│            │                 │ m^-2     │                                  │                 │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ par        │ Photosynthetica │ mol      │ The subset of total radiation    │ photosynthetica │
+│            │ lly Active      │ photons  │ that activates photosynthesis    │ lly_active_radi │
+│            │ Radiation       │ m^-2     │ reaching the canopy.             │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ qsfc       │ Surface         │          │ Ratio of water vapor mass to     │ surface_specifi │
+│            │ Specific        │          │ total moist air parcel mass.     │ c_humidity      │
+│            │ Humidity        │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ra         │ Autotrophic     │ mol CO2  │ The canopy autotrophic           │ autotrophic_res │
+│            │ Respiration     │ m^-2     │ respiration, the sum of leaves,  │ piration        │
+│            │                 │ s^-1     │ stems and roots respiration.     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rae        │ Aerodynamic     │ m s^-1   │ Effiency of turbulent transport  │ aerodynamic_res │
+│            │ Resistance      │          │ controlling the land-atmosphere  │ istance         │
+│            │                 │          │ exchange of sensible and latent  │                 │
+│            │                 │          │ heat.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rai        │ Root area Index │ m^2 m^-2 │ The area index of roots,         │ root_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ roots per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rain       │ Rainfall        │ m s^-1   │ Precipitation of liquid water    │ rainfall        │
+│            │                 │          │ volume (m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground per second).              │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rd         │ Leaf            │ mol CO2  │ Leaf respiration, called dark    │ leaf_dark_respi │
+│            │ Respiration     │ m^-2     │ respiration because usually      │ ration          │
+│            │                 │ s^-1     │ measured in the abscence of      │                 │
+│            │                 │          │ radiation.                       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rhosfc     │ Surface Air     │ kg m^−3  │ Density of air at the            │ surface_air_den │
+│            │ Density         │          │ land-atmosphere interface.       │ sity            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rn         │ Net Radiation   │ W m^-2   │ Difference between incoming and  │ net_radiation   │
+│            │                 │          │ outgoing shortwave and longwave  │                 │
+│            │                 │          │ radiation at the land surface.   │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rnir       │ Reflected Near  │ mol      │ The amount of near infrared      │ reflected_near_ │
+│            │ Infrared        │ photons  │ radiation reflected by the       │ infrared_radiat │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ion             │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rpar       │ Reflected       │ mol      │ The amount of                    │ reflected_photo │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ synthetically_a │
+│            │ lly Active      │ m^-2     │ radiation reflected by leaves.   │ ctive_radiation │
+│            │ Radiation       │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sai        │ Stem area Index │ m^2 m^-2 │ The area index of stems,         │ stem_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ stems per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scd        │ Soil CO2        │ m^2 s^-1 │ The diffusivity of CO2 in the    │ soil_co2_diffus │
+│            │ Diffusivity     │          │ porous phase of the soil.        │ ivity           │
+│            │                 │          │ Depends on soil texture,         │                 │
+│            │                 │          │ moisture, and temperature.       │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scms       │ Soil CO2        │ kg C     │ The production of CO2 by         │ soil_co2_microb │
+│            │ Microbial       │ m^-3     │ microbes in the soil. Vary by    │ ial_source      │
+│            │ Source          │ s^-1     │ layers of soil depth. (depth     │                 │
+│            │                 │          │ resolved)                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sco2       │ Soil CO2        │ kg C m^3 │ Concentration of CO2 in the      │ soil_co2        │
+│            │                 │          │ porous air space of the soil.    │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shc        │ Soil Hydraulic  │ m s^-1   │ Soil hydraulic conductivity.     │ soil_hydraulic_ │
+│            │ Conductivity    │          │ (depth resolved)                 │ conductivity    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shf        │ Sensible Heat   │ W m^-2   │ Exchange of energy at the        │ sensible_heat_f │
+│            │ Flux            │          │ land-atmosphere interface due    │ lux             │
+│            │                 │          │ to temperature difference.       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ si         │ Soil Ice        │ m^3 m^-3 │ The volume of soil ice per       │ soil_ice        │
+│            │                 │          │ volume of soil. (depth resolved) │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sie        │ Soil Internal   │ W m^-2   │ The energy per volume of soil.   │ soil_internal_e │
+│            │ Energy          │          │ (depth resolved)                 │ nergy           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sif        │ Solar Induced   │ W m^-2   │ The fluorescence of leaves       │ solar_induced_f │
+│            │ Fluorescence    │          │ induced by solar radiation at    │ luorescence     │
+│            │                 │          │ 755nm. This quantity is          │                 │
+│            │                 │          │ correlated with photosynthesis   │                 │
+│            │                 │          │ activity.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ snow       │ Snowfall        │ m s^-1   │ The precipitation of snow in     │ snowfall        │
+│            │                 │          │ liquid water volume (m^3 of      │                 │
+│            │                 │          │ water per m^2 of ground per      │                 │
+│            │                 │          │ second).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soc        │ Soil organic    │ kg C     │ Mass of organic carbon per       │ soil_organic_ca │
+│            │ carbon          │ m^-3     │ volume of soil. (depth resolved) │ rbon            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soillhf    │ Soil Latent     │ W m^-2   │ Soil latent heat flux, the       │ soil_Latent_Hea │
+│            │ Heat Flux       │          │ amount of liquid water           │ t_Flux          │
+│            │                 │          │ evaporated by the soil,          │                 │
+│            │                 │          │ expressed in energy units (W     │                 │
+│            │                 │          │ m^-2).                           │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrae    │ Soil            │ s m^-1   │ The soil aerodynamic             │ soil_aerodynami │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ c_resistance    │
+│            │ Resistance      │          │ resistance is a measure of how   │                 │
+│            │                 │          │ much the air near the Earth's    │                 │
+│            │                 │          │ surface resists the movement of  │                 │
+│            │                 │          │ water vapor and heat from the    │                 │
+│            │                 │          │ surface into the air.            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrn     │ Soil Net        │ W m^-2   │ Net radiation at the soil        │ soil_net_radiat │
+│            │ Radiation       │          │ surface.                         │ ion             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilshf    │ Soil Sensible   │ W m^-2   │ Soil sensible heat flux, the     │ soil_sensible_H │
+│            │ Heat Flux       │          │ amount of energy exchanged       │ eat_Flux        │
+│            │                 │          │ between the soil and atmosphere  │                 │
+│            │                 │          │ to change the temperature of     │                 │
+│            │                 │          │ the soil.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ssfc       │ Snow Water      │ m        │ Snow at the soil surface,        │ snow_water_equi │
+│            │ Equivalent      │          │ expressed in water equivalent.   │ valent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ stc        │ Soil Thermal    │ W m^-1   │ Soil thermal conductivity.       │ soil_thermal_co │
+│            │ Conductivity    │ K^-1     │ (depth resolved)                 │ nductivity      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swc        │ Soil Water      │ m^3 m^-3 │ The volume of soil water per     │ soil_water_cont │
+│            │ Content         │          │ volume of soil. (depth resolved) │ ent             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swd        │ Short Longwave  │ W m^-2   │ The short (in) longwave          │ short_longwave_ │
+│            │ Radiation       │          │ radiation at the surface.        │ radiation       │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swn        │ Net Shortwave   │ W m^-2   │ The net (in minus out)           │ net_shortwave_r │
+│            │ Radiation       │          │ shortwave radiation at the       │ adiation        │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swp        │ Soil Water      │ Pa       │ Soil water potential. (depth     │ soil_water_pote │
+│            │ Potential       │          │ resolved)                        │ ntial           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sza        │ Solar Zenith    │          │ Solar zenith angle.              │ solar_zenith_an │
+│            │ Angle           │          │                                  │ gle             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tnir       │ Transmitted     │ mol      │ The amount of near infrared      │ transmitted_nea │
+│            │ Near Infrared   │ photons  │ radiation transmitted by the     │ r_infrared_radi │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tpar       │ Transmitted     │ mol      │ The amount of                    │ transmitted_pho │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ tosynthetically │
+│            │ lly Active      │ m^-2     │ radiation transmitted by leaves. │ _active_radiati │
+│            │ Radiation       │ s^-1     │                                  │ on              │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ trans      │ Canopy          │ m s^-1   │ The water evaporated from the    │ canopy_transpir │
+│            │ Transpiration   │          │ canopy due to leaf               │ ation           │
+│            │                 │          │ transpiration (flux of water     │                 │
+│            │                 │          │ volume, m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsfc       │ Surface         │ K        │ Temperature of the land surface. │ surface_tempera │
+│            │ Temperature     │          │                                  │ ture            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsoil      │ Soil            │ K        │ Soil temperature at multiple     │ soil_temperatur │
+│            │ temperature     │          │ soil depth. (depth resolved)     │ e               │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vcmax25    │ Vcmax25         │ mol CO2  │ The parameter vcmax at 25        │ vcmax25         │
+│            │                 │ m^-2     │ degree celsius. Important for    │                 │
+│            │                 │ s^-1     │ the Farquhar model of leaf       │                 │
+│            │                 │          │ photosynthesis.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vflux      │ Liquid water    │ m s^-1   │ Flux of water from the land      │ vapor_flux      │
+│            │ evaporation     │          │ surface to the atmosphere.       │                 │
+│            │                 │          │ E.g., evaporation or             │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ws         │ Wind Speed      │ m s^-1   │ The average wind speed.          │ wind_speed      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsfc       │ Surface Water   │ m        │ Water at the soil surface.       │ surface_water_c │
+│            │ Content         │          │                                  │ ontent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsoil      │ subsurface      │ m        │ Soil water content.              │ subsurface_wate │
+│            │ Water Storage   │          │                                  │ r_storage       │
+└────────────┴─────────────────┴──────────┴──────────────────────────────────┴─────────────────┘
diff --git a/previews/PR753/diagnostics/developers_diagnostics/index.html b/previews/PR753/diagnostics/developers_diagnostics/index.html new file mode 100644 index 0000000000..809acab523 --- /dev/null +++ b/previews/PR753/diagnostics/developers_diagnostics/index.html @@ -0,0 +1,44 @@ + +For developers · ClimaLand.jl
GitHub

ClimaLand Diagnostics: why and how

ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in "K" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.

Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,

  • Diagnostics.jl defines the module,
  • diagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function

add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.

  • define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.

add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).

  • compute methods are defined in a separate file, for example, bucket_compute_methods.jl.
  • standard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.
  • default_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).

will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.

The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.

Compute methods

Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:

@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.α_sfc)
+    else
+        out .= p.bucket.α_sfc
+    end
+end

It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:

@diagnostic_compute "albedo" BucketModel p.bucket.α\_sfc

The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.

Define diagnostics

Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:

add_diagnostic_variable!(
+        short_name = "alpha",
+        long_name = "Albedo",
+        standard_name = "albedo",
+        units = "",
+        compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),
+    )

Default diagnostics

For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,

function default_diagnostics(land_model::BucketModel, t_start; output_writer)
+
+    define_diagnostics!(land_model)
+
+    bucket_diagnostics = [
+        "alpha",
+        "rn",
+        "tsfc",
+        "qsfc",
+        "lhf",
+        "rae",
+        "shf",
+        "vflux",
+        "rhosfc",
+        "t",
+        "w",
+        "ws",
+        "sigmas",
+    ]
+
+    default_outputs =
+        hourly_averages(bucket_diagnostics...; output_writer, t_start)
+    return [default_outputs...]
+end

is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).

For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = ["gpp", "ct", "lai", "swc", "si"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.

Standard diagnostic frequencies

We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example

hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(
+    60 * 60 * one(t_start),
+    (+),
+    output_writer,
+    t_start,
+    short_names...;
+    pre_output_hook! = average_pre_output_hook!,
+)

will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.

diff --git a/previews/PR753/diagnostics/make_diagnostic_table.jl b/previews/PR753/diagnostics/make_diagnostic_table.jl new file mode 100644 index 0000000000..4839efd307 --- /dev/null +++ b/previews/PR753/diagnostics/make_diagnostic_table.jl @@ -0,0 +1,35 @@ +import ClimaLand as CL +using PrettyTables + +# Print all available diagnostics to an ASCII table + +CL.Diagnostics.define_diagnostics!(nothing) +short_names = [] +long_names = [] +units = [] +comments = [] +standard_names = [] +for d in values(CL.Diagnostics.ALL_DIAGNOSTICS) + push!(short_names, d.short_name) + push!(long_names, d.long_name) + push!(units, d.units) + push!(comments, d.comments) + push!(standard_names, d.standard_name) +end +i = sortperm(short_names) # indices of short_names sorted alphabetically +data = hcat( + short_names[i], + long_names[i], + units[i], + comments[i], + standard_names[i], +) +pretty_table( + data; + autowrap = true, + linebreaks = true, + columns_width = [10, 15, 8, 32, 15], # Width = 80 + body_hlines = collect(1:size(data)[1]), + header = ["Short name", "Long name", "Units", "Comments", "Standard name"], + alignment = :l, +) diff --git a/previews/PR753/diagnostics/users_diagnostics/index.html b/previews/PR753/diagnostics/users_diagnostics/index.html new file mode 100644 index 0000000000..c6332f8f3f --- /dev/null +++ b/previews/PR753/diagnostics/users_diagnostics/index.html @@ -0,0 +1,58 @@ + +For users · ClimaLand.jl
GitHub

Using ClimaLand Diagnostics when running a simulation

When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.

This is where ClimaLand Diagnostics comes in for users.

In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.

Default Diagnostics

Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:

define an output folder

output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path("base_output_dir/")

define a space

Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:

space = bucket_domain.space.subsurface

define your writter

Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:

nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)

providing the space and output_dir defined in steps 1. and 2.

make your diagnostics on your model, using your writter, and define a callback

Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:

t0 = 0 # the starting time of your simulation
+
+reference_date = DateTime(2024) # reference_date is the DateTime of your starting time
+
+diags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)
+
+diagnostic_handler =
+    ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)
+
+diag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)

Your diagnostics have now been written in netcdf files in your output folder.

Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.

Custom Diagnostics

When defining a custom diagnostic, follow these steps:

Define how to compute your diagnostic variable from your model state and cache.

For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.

function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)
+    else
+        out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf
+    end
+end

Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,

@diagnostic_compute "latent_heat_flux" BucketModel p.bucket.turbulent_fluxes.lhf

Add that diagnostic(s) variable to your list of variables

 add_diagnostic_variable!(
+    short_name = "bor",
+    long_name = "Bowen ratio",
+    standard_name = "bowen_ratio",
+    units = "",
+    comments = "Ratio of sensible to latent heat flux.",
+    compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),
+)
+
+add_diagnostic_variable!(
+    short_name = "lhf",
+    long_name = "Latent Heat Flux",
+    standard_name = "latent_heat_flux",
+    units = "W m^-2",
+    comments = "Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.",
+    compute! = (out, Y, p, t) ->
+    compute_latent_heat_flux!(out, Y, p, t, land_model),
+)

Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.

seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(
+    90 * 24 * 60 * 60 * one(t_start),
+    max,
+    output_writer,
+    t_start,
+    short_names...,
+)

Define a function to return your ScheduledDiagnostics

Now, you can call your schedule with your variables.

my_custom_diagnostics = ["lhf", "bor"]
+
+diags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)

Analyze your simulation output

Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.

Here is an example of how to plot a variable:

import ClimaAnalysis
+
+import ClimaAnalysis.Visualize as viz
+
+import CairoMakie # the plotting package used by ClimaAnalysis
+
+simdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.
+
+var = get(simdir; "lhf") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.
+
+fig = CairoMakie.Figure() # creates an empty figure object
+
+viz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.
+
+CairoMakie.save(fig) # saves the figure in current working directory
diff --git a/previews/PR753/folderstructure/index.html b/previews/PR753/folderstructure/index.html new file mode 100644 index 0000000000..7111c98c73 --- /dev/null +++ b/previews/PR753/folderstructure/index.html @@ -0,0 +1,2 @@ + +Repository structure · ClimaLand.jl
GitHub

ClimaLand folder structure

ClimaLand home directory has 5 main folders:

  • docs: contains files to generate the documentation website.
  • experiments: contains simple runs of ClimaLand models.
  • parameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc.
  • src: contains the code of ClimaLand models.
  • test: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.

and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push.

  • .buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR.
  • .dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main).
  • .github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand.

as well as 5 files:

  • .gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git.
  • LICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code.
  • Project.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file.
  • README.md: This markdown file contains the info that you can read on ClimaLand GitHub web page

/docs folder

Julia packages are recommended to have a \docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.

The folder /docs contains:

  • a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation "contribution guide" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file.
  • a make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch.

Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl.

/experiments folder

The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified.

For example, /experiments/LSM/ozark/ contains:

  • ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).
  • ozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.
  • ozark_parameters.jl: In this file, parameters values are defined.
  • ozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm.
  • ozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.

/src folder

The /src folder contains the source code of ClimaLand models. It contains 3 folders:

  • sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.
  • standalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder.
  • integrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.

As well as one file:

  • ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase.
diff --git a/previews/PR753/generated/integrated/ozark_canopy_flux_test.png b/previews/PR753/generated/integrated/ozark_canopy_flux_test.png new file mode 100644 index 0000000000..3545594673 Binary files /dev/null and b/previews/PR753/generated/integrated/ozark_canopy_flux_test.png differ diff --git a/previews/PR753/generated/integrated/ozark_soil_plant_flux.png b/previews/PR753/generated/integrated/ozark_soil_plant_flux.png new file mode 100644 index 0000000000..5df1f84d8b Binary files /dev/null and b/previews/PR753/generated/integrated/ozark_soil_plant_flux.png differ diff --git a/previews/PR753/generated/integrated/ozark_soil_test.png b/previews/PR753/generated/integrated/ozark_soil_test.png new file mode 100644 index 0000000000..6589f9d47b Binary files /dev/null and b/previews/PR753/generated/integrated/ozark_soil_test.png differ diff --git a/previews/PR753/generated/integrated/soil_canopy_tutorial/index.html b/previews/PR753/generated/integrated/soil_canopy_tutorial/index.html new file mode 100644 index 0000000000..9cca66c383 --- /dev/null +++ b/previews/PR753/generated/integrated/soil_canopy_tutorial/index.html @@ -0,0 +1,338 @@ + +Coupled Canopy and Soil · ClimaLand.jl
GitHub

Coupling the CliMA Canopy and Soil Hydraulics Models

In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.

In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Soil.Biogeochemistry
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the domain for the model:

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4)
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Column(; zlim = (zmin, zmax), nelements = nelements);
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32) # m
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Setup the Coupled Canopy and Soil Physics Model

We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.

For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.

Define the parameters for the soil model and provide them to the model parameters struct:

Soil parameters

soil_ν = FT(0.5) # m3/m3
+soil_K_sat = FT(4e-7) # m/s
+soil_S_s = FT(1e-3) # 1/m
+soil_vg_n = FT(2.05) # unitless
+soil_vg_α = FT(0.04) # inverse meters
+θ_r = FT(0.067); # m3/m3

Soil heat transfer parameters

ν_ss_quartz = FT(0.1)
+ν_ss_om = FT(0.1)
+ν_ss_gravel = FT(0.0);
+z_0m_soil = FT(0.1)
+z_0b_soil = FT(0.1)
+soil_ϵ = FT(0.98)
+soil_α_PAR = FT(0.2)
+soil_α_NIR = FT(0.4)
+
+soil_domain = land_domain
+soil_ps = Soil.EnergyHydrologyParameters(
+    FT;
+    ν = soil_ν,
+    ν_ss_om = ν_ss_om,
+    ν_ss_quartz = ν_ss_quartz,
+    ν_ss_gravel = ν_ss_gravel,
+    hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),
+    K_sat = soil_K_sat,
+    S_s = soil_S_s,
+    θ_r = θ_r,
+    earth_param_set = earth_param_set,
+    z_0m = z_0m_soil,
+    z_0b = z_0b_soil,
+    emissivity = soil_ϵ,
+    PAR_albedo = soil_α_PAR,
+    NIR_albedo = soil_α_NIR,
+);
+
+soil_args = (domain = soil_domain, parameters = soil_ps)
+soil_model_type = Soil.EnergyHydrology{FT}
ClimaLand.Soil.EnergyHydrology{Float32}

For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.

soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}
+
+soilco2_ps = SoilCO2ModelParameters(FT);

soil microbes args

Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))
+
+soilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()
+soilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);
+soilco2_sources = (MicrobeProduction{FT}(),);
+
+soilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);
+
+soilco2_args = (;
+    boundary_conditions = soilco2_boundary_conditions,
+    sources = soilco2_sources,
+    domain = soil_domain,
+    parameters = soilco2_ps,
+);

Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.

Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.

canopy_component_types = (;
+    autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},
+    radiative_transfer = Canopy.TwoStreamModel{FT},
+    photosynthesis = Canopy.FarquharModel{FT},
+    conductance = Canopy.MedlynConductanceModel{FT},
+    hydraulics = Canopy.PlantHydraulicsModel{FT},
+);

Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.

autotrophic_respiration_args =
+    (; parameters = AutotrophicRespirationParameters(FT))
+
+radiative_transfer_args = (;
+    parameters = TwoStreamParameters(
+        FT;
+        G_Function = ConstantGFunction(FT(0.5)),
+        α_PAR_leaf = 0.1,
+        α_NIR_leaf = 0.45,
+        τ_PAR_leaf = 0.05,
+        τ_NIR_leaf = 0.25,
+        Ω = 0.69,
+    )
+)
+
+conductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))
+
+photosynthesis_args =
+    (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));
+
+K_sat_plant = FT(1.8e-8)
+RAI = (SAI + maxLAI) * f_root_to_shoot;

Note: LAIfunction was determined from data in the script we included above.

ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)
+function root_distribution(z::T; rooting_depth = FT(1.0)) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m
+end
+
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)
+
+plant_ν = FT(0.7)
+plant_S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = plant_ν,
+    S_s = plant_S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+)
+
+plant_hydraulics_args = (
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_midpoints = compartment_midpoints,
+    compartment_surfaces = compartment_surfaces,
+);

We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.

canopy_component_args = (;
+    autotrophic_respiration = autotrophic_respiration_args,
+    radiative_transfer = radiative_transfer_args,
+    photosynthesis = photosynthesis_args,
+    conductance = conductance_args,
+    hydraulics = plant_hydraulics_args,
+);

We also need to provide the shared parameter struct to the canopy.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+)
+canopy_domain = obtain_surface_domain(land_domain)
+canopy_model_args = (; parameters = shared_params, domain = canopy_domain);

We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.

land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)
+
+land = SoilCanopyModel{FT}(;
+    soilco2_type = soilco2_type,
+    soilco2_args = soilco2_args,
+    land_args = land_input,
+    soil_model_type = soil_model_type,
+    soil_args = soil_args,
+    canopy_component_types = canopy_component_types,
+    canopy_component_args = canopy_component_args,
+    canopy_model_args = canopy_model_args,
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.

Y, p, coords = initialize(land);
+exp_tendency! = make_exp_tendency(land);
+imp_tendency! = make_imp_tendency(land);
+jacobian! = make_jacobian(land);
+jac_kwargs =
+    (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);

We need to provide initial conditions for the soil and canopy hydraulics models:

Y.soil.ϑ_l = FT(0.4)
+Y.soil.θ_i = FT(0.0)
+T_0 = FT(288.7)
+ρc_s =
+    volumetric_heat_capacity.(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        land.soil.parameters.ρc_ds,
+        earth_param_set,
+    )
+Y.soil.ρe_int =
+    volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)
+
+Y.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air
+
+ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        plant_ν,
+        plant_S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .=
+        augmented_liquid_fraction.(plant_ν, S_l_ini[i])
+end;

Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.

t0 = Float64(150 * 3600 * 24)# start mid year
+N_days = 100
+tf = t0 + Float64(3600 * 24 * N_days)
+dt = Float64(30)
+n = 120
+saveat = Array(t0:(n * dt):tf)
+
+timestepper = CTS.ARS343()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Now set the initial values for the cache variables for the combined soil and plant model.

set_initial_cache! = make_set_initial_cache(land)
+set_initial_cache!(p, Y, t0);

Set the callbacks, which govern how often we save output, and how often we update the forcing data ("drivers")

sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+model_drivers = ClimaLand.get_drivers(land)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+updateat = Array(t0:1800:tf)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Carry out the simulation

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(
+    prob,
+    ode_algo;
+    dt = dt,
+    callback = cb,
+    adaptive = false,
+    saveat = saveat,
+);

Plotting

Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_canopy_flux_test.png");

Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.

plt1 = Plots.plot(size = (500, 700));
+ϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]
+plt1 = Plots.plot(
+    daily,
+    ϑ_l_10,
+    label = "10 cm",
+    xlabel = "Days",
+    ylabel = "SWC [m/m]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+    color = "blue",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],
+    label = "20cm",
+    color = "red",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],
+    label = "30cm",
+    color = "purple",
+);

Save the output:

savefig("ozark_soil_test.png");

And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:

root_stem_flux = [
+    sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for
+    k in 1:length(sol.t)
+]
+plt1 = Plots.plot(
+    daily,
+    root_stem_flux,
+    label = "soil-root-stem water flux",
+    ylabel = "Water flux[mm/day]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+);

And save the output

savefig("ozark_soil_plant_flux.png");

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/shared_utilities/driver_tutorial/index.html b/previews/PR753/generated/shared_utilities/driver_tutorial/index.html new file mode 100644 index 0000000000..6e6ec56d9f --- /dev/null +++ b/previews/PR753/generated/shared_utilities/driver_tutorial/index.html @@ -0,0 +1,49 @@ + +Intro to forced site-level runs · ClimaLand.jl
GitHub

Using atmospheric and radiative drivers

The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.

Types of forcing data

We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.

The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).

The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.

Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.

Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.

Creating site-level drivers for radiation

First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.

using Dates
+using Insolation # for computing zenith angle given lat, lon, time.
+using ClimaLand
+import ClimaLand.Parameters as LP
+import ClimaParams

Assume the local_datetime array is read in from the data file.

local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data

Timezone (offset of local time from UTC in hrs)

time_offset = 7;

Site latitude and longitude

lat = 38.7441; # degree
+long = -92.2000; # degree

Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time

ref_time = local_datetime[1] + Dates.Hour(time_offset);
+data_dt = 3600.0;
+seconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);

Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:

T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));
+LW_d = 5.67 * 10^(-8) .* T .^ 4;
+SW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);

Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.

LW_d = TimeVaryingInput(seconds, LW_d)
+SW_d = TimeVaryingInput(seconds, SW_d);

Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:

earth_param_set = LP.LandParameters(Float64);
+insol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation
+function zenith_angle(
+    t,
+    ref_time;
+    latitude = lat,
+    longitude = long,
+    insol_params = insol_params,
+)
+    current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC
+
+    d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(
+        current_datetime,
+        ref_time,
+        insol_params,
+    ))
+
+
+    return Insolation.instantaneous_zenith_angle(
+        d,
+        δ,
+        η_UTC,
+        longitude,
+        latitude,
+    )[1]
+end;

Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.

radiation = ClimaLand.PrescribedRadiativeFluxes(
+    Float64,
+    SW_d,
+    LW_d,
+    ref_time;
+    θs = zenith_angle,
+);

Updating the driver variables during the simulation

The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:

p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));

In order to update them, we can make use of default update functions:

update_radiation! = ClimaLand.make_update_drivers(radiation)
+t0 = seconds[1] # midnight local time
+update_radiation!(p, t0);
+@show(p.drivers);
p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])
+

During a simulation, the drivers are updated in place in p.drivers via a "callback", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:

updateat = collect(seconds[1]:(3600 * 3):seconds[end]);
+updatefunc = update_radiation!;
+cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);

This callback must then be provided to the simulation solve function.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Bucket/bucket_tutorial/index.html b/previews/PR753/generated/standalone/Bucket/bucket_tutorial/index.html new file mode 100644 index 0000000000..78d2882022 --- /dev/null +++ b/previews/PR753/generated/standalone/Bucket/bucket_tutorial/index.html @@ -0,0 +1,145 @@ + +Introduction to the Land Bucket Model · ClimaLand.jl
GitHub

Introduction to the Land Bucket Model

The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.

This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.

At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.

In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.

All equation variables are defined immediately below. We have:

$\frac{d W}{dt} = -I,$

$\frac{d Ws}{dt} = -(P_{liq} + σM + (1-σ) E_{soil} - I),$

$\frac{d σS}{dt} = -(P_{snow} + σE_{snow} - σM),$

$ρc \frac{\partial T}{\partial t} = κ_{soil} \frac{\partial T}{\partial z}$

$F_{bot} = 0.0 = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{bot}}$

$(1-σ) (R_n+ SHF + LHF)_{soil} + σG_{undersnow} = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{sfc}}$

$G_{undersnow} = (R_n+ SHF + LHF)_{snow} - F_{intosnow}$

$F_{intosnow} = -ρ_l L_{f,0} (P_{snow} + E_{snow}-M)$

$R_n = -(1-α)*SW↓ -LW↓ + σ_{SB} T_{sfc}^4$

where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.

For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.

Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.

Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.

As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have

$E_{soil} = β(W, W_f) E(q_{sat}(T_{sfc}, ρ_{sfc}; liquid),$

where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have

$E_{snow} = E(q_{sat}(T_{sfc}, ρ_{sfc}; ice).$

Simulating a standalone bucket model

First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.

import SciMLBase
+import ClimaTimeSteppers as CTS

We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.

using ClimaCore

We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.

import ClimaParams as CP

We also use Insolation to calculate solar zenith angle and solar insolation.

Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.

using ClimaLand.Bucket:
+    BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo
+using ClimaLand.Domains: coordinates, Column
+using ClimaLand:
+    initialize,
+    make_update_aux,
+    make_exp_tendency,
+    make_set_initial_cache,
+    PrescribedAtmosphere,
+    PrescribedRadiativeFluxes
+using ClimaUtilities.TimeVaryingInputs: TimeVaryingInput

We also want to plot the solution

using Plots

And we need to use the DateTime type to store reference times

using Dates
+
+FT = Float32;

As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:

import ClimaLand
+import ClimaLand.Parameters as LP
+earth_param_set = LP.LandParameters(FT);

Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.

soil_depth = FT(3.5);
+bucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);
+surface_space = bucket_domain.space.surface
ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}
+  Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])

Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.

α_bareground_func = (coordinate_point) -> 0.2;
+α_snow = FT(0.8);
+albedo =
+    PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);

The critical snow level setting the scale for when we interpolate between snow and surface albedo

σS_c = FT(0.2);

The field capacity of the soil

W_f = FT(0.15);

Roughness lengths (meters)

z_0m = FT(1e-2);
+z_0b = FT(1e-3);

Thermal parameters of soil

κ_soil = FT(0.7);
+ρc_soil = FT(2e6);

Snow melt timescale

τc = FT(3600);

Simulation start time, end time, and timestep

t0 = 0.0;
+tf = 7 * 86400;
+Δt = 3600.0;
+
+bucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);

The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.

ref_time = DateTime(2005);

To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Here we define the model drivers Prescribed atmospheric variables

Precipitation:

precip = (t) -> 0;
+snow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);

Diurnal temperature variations:

T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);

Constant otherwise:

u_atmos = (t) -> 3.0;
+q_atmos = (t) -> 0.005;
+h_atmos = FT(2);
+P_atmos = (t) -> 101325;

We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)

bucket_atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(snow_precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set,
+);

Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:

SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));
+LW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;
+bucket_rad = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:

model = BucketModel(
+    parameters = bucket_parameters,
+    domain = bucket_domain,
+    atmosphere = bucket_atmos,
+    radiation = bucket_rad,
+);

Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.

Like all ClimaLand models, we set up the state vector using initialize:

Y, p, coords = initialize(model);

We can inspect the prognostic and auxiliary variables of the model:

ClimaLand.prognostic_vars(model)
+Y.bucket |> propertynames
(:W, :T, :Ws, :σS)

The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.

ClimaLand.auxiliary_vars(model)
+p.bucket |> propertynames
(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)

Next is to set initial conditions.

Y.bucket.T .= FT(270);
+Y.bucket.W .= FT(0.05);
+Y.bucket.Ws .= FT(0.0);
+Y.bucket.σS .= FT(0.08);

We also set the initial values of the cache here:

set_initial_cache! = make_set_initial_cache(model);
+set_initial_cache!(p, Y, t0);

Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:

exp_tendency! = make_exp_tendency(model);

Now we choose our timestepping algorithm.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

Then we can set up the simulation and solve it:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)

saveat = collect(t0:Δt:tf);
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+saving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);
+updateat = copy(saveat)
+model_drivers = ClimaLand.get_drivers(model)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);

Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.

W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];
+Ws = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];
+σS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];
+T_sfc =
+    [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];
+evaporation = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]
+    for k in 1:length(sol.t)
+];
+R_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];

The turbulent energy flux is the sum of latent and sensible heat fluxes.

LHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for
+    k in 1:length(sol.t)
+];
+SHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for
+    k in 1:length(sol.t)
+];
+turbulent_energy_flux = SHF .+ LHF
+
+plot(
+    sol.t ./ 86400,
+    W,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "W (m)",
+    title = "Land water storage (m)",
+)
+savefig("w.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png"

plot(
+    sol.t ./ 86400,
+    σS,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "σS (m)",
+    title = "Area weighted SWE (m) ",
+)
+savefig("swe.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png"

plot(
+    sol.t ./ 86400,
+    snow_precip.(sol.t),
+    label = "Net precipitation",
+    xlabel = "time (days)",
+    ylabel = "Flux (m/s)",
+    title = "Surface water fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, evaporation, label = "Sublimation/Evaporation")
+savefig("water_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png"

plot(
+    sol.t ./ 86400,
+    T_sfc,
+    title = "Surface Temperatures",
+    label = "Ground temperature",
+    xlabel = "time (days)",
+    ylabel = "T_sfc (K)",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, T_atmos.(sol.t), label = "Atmospheric Temperature")
+savefig("t.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png"

plot(
+    sol.t ./ 86400,
+    R_n,
+    label = "Net radiative flux",
+    xlabel = "time (days)",
+    ylabel = "Flux (W/m^2)",
+    title = "Surface energy fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, turbulent_energy_flux, label = "Turbulent fluxes")
+plot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = "Net flux")
+savefig("energy_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png"

References

[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Bucket/coupled_bucket/index.html b/previews/PR753/generated/standalone/Bucket/coupled_bucket/index.html new file mode 100644 index 0000000000..44e754f497 --- /dev/null +++ b/previews/PR753/generated/standalone/Bucket/coupled_bucket/index.html @@ -0,0 +1,2 @@ + +Setting up a Coupled Simulation · ClimaLand.jl
GitHub

Setting up a Coupled Simulation

For more information about the bucket model, please see the bucket model tutorial.

This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.

Background

Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.

In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.

In our current setup, "passed back to the land model via the coupler" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.

In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.

Turbulent Surface Fluxes and Radiation

Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:

prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)

These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.

In the coupled case, we want different behavior. We have defined new $coupled$ types to use instead of the "prescribed" types:

struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end

Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end

similarily:

function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end

These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!

Surface air density

Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.

In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end

Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Bucket/energy_f.png b/previews/PR753/generated/standalone/Bucket/energy_f.png new file mode 100644 index 0000000000..2fe9f0d606 Binary files /dev/null and b/previews/PR753/generated/standalone/Bucket/energy_f.png differ diff --git a/previews/PR753/generated/standalone/Bucket/swe.png b/previews/PR753/generated/standalone/Bucket/swe.png new file mode 100644 index 0000000000..9201c070e6 Binary files /dev/null and b/previews/PR753/generated/standalone/Bucket/swe.png differ diff --git a/previews/PR753/generated/standalone/Bucket/t.png b/previews/PR753/generated/standalone/Bucket/t.png new file mode 100644 index 0000000000..387db53ad8 Binary files /dev/null and b/previews/PR753/generated/standalone/Bucket/t.png differ diff --git a/previews/PR753/generated/standalone/Bucket/w.png b/previews/PR753/generated/standalone/Bucket/w.png new file mode 100644 index 0000000000..b11d3a746a Binary files /dev/null and b/previews/PR753/generated/standalone/Bucket/w.png differ diff --git a/previews/PR753/generated/standalone/Bucket/water_f.png b/previews/PR753/generated/standalone/Bucket/water_f.png new file mode 100644 index 0000000000..a5ee08bbf4 Binary files /dev/null and b/previews/PR753/generated/standalone/Bucket/water_f.png differ diff --git a/previews/PR753/generated/standalone/Canopy/canopy_tutorial/index.html b/previews/PR753/generated/standalone/Canopy/canopy_tutorial/index.html new file mode 100644 index 0000000000..bc3db437cf --- /dev/null +++ b/previews/PR753/generated/standalone/Canopy/canopy_tutorial/index.html @@ -0,0 +1,202 @@ + +Standalone Canopy · ClimaLand.jl
GitHub

Introduction to the Canopy Model

This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.

The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.

ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.

The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.

The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using StaticArrays
+using ClimaLand
+using ClimaLand.Domains: Point
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the Canopy Model

We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.

First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4) # kg/m^2
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Point(; z_sfc = FT(0.0))
ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}
+  Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32)
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+);

For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:

ψ_soil0 = FT(0.0)
+
+soil_driver = PrescribedSoil(
+    FT;
+    root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),
+    ψ = t -> ψ_soil0,
+    α_PAR = FT(0.2),
+    α_NIR = FT(0.4),
+    T = t -> 298.0,
+    ϵ = FT(0.99),
+);

Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:

rt_params = TwoStreamParameters(
+    FT;
+    G_Function = ConstantGFunction(FT(0.5)),
+    α_PAR_leaf = FT(0.1),
+    α_NIR_leaf = FT(0.45),
+    τ_PAR_leaf = FT(0.05),
+    τ_NIR_leaf = FT(0.25),
+    Ω = FT(0.69),
+    λ_γ_PAR = FT(5e-7),
+    λ_γ_NIR = FT(1.65e-6),
+)
+
+rt_model = TwoStreamModel{FT}(rt_params);

Arguments for conductance model:

cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))
+
+stomatal_model = MedlynConductanceModel{FT}(cond_params);

Arguments for photosynthesis model:

photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))
+
+photosynthesis_model = FarquharModel{FT}(photo_params);

Arguments for autotrophic respiration model:

AR_params = AutotrophicRespirationParameters(FT)
+AR_model = AutotrophicRespirationModel{FT}(AR_params);

Arguments for plant hydraulics model are more complicated.

Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.

LAI = 4.2
+LAIfunction = (t) -> LAI
+SAI = FT(0.00242)
+f_root_to_shoot = FT(3.5)
+RAI = FT((SAI + LAI) * f_root_to_shoot)
+ai_parameterization =
+    PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)
+rooting_depth = FT(1.0);

Define the root distribution function p(z):

function root_distribution(z::T; rooting_depth = rooting_depth) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))
+end;

Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.

K_sat_plant = FT(1.8e-8)
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);

Use these values to populate the parameters of the PlantHydraulics model:

ν = FT(0.7)
+S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = ν,
+    S_s = S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+);

Define the remaining variables required for the plant hydraulics model.

plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_surfaces = compartment_surfaces,
+    compartment_midpoints = compartment_midpoints,
+);

Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.

canopy = ClimaLand.Canopy.CanopyModel{FT}(;
+    parameters = shared_params,
+    domain = land_domain,
+    autotrophic_respiration = AR_model,
+    radiative_transfer = rt_model,
+    photosynthesis = photosynthesis_model,
+    conductance = stomatal_model,
+    hydraulics = plant_hydraulics,
+    soil_driver = soil_driver,
+    atmos = atmos,
+    radiation = radiation,
+);
[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.

Y, p, coords = ClimaLand.initialize(canopy)
+exp_tendency! = make_exp_tendency(canopy);

Provide initial conditions for the canopy hydraulics model

ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        ν,
+        S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])
+end;

Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

t0 = 0.0
+N_days = 364
+tf = t0 + 3600 * 24 * N_days
+dt = 225.0;

Initialize the cache variables for the canopy using the initial conditions and initial time.

set_initial_cache! = make_set_initial_cache(canopy)
+set_initial_cache!(p, Y, t0);

Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.

n = 16
+saveat = Array(t0:(n * dt):tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);

Create the callback function which updates the forcing variables, or drivers.

updateat = Array(t0:1800:tf)
+model_drivers = ClimaLand.get_drivers(canopy)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Select a timestepping algorithm and setup the ODE problem.

timestepper = CTS.RK4();
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Create some plots

We can now plot the data produced in the simulation. For example, GPP:

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_standalone_canopy_test.png");

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Canopy/ozark_standalone_canopy_test.png b/previews/PR753/generated/standalone/Canopy/ozark_standalone_canopy_test.png new file mode 100644 index 0000000000..7a17facc9c Binary files /dev/null and b/previews/PR753/generated/standalone/Canopy/ozark_standalone_canopy_test.png differ diff --git a/previews/PR753/generated/standalone/Snow/base_tutorial/index.html b/previews/PR753/generated/standalone/Snow/base_tutorial/index.html new file mode 100644 index 0000000000..98ed735352 --- /dev/null +++ b/previews/PR753/generated/standalone/Snow/base_tutorial/index.html @@ -0,0 +1,74 @@ + +Seasonal Snow Timeseries Generation with a Neural Network · ClimaLand.jl
GitHub

Seasonal Snow Timeseries Generation with a Neural Network

This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.

The updates of the neural snow model follow the equation

$\frac{dz}{dt} = \mathcal{M}\left(z, SWE, φ, R, v, T_{air}, P_{snow}\right),$

where

  • $t$ is the time (s),

  • $z$ is the snow depth (m),

  • $\mathcal{M}$ is the neural network,

  • $SWE$ is the Snow Water Equivalent, or the height of water if all the snow melted (m),

  • $φ$ is the relative humidity (0-1),

  • $R$ is the solar radiation (W/m²).

  • $v$ is the wind speed (W/m²).

  • $T_{air}$ is the air temperature (degrees C).

  • $P_{snow}$ is the water equivalent rate of snow precipitation (m/s).

The model is a 1D model to permit utilization over any desired grid resolution and shape.

We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.

We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN
┌ Warning: Package cuDNN not found in current path.
+│ - Run `import Pkg; Pkg.add("cuDNN")` to install the cuDNN package, then restart julia.
+│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.
+└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57
+

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools
+ModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;

and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.

using ClimaLand
+code_dir = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Snow")
+include(joinpath(code_dir, "display_tools.jl"));

Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter $n$ as outlined in the associated parameter, and the two loss function hyperparameters $n_1$, $n_2$.

n = 4
+n1 = 2
+n2 = 4;

We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the $\frac{dz}{dt}$ column.

pred_vars = [
+    :z,
+    :SWE,
+    :rel_hum_avg,
+    :sol_rad_avg,
+    :wind_speed_avg,
+    :air_temp_avg,
+    :dprecipdt_snow,
+]
+target = :dzdt;

Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.

nfeatures = length(pred_vars)
+z_idx = 1
+p_idx = 7;

We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.

training_data_download_link = "https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv"
+testing_data_download_link = "https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv"
+data_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)
+valdata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)
+Δt = Second(86400)
+hole_thresh = 5;

With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):

usedata = DataTools.prep_data(data_train);

After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of "0" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.

out_scale = maximum(abs.(usedata[!, target]))
+in_scales = std.(eachcol(select(usedata, pred_vars)))
+x_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);

We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.

model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)
+ps = ModelTools.get_model_ps(model);

As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.

ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)
+ModelTools.setoutscale!(model, 1.0);

With that, training is as simple as calling the trainmodel! function:

print("\nTraining model!\n")
+ModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);

+Training model!
+Epoch: 10 | training loss: 0.002703763
+Epoch: 20 | training loss: 0.0026803392
+Epoch: 30 | training loss: 0.0026741433
+Epoch: 40 | training loss: 0.0025964214
+Epoch: 50 | training loss: 0.0025694624
+Epoch: 60 | training loss: 0.0026297325
+Epoch: 70 | training loss: 0.0025780287
+Epoch: 80 | training loss: 0.0025821207
+Epoch: 90 | training loss: 0.0025638747
+Epoch: 100 | training loss: 0.002566546
+

To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.

ModelTools.setoutscale!(model, out_scale)
+ModelTools.settimescale!(model, Dates.value(Δt));

For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):

Note that gaps in the data are shown as shaded regions on the plotted timeseries.

site_id = 1286
+sitedata = usedata[usedata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Slagamount Lakes, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot1.png",
+    display_plot = false,
+);

Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:

site_id = "1070" #string format for the testing ids is due to non-numerical testing site codes.
+sitedata = valdata[valdata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Anchorage Hillside, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot2.png",
+    display_plot = false,
+);

Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Snow/base_tutorial_plot1.png b/previews/PR753/generated/standalone/Snow/base_tutorial_plot1.png new file mode 100644 index 0000000000..dd4b728e4c Binary files /dev/null and b/previews/PR753/generated/standalone/Snow/base_tutorial_plot1.png differ diff --git a/previews/PR753/generated/standalone/Snow/base_tutorial_plot2.png b/previews/PR753/generated/standalone/Snow/base_tutorial_plot2.png new file mode 100644 index 0000000000..9d319e8ddb Binary files /dev/null and b/previews/PR753/generated/standalone/Snow/base_tutorial_plot2.png differ diff --git a/previews/PR753/generated/standalone/Snow/data_tutorial/index.html b/previews/PR753/generated/standalone/Snow/data_tutorial/index.html new file mode 100644 index 0000000000..677c847981 --- /dev/null +++ b/previews/PR753/generated/standalone/Snow/data_tutorial/index.html @@ -0,0 +1,132 @@ + +Scraping SNOTEL Data · ClimaLand.jl
GitHub

Scraping SNOTEL Data

This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.

We begin by importing all required packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;

We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.

metadata = DataTools.snotel_metadata();
+metacols = ["id", "name", "state", "elev", "lat", "lon"]
+DataFrames.rename!(metadata, Symbol.(metacols));

At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:

example_ID = 1030
+example_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]
+example_data = DataTools.scrape_site_paper(example_ID, example_state);

And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.

We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.

const inch2meter = 0.0254
+const kmphr2mps = 5.0 / 18.0
+
+filter_val = Dict{Symbol, Tuple{Real, Real}}(
+    :SWE => (0.0, 250.0),
+    :z => (0.0, 420.0),
+    :precip => (0.0, 250.0),
+    :rel_hum_avg => (10.0, 100.0),
+    :sol_rad_avg => (0.0, 1500.0),
+    :wind_speed_avg => (0.0, 216.0),
+    :air_temp_avg => (-40.0, 60.0),
+)
+
+scales = Dict{Symbol, Real}(
+    :SWE => inch2meter,
+    :z => inch2meter,
+    :precip => inch2meter,
+    :rel_hum_avg => 0.01,
+    :wind_speed_avg => kmphr2mps,
+);

We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. "start" refers to 1850-01-01 or the first available date, while "end" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:

  • * Indicates alternative handling of the rectify_daily_hourly() function.

  • ^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.

  • A Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.

  • T Requires a site that already has had the temperature bias correction at the portal level as of May 2024.

  • X Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.

good_stations = Dict{Int, Tuple{String, String}}(
+    #306 => ("start", "end"), #*
+    316 => ("start", "end"),
+    344 => ("start", "end"),
+    #=367 => ("start", "end"),
+    395 => ("start", "end"),
+    457 => ("start", "end"),
+    482 => ("start", "end"),
+    491 => ("start", "end"),
+    515 => ("start", "2023-06-02"), #X
+    532 => ("start", "end"),
+    551 => ("start", "end"),
+    571 => ("start", "end"),
+    599 => ("start", "end"),
+    608 => ("start", "end"),
+    613 => ("start", "end"),
+    641 => ("start", "end"), #A^
+    665 => ("start", "end"),
+    708 => ("start", "end"),
+    715 => ("start", "end"),
+    734 => ("start", "end"),
+    737 => ("start", "end"),
+    744 => ("start", "end"),
+    832 => ("start", "end"),
+    845 => ("start", "end"),
+    854 => ("start", "end"),
+    857 => ("start", "end"),
+    921 => ("start", "end"),
+    922 => ("start", "end"),
+    927 => ("start", "end"),
+    942 => ("start", "end"),
+    963 => ("start", "end"), #A^
+    969 => ("start", "end"),
+    974 => ("start", "end"),
+    978 => ("start", "end"), #*
+    1030 => ("start", "end"),
+    1035 => ("start", "end"), #A^
+    1053 => ("start", "end"),
+    1070 => ("start", "end"), #A^T
+    1083 => ("start", "end"),
+    1091 => ("start", "end"), #A^T
+    1092 => ("start", "end"), #A^T
+    1105 => ("start", "end"),
+    1122 => ("start", "end"), #*
+    1123 => ("start", "end"),
+    1159 => ("start", "end"),
+    1168 => ("start", "end"),
+    1170 => ("start", "end"),
+    1254 => ("start", "end"),
+    1286 => ("start", "end"),
+    2080 => ("start", "end"), #A^
+    2170 => ("start", "end"), #^
+    =#
+);

We then loop through each site to scrape and follow an automated data pipeline, consisting of:

  • Extracting the daily and hourly timeseries from the site
  • Applying the sensor bounds over each data timeseries (i.e. remove sensor error)
  • Converting the hourly dataset into a daily dataset
  • Coalescing the converted-hourly and daily data into one dataset
  • Scaling all data to the appropriate metric units
  • Restricting data to complete cases
  • Making the differential variables ( $\frac{dz}{dt}$, etc.)
  • Resetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip
  • Attaching appropriate metadata

A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.

allsites = Any[];
+for site in sort(collect(keys(good_stations)))
+    state = metadata[metadata[!, :id] .== site, :state][1]
+    start_date = good_stations[site][1]
+    end_date = good_stations[site][2]
+
+    hourly = DataTools.apply_bounds(
+        DataTools.sitedata_hourly(
+             site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+       filter_val,
+    )
+    hourly[!, :id] .= site
+    #hourly = DataTools.bcqc_hourly(hourly)
+    hourly_d = DataTools.hourly2daily(hourly)
+    #DataFrames.allowmissing!(hourly_d)
+    #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)
+   #hourly_d[sflags, :sol_rad_avg] .= missing
+
+    daily = DataTools.apply_bounds(
+        DataTools.sitedata_daily(
+            site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+        filter_val,
+    )
+    daily[!, :id] .= site
+    gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)
+    #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)
+    #gap_daily = DataTools.d_impute(gap_daily)
+    daily_scaled = DataTools.scale_cols(gap_daily, scales)
+    daily_clean = daily_scaled[completecases(daily_scaled), :]
+    daily_clean = DataTools.makediffs(daily_clean, Day(1))
+    good_vals = daily_clean[!, :dprecipdt] .>= 0.0
+    daily_clean[(!).(good_vals), :dprecipdt] .= 0.0
+    daily_clean = daily_clean[!, Not(:precip)]
+    #show(describe(daily_clean), allrows = true, allcols = true)
+    #print("\nSIZE: ", nrow(daily_clean), "\n")
+
+    daily_clean[!, :id] .= site
+    daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]
+    daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]
+    daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]
+
+    push!(allsites, daily_clean)
+end;

With the sites complete, we condense all sites into a single DataFrame,

totaldata = deepcopy(allsites[1])
+for site in allsites[2:end]
+    append!(totaldata, site)
+end

and a final CSV.write("data.csv", totaldata) call will save the file.

Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/boundary_conditions/index.html b/previews/PR753/generated/standalone/Soil/boundary_conditions/index.html new file mode 100644 index 0000000000..998eed7b71 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/boundary_conditions/index.html @@ -0,0 +1,2 @@ + +Boundary conditions · ClimaLand.jl
GitHub

Boundary conditions for the soil model

In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.

Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.

Boundary conditions for Richards equation

  1. FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.

  2. WaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).

  3. MoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).

  4. RichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.

Boundary conditions for the soil heat equation

  1. HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).

  2. TemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).

Boundary conditions for the soil heat + water equations (EnergyHydrology model)

The full soil model requires boundary conditions for both Richards equation and the soil heat equation.

  1. WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).

  2. AtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/eq_moisture_plot.png b/previews/PR753/generated/standalone/Soil/eq_moisture_plot.png new file mode 100644 index 0000000000..df15e83379 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/eq_moisture_plot.png differ diff --git a/previews/PR753/generated/standalone/Soil/eq_temperature_plot.png b/previews/PR753/generated/standalone/Soil/eq_temperature_plot.png new file mode 100644 index 0000000000..f50685a410 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/eq_temperature_plot.png differ diff --git "a/previews/PR753/generated/standalone/Soil/equilibrium_test_\317\221_l.png" "b/previews/PR753/generated/standalone/Soil/equilibrium_test_\317\221_l.png" new file mode 100644 index 0000000000..e19d4c660e Binary files /dev/null and "b/previews/PR753/generated/standalone/Soil/equilibrium_test_\317\221_l.png" differ diff --git a/previews/PR753/generated/standalone/Soil/evaporation/index.html b/previews/PR753/generated/standalone/Soil/evaporation/index.html new file mode 100644 index 0000000000..9e9f1b6c92 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/evaporation/index.html @@ -0,0 +1,229 @@ + +Coarse Sand Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.

Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.

ref_time = DateTime(2005) # required argument, but not used in this case
+SW_d = (t) -> 0
+LW_d = (t) -> 301.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 5
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)
+    Y.soil.θ_i .= 0
+    T = FT(296.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 13)
+dt = Float64(900.0)
900.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Define the tendency functions

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+evaporation_data =
+    ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();
+ref_soln_E = readdlm(evaporation_data, ',')
+ref_soln_E_350mm = ref_soln_E[2:end, 1:2]
+data_dates = ref_soln_E_350mm[:, 1]
+data_e = ref_soln_E_350mm[:, 2];
+
+fig = Figure(size = (800, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Evaporation rate (mm/d)",
+    title = "Bare soil evaporation",
+)
+CairoMakie.xlims!(minimum(data_dates), maximum(data_dates))
+CairoMakie.lines!(
+    ax,
+    FT.(data_dates),
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+ax = Axis(
+    fig[1, 2],
+    xlabel = "Mass (g)",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+A_col = π * (0.027)^2
+mass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col
+mass_loss =
+    [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]
+CairoMakie.lines!(
+    ax,
+    cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    mass_loss,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+save("evaporation_lehmann2008_fig8b.png", fig);

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/evaporation_gardner_fig1.png b/previews/PR753/generated/standalone/Soil/evaporation_gardner_fig1.png new file mode 100644 index 0000000000..4634fa4cf2 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/evaporation_gardner_fig1.png differ diff --git a/previews/PR753/generated/standalone/Soil/evaporation_gilat_loess/index.html b/previews/PR753/generated/standalone/Soil/evaporation_gilat_loess/index.html new file mode 100644 index 0000000000..4f3b18d778 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/evaporation_gilat_loess/index.html @@ -0,0 +1,374 @@ + +Gilat Loess Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.

For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations

The same experiment is carried out 3 times

  1. No evaporation (zero flux boundary conditions)
  2. With evaporation but no drainage (Ksat = 0)
  3. With evaporation and drainage
using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

Parameters

K_sat = FT(0.01 / 3600 / 24)
+vg_n = FT(1.45)
+vg_α = FT(1.5)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.4)
+θ_r = FT(0.04)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(0.3)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)# 10mm
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 294.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+)
PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}(Main.var"##317".var"#1#2"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}(Main.var"##317".var"#3#4"()), Dates.DateTime("2005-01-01T00:00:00"), nothing)

Atmos

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+)
PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}(Main.var"##317".var"#7#8"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}(Main.var"##317".var"#9#10"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}(Main.var"##317".var"#11#12"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}(Main.var"##317".var"#13#14"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}(ClimaLand.var"#21#24"()), Dates.DateTime("2005-01-01T00:00:00"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))

Simulation setup - no evaporation Boundary conditions

zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+no_flux_boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
+
+t0 = Float64(0)
+tf = Float64(24 * 3600 * 15)
+dt = Float64(900.0)
+Δz = 0.01
+zmax = FT(0)
+zmin = FT(-1.6)
+nelems = Int((zmax - zmin) / Δz)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_flux_boundary_fluxes,
+    sources = (),
+);

Initial conditions

Y, p, cds = initialize(soil)
+function estimated_ic(z)
+    0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= estimated_ic.(z)
+    Y.soil.θ_i .= 0
+    T = FT(294.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+cb = SciMLBase.CallbackSet(saving_cb);
+
+sol_no_evap =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Repeat with evaporation and drainage This requires different initial conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+evap_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+)
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = evap_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+soil_exp_tendency! = make_exp_tendency(soil)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];
+
+# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.

bottom_water_bc = MoistureStateBC((p, t) -> 0.35)
+no_drainage_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),
+)
+zmax = FT(0)
+zmin = FT(-0.16)
+nelems = Int((zmax - zmin) / Δz)
+dt = Float64(10.0)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_drainage_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z_no_evap, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol_no_drainage =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap_no_drainage = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];

Figures

savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (800, 400))
+ax = Axis(fig[1, 1], xlabel = "Day", ylabel = "Evaporation rate (mm/d)")
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "With drainage",
+    color = :red,
+)
+CairoMakie.lines!(
+    ax,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    evap_no_drainage .* (1000 * 3600 * 24),
+    label = "No drainage",
+    color = :blue,
+)
+
+CairoMakie.axislegend(ax)
+ax2 = Axis(fig[1, 2], xlabel = "Day", ylabel = "Cumulative evaporation (mm)")
+CairoMakie.lines!(
+    ax2,
+    sol.t ./ 3600 ./ 24,
+    cumsum(evap) .* (1000 * 3600),
+    color = :red,
+)
+CairoMakie.lines!(
+    ax2,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    cumsum(evap_no_drainage) .* (1000 * 3600),
+    color = :blue,
+)
+save("evaporation_lehmann2024_figS6.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Drainage only")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.4)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 10]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax1,
+        parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Evap+Drainage", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.3, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 1, 2, 5, 13]
+for i in 1:1:5
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Evap only", xlabel = "Volumetric Water Content")
+CairoMakie.ylims!(-0.15, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 2, 9, 14]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax3,
+        parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        label = "$(days[i]) days",
+        parent(z_no_evap)[:],
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("evaporation_gardner_fig1.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png b/previews/PR753/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png new file mode 100644 index 0000000000..95012f78db Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png differ diff --git a/previews/PR753/generated/standalone/Soil/evaporation_lehmann2024_figS6.png b/previews/PR753/generated/standalone/Soil/evaporation_lehmann2024_figS6.png new file mode 100644 index 0000000000..f6f7ed2cc8 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/evaporation_lehmann2024_figS6.png differ diff --git a/previews/PR753/generated/standalone/Soil/freezing_front/index.html b/previews/PR753/generated/standalone/Soil/freezing_front/index.html new file mode 100644 index 0000000000..05a6f34945 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/freezing_front/index.html @@ -0,0 +1,177 @@ + +Phase Changes · ClimaLand.jl
GitHub

Modeling a freezing front in unsaturated soil

Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...) -\frac{F_T}{ρ_l}$

$\frac{ ∂ θ_i}{∂ t} = \frac{F_T}{ρ_i}$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity, and

$F_T$ is the freeze-thaw term.

To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.

We solve these equations in an effectively 1-d domain with $z ∈ [-0.2,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 28 W/m^2/K (T - 267.15K) ẑ$

$- κ ∇T(t, z= -0.2) = -3 W/m^2/K (T - 279.85K) ẑ$

$T(t = 0, z) = 279.85 K$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -0.2) = 0 ẑ$

$ϑ_l(t = 0, z) = 0.33$.

The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.

Import necessary modules

import SciMLBase
+import ClimaTimeSteppers as CTS
+using DelimitedFiles
+using CairoMakie
+
+using ClimaCore
+import ClimaParams as CP
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Preliminary set-up

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
Float32

Set the values of other parameters required by the model:

ν = FT(0.535)
+K_sat = FT(3.2e-6) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.48)
+vg_α = FT(1.11) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);

You could also try the Brooks and Corey model:

#ψb = FT(-0.6)
+#c = FT(0.43)
+#hcm = BrooksCorey(;ψb = ψb, c = c);
+θ_r = FT(0.05)
+ν_ss_om = FT(0.3)
+ν_ss_quartz = FT(0.7)
+ν_ss_gravel = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat,
+    S_s,
+    θ_r,
+);

Choose the domain and discretization:

zmax = FT(0)
+zmin = FT(-0.2)
+nelems = 20
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Set the boundary conditions:

zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)
+function top_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))
+    T_c = ClimaCore.Fields.level(p.soil.T, p_len)
+    return @. FT(28 * (T_c - 267.15))
+end
+function bottom_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    T_c = ClimaCore.Fields.level(p.soil.T, 1)
+    return @. FT(-3 * (T_c - 279.85))
+end
+top_heat_flux_bc = HeatFluxBC(top_heat_flux)
+bottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)
+boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),
+    bottom = WaterHeatBC(;
+        water = zero_water_flux_bc,
+        heat = bottom_heat_flux_bc,
+    ),
+);

Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.

sources = (PhaseChange{FT}(),);

Now we can package this up in the EnergyHydrology model struct:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Running a simulation

Once we have the model, we can initialize the state vectors and obtain the coordinates

Y, p, coords = initialize(soil);

After which, we can specify the initial condition function, and initialze the variables:

function init_soil!(Ysoil, z, params)
+    ν = params.ν
+    FT = eltype(Ysoil.soil.ϑ_l)
+    Ysoil.soil.ϑ_l .= FT(0.33)
+    Ysoil.soil.θ_i .= FT(0.0)
+    T = FT(279.85)
+    ρc_s = Soil.volumetric_heat_capacity(
+        FT(0.33),
+        FT(0.0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Ysoil.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            FT(0.0),
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 50);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Create the tendency function, and choose a timestep, and timestepper:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+dt = Float64(100)
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 3,
+        update_j = CTS.UpdateEvery(CTS.NewTimeStep),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);

Comparison to data

This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.

dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();
+ds = readdlm(dataset_path, ',')
+hours = ds[:, 1][2:end]
+vwc = ds[:, 2][2:end] ./ 100.0
+depth = ds[:, 3][2:end]
+mask_12h = hours .== 12
+mask_24h = hours .== 24
+mask_50h = hours .== 50;
+
+fig = Figure(size = (900, 300))
+ax1 = Axis(
+    fig[1, 1],
+    title = "12 hours",
+    xlabel = L"θ_l + θ_i",
+    ylabel = "Soil depth (m)",
+)
+limits!(ax1, 0.2, 0.5, -0.2, 0.0)
+ax2 = Axis(
+    fig[1, 2],
+    title = "24 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax2, 0.2, 0.5, -0.2, 0.0)
+ax3 = Axis(
+    fig[1, 3],
+    title = "50 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax3, 0.2, 0.5, -0.2, 0.0)
+
+
+z = parent(coords.subsurface.z)[:];
+
+scatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = "", color = "purple")
+lines!(
+    ax1,
+    parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+
+scatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = "", color = "purple")
+lines!(
+    ax2,
+    parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+scatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = "Data", color = "purple")
+lines!(
+    ax3,
+    parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],
+    z,
+    label = "Simulation",
+    color = :green,
+)
+axislegend(ax3, position = :rb)
+
+save("mizoguchi_data_comparison.png", fig);

Discussion and Model Explanation

To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies

$ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0$

Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing ($T < T_f$)

$\frac{dp_l}{ρ_l} = L_f \frac{dT}{T},$

or

$p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)$

where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).

What this implies is that above the freezing point, the pressure is equal to $p_{l,0}$, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure $p_{l,0}$ is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):

$p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)$

where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for

$θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.$

For freezing, the freeze thaw function F_T is equal to

$F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)$

which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).

Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).

This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time

$τ_{LTE}= c̃ Δz²/κ,$

which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).

References

  • Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.
  • Hansson et al., Vadose Zone Journal 3:693–704 (2004).
  • M. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).
  • Kurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)
  • Watanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58
  • Painter and Karra, Vadose Zone Journal (2014) 13 (4)

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/layered_soil/index.html b/previews/PR753/generated/standalone/Soil/layered_soil/index.html new file mode 100644 index 0000000000..ccb859b31b --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/layered_soil/index.html @@ -0,0 +1,105 @@ + +Layered Soil · ClimaLand.jl
GitHub

This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.

using Plots
+import ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using ClimaCore
+import ClimaParams as CP
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+FT = Float64;

Define simulation times

t0 = Float64(0)
+tf = Float64(60 * 60)
+dt = Float64(30);

Define the domain

zmax = FT(0)
+zmin = FT(-1.1)
+nelems = 75
+Δ = FT((zmax - zmin) / nelems / 2)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118

data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();
+parameter_data = readdlm(data_file, ',');

Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.

depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m
+ksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s
+vgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).
+vgn = reverse(parameter_data[5, :])
+residual_frac = reverse(parameter_data[2, :])
+porosity = reverse(parameter_data[3, :]);

Create fields corresponding to the parameter

ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)
+K_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)
+θ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);

The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:

S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;

The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:

hcm = SpaceVaryingInput(
+    depth,
+    (; α = vgα, n = vgn),
+    soil_domain.space.subsurface,
+    vanGenuchten{FT},
+);

The parameter struct:

params = ClimaLand.Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.

function top_flux_function(p, t)
+    return -0.0001033
+end
+top_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)
+bottom_bc = ClimaLand.Soil.FreeDrainage()
+boundary_fluxes = (; top = top_bc, bottom = bottom_bc)
+soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+);

Initial the state vectors, and set initial conditions

Y, p, cds = initialize(soil);

Initial conditions

Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.

We also set the initial conditions of the auxiliary state here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

stepper = CTS.ARS111()
+@assert FT in (Float32, Float64)
+err = (FT == Float64) ? 1e-8 : 1e-4
+convergence_cond = CTS.MaximumError(err)
+conv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 10,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+        convergence_checker = conv_checker,
+    ),
+)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = make_jacobian(soil)
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);
+
+z = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]
+plot(ϑ_l[1], z, label = "initial", color = "grey", aspect_ratio = 0.8)
+plot!(ϑ_l[2], z, label = "8min", color = "orange")
+plot!(ϑ_l[3], z, label = "16min", color = "red")
+plot!(ϑ_l[4], z, label = "24min", color = "teal")
+plot!(ϑ_l[5], z, label = "32min", color = "blue")
+plot!(ϑ_l[6], z, label = "40min", color = "purple")
+plot!(ϑ_l[7], z, label = "60min", color = "green")
+scatter!(porosity, depth, label = "Porosity")
+plot!(legend = :bottomright)
+
+plot!(xlim = [0, 0.7])
+
+plot!(
+    ylim = [-1.1, 0],
+    yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],
+)
+
+plot!(ylabel = "Depth (m)")
+
+plot!(xlabel = "Volumeteric Water Content")
+
+savefig("./sv62_alpha_2_inf_updated_data_climaland.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png"

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/mizoguchi_data_comparison.png b/previews/PR753/generated/standalone/Soil/mizoguchi_data_comparison.png new file mode 100644 index 0000000000..a7b91672fd Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/mizoguchi_data_comparison.png differ diff --git a/previews/PR753/generated/standalone/Soil/profiles.png b/previews/PR753/generated/standalone/Soil/profiles.png new file mode 100644 index 0000000000..0e27696c63 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/profiles.png differ diff --git a/previews/PR753/generated/standalone/Soil/richards_equation/index.html b/previews/PR753/generated/standalone/Soil/richards_equation/index.html new file mode 100644 index 0000000000..8672b6c392 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/richards_equation/index.html @@ -0,0 +1,90 @@ + +Richards Equation · ClimaLand.jl
GitHub

Hydrostatic Equilibrium test for Richards Equation

This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.

The equations are:

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (ϑ_l; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in a 1-d domain with $z ∈ [-5,0]$, and with the following boundary and initial conditions:

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -5) = 0 ẑ$

$ϑ(t = 0, z) = ν-0.001$

$θ_i(t = 0, z) = 0.0.$

where $\nu$ is the porosity.

When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.

Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).

Preliminary setup

  • Load external packages
import SciMLBase
+using Plots
  • Load CliMA packages and ClimaLand modules
using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP
  • Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:
const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Set up the soil model

We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.

First, we define the parameters: porosity \nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.

K_sat = FT(0.0443 / (3600 * 100))
+S_s = FT(1e-3)
+ν = FT(0.495)
+vg_α = FT(2.6)
+vg_n = FT(2)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0)
+params = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:

zmax = FT(0)
+zmin = FT(-5)
+nelems = 10
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:

surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+bottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+boundary_conditions = (; top = surface_flux, bottom = bottom_flux);

Lastly, in this case we don't have any sources, so we pass an empty tuple:

sources = ();

Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.

soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_conditions,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = ClimaLand.make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.

Y.soil.ϑ_l .= FT(0.494);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 24 * 36);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

dt = Float64(1e3);

Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.

stepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Here we set up the information used for our Jacobian.

jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);

And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);

Create some plots

We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).

t = sol.t ./ (60 * 60 * 24);
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+z = parent(coords.subsurface.z)
+plot(
+    ϑ_l[1],
+    z,
+    label = string("t = ", string(t[1]), "days"),
+    xlim = [0.47, 0.501],
+    ylabel = "z",
+    xlabel = "ϑ_l",
+    legend = :bottomleft,
+    title = "Equilibrium test",
+);
+plot!(ϑ_l[end], z, label = string("t = ", string(t[end]), "days"));
+function hydrostatic_equilibrium(z, z_interface)
+    ν = 0.495
+    S_s = 1e-3
+    α = 2.6
+    n = 2.0
+    m = 0.5
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+plot!(hydrostatic_equilibrium.(z, -0.56), z, label = "equilibrium solution");
+
+plot!(1e-3 .+ ϑ_l[1], z, label = "porosity");

Save the output:

savefig("equilibrium_test_ϑ_l.png");

References

  • Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/soil_energy_hydrology/index.html b/previews/PR753/generated/standalone/Soil/soil_energy_hydrology/index.html new file mode 100644 index 0000000000..9532295bdd --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/soil_energy_hydrology/index.html @@ -0,0 +1,125 @@ + +Energy and Hydrology · ClimaLand.jl
GitHub

Coupled heat and water equations tending towards equilibrium

The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.

Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.

The equations are:

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction, and

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in an effectively 1-d domain with $z ∈ [-1,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 0 ẑ$

$-κ ∇T(t, z = -1) = 0 ẑ$

$T(t = 0, z) = T_{min} + (T_{max}-T_{min}) e^{Cz}$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -1) = 0 ẑ$

$ϑ(t = 0, z) = ϑ_{min} + (ϑ_{max}-ϑ_{min}) e^{Cz},$

where $C, T_{min}, T_{max}, ϑ_{min},$ and $ϑ_{max}$ are constants.

If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy $∂h/∂z = 0$ and $∂T/∂z = 0$. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.

We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.

Import necessary modules

External (non - CliMA) modules

import SciMLBase
+using Statistics
+using Plots

CliMA packages and ClimaLand modules

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
+earth_param_set = LP.LandParameters(FT);

Create the model

Set the values of other parameters required by the model:

ν = FT(0.395)
0.395f0

Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).

ν_ss_quartz = FT(0.92)
+ν_ss_om = FT(0.0)
+ν_ss_gravel = FT(0.0)
0.0f0

Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.

Ksat = FT(4.42 / 3600 / 100) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.89)
+vg_α = FT(7.5) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+θ_r = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat = Ksat,
+    S_s,
+    θ_r,
+);

We also need to pick a domain on which to solve the equations:

zmax = FT(0)
+zmin = FT(-1.0)
+nelems = 50
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.

Water boundary conditions:

surface_water_flux = WaterFluxBC((p, t) -> 0.0)
+bottom_water_flux = WaterFluxBC((p, t) -> 0.0);

The boundary conditions for the heat equation:

surface_heat_flux = HeatFluxBC((p, t) -> 0.0)
+bottom_heat_flux = HeatFluxBC((p, t) -> 0.0);

We wrap up all of those in a WaterHeatBC struct:

boundary_fluxes = (;
+    top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),
+    bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),
+);

We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.

sources = ();

Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.

function init_soil!(Y, z, params)
+    ν = params.ν
+    θ_r = params.θ_r
+    FT = eltype(Y.soil.ϑ_l)
+    zmax = FT(0)
+    zmin = FT(-1)
+
+    theta_max = FT(ν * 0.5)
+    theta_min = FT(ν * 0.4)
+    T_max = FT(289.0)
+    T_min = FT(288.0)
+
+    c = FT(20.0)
+    @. Y.soil.ϑ_l =
+        theta_min +
+        (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)
+    Y.soil.θ_i .= FT(0.0)
+
+    T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))
+
+    θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)
+    ρc_s =
+        Soil.volumetric_heat_capacity.(
+            θ_l,
+            Y.soil.θ_i,
+            params.ρc_ds,
+            params.earth_param_set,
+        )
+    Y.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 72);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

We use ClimaTimesteppers.jl for carrying out the time integration.

Choose a timestepper and set up the ODE problem:

dt = Float64(1000.0);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:

saveat = collect(t0:FT(30000):tf)
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);

Extract output

z = parent(coords.subsurface.z)
+t = parent(sol.t)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+T = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];

Let's look at the initial and final times:

plot(ϑ_l[1], z, xlabel = "ϑ_l", ylabel = "z (m)", label = "t = 0d")
+plot!(ϑ_l[4], z, label = "t = 1.5d")
+plot!(ϑ_l[end], z, label = "t = 3d")
+savefig("eq_moisture_plot.png");

plot(T[1], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 0d")
+plot!(T[4], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 1.5d")
+plot!(T[end], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 3d")
+savefig("eq_temperature_plot.png");

Analytic Expectations

We can determine a priori what we expect the final temperature to be in equilibrium.

Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).

Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.

One could also solve the equation for ϑ_l specified by $∂ h/∂ z = 0$ to determine the functional form of the equilibrium profile of the liquid water.

References

  • Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.
  • Balland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/sublimation/index.html b/previews/PR753/generated/standalone/Soil/sublimation/index.html new file mode 100644 index 0000000000..bed2eda8c0 --- /dev/null +++ b/previews/PR753/generated/standalone/Soil/sublimation/index.html @@ -0,0 +1,255 @@ + +Bare soil site · ClimaLand.jl
GitHub

Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 270.0^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(270.0)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 12
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

sources = (PhaseChange{FT}(),);
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)
+    Y.soil.θ_i .= 0
+    T = FT(275.0)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 4)
+dt = Float64(5)
5.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping functions:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = ClimaLand.make_jacobian(soil)
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+
+timestepper = CTS.ARS111()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+sub = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        sv.saveval[k].soil.ice_frac,
+    )[1] for k in 1:length(sol.t)
+]
+
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (400, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Rate (mm/d)",
+    title = "Vapor Fluxes",
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    sub .* (1000 * 3600 * 24),
+    label = "Sublimation",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Evaporation",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+save("water_fluxes.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Temperature")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(260, 280)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 3, 4]
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax1,
+        parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Ice", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Liquid Water", xlabel = "")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax3,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("profiles.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png b/previews/PR753/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png new file mode 100644 index 0000000000..1d25aedb18 Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png differ diff --git a/previews/PR753/generated/standalone/Soil/water_fluxes.png b/previews/PR753/generated/standalone/Soil/water_fluxes.png new file mode 100644 index 0000000000..9a54a6023d Binary files /dev/null and b/previews/PR753/generated/standalone/Soil/water_fluxes.png differ diff --git a/previews/PR753/generated/standalone/Usage/LSM_single_column_tutorial/index.html b/previews/PR753/generated/standalone/Usage/LSM_single_column_tutorial/index.html new file mode 100644 index 0000000000..1507ffe8e6 --- /dev/null +++ b/previews/PR753/generated/standalone/Usage/LSM_single_column_tutorial/index.html @@ -0,0 +1,82 @@ + +Intro to multi-component models · ClimaLand.jl
GitHub

The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.

The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.

We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.

First, let's load the required modules:

using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Pond
+
+FT = Float32;

The individual component models I - Soil Hydrology

The RR equation for the volumetric water content of soil is given by

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S(x,y,z, t)$

In order to solve this, one must specify:

  • boundary conditions,
  • relevant parameters (closure models for K and ψ),
  • a domain and a spatial discretization scheme,
  • additional source terms S, if applicable,
  • a time-stepping algorithm,
  • initial conditions.

We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).

Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.

For the RR equation, we can create this as follows. First, we specify parameters:

ν = FT(0.495);
+K_sat = FT(0.0443 / 3600 / 100); # m/s
+S_s = FT(1e-3); #inverse meters
+vg_n = FT(2.0);
+vg_α = FT(2.6); # inverse meters
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0);
+soil_ps = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, let's define the spatial domain and discretization:

zmax = FT(0);
+zmin = FT(-1);
+nelems = 20;
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

And boundary conditions and source terms (none currently):

top_flux_bc = WaterFluxBC((p, t) -> 0.0);
+bot_flux_bc = WaterFluxBC((p, t) -> 0.0);
+sources = ()
+boundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)
(top = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#1#2"}(Main.var"##494".var"#1#2"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#3#4"}(Main.var"##494".var"#3#4"()))

With this information, we can make our model:

soil = Soil.RichardsModel{FT}(;
+    parameters = soil_ps,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,

Y_soil, p_soil, coords_soil = initialize(soil);

and we can set up the tendency function using the default as well,

soil_ode! = make_exp_tendency(soil);

which computes, for the column domain,

$-\frac{∂ }{∂z} (-K\frac{∂(ψ+z)}{∂ z})$

for each value of ϑ on the mesh of our soil_domain.

Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:

function soil_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place
+         update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place
+         compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.
+end

It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.

Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.

Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.

The individual component models II - Surface Water

The pond model has a single variable, the pond height η, which satisfies the ODE:

$\frac{∂ η}{∂ t} = -(P - I) = R,$

where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.

To write down the pond equations, we need to specify

  • P
  • I

which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:

precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s
+
+infiltration(t) = -(1e-6) #m/s
+pond_model =
+    Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));

Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.

Again we can initialize the state vector and auxiliary vectors:

Y_pond, p_pond, coords_pond = initialize(pond_model);

We can make the tendency function in the same way, for stepping the state forward in time:

pond_ode! = make_exp_tendency(pond_model);

The pond_ode! function works in the same way as for the soil model:

function pond_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # falls back to default; does nothing
+         update_boundary_fluxes!(p,Y,t)  # p.surface_water.runoff in place
+         compute_exp_tendency!(dY, Y, p, t)
+end

An LSM with pond and soil:

The LSM model must contain everything needed to write down the joint system of equations

$\frac{\partial \eta}{\partial t} = -(P(t) - I(ϑ, η, P)) = R,$

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S$

$-K ∇(ψ+z)|_{z = zmax} ⋅ ẑ = I(ϑ, η, P)$

$-K ∇(ψ+z)|_{z = zmin} ⋅ ẑ = 0.0.$

These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.

As in the standalone cases, defining our model requires specifying

  • parameters,
  • domains, discretizations
  • precipitation,
  • boundary conditions,
  • sources in the soil equation, if any.

First, let's make our single column domain.

lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.

soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());
+surface_water_args = NamedTuple();

Atmospheric drivers don't "belong" to either component alone:

land_args = (precip = precipitation,);
+land = LandHydrology{FT}(;
+    land_args = land_args,
+    soil_model_type = Soil.RichardsModel{FT},
+    soil_args = soil_args,
+    surface_water_model_type = Pond.PondModel{FT},
+    surface_water_args = surface_water_args,
+);

Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).

Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?

Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.

To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:

function make_update_aux(land)
+         soil_update_aux! = make_update_aux(land.soil)
+         surface_update_aux! = make_update_aux(land.surface_water)
+         function update_aux!(p,Y,t)
+                  surface_update_aux!(p,Y,t) # does nothing to `p`
+                  soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ
+         end
+         return update_aux!
+end
function make_update_boundary_fluxes(land)
+         update_soil_bf! = make_update_boundary_fluxes(land.soil)
+         update_pond_bf! = make_update_boundary_fluxes(land.surface_water)
+         function update_boundary_fluxes!(p,Y,t)
+                  p.soil_infiltration = compute_infiltration(Y,p, t)
+                  update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration
+                  update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration
+         end
+         return update_boundary_fluxes!
+end

and similarily for the compute_exp_tendency! functions:

function make_compute_exp_tendency(land)
+         soil_compute_exp_tendency! = make_update_aux(land.soil)
+         surface_compute_exp_tendency! = make_update_aux(land.surface_water)
+         function compute_exp_tendency!(dY,Y,p,t)
+                  surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η
+                  soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ
+         end
+         return compute_exp_tendency!
+end

The exp_tendency! for the land model is then again just

function exp_tendency!(dY, Y, p, t)
+         update_aux!(p,Y,t)
+         update_boundary_fluxes!(p,Y,t)
+         compute_exp_tendency!(dY, Y, p, t)
+end

In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.

A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:

  • initialize(land.soil)
  • initialize(land.surface_water)
  • initializes additional auxiliary variables, like p.soil_infiltration
  • append these into Y, p, and coords:
Y, p, coords = initialize(land);

We have volumetric liquid water fraction:

propertynames(Y.soil)
(:ϑ_l,)

and surface height of the pond:

propertynames(Y.surface_water)
(:η,)

as well as auxiliary variables for the soil:

propertynames(p.soil)
(:K, :ψ, :top_bc, :bottom_bc)

and the runoff for surface water:

propertynames(p.surface_water)
(:runoff,)

and the additional variable required in the LSM is stored here as well:

propertynames(p)
(:soil_infiltration, :soil, :surface_water)

and finally, coordinates - useful for visualization of solutions:

coords.subsurface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]

and the coordinates of the surface variables:

coords.surface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[0.0]

And we can make the tendency function as before:

land_ode! = make_exp_tendency(land);

Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.

Advantages and disadvantages

Some advantages to our interface design are as follows:

  • a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.
  • likewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.
  • the exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).
  • although we wrote it here in a $hardwired$ fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.
  • the order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.
  • the code is also modular in terms of swapping out a simple component model for a more complex version.

Possible disadvantages to our interface design:

  • Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.
  • To accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.
  • standalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.
  • we need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.

This page was generated using Literate.jl.

diff --git a/previews/PR753/generated/standalone/Usage/domain_tutorial/index.html b/previews/PR753/generated/standalone/Usage/domain_tutorial/index.html new file mode 100644 index 0000000000..4f2c46d35b --- /dev/null +++ b/previews/PR753/generated/standalone/Usage/domain_tutorial/index.html @@ -0,0 +1,2 @@ + +Intro to ClimaLand Domains · ClimaLand.jl
GitHub

Domain Tutorial

Goals of the tutorial

The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.

Background

In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question "only" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).

For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.

In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.

ClimaLand Domains were designed with this in mind. The domains are defined so that

  1. the user can easily switch geometries, e.g. single column to global model,
  2. individual component models can be run by themselves, using a single domain,
  3. the same domains can be used to set up multi-component models (LSMs),
  4. different variables can exist on different parts of the domain.

What is a ClimaLand Domain?

A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.

Domain types

All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:

As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.

DomainSurface DomainSubsurface Domain
ColumnPointColumn
HybridBoxPlaneHybridBox
SphericalShellSphericalSurfaceSphericalShell

There is a single key method which take a ClimaLand domain as an argument.

the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.

It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.

How variable initialization depends on domains

Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.

When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.

They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.

The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].

How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.

This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.

Future work

Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into "vertical" and "horizontal" pieces.

We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.

This page was generated using Literate.jl.

  • 1finite differencing is used in the vertical, and spectral elements are used in the horizontal.
  • 2a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.
  • 3We also will support having an array-like type of variable.
diff --git a/previews/PR753/generated/standalone/Usage/model_tutorial/index.html b/previews/PR753/generated/standalone/Usage/model_tutorial/index.html new file mode 100644 index 0000000000..e06b1028ea --- /dev/null +++ b/previews/PR753/generated/standalone/Usage/model_tutorial/index.html @@ -0,0 +1,133 @@ + +Intro to standalone models · ClimaLand.jl
GitHub

The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.

This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.

General setup

We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.

Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.

Your model defines a system of equations of the following form:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in $\vec{Y}$. Generically, we will speak of the functions $\vec{f}$ as tendencies; these can be functions of the prognostic state, of space $\vec{x}$, and of time $t$, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions

The cache ("auxiliary variables")

There are typically quantities, which depend on the state vector $\vec{Y}$, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require "a lot" of memory to store (e.g., most variables in global runs). Allocating memory "on-the-fly" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.

Denoting the cache as $\vec{p}$, your equations may be rewritten as:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{p}, \vec{x}, t; \mbox{params} \ldots)$

$\vec{p}(\vec{x}, t) = \vec{g}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables $\vec{p}$ at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported ($\vec{Y}$ cannot be zero dimensional).

In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.

The Model

All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.

The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.

The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.

As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.

Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to "imex" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as "explicit" or "implicit" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.

Let's first import some needed packages.

import ClimaTimeSteppers as CTS
+using SciMLBase
+using Plots
+using ClimaCore
+using ClimaLand

Import the functions we are extending for our model:

import ClimaLand:
+    name,
+    make_exp_tendency,
+    make_compute_exp_tendency,
+    make_update_aux,
+    make_update_boundary_fluxes,
+    prognostic_vars,
+    prognostic_types,
+    prognostic_domain_names,
+    auxiliary_vars,
+    auxiliary_types,
+    auxiliary_domain_names

The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).

struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}
+    "van Genuchten model parameters"
+    vGmodel::ClimaLand.Soil.vanGenuchten{FT}
+    "Porosity [unitless]"
+    ν::FT
+    "Residual water fraction [unitless]"
+    θ_r::FT
+    "Saturated hydraulic conductiity [m/s]"
+    Ksat::FT
+    "Surface flux, used as boundary condition [m/s]"
+    F_sfc::FT
+    "Domain of the model"
+    domain::D
+end;

For reasons that will be clear momentarily, let's also define the name of the model:

ClimaLand.name(model::RichardsTutorialModel) = :soil;

Explicit tendency

Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:

$\frac{\partial ϑ_l}{\partial t} = - ∇⋅[-K(θ) ∇(ψ(θ)+z)],$

where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:

function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)
+    function compute_exp_tendency!(dY, Y, p, t)
+        gradc2f = ClimaCore.Operators.GradientC2F()
+        interpc2f = ClimaCore.Operators.InterpolateC2F()
+        FT = FTfromY(Y)
+        divf2c = ClimaCore.Operators.DivergenceF2C(
+            top = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(model.F_sfc),
+            ),
+            bottom = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(FT(0)),
+            ),
+        )
+
+        @. dY.soil.θ =
+            -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))
+    end
+    return compute_exp_tendency!
+end;

A couple of notes: the vector $\vec{dY}$ contains the evaluation of the tendency function for each variable in $\vec{Y}$. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.

The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.

The prognostic state vector $\vec{Y}$ and cache $\vec{p}$

We have given the state vector $\vec{Y}$ a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.

ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);
+ClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);
+ClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);

The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).

ClimaLand.auxiliary_vars(::RichardsTutorialModel) =
+    (:K, :ψ, :top_flux, :bottom_flux, :z)
+ClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =
+    (FT, FT, FT, FT, FT);
+ClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =
+    (:subsurface, :subsurface, :surface, :surface, :subsurface);

Updating the cache

We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.

function ClimaLand.make_update_aux(model::RichardsTutorialModel)
+    function update_aux!(p, Y, t)
+        p.soil.z .=
+            ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step
+        @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(
+            model.vGmodel,
+            model.Ksat,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+        @. p.soil.ψ = ClimaLand.Soil.matric_potential(
+            model.vGmodel,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+    end
+    return update_aux!
+end;
+
+function ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)
+    function update_boundary_fluxes!(p, Y, t)
+        FT = ClimaLand.FTfromY(Y)
+        p.soil.top_flux .= model.F_sfc
+        p.soil.bottom_flux .= FT(0)
+    end
+    return update_boundary_fluxes!
+end;

The default tendency function in ClimaLand for any AbstractModel carries out the following:

function make_exp_tendency(model::AbstractModel)
+    update_aux! = make_update_aux(model)
+    update_boundary_fluxes! = make_update_boundary_fluxes(model)
+    compute_exp_tendency! = make_compute_exp_tendency(model)
+    function exp_tendency!(dY,Y,p,t)
+        update_aux!(p,Y,t)
+        update_boundary_fluxes!(p,Y,t)
+        compute_exp_tendency!(dY,Y,p,t)
+    end
+    return exp_tendency!
+end;

Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.

Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone "component" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.

More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.

Running a simulation

Create a model instance.

FT = Float32
+vGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)
+Ksat = FT(4.0e-7)
+ν = 0.5f0
+θ_r = 0.0f0
+F_sfc = FT(-3.0e-8)
+domain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)
+soil = RichardsTutorialModel{Float32, typeof(domain)}(
+    vGmodel,
+    ν,
+    θ_r,
+    Ksat,
+    F_sfc,
+    domain,
+);

Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.

Y, p, cds = initialize(soil);

Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,

Y.soil
1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0

The same is true for p:

p.soil
(K = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:
+  Float32[0.0], bottom_flux = Float32-valued Field:
+  Float32[0.0], z = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

Here we now update Y in place with initial conditions of our choosing.

Y.soil.θ = 0.25f0;

Set initial cache variable values, and inspect values:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, 0.0);
+@show p.soil.K
+
+@show p.soil.ψ
+
+@show p.soil.top_flux
Float32-valued Field:
+  Float32[-3.0f-8]

Next up is to create the exp_tendency! function:

exp_tendency! = make_exp_tendency(soil);

Running the simulation

Set the initial and end times, timestep:

t0 = 0.0;
+tf = 7 * 24 * 3600.0;
+dt = 1800.0;

Select the timestepping algorithm we want to use from CTS.jl.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

SciMLBase problem statement using CTS.jl internals:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!),
+    Y,
+    (t0, tf),
+    p,
+);

Solve command:

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);

The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.

This page was generated using Literate.jl.

diff --git a/previews/PR753/getting_started/index.html b/previews/PR753/getting_started/index.html new file mode 100644 index 0000000000..7ceacba1e7 --- /dev/null +++ b/previews/PR753/getting_started/index.html @@ -0,0 +1,32 @@ + +Getting Started · ClimaLand.jl
GitHub

Getting Started

For Users

Installation

First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:

julia> ] # Enter Package REPL mode
+Pkg> add ClimaLand # Install ClimaLand
+Pkg> # Go back to Julia REPL mode
+Julia> using ClimaLand

A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.

Parameterization

Let's start with a basic example: compute canopy gross photosynthesis (GPP).

julia> using ClimaLand
julia> @doc ClimaLand.Canopy.compute_GPP  compute_GPP(An::FT,
+           K::FT,
+           LAI::FT,
+           Ω::FT) where {FT}
+
+  Computes the total canopy photosynthesis (GPP) as a function of the total
+  net carbon assimilation (An), the extinction coefficient (K), leaf area
+  index (LAI) and the clumping index (Ω).

As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:

julia> import ClimaLand.Canopy as canopy
julia> canopy.compute_GPP(5.0, 0.5, 3.0, 0.7)9.28660358412635

Et voilà!

Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.

ClimaLand structure

ClimaLand contains multiple modules. They are listed below:

julia> using MethodAnalysis, ClimaLand
julia> child_modules(ClimaLand)13-element Vector{Module}:
+ ClimaLand
+ ClimaLand.Artifacts
+ ClimaLand.Bucket
+ ClimaLand.Canopy
+ ClimaLand.Canopy.PlantHydraulics
+ ClimaLand.Diagnostics
+ ClimaLand.Domains
+ ClimaLand.Parameters
+ ClimaLand.Pond
+ ClimaLand.Snow
+ ClimaLand.Soil
+ ClimaLand.Soil.Biogeochemistry
+ ClimaLand.Soil.Runoff

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia> using ClimaLand
julia> using About
julia> about(ClimaLand.Soil.Biogeochemistry)Module ClimaLand.Soil.Biogeochemistry
+
+Re-exports 13 names (from ClimaLand):
+• Biogeochemistry         • AtmosCO2StateBC    • SoilCO2ModelParameters
+• co2_diffusivity         • MicrobeProduction  • SoilCO2StateBC        
+• microbe_source          • PrescribedMet      • SoilDrivers           
+• volumetric_air_content  • SoilCO2FluxBC    
+• AbstractSoilDriver      • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

diff --git a/previews/PR753/index.html b/previews/PR753/index.html new file mode 100644 index 0000000000..1e03b79a33 --- /dev/null +++ b/previews/PR753/index.html @@ -0,0 +1,2 @@ + +Home · ClimaLand.jl
GitHub

ClimaLand.jl Documentation (v0.14.3)

Introduction

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
diff --git a/previews/PR753/objects.inv b/previews/PR753/objects.inv new file mode 100644 index 0000000000..101874932f Binary files /dev/null and b/previews/PR753/objects.inv differ diff --git a/previews/PR753/search_index.js b/previews/PR753/search_index.js new file mode 100644 index 0000000000..90179dea40 --- /dev/null +++ b/previews/PR753/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"APIs/canopy/RadiativeTransfer/#Radiative-Transfer","page":"Canopy RT","title":"Radiative Transfer","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#Parameters","page":"Canopy RT","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.BeerLambertParameters","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.BeerLambertParameters","page":"Canopy RT","title":"ClimaLand.Canopy.BeerLambertParameters","text":"BeerLambertParameters{FT <: AbstractFloat}\n\nThe required parameters for the Beer-Lambert radiative transfer model.\n\nα_PAR_leaf: PAR leaf reflectance (unitless)\nα_NIR_leaf: NIR leaf reflectance\nϵ_canopy: Emissivity of the canopy\nΩ: Clumping index following Braghiere (2021) (unitless)\nλ_γ_PAR: Typical wavelength per PAR photon (m)\nλ_γ_NIR: Typical wavelength per NIR photon (m)\nG_Function: Leaf angle distribution function\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/RadiativeTransfer/#Methods","page":"Canopy RT","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.compute_absorbances\nClimaLand.Canopy.plant_absorbed_pfd\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.canopy_radiant_energy_fluxes!","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.plant_absorbed_pfd","page":"Canopy RT","title":"ClimaLand.Canopy.plant_absorbed_pfd","text":"plant_absorbed_pfd(\n RT::BeerLambertModel{FT},\n SW_IN:FT,\n α_leaf::FT,\n LAI::FT,\n K::FT,\n α_soil::FT\n)\n\nComputes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.\n\nThis applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). \n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\nplant_absorbed_pfd(\n RT::TwoStreamModel{FT},\n α_leaf,\n SW_IN::FT,\n LAI::FT,\n K::FT,\n τ_leaf,\n θs::FT,\n α_soil::FT,\n)\n\nComputes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band. \n\nThis applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.\n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.extinction_coeff","page":"Canopy RT","title":"ClimaLand.Canopy.extinction_coeff","text":"extinction_coeff(ld::FT,\n θs::FT) where {FT}\n\nComputes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.canopy_radiant_energy_fluxes!","page":"Canopy RT","title":"ClimaLand.Canopy.canopy_radiant_energy_fluxes!","text":"Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrognosticSoil{F},\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {FT, PSE}\n\nIn standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.\n\nIn integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\ncanopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrescribedSoil,\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {PSE}\n\nComputes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/available_diagnostics/#Available-diagnostic-variables","page":"Available diagnostics","title":"Available diagnostic variables","text":"","category":"section"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"Autogenerate table of available diagnostics:","category":"page"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"include(\"make_diagnostic_table.jl\")","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"ref_time = DateTime(2005) # required argument, but not used in this case\nSW_d = (t) -> 0\nLW_d = (t) -> 301.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 5\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)\n Y.soil.θ_i .= 0\n T = FT(296.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 13)\ndt = Float64(900.0)","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"900.0","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the tendency functions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\nevaporation_data =\n ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();\nref_soln_E = readdlm(evaporation_data, ',')\nref_soln_E_350mm = ref_soln_E[2:end, 1:2]\ndata_dates = ref_soln_E_350mm[:, 1]\ndata_e = ref_soln_E_350mm[:, 2];\n\nfig = Figure(size = (800, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Evaporation rate (mm/d)\",\n title = \"Bare soil evaporation\",\n)\nCairoMakie.xlims!(minimum(data_dates), maximum(data_dates))\nCairoMakie.lines!(\n ax,\n FT.(data_dates),\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nax = Axis(\n fig[1, 2],\n xlabel = \"Mass (g)\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nA_col = π * (0.027)^2\nmass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col\nmass_loss =\n [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]\nCairoMakie.lines!(\n ax,\n cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n mass_loss,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nsave(\"evaporation_lehmann2008_fig8b.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#General-setup","page":"Intro to standalone models","title":"General setup","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Your model defines a system of equations of the following form:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in vecY. Generically, we will speak of the functions vecf as tendencies; these can be functions of the prognostic state, of space vecx, and of time t, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-cache-(\"auxiliary-variables\")","page":"Intro to standalone models","title":"The cache (\"auxiliary variables\")","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"There are typically quantities, which depend on the state vector vecY, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require \"a lot\" of memory to store (e.g., most variables in global runs). Allocating memory \"on-the-fly\" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Denoting the cache as vecp, your equations may be rewritten as:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecp vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"vecp(vecx t) = vecg(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables vecp at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported (vecY cannot be zero dimensional).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-Model","page":"Intro to standalone models","title":"The Model","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to \"imex\" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as \"explicit\" or \"implicit\" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Let's first import some needed packages.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaTimeSteppers as CTS\nusing SciMLBase\nusing Plots\nusing ClimaCore\nusing ClimaLand","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Import the functions we are extending for our model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaLand:\n name,\n make_exp_tendency,\n make_compute_exp_tendency,\n make_update_aux,\n make_update_boundary_fluxes,\n prognostic_vars,\n prognostic_types,\n prognostic_domain_names,\n auxiliary_vars,\n auxiliary_types,\n auxiliary_domain_names","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}\n \"van Genuchten model parameters\"\n vGmodel::ClimaLand.Soil.vanGenuchten{FT}\n \"Porosity [unitless]\"\n ν::FT\n \"Residual water fraction [unitless]\"\n θ_r::FT\n \"Saturated hydraulic conductiity [m/s]\"\n Ksat::FT\n \"Surface flux, used as boundary condition [m/s]\"\n F_sfc::FT\n \"Domain of the model\"\n domain::D\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"For reasons that will be clear momentarily, let's also define the name of the model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.name(model::RichardsTutorialModel) = :soil;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Explicit-tendency","page":"Intro to standalone models","title":"Explicit tendency","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracpartial ϑ_lpartial t = - -K(θ) (ψ(θ)+z)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)\n function compute_exp_tendency!(dY, Y, p, t)\n gradc2f = ClimaCore.Operators.GradientC2F()\n interpc2f = ClimaCore.Operators.InterpolateC2F()\n FT = FTfromY(Y)\n divf2c = ClimaCore.Operators.DivergenceF2C(\n top = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(model.F_sfc),\n ),\n bottom = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(FT(0)),\n ),\n )\n\n @. dY.soil.θ =\n -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))\n end\n return compute_exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"A couple of notes: the vector vecdY contains the evaluation of the tendency function for each variable in vecY. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-prognostic-state-vector-\\vec{Y}-and-cache-\\vec{p}","page":"Intro to standalone models","title":"The prognostic state vector vecY and cache vecp","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We have given the state vector vecY a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);\nClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);\nClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.auxiliary_vars(::RichardsTutorialModel) =\n (:K, :ψ, :top_flux, :bottom_flux, :z)\nClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =\n (FT, FT, FT, FT, FT);\nClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =\n (:subsurface, :subsurface, :surface, :surface, :subsurface);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Updating-the-cache","page":"Intro to standalone models","title":"Updating the cache","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_update_aux(model::RichardsTutorialModel)\n function update_aux!(p, Y, t)\n p.soil.z .=\n ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step\n @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(\n model.vGmodel,\n model.Ksat,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n @. p.soil.ψ = ClimaLand.Soil.matric_potential(\n model.vGmodel,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n end\n return update_aux!\nend;\n\nfunction ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)\n function update_boundary_fluxes!(p, Y, t)\n FT = ClimaLand.FTfromY(Y)\n p.soil.top_flux .= model.F_sfc\n p.soil.bottom_flux .= FT(0)\n end\n return update_boundary_fluxes!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The default tendency function in ClimaLand for any AbstractModel carries out the following:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function make_exp_tendency(model::AbstractModel)\n update_aux! = make_update_aux(model)\n update_boundary_fluxes! = make_update_boundary_fluxes(model)\n compute_exp_tendency! = make_compute_exp_tendency(model)\n function exp_tendency!(dY,Y,p,t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY,Y,p,t)\n end\n return exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone \"component\" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-a-simulation","page":"Intro to standalone models","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create a model instance.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"FT = Float32\nvGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)\nKsat = FT(4.0e-7)\nν = 0.5f0\nθ_r = 0.0f0\nF_sfc = FT(-3.0e-8)\ndomain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)\nsoil = RichardsTutorialModel{Float32, typeof(domain)}(\n vGmodel,\n ν,\n θ_r,\n Ksat,\n F_sfc,\n domain,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The same is true for p:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"p.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"(K = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:\n Float32[0.0], bottom_flux = Float32-valued Field:\n Float32[0.0], z = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here we now update Y in place with initial conditions of our choosing.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil.θ = 0.25f0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set initial cache variable values, and inspect values:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, 0.0);\n@show p.soil.K\n\n@show p.soil.ψ\n\n@show p.soil.top_flux","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Float32-valued Field:\n Float32[-3.0f-8]","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Next up is to create the exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"exp_tendency! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-the-simulation","page":"Intro to standalone models","title":"Running the simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set the initial and end times, timestep:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"t0 = 0.0;\ntf = 7 * 24 * 3600.0;\ndt = 1800.0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Select the timestepping algorithm we want to use from CTS.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"SciMLBase problem statement using CTS.jl internals:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Solve command:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand","page":"ClimaLand","title":"ClimaLand","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/ClimaLand/#LSM-Model-Types-and-methods","page":"ClimaLand","title":"LSM Model Types and methods","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.SoilPlantHydrologyModel\nClimaLand.LandSoilBiogeochemistry\nClimaLand.LandHydrology\nClimaLand.make_interactions_update_aux\nClimaLand.initialize_interactions\nClimaLand.land_components\nClimaLand.lsm_aux_vars\nClimaLand.lsm_aux_types\nClimaLand.lsm_aux_domain_names\nClimaLand.domain_name","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.LandSoilBiogeochemistry","page":"ClimaLand","title":"ClimaLand.LandSoilBiogeochemistry","text":"struct LandSoilBiogeochemistry{\n FT,\n SEH <: Soil.EnergyHydrology{FT},\n SB <: Soil.Biogeochemistry.SoilCO2Model{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.\n\nsoil: The soil model\nsoilco2: The biochemistry model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.LandHydrology","page":"ClimaLand","title":"ClimaLand.LandHydrology","text":"struct LandHydrology{\n FT,\n SM <: Soil.AbstractSoilModel{FT},\n SW <: Pond.AbstractSurfaceWaterModel{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil and surface water component.\n\nsoil: The soil model\nsurface_water: The surface water model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.land_components","page":"ClimaLand","title":"ClimaLand.land_components","text":"land_components(land::AbstractLandModel)\n\nReturns the component names of the land model, by calling propertynames(land).\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_vars","page":"ClimaLand","title":"ClimaLand.lsm_aux_vars","text":"lsmauxvars(m::AbstractLandModel)\n\nReturns the additional aux variable symbols for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_vars(m::SoilCanopyModel)\n\nThe names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_types","page":"ClimaLand","title":"ClimaLand.lsm_aux_types","text":"lsmauxtypes(m::AbstractLandModel)\n\nReturns the shared additional aux variable types for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_types(m::SoilCanopyModel)\n\nThe types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_domain_names","page":"ClimaLand","title":"ClimaLand.lsm_aux_domain_names","text":"lsmauxdomain_names(m::AbstractLandModel)\n\nReturns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.\n\nThis is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.\n\n\n\n\n\nlsm_aux_domain_names(m::SoilCanopyModel)\n\nThe domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#Land-Hydrology","page":"ClimaLand","title":"Land Hydrology","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.infiltration_capacity\nClimaLand.infiltration_at_point\nClimaLand.PrognosticRunoff\nClimaLand.RunoffBC","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_capacity","page":"ClimaLand","title":"ClimaLand.infiltration_capacity","text":"function infiltration_capacity(\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n)\n\nFunction which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.\n\nDefined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_at_point","page":"ClimaLand","title":"ClimaLand.infiltration_at_point","text":"infiltration_at_point(η::FT, i_c::FT, P::FT)\n\nReturns the infiltration given pond height η, infiltration capacity, and precipitation.\n\nThis is defined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticRunoff","page":"ClimaLand","title":"ClimaLand.PrognosticRunoff","text":"PrognosticRunoff <: Pond.AbstractSurfaceRunoff\n\nConcrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.\n\nThis is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RunoffBC","page":"ClimaLand","title":"ClimaLand.RunoffBC","text":"RunoffBC <: Soil.AbstractSoilBC\n\nConcrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.\n\nThis is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#SoilCanopyModel","page":"ClimaLand","title":"SoilCanopyModel","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticSoil\nClimaLand.RootExtraction","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticSoil","page":"ClimaLand","title":"ClimaLand.PrognosticSoil","text":" PrognosticSoil{FT} <: AbstractSoilDriver\n\nConcrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.\n\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RootExtraction","page":"ClimaLand","title":"ClimaLand.RootExtraction","text":"RootExtraction{FT} <: Soil.AbstractSoilSource{FT}\n\nConcrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.\n\nThis is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#LandSoilBiogeochemistry","page":"ClimaLand","title":"LandSoilBiogeochemistry","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticMet","category":"page"},{"location":"APIs/shared_utilities/#Shared-Utilities","page":"Shared Utilities","title":"Shared Utilities","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/shared_utilities/#Domains","page":"Shared Utilities","title":"Domains","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.Domains.AbstractDomain\nClimaLand.Domains.AbstractLSMDomain\nClimaLand.Domains.SphericalShell\nClimaLand.Domains.SphericalSurface\nClimaLand.Domains.HybridBox\nClimaLand.Domains.Column\nClimaLand.Domains.Plane\nClimaLand.Domains.Point\nClimaLand.Domains.coordinates\nClimaLand.Domains.obtain_face_space\nClimaLand.Domains.obtain_surface_space\nClimaLand.Domains.obtain_surface_domain\nClimaLand.Domains.top_center_to_surface\nClimaLand.Domains.top_face_to_surface\nClimaLand.Domains.linear_interpolation_to_surface!\nClimaLand.Domains.get_Δz\n","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.AbstractDomain","page":"Shared Utilities","title":"ClimaLand.Domains.AbstractDomain","text":"AbstractDomain{FT <:AbstractFloat}\n\nAn abstract type for domains.\n\nThe domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.\n\nAdditionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalShell","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalShell","text":"struct SphericalShell{FT} <: AbstractDomain{FT}\n radius::FT\n depth::FT\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nradius: The radius of the shell\ndepth: The radial extent of the shell\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: The number of elements to be used in the non-radial and radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalSurface","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalSurface","text":"struct SphericalSurface{FT} <: AbstractDomain{FT}\n radius::FT\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).\n\nFields\n\nradius: The radius of the surface\nnelements: The number of elements to be used in the non-radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.HybridBox","page":"Shared Utilities","title":"ClimaLand.Domains.HybridBox","text":"struct HybridBox{FT} <: AbstractDomain{FT}\n xlim::Tuple{FT, FT}\n ylim::Tuple{FT, FT}\n zlim::Tuple{FT, FT}\n longlat::Union{Nothing, Tuple{FT, FT}},\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int, Int}\n npolynomial::Int\n periodic::Tuple{Bool, Bool}\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.\n\nWhen longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nzlim: Domain interval limits along z axis, in meters\nlonglat: When not nothing, a Tuple that contains the center long and lat.\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: Number of elements to discretize interval, (nx, ny,nz)\nnpolynomial: Polynomial order for the horizontal directions\nperiodic: Flag indicating periodic boundaries in horizontal\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Column","page":"Shared Utilities","title":"ClimaLand.Domains.Column","text":"Column{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nzlim: Domain interval limits, (zmin, zmax), in meters\nnelements: Number of elements used to discretize the interval\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nboundary_names: Boundary face identifiers\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Plane","page":"Shared Utilities","title":"ClimaLand.Domains.Plane","text":"Plane{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.\n\nWhen longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.\n\nlonglat are in degrees, with longitude going from -180 to 180.\n\n:warning: Only independent columns are supported! (No lateral flow).\n\nspace is a NamedTuple holding the surface space (in this case, the entire Plane space).\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nlonglat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).\nnelements: Number of elements to discretize interval, (nx, ny)\nperiodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.\nnpolynomial: Polynomial order for both x and y\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Point","page":"Shared Utilities","title":"ClimaLand.Domains.Point","text":"Point{FT} <: AbstractDomain{FT}\n\nA domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.\n\nspace is a NamedTuple holding the surface space (in this case, the Point space).\n\nFields\n\nz_sfc: Surface elevation relative to a reference (m)\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.coordinates","page":"Shared Utilities","title":"ClimaLand.Domains.coordinates","text":"coordinates(domain::AbstractDomain)\n\nReturns the coordinate fields for the domain as a NamedTuple.\n\nThe returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.\n\n\n\n\n\nDomains.coordinates(model::AbstractLandModel)\n\nReturns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.\n\nFor example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_face_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_face_space","text":"obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the face space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the face space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_space","text":"obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the surface space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_domain","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_domain","text":"obtain_surface_domain(d::AbstractDomain) where {FT}\n\nDefault method throwing an error; any domain with a corresponding domain should define a new method of this function.\n\n\n\n\n\nobtain_surface_domain(c::Column{FT}) where {FT}\n\nReturns the Point domain corresponding to the top face (surface) of the Column domain c.\n\n\n\n\n\nobtain_surface_domain(b::HybridBox{FT}) where {FT}\n\nReturns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.\n\n\n\n\n\nobtain_surface_domain(s::SphericalShell{FT}) where {FT}\n\nReturns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_center_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_center_to_surface","text":"top_center_to_surface(center_field::ClimaCore.Fields.Field)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.\n\nFor example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\ntop_center_to_surface(val)\n\nWhen val is a scalar (e.g. a single float or struct), returns val.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_face_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_face_to_surface","text":"top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.\n\nGiven a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.linear_interpolation_to_surface!","page":"Shared Utilities","title":"ClimaLand.Domains.linear_interpolation_to_surface!","text":"linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)\n\nLinearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.get_Δz","page":"Shared Utilities","title":"ClimaLand.Domains.get_Δz","text":"get_Δz(z::ClimaCore.Fields.Field)\n\nA function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Models","page":"Shared Utilities","title":"Models","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.AbstractModel\nClimaLand.AbstractImExModel\nClimaLand.AbstractExpModel\nClimaLand.make_exp_tendency\nClimaLand.make_imp_tendency\nClimaLand.make_compute_exp_tendency\nClimaLand.make_compute_imp_tendency\nClimaLand.make_update_aux\nClimaLand.make_update_boundary_fluxes\nClimaLand.make_set_initial_cache\nClimaLand.make_update_drivers\nClimaLand.prognostic_vars\nClimaLand.prognostic_types\nClimaLand.prognostic_domain_names\nClimaLand.auxiliary_vars\nClimaLand.auxiliary_types\nClimaLand.auxiliary_domain_names\nClimaLand.initialize_prognostic\nClimaLand.initialize_auxiliary\nClimaLand.initialize\nClimaLand.name\nClimaLand.AbstractBC\nClimaLand.AbstractSource\nClimaLand.source!\nClimaLand.AbstractBoundary\nClimaLand.TopBoundary\nClimaLand.BottomBoundary\nClimaLand.boundary_flux\nClimaLand.diffusive_flux\nClimaLand.boundary_vars\nClimaLand.boundary_var_domain_names\nClimaLand.boundary_var_types\nClimaLand.make_jacobian\nClimaLand.make_compute_jacobian\nClimaLand.set_dfluxBCdY!\nClimaLand.get_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractModel","page":"Shared Utilities","title":"ClimaLand.AbstractModel","text":"abstract type AbstractModel{FT <: AbstractFloat}\n\nAn abstract type for all models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractImExModel","page":"Shared Utilities","title":"ClimaLand.AbstractImExModel","text":"AbstractImExModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractExpModel","page":"Shared Utilities","title":"ClimaLand.AbstractExpModel","text":"AbstractExpModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.make_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_exp_tendency","text":"make_exp_tendency(model::AbstractModel)\n\nReturns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_imp_tendency","text":"make_imp_tendency(model::AbstractImExModel)\n\nReturns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\nmake_imp_tendency(model::AbstractModel)\n\nReturns an imp_tendency that does nothing. This model type is not stepped explicity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_exp_tendency","text":"make_explicit_tendency(model::Soil.RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nConstruct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.\n\n\n\n\n\nmake_compute_exp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::BucketModel{FT}) where {FT}\n\nCreates the computeexptendency! function for the bucket model.\n\n\n\n\n\nmake_compute_exp_tendency(model::AbstractModel)\n\nReturn a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)\n\nCreates the computeexptendency!(dY,Y,p,t) function for the canopy component.\n\nSince component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.\n\n\n\n\n\nmake_compute_exp_tendency(canopy::CanopyModel)\n\nCreates and returns the computeexptendency! for the CanopyModel.\n\n\n\n\n\nmake_compute_exp_tendency(model::SoilCO2Model)\n\nAn extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::PlantHydraulicsModel, _)\n\nA function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.\n\nBelow, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).\n\nNote that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.\n\nTo prevent dividing by zero, we change AI/(AI x dz)\" to \"AI/max(AI x dz, eps(FT))\"\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_imp_tendency","text":"make_compute_imp_tendency(model::RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.\n\n\n\n\n\nmake_compute_imp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_imp_tendency(model::AbstractModel)\n\nReturn a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_aux","page":"Shared Utilities","title":"ClimaLand.make_update_aux","text":"make_update_aux(model::RichardsModel)\n\nAn extension of the function make_update_aux, for the Richardson- Richards equation.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::EnergyHydrology)\n\nAn extension of the function make_update_aux, for the integrated soil hydrology and energy model.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::BucketModel{FT}) where {FT}\n\nCreates the update_aux! function for the BucketModel.\n\n\n\n\n\nmake_update_aux(model::AbstractModel)\n\nReturn an update_aux! function that updates auxiliary parameters p.\n\n\n\n\n\n ClimaLand.make_update_aux(canopy::CanopyModel{FT,\n <:AutotrophicRespirationModel,\n <:Union{BeerLambertModel, TwoStreamModel},\n <:FarquharModel,\n <:MedlynConductanceModel,\n <:PlantHydraulicsModel,},\n ) where {FT}\n\nCreates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.\n\nPlease note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.\n\nThe other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.\n\n\n\n\n\nmake_update_aux(model::SoilCO2Model)\n\nAn extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_boundary_fluxes","page":"Shared Utilities","title":"ClimaLand.make_update_boundary_fluxes","text":"make_update_boundary_fluxes(model::AbstractModel)\n\nReturn an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::LandHydrology{FT, SM, SW},\n) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}\n\nA method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.\n\nThis function is called each ode function evaluation.\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::SoilCanopyModel{FT, MM, SM, RM},\n) where {\n FT,\n MM <: Soil.Biogeochemistry.SoilCO2Model{FT},\n SM <: Soil.RichardsModel{FT},\n RM <: Canopy.CanopyModel{FT}\n }\n\nA method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. \n\nThis function is called each ode function evaluation, prior to the tendency function evaluation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_set_initial_cache","page":"Shared Utilities","title":"ClimaLand.make_set_initial_cache","text":"make_set_initial_cache(model::AbstractModel)\n\nReturns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.\n\nIn principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.\n\nFurthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_drivers","page":"Shared Utilities","title":"ClimaLand.make_update_drivers","text":"make_update_drivers(::AbstractClimaLandDrivers)\n\nCreates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.\n\n\n\n\n\nmake_update_drivers(driver_tuple)\n\nCreates and returns a function which updates the forcing variables (\"drivers\"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.\n\n\n\n\n\nmake_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.\n\n\n\n\n\nmake_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.\n\n\n\n\n\nmake_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.\n\n\n\n\n\nmake_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_vars","page":"Shared Utilities","title":"ClimaLand.prognostic_vars","text":"prognostic_vars(soil::RichardsModel)\n\nA function which returns the names of the prognostic variables of RichardsModel.\n\n\n\n\n\nprognostic_vars(soil::EnergyHydrology)\n\nA function which returns the names of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_vars(::SnowModel)\n\nReturns the prognostic variable names of the snow model.\n\nFor this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.\n\n\n\n\n\nprognostic_vars(m::AbstractModel)\n\nReturns the prognostic variable symbols for the model in the form of a tuple.\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_vars(::AbstractCanopyComponent)\n\nReturns the prognostic vars of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_vars(canopy::CanopyModel)\n\nReturns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nprognostic_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the prognostic variables of the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_types","page":"Shared Utilities","title":"ClimaLand.prognostic_types","text":"prognostic_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_types(::SnowModel{FT})\n\nReturns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.\n\n\n\n\n\nprognostic_types(m::AbstractModel{FT}) where {FT}\n\nReturns the prognostic variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nHere, the coordinate points are those returned by coordinates(model).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_types(::AbstractCanopyComponent)\n\nReturns the prognostic types of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_types(canopy::CanopyModel)\n\nReturns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the prognostic types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_domain_names","page":"Shared Utilities","title":"ClimaLand.prognostic_domain_names","text":"prognostic_domain_names(::SnowModel)\n\nReturns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is \":surface\".\n\n\n\n\n\nprognosticdomainnames(m::AbstractModel)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nprognosticdomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_vars","page":"Shared Utilities","title":"ClimaLand.auxiliary_vars","text":"auxiliary_vars(soil::RichardsModel)\n\nA function which returns the names of the auxiliary variables of RichardsModel.\n\n\n\n\n\nauxiliary_vars(soil::EnergyHydrology)\n\nA function which returns the names of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_vars(::SnowModel)\n\nReturns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.\n\nSince the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.\n\n\n\n\n\nauxiliary_vars(m::AbstractModel)\n\nReturns the auxiliary variable symbols for the model in the form of a tuple.\n\n\n\n\n\nClimaLand.auxiliary_vars(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_vars(canopy::CanopyModel)\n\nReturns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nauxiliary_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_types","page":"Shared Utilities","title":"ClimaLand.auxiliary_types","text":"auxiliary_types(soil::RichardsModel)\n\nA function which returns the names of the auxiliary types of RichardsModel.\n\n\n\n\n\nauxiliary_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_types(m::AbstractModel{FT}) where {FT}\n\nReturns the auxiliary variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nNote that Arrays, MVectors are not isbits and cannot be used.\n\nHere, the coordinate points are those returned by coordinates(model).\n\n\n\n\n\nClimaLand.auxiliary_types(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_types(canopy::CanopyModel)\n\nReturns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the auxiliary types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_domain_names","page":"Shared Utilities","title":"ClimaLand.auxiliary_domain_names","text":"auxiliary_domain_names(soil::RichardsModel)\n\nA function which returns the names of the auxiliary domain names of RichardsModel.\n\n\n\n\n\nauxiliarydomainnames(m::AbstractModel)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\n\n\n\n\nauxiliarydomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_prognostic","page":"Shared Utilities","title":"ClimaLand.initialize_prognostic","text":"initialize_prognostic(model::AbstractModel, state::NamedTuple)\n\nReturns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.\n\nIf a model has no prognostic variables, the returned FieldVector contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_prognostic(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_prognostic(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input state is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_auxiliary","page":"Shared Utilities","title":"ClimaLand.initialize_auxiliary","text":"initialize_auxiliary(model::AbstractModel, state::NamedTuple)\n\nReturns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.\n\nIf a model has no auxiliary variables, the returned NamedTuple contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_auxiliary(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_auxiliary(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input coords is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize","page":"Shared Utilities","title":"ClimaLand.initialize","text":"initialize(model::AbstractModel)\n\nCreates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.name","page":"Shared Utilities","title":"ClimaLand.name","text":"name(model::AbstractModel)\n\nReturns a symbol of the model component name, e.g. :soil or :vegetation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBC","page":"Shared Utilities","title":"ClimaLand.AbstractBC","text":"AbstractBC\n\nAn abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractSource","page":"Shared Utilities","title":"ClimaLand.AbstractSource","text":"AbstractSource{FT <: AbstractFloat}\n\nAn abstract type for types of source terms.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.source!","page":"Shared Utilities","title":"ClimaLand.source!","text":" source!(dY::ClimaCore.Fields.FieldVector,\n src::PhaseChange{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nComputes the source terms for phase change.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::SoilSublimation{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nUpdates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::AbstractSource,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n )::ClimaCore.Fields.Field\n\nA stub function, which is extended by ClimaLand.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::RootExtraction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n model::EnergyHydrology)\n\nAn extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::MicrobeProduction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n params)\n\nA method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.\n\n\n\n\n\nClimaLand.source!(\n dY::ClimaCore.Fields.FieldVector,\n src::TOPMODELSubsurfaceRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model::AbstractSoilModel{FT},\n) where {FT}\n\nAdjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.\n\nThe sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBoundary","page":"Shared Utilities","title":"ClimaLand.AbstractBoundary","text":"AbstractBoundary\n\nAn abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.TopBoundary","page":"Shared Utilities","title":"ClimaLand.TopBoundary","text":"TopBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the top boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.BottomBoundary","page":"Shared Utilities","title":"ClimaLand.BottomBoundary","text":"BottomBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the bottom boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_flux","page":"Shared Utilities","title":"ClimaLand.boundary_flux","text":"boundary_flux(bc::WaterFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(bc::RichardsAtmosDrivenFluxBC,\n boundary::ClimaLand.AbstractBoundary,\n model::RichardsModel{FT},\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field where {FT}\n\nA method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.\n\nIf model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(bc::FreeDrainage,\n boundary::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which enforces free drainage at the bottom of the domain.\n\n\n\n\n\nboundary_flux(bc::HeatFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.BottomBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field\n\nA function which returns the correct boundary flux given any boundary condition (BC). \n\n\n\n\n\nfunction ClimaLand.boundary_flux(\n bc::RunoffBC,\n ::TopBoundary,\n model::Soil.RichardsModel,\n Δz::FT,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n params,\n)::ClimaCore.Fields.Field\n\nExtension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2FluxBC,\n boundary::ClimaLand.AbstractBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::SoilCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2StateBC,\n boundary::ClimaLand.BottomBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::AtmosCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.diffusive_flux","page":"Shared Utilities","title":"ClimaLand.diffusive_flux","text":"diffusive_flux(K, x_2, x_1, Δz)\n\nCalculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_vars","page":"Shared Utilities","title":"ClimaLand.boundary_vars","text":"boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.\n\nThese variables are updated in place in soil_boundary_fluxes!.\n\n\n\n\n\nboundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for MoistureStateBC at the top boundary.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\nboundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_domain_names","page":"Shared Utilities","title":"ClimaLand.boundary_var_domain_names","text":"boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.\n\n\n\n\n\nboundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)\n\nThe list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. \n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_types","page":"Shared Utilities","title":"ClimaLand.boundary_var_types","text":"boundary_var_types(::RichardsModel{FT},\n ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <: Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.\n\nBecause we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.\n\n\n\n\n\nboundary_var_types(\n ::EnergyHydrology{FT},\n ::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere{FT},\n <:AbstractRadiativeDrivers{FT},\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary,\n) where {FT}\n\nAn extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.\n\n\n\n\n\nboundary_var_types(::RichardsModel{FT},\n ::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_jacobian","page":"Shared Utilities","title":"ClimaLand.make_jacobian","text":"make_jacobian(model::AbstractModel)\n\nCreates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.\n\nThe default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.\n\nNote that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_jacobian","page":"Shared Utilities","title":"ClimaLand.make_compute_jacobian","text":"ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nmake_compute_jacobian(model::AbstractModel)\n\nCreates and returns a function which computes the entries of the Jacobian matrix W in place.\n\nIf the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.\n\nThe default is that no updates are required, but this function must be extended for models that use implicit timestepping.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_dfluxBCdY!","page":"Shared Utilities","title":"ClimaLand.set_dfluxBCdY!","text":"ClimaLand.set_dfluxBCdY!(\n model::RichardsModel,\n ::MoistureStateBC,\n boundary::ClimaLand.TopBoundary,\n Δz,\n Y,\n p,\n t,\n\n)\n\nComputes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.\n\nFor Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.\n\n\n\n\n\nset_dfluxBCdY!(::AbstractModel,\n ::AbstractBC,\n ::AbstractBoundary,\n _...)::Union{ClimaCore.Fields.FieldVector, Nothing}\n\nA function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.get_drivers","page":"Shared Utilities","title":"ClimaLand.get_drivers","text":"ClimaLand.get_drivers(model::RichardsModel)\n\nReturns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.\n\n\n\n\n\nClimaLand.get_drivers(model::SnowModel)\n\nReturns the driver variable symbols for the SnowModel.\n\n\n\n\n\nget_drivers(model::AbstractModel)\n\nReturns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Drivers","page":"Shared Utilities","title":"Drivers","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.PrescribedAtmosphere\nClimaLand.PrescribedPrecipitation\nClimaLand.PrescribedRadiativeFluxes\nClimaLand.PrescribedSoilOrganicCarbon\nClimaLand.CoupledAtmosphere\nClimaLand.CoupledRadiativeFluxes\nClimaLand.AbstractAtmosphericDrivers\nClimaLand.AbstractRadiativeDrivers\nClimaLand.turbulent_fluxes\nClimaLand.turbulent_fluxes_at_a_point\nClimaLand.radiative_fluxes_at_a_point\nClimaLand.set_atmos_ts!\nClimaLand.surface_air_density\nClimaLand.surface_temperature\nClimaLand.surface_resistance\nClimaLand.surface_specific_humidity\nClimaLand.make_update_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedAtmosphere","page":"Shared Utilities","title":"ClimaLand.PrescribedAtmosphere","text":"PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.\n\nThe default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.\n\nSince not all models require co2 concentration, the default for that is nothing.\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\nsnow_precip: Snow precipitation (m/s) function of time: positive by definition\nT: Prescribed atmospheric temperature (function of time) at the reference height (K)\nu: Prescribed wind speed (function of time) at the reference height (m/s)\nq: Prescribed specific humidity (function of time) at the reference height (_)\nP: Prescribed air pressure (function of time) at the reference height (Pa)\nc_co2: CO2 concentration in atmosphere (mol/mol)\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nh: Reference height (m), relative to surface elevation\ngustiness: Minimum wind speed (gustiness; m/s)\nthermo_params: Thermodynamic parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedPrecipitation","page":"Shared Utilities","title":"ClimaLand.PrescribedPrecipitation","text":"PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.PrescribedRadiativeFluxes","text":"PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}\n\nContainer for the prescribed radiation functions needed to drive land models in standalone mode.\n\nSW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface\nLW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nθs: Sun zenith angle, in radians\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedSoilOrganicCarbon","page":"Shared Utilities","title":"ClimaLand.PrescribedSoilOrganicCarbon","text":" PrescribedSoilOrganicCarbon{FT}\n\nA type for prescribing soil organic carbon.\n\nsoc: Soil organic carbon, function of time and space: kg C/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledAtmosphere","page":"Shared Utilities","title":"ClimaLand.CoupledAtmosphere","text":"CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.CoupledRadiativeFluxes","text":"CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractAtmosphericDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractAtmosphericDrivers","text":" AbstractAtmosphericDrivers{FT}\n\nAn abstract type of atmospheric drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractRadiativeDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractRadiativeDrivers","text":" AbstractRadiativeDrivers{FT}\n\nAn abstract type of radiative drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes","text":"turbulent_fluxes(atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t\n )\n\nComputes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.\n\nIt solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.\n\n\n\n\n\nturbulent_fluxes(atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t)\n\nComputes the turbulent surface fluxes terms at the ground for a coupled simulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes_at_a_point","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes_at_a_point","text":"turbulent_fluxes_at_a_point(T_sfc::FT,\n q_sfc::FT,\n ρ_sfc::FT,\n β_sfc::FT,\n h_sfc::FT,\n r_sfc::FT,\n d_sfc::FT,\n ts_in,\n u::FT,\n h::FT,\n gustiness::FT,\n z_0m::FT,\n z_0b::FT,\n earth_param_set::EP,\n ) where {FT <: AbstractFloat, P}\n\nComputes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc\n\nThis returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_atmos_ts!","page":"Shared Utilities","title":"ClimaLand.set_atmos_ts!","text":"set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)\n\nFill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_air_density","page":"Shared Utilities","title":"ClimaLand.surface_air_density","text":"surface_air_density(\n atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t,\n T_sfc,\n )\n\nA helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.\n\nWe additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.\n\nExtending this function for your model is only necessary if you need to compute the air density in a different way.\n\n\n\n\n\nClimaLand.surface_air_density(\n atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n _...,\n )\n\nReturns the air density at the surface in the case of a coupled simulation.\n\nThis requires the field ρ_sfc to be present in the cache p under the name of the model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_temperature","page":"Shared Utilities","title":"ClimaLand.surface_temperature","text":"ClimaLand.surface_temperature(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface temperature field of the EnergyHydrology soil model.\n\nThe assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.\n\n\n\n\n\nClimaLand.surface_temperature(model::SnowModel, Y, p)\n\na helper function which returns the surface temperature for the snow model, which is stored in the aux state.\n\n\n\n\n\nClimaLand.surface_temperature(model::BucketModel, Y, p)\n\na helper function which returns the surface temperature for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_temperature(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_temperature(model::CanopyModel, Y, p, t)\n\nA helper function which returns the temperature for the canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_resistance","page":"Shared Utilities","title":"ClimaLand.surface_resistance","text":"ClimaLand.surface_resistance(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface resistance field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_resistance(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\nThe default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.\n\n\n\n\n\nClimaLand.surface_resistance(\n model::CanopyModel{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the stomatal resistance field of the CanopyModel canopy.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_specific_humidity","page":"Shared Utilities","title":"ClimaLand.surface_specific_humidity","text":"ClimaLand.surface_specific_humidity(\n model::EnergyHydrology{FT},\n Y,\n p,\n T_sfc,\n ρ_sfc\n) where {FT}\n\nReturns the surface specific humidity field of the EnergyHydrology soil model.\n\nThis models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.\n\nOver the soil ice, the specific humidity is the saturated value.\n\nThe total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\nComputes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\na helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)\n\nA helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)\n\nA helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.\n\n\n\n\n\n","category":"function"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Farquhar-Model","page":"Farquhar model","title":"Farquhar Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO_2 into the leaf, the assimilation of CO_2 during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO_2 concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Rubisco-limited-rate","page":"Farquhar model","title":"Rubisco limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_1(T c_a VPD) =\nbegincases\n V_cmax(T) frac(c_i(T c_a VPD) - Gamma^*(T))(c_i(T c_a VPD) + K_c(T)*(1+o_iK_o(T))) textfor C3\n V_cmax(T) textfor C4\nendcases\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The dependence on the atmospheric CO_2 concentration c_a (mol/mol) and vapor pressure deficit VPD arise in the expression for c_i,","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n c_i(T c_a VPD) = max(c_a(1-1m(VPD)) Gamma^*(T))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where and m is the Medlyn factor (see Stomatal Conductance).","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We also have","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":" Gamma^*(T) = Gamma^*_25expleft(Delta H_Gamma^*fracT - T_oT_o R Tright)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where Delta H_Gamma^* is the activation energy per mol for Gamma^*.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Light-limited-rate","page":"Farquhar model","title":"Light limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_2 =\nbegincases\n J(T PAR) (c_i - Gamma^*)4(c_i + 2 Gamma^*) textfor C3\n J(T PAR) textfor C4\nendcases \nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where J is the rate of electron transport, which has units of mol photon per m^2 per s. It depends on PAR via APAR, as described below, and on T via the dependence on J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"J is given by the root of the equation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n theta_j J^2 - (I + J_max) J + I J_max = 0 nonumber \n I = fracphi2 (APAR) nonumber \n J_max(T) = V_cmax(T)times e expleft(Delta H_J_maxfracT - T_oT_o R Tright)nonumber \nJ(T PAR) = frac(I + J_max - sqrt(I + J_max)^2 - 4theta_j I times J_max2theta_j\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where phi = 06 and theta_j = 09 are the quantum yield of photosystem II and a curvature function (Bonan's book), and Delta H_J_max is the energy of activation of J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The total net carbon assimilation (A_n, mol CO_2 m^-2 s^-1) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nA_n(T PAR VPD c_a) = textmax(0 textmin(a_1 beta a_2) - R_d)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where beta is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R_d is the leaf dark respiration calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n R_d25(psi_l) = f V_cmax25beta(psi_l) nonumber \n R_d (T psi_l) = R_d25(psi_l)expleft(Delta H_R_dfracT - T_oT_o R Tright)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where f = 0015 is a constant, Delta H_R_d is the energy of activation for R_d, and finally Vcmax is calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\nV_cmax(T) = V_cmax25 expleft(Delta H_VcmaxfracT - T_oT_o R Tright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"with V_cmax25 is a parameter (Vcmax at the reference temperature 25 C), and Delta H_Vcmax = 65330 Jmol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The moisture stress factor is related to the leaf water potential psi_l as","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n beta = frac1+ exp(s_c psi_c)1+ exp(s_c(psi_c - psi_l))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where s_c = 4MPa^-1, psi_c = -2MPa, and psi_l is the leaf water potential computed by the plant hydraulics model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nGPP(T PAR c_a VPD theta_s) = A_n (1 - exp(-K LAI Omega))K\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This is not currently needed by other components, but is used for offline validation of the model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We need to supply the following parameters and “drivers\"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"K_c25 and K_o25, V_cmax 25, Gamma^*_25phi, theta_j, o_i, s_c, psi_c\npsi_l, to compute beta\nTemperature T, PAR, c_a, VPD, theta_s.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Output Symbol Unit Range\nTotal net carbon assimilation A_n μmol CO_2 m^-2 s^-1 0–25","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nTemperature T °C 0–50","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Parameters Symbol Unit Range\nMoisture stress β - 0-1\nLeaf Area Index LAI m² m⁻² 1–10\nCO_2 concentration c_a ppm 300e–500\nVapor pressure deficit VPD kPa 1-10","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Constants Symbol Unit Value\nZenith angle θ_s rad 0.6\nLeaf angle distribution l_d - 0.5\nCanopy reflectance ρ_leaf - 0.1\nClumping index Ω - 0.69\nCO_2 compensation at 25°C Γ^*_25 mol/mol 4.275e-5\nEnergy of activation for Γ^* ΔH_Γ^* J/mol 37830\nStandard temperature T_o K 298.15\nUniversal gas constant R J/mol 8.314\nThe maximum rate of carboxylation of Rubisco V_cmax25 mol CO_2 m^-2 s^-1 5e-5\nEnergy of activation for J_max ΔH_J_max J/mol 43540\nCurvature parameter, a fitting constant to compute J θ_j - 0.9\nThe quantum yied of photosystem II phi - 0.6\nEnergy of activation for V_cmax ΔH_V_cmax J/mol 58520\nSlope parameter for stomatal conductance models g_1 - 141\nMichaelis Menten constant for CO_2 and at 25\u000e°C K_c25 mol/mol 4.049e-4\nEnergy of activation for CO_2 ΔH_K_c J/mol 79430\nMichaelis Menten constant for O_2 at 25 °C\u000e K_o25 mmol/mol 0.2874\nEnergy of activation for O_2 ΔH_K_o J/mol 36380\nIntercellular O_2 concentration o_i mol/mol 0.209\nConstant factor appearing the dark respiration term\u000e f - 0.015\nEnergy of activation for R_d ΔH_R_d J/mol 43390","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Soil-Biogeochemistry","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"CurrentModule = ClimaLand.Soil.Biogeochemistry","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Model-Structure","page":"Soil Biogeochemistry","title":"Model Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2Model","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","text":"SoilCO2Model\n\nA model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type NamedTuple\nsources: A tuple of sources, each of type AbstractSource\ndrivers: Drivers\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Parameter-Structure","page":"Soil Biogeochemistry","title":"Parameter Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","text":"SoilCO2ModelParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SoilCO2Model.\n\nAll of these parameters are currently treated as global constants.\n\nD_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)\nD_liq: Diffusivity of soil C substrate in liquid (unitless)\nα_sx: Pre-exponential factor (kg C m-3 s-1)\nEa_sx: Activation energy (J mol-1)\nkM_sx: Michaelis constant (kg C m-3)\nkM_o2: Michaelis constant for O2 (m3 m-3)\nO2_a: Volumetric fraction of O₂ in the soil air, dimensionless\nD_oa: Diffusion coefficient of oxygen in air, dimensionless\np_sx: Fraction of soil carbon that is considered soluble, dimensionless\nearth_param_set: Physical constants used Clima-wide\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Model-specific-Types","page":"Soil Biogeochemistry","title":"Model-specific Types","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction\nClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC\nClimaLand.Soil.Biogeochemistry.SoilCO2StateBC\nClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC\nClimaLand.Soil.Biogeochemistry.AbstractSoilDriver\nClimaLand.Soil.Biogeochemistry.SoilDrivers\nClimaLand.Soil.Biogeochemistry.PrescribedMet","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.MicrobeProduction","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction","text":"MicrobeProduction{FT} <: AbstractCarbonSource{FT}\n\nStruct for the microbe production of CO2, appearing as a source term in the differential equation.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","text":"SoilCO2FluxBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 flux boundary condition, which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","text":"SoilCO2StateBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 state boundary condition (kg CO2 m−3), which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","text":"AtmosCO2StateBC <: ClimaLand.AbstractBC\n\nSet the CO2 concentration to the atmospheric one.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","text":"AbstractSoilDriver\n\nAn abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilDrivers","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilDrivers","text":"SoilDrivers\n\nA container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).\n\nmet: Soil temperature and moisture drivers - Prescribed or Prognostic\nsoc: Soil SOM driver - Prescribed only\natmos: Prescribed atmospheric variables\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.PrescribedMet","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.PrescribedMet","text":"PrescribedMet <: AbstractSoilDriver\n\nA container which holds the prescribed functions for soil temperature and moisture.\n\nThis is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.\n\ntemperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat\nvolumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat\nν: Soil porosity (m³ m⁻³)\nθ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)\nb: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Functions-of-State","page":"Soil Biogeochemistry","title":"Functions of State","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content\nClimaLand.Soil.Biogeochemistry.co2_diffusivity\nClimaLand.Soil.Biogeochemistry.microbe_source","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.volumetric_air_content","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content","text":"volumetric_air_content(θ_w::FT,\n ν::FT,\n ) where {FT}\n\nComputes the volumetric air content (θ_a) in the soil, which is related to the total soil porosity (ν) and volumetric soil water content (θ_w = θ_l+θ_i).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.co2_diffusivity","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.co2_diffusivity","text":"co2_diffusivity(\n T_soil::FT,\n θ_w::FT,\n P_sfc::FT,\n θ_a100::FT,\n b::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT},\n ) where {FT}\n\nComputes the diffusivity of CO₂ within the soil (D).\n\nFirst, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.\n\nThis parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.microbe_source","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.microbe_source","text":"microbe_source(T_soil::FT,\n θ_l::FT,\n Csom::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT}\n ) where {FT}\n\nComputes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#Extendible-Functions","page":"Soil Biogeochemistry","title":"Extendible Functions","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.soil_moisture\nClimaLand.Soil.Biogeochemistry.soil_temperature\nClimaLand.Soil.Biogeochemistry.soil_SOM_C","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_moisture","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_moisture","text":"soil_moisture(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.\n\n\n\n\n\nsoil_moisture(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil moisture at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_temperature","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_temperature","text":"soil_temperature(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the prognostic soil temperature.\n\n\n\n\n\nsoil_temperature(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil temperature at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/users_diagnostics/#Using-ClimaLand-Diagnostics-when-running-a-simulation","page":"For users","title":"Using ClimaLand Diagnostics when running a simulation","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"This is where ClimaLand Diagnostics comes in for users.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.","category":"page"},{"location":"diagnostics/users_diagnostics/#Default-Diagnostics","page":"For users","title":"Default Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#define-an-output-folder","page":"For users","title":"define an output folder","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path(\"base_output_dir/\")","category":"page"},{"location":"diagnostics/users_diagnostics/#define-a-space","page":"For users","title":"define a space","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"space = bucket_domain.space.subsurface","category":"page"},{"location":"diagnostics/users_diagnostics/#define-your-writter","page":"For users","title":"define your writter","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"providing the space and output_dir defined in steps 1. and 2.","category":"page"},{"location":"diagnostics/users_diagnostics/#make-your-diagnostics-on-your-model,-using-your-writter,-and-define-a-callback","page":"For users","title":"make your diagnostics on your model, using your writter, and define a callback","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"t0 = 0 # the starting time of your simulation\n\nreference_date = DateTime(2024) # reference_date is the DateTime of your starting time\n\ndiags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)\n\ndiagnostic_handler =\n ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)\n\ndiag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics have now been written in netcdf files in your output folder.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.","category":"page"},{"location":"diagnostics/users_diagnostics/#Custom-Diagnostics","page":"For users","title":"Custom Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When defining a custom diagnostic, follow these steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-compute-your-diagnostic-variable-from-your-model-state-and-cache.","page":"For users","title":"Define how to compute your diagnostic variable from your model state and cache.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)\n else\n out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf\n end\nend","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"@diagnostic_compute \"latent_heat_flux\" BucketModel p.bucket.turbulent_fluxes.lhf","category":"page"},{"location":"diagnostics/users_diagnostics/#Add-that-diagnostic(s)-variable-to-your-list-of-variables","page":"For users","title":"Add that diagnostic(s) variable to your list of variables","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":" add_diagnostic_variable!(\n short_name = \"bor\",\n long_name = \"Bowen ratio\",\n standard_name = \"bowen_ratio\",\n units = \"\",\n comments = \"Ratio of sensible to latent heat flux.\",\n compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),\n)\n\nadd_diagnostic_variable!(\n short_name = \"lhf\",\n long_name = \"Latent Heat Flux\",\n standard_name = \"latent_heat_flux\",\n units = \"W m^-2\",\n comments = \"Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.\",\n compute! = (out, Y, p, t) ->\n compute_latent_heat_flux!(out, Y, p, t, land_model),\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-schedule-your-variables.-For-example,-you-want-the-seasonal-maximum-of-your-variables,-where-season-is-defined-as-90-days.","page":"For users","title":"Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(\n 90 * 24 * 60 * 60 * one(t_start),\n max,\n output_writer,\n t_start,\n short_names...,\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-a-function-to-return-your-ScheduledDiagnostics","page":"For users","title":"Define a function to return your ScheduledDiagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now, you can call your schedule with your variables.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"my_custom_diagnostics = [\"lhf\", \"bor\"]\n\ndiags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)","category":"page"},{"location":"diagnostics/users_diagnostics/#Analyze-your-simulation-output","page":"For users","title":"Analyze your simulation output","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Here is an example of how to plot a variable:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"import ClimaAnalysis\n\nimport ClimaAnalysis.Visualize as viz\n\nimport CairoMakie # the plotting package used by ClimaAnalysis\n\nsimdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.\n\nvar = get(simdir; \"lhf\") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.\n\nfig = CairoMakie.Figure() # creates an empty figure object\n\nviz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.\n\nCairoMakie.save(fig) # saves the figure in current working directory","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/#Optimality-Model","page":"Optimality model","title":"Optimality Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation (V_textcmax). ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates V_textcmax and J_textmax as a function of environmental variables as follows:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n V_textcmax^* = varphi I left(fracmm_cright)left(fracoverlineomega^*8thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega^* = 1 + overlineomega - sqrt(1 + overlineomega)^2 - 4thetaoverlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega = -(1 - 2theta) + sqrt(1 - theta)left(frac1frac4cmleft(1 - thetafrac4cmright) - 4thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n c = fracm8thetaleft(1 - fracvarphi I + J_textmax - 2thetavarphi Isqrt(varphi I + J_textmax)^2 - 4thetavarphi I J_textmaxright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n J_textmax = varphi I overlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m = fracC_i - Gamma^*C_i + 2Gamma^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n C_i = Gamma^* + (C_a - Gamma^*)fracxixi + sqrtD_g\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n xi = sqrtbeta fracK + Gamma^*16eta^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n K = K_cleft(1 + fracO_iK_oright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m_c = fracC_i - Gamma^*C_i + K\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Gamma^*","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"is the CO_2 compensation point in the absence of mitochondrial respiration","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n Gamma^* = Gamma^*_0 f(T Delta H_a) pp_0\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where Gamma^*_0 = 4332 Pa, p is the atmospheric pressure, p_0 = 101325 Pa, and Delta H_a = 37830 J/mol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"varphi is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.\ntheta is the curvature of the light response curve (dimensionless). Estimated at 0.85.\nbeta is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"For Smith et al. (2019) Vcmax model:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Altitude\nD_g is the vapor pressure deficit (VPD) at altitude\nC_a is the CO_2 partial pressure\nI is the incident photosynthetically active photon flux (PAR)\nT is the temperature","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Introduction-to-the-Canopy-Model","page":"Standalone Canopy","title":"Introduction to the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Preliminary-Setup","page":"Standalone Canopy","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load External Packages:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing StaticArrays\nusing ClimaLand\nusing ClimaLand.Domains: Point\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Setup-the-Canopy-Model","page":"Standalone Canopy","title":"Setup the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4) # kg/m^2\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Point(; z_sfc = FT(0.0))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}\n Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"time_offset = 7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Site latitude and longitude","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"-92.2f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Height of the sensor at the site","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"atmos_h = FT(32)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"32.0f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_soil0 = FT(0.0)\n\nsoil_driver = PrescribedSoil(\n FT;\n root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),\n ψ = t -> ψ_soil0,\n α_PAR = FT(0.2),\n α_NIR = FT(0.4),\n T = t -> 298.0,\n ϵ = FT(0.99),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"rt_params = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = FT(0.1),\n α_NIR_leaf = FT(0.45),\n τ_PAR_leaf = FT(0.05),\n τ_NIR_leaf = FT(0.25),\n Ω = FT(0.69),\n λ_γ_PAR = FT(5e-7),\n λ_γ_NIR = FT(1.65e-6),\n)\n\nrt_model = TwoStreamModel{FT}(rt_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for conductance model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))\n\nstomatal_model = MedlynConductanceModel{FT}(cond_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for photosynthesis model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))\n\nphotosynthesis_model = FarquharModel{FT}(photo_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for autotrophic respiration model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"AR_params = AutotrophicRespirationParameters(FT)\nAR_model = AutotrophicRespirationModel{FT}(AR_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for plant hydraulics model are more complicated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"LAI = 4.2\nLAIfunction = (t) -> LAI\nSAI = FT(0.00242)\nf_root_to_shoot = FT(3.5)\nRAI = FT((SAI + LAI) * f_root_to_shoot)\nai_parameterization =\n PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)\nrooting_depth = FT(1.0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the root distribution function p(z):","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"function root_distribution(z::T; rooting_depth = rooting_depth) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"K_sat_plant = FT(1.8e-8)\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use these values to populate the parameters of the PlantHydraulics model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ν = FT(0.7)\nS_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = ν,\n S_s = S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the remaining variables required for the plant hydraulics model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_surfaces = compartment_surfaces,\n compartment_midpoints = compartment_midpoints,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"canopy = ClimaLand.Canopy.CanopyModel{FT}(;\n parameters = shared_params,\n domain = land_domain,\n autotrophic_respiration = AR_model,\n radiative_transfer = rt_model,\n photosynthesis = photosynthesis_model,\n conductance = stomatal_model,\n hydraulics = plant_hydraulics,\n soil_driver = soil_driver,\n atmos = atmos,\n radiation = radiation,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Y, p, coords = ClimaLand.initialize(canopy)\nexp_tendency! = make_exp_tendency(canopy);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide initial conditions for the canopy hydraulics model","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n ν,\n S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"t0 = 0.0\nN_days = 364\ntf = t0 + 3600 * 24 * N_days\ndt = 225.0;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the cache variables for the canopy using the initial conditions and initial time.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"set_initial_cache! = make_set_initial_cache(canopy)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"n = 16\nsaveat = Array(t0:(n * dt):tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the callback function which updates the forcing variables, or drivers.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"updateat = Array(t0:1800:tf)\nmodel_drivers = ClimaLand.get_drivers(canopy)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a timestepping algorithm and setup the ODE problem.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"timestepper = CTS.RK4();\node_algo = CTS.ExplicitAlgorithm(timestepper)\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Create-some-plots","page":"Standalone Canopy","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We can now plot the data produced in the simulation. For example, GPP:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Transpiration plot:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Show the two plots together:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Save the output:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"savefig(\"ozark_standalone_canopy_test.png\");","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"(Image: )","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Introduction-to-the-Land-Bucket-Model","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"All equation variables are defined immediately below. We have:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wdt = -I","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wsdt = -(P_liq + σM + (1-σ) E_soil - I)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd σSdt = -(P_snow + σE_snow - σM)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ρc fracpartial Tpartial t = κ_soil fracpartial Tpartial z","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_bot = 00 = -κ_soil fracpartial Tpartial z_z = z_bot","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(1-σ) (R_n+ SHF + LHF)_soil + σG_undersnow = -κ_soil fracpartial Tpartial z_z = z_sfc","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"G_undersnow = (R_n+ SHF + LHF)_snow - F_intosnow","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_intosnow = -ρ_l L_f0 (P_snow + E_snow-M)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"R_n = -(1-α)*SW -LW + σ_SB T_sfc^4","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_soil = β(W W_f) E(q_sat(T_sfc ρ_sfc liquid)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_snow = E(q_sat(T_sfc ρ_sfc ice)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Simulating-a-standalone-bucket-model","page":"Introduction to the Land Bucket Model","title":"Simulating a standalone bucket model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaCore","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaParams as CP","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use Insolation to calculate solar zenith angle and solar insolation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaLand.Bucket:\n BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo\nusing ClimaLand.Domains: coordinates, Column\nusing ClimaLand:\n initialize,\n make_update_aux,\n make_exp_tendency,\n make_set_initial_cache,\n PrescribedAtmosphere,\n PrescribedRadiativeFluxes\nusing ClimaUtilities.TimeVaryingInputs: TimeVaryingInput","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also want to plot the solution","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Plots","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"And we need to use the DateTime type to store reference times","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Dates\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaLand\nimport ClimaLand.Parameters as LP\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"soil_depth = FT(3.5);\nbucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);\nsurface_space = bucket_domain.space.surface","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}\n Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"α_bareground_func = (coordinate_point) -> 0.2;\nα_snow = FT(0.8);\nalbedo =\n PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The critical snow level setting the scale for when we interpolate between snow and surface albedo","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"σS_c = FT(0.2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The field capacity of the soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W_f = FT(0.15);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Roughness lengths (meters)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"z_0m = FT(1e-2);\nz_0b = FT(1e-3);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Thermal parameters of soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"κ_soil = FT(0.7);\nρc_soil = FT(2e6);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Snow melt timescale","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"τc = FT(3600);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Simulation start time, end time, and timestep","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"t0 = 0.0;\ntf = 7 * 86400;\nΔt = 3600.0;\n\nbucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ref_time = DateTime(2005);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Here we define the model drivers Prescribed atmospheric variables","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Precipitation:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"precip = (t) -> 0;\nsnow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Diurnal temperature variations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Constant otherwise:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"u_atmos = (t) -> 3.0;\nq_atmos = (t) -> 0.005;\nh_atmos = FT(2);\nP_atmos = (t) -> 101325;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"bucket_atmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(snow_precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));\nLW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;\nbucket_rad = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"model = BucketModel(\n parameters = bucket_parameters,\n domain = bucket_domain,\n atmosphere = bucket_atmos,\n radiation = bucket_rad,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Like all ClimaLand models, we set up the state vector using initialize:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y, p, coords = initialize(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We can inspect the prognostic and auxiliary variables of the model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.prognostic_vars(model)\nY.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:W, :T, :Ws, :σS)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.auxiliary_vars(model)\np.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Next is to set initial conditions.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y.bucket.T .= FT(270);\nY.bucket.W .= FT(0.05);\nY.bucket.Ws .= FT(0.0);\nY.bucket.σS .= FT(0.08);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also set the initial values of the cache here:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"set_initial_cache! = make_set_initial_cache(model);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"exp_tendency! = make_exp_tendency(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Now we choose our timestepping algorithm.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then we can set up the simulation and solve it:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"saveat = collect(t0:Δt:tf);\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\nsaving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);\nupdateat = copy(saveat)\nmodel_drivers = ClimaLand.get_drivers(model)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];\nWs = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];\nσS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];\nT_sfc =\n [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];\nevaporation = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]\n for k in 1:length(sol.t)\n];\nR_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The turbulent energy flux is the sum of latent and sensible heat fluxes.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"LHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for\n k in 1:length(sol.t)\n];\nSHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for\n k in 1:length(sol.t)\n];\nturbulent_energy_flux = SHF .+ LHF\n\nplot(\n sol.t ./ 86400,\n W,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"W (m)\",\n title = \"Land water storage (m)\",\n)\nsavefig(\"w.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n σS,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"σS (m)\",\n title = \"Area weighted SWE (m) \",\n)\nsavefig(\"swe.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n snow_precip.(sol.t),\n label = \"Net precipitation\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (m/s)\",\n title = \"Surface water fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, evaporation, label = \"Sublimation/Evaporation\")\nsavefig(\"water_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n T_sfc,\n title = \"Surface Temperatures\",\n label = \"Ground temperature\",\n xlabel = \"time (days)\",\n ylabel = \"T_sfc (K)\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, T_atmos.(sol.t), label = \"Atmospheric Temperature\")\nsavefig(\"t.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n R_n,\n label = \"Net radiative flux\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (W/m^2)\",\n title = \"Surface energy fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, turbulent_energy_flux, label = \"Turbulent fluxes\")\nplot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = \"Net flux\")\nsavefig(\"energy_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#References","page":"Introduction to the Land Bucket Model","title":"References","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/#The-Two-Stream-Scheme","page":"Two-Stream model","title":"The Two-Stream Scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"-overlinemu(dI^uparrow)dL + 1 - (1 - beta)omegaI^uparrow - omega beta I^downarrow = omega overlinemu K beta_0 exp(-KL)\n-overlinemu(dI^downarrow)dL + 1 - (1 - beta)omegaI^downarrow - omega beta I^uparrow = omega overlinemu K (1-beta_0) exp(-KL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"int_0^1mu^primeG(mu^prime)dmu^prime","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = frach_1exp(-KL)sigma + h_2exp(-hL) + h_3exp(hL)\nI^downarrow = frach_4exp(-KL)sigma + h_5exp(-hL) + h_6exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = h_7exp(-hL) + h_8exp(hL)\nI^downarrow = h_9exp(-hL) + h_10exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Output Symmbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500\nAbsorbed Near-Infrared Radiation ANIR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Parameters Symbol Unit Range\nCanopy PAR Reflectance alpha_PAR_leaf - 0.0–1.0\nCanopy NIR Reflectance alpha_NIR_leaf - 0.0–1.0\nCanopy PAR Transmittance tau_PAR_leaf - 0.0–1.0\nCanopy NIR Transmittance tau_NIR_leaf - 0.0–1.0\nCanopy Emissivity ϵ_canopy - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5\nTypical wavelength per photon PAR lambda_gamma_PAR m 5e-7\nTypical wavelength per photon NIR lambda_gamma_NIR m 1.65e-6","category":"page"},{"location":"APIs/Bucket/#Bucket","page":"Bucket Model","title":"Bucket","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"CurrentModule = ClimaLand.Bucket","category":"page"},{"location":"APIs/Bucket/#Types","page":"Bucket Model","title":"Types","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.BucketModelParameters\nClimaLand.Bucket.PrescribedBaregroundAlbedo\nClimaLand.Bucket.PrescribedSurfaceAlbedo\nClimaLand.Bucket.BucketModel","category":"page"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModelParameters","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModelParameters","text":"struct BucketModelParameters{\n FT <: AbstractFloat,\n PSE,\n}\n\nContainer for holding the parameters of the bucket model.\n\nκ_soil: Conductivity of the soil (W/K/m); constant\nρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant\nalbedo: Albedo Model\nσS_c: Critical σSWE amount (m) where surface transitions from to snow-covered\nf_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)\nW_f: Capacity of the land bucket (m)\nf_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)\np: Exponent used in β decay (unitless)\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nτc: τc timescale on which snow melts\nearth_param_set: Earth Parameter set; physical constants, etc\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedBaregroundAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedBaregroundAlbedo","text":"PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel\n\nAn albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedSurfaceAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedSurfaceAlbedo","text":"PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}\n <: AbstractBucketAlbedoModel\n\nAn albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.\n\nNote that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModel","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModel","text":"struct BucketModel{\n FT,\n PS <: BucketModelParameters{FT},\n ATM <: AbstractAtmosphericDrivers{FT},\n RAD <: AbstractRadiativeDrivers{FT},\n D,\n } <: AbstractBucketModel{FT}\n\nConcrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.\n\nparameters: Parameters required by the bucket model\natmos: The atmospheric drivers: Prescribed or Coupled\nradiation: The radiation drivers: Prescribed or Coupled\ndomain: The domain of the model\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#Misc-Functions","page":"Bucket Model","title":"Misc Functions","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.surface_albedo\nClimaLand.Bucket.beta_factor","category":"page"},{"location":"APIs/Bucket/#ClimaLand.surface_albedo","page":"Bucket Model","title":"ClimaLand.surface_albedo","text":"ClimaLand.surface_albedo(\n model::EnergyHydrology{FT},\n Y,\n p,\n) where {FT}\n\nReturns the surface albedo field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_albedo(model::SnowModel, Y, p)\n\nA helper function which computes and returns the snow albedo.\n\n\n\n\n\nsurface_albedo(model::BucketModel, Y, p)\n\nReturns the bulk surface albedo, which gets updated in update_aux via next_albedo.\n\n\n\n\n\nsurface_albedo(model::AbstractModel, Y, p)\n\nA helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Bucket/#ClimaLand.Bucket.beta_factor","page":"Bucket Model","title":"ClimaLand.Bucket.beta_factor","text":"beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}\n\nComputes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:\n\nβ = (x/xc)^p x < xc 1 otherwise\n\nwhere x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.\n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Coupled-heat-and-water-equations-tending-towards-equilibrium","page":"Energy and Hydrology","title":"Coupled heat and water equations tending towards equilibrium","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"θ_i is the volumetric ice fraction, and","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We will solve this equation in an effectively 1-d domain with z -10, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- κ T(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-κ T(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T(t = 0 z) = T_min + (T_max-T_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-K h(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ(t = 0 z) = ϑ_min + (ϑ_max-ϑ_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"where C T_min T_max ϑ_min and ϑ_max are constants.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy hz = 0 and Tz = 0. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Import-necessary-modules","page":"Energy and Hydrology","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"External (non - CliMA) modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"import SciMLBase\nusing Statistics\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"FT = Float32\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Create-the-model","page":"Energy and Hydrology","title":"Create the model","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν = FT(0.395)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.395f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν_ss_quartz = FT(0.92)\nν_ss_om = FT(0.0)\nν_ss_gravel = FT(0.0)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.0f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Ksat = FT(4.42 / 3600 / 100) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.89)\nvg_α = FT(7.5) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nθ_r = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat = Ksat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We also need to pick a domain on which to solve the equations:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"zmax = FT(0)\nzmin = FT(-1.0)\nnelems = 50\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Water boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_water_flux = WaterFluxBC((p, t) -> 0.0)\nbottom_water_flux = WaterFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary conditions for the heat equation:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_heat_flux = HeatFluxBC((p, t) -> 0.0)\nbottom_heat_flux = HeatFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We wrap up all of those in a WaterHeatBC struct:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"boundary_fluxes = (;\n top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),\n bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Set-up-the-simulation","page":"Energy and Hydrology","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"function init_soil!(Y, z, params)\n ν = params.ν\n θ_r = params.θ_r\n FT = eltype(Y.soil.ϑ_l)\n zmax = FT(0)\n zmin = FT(-1)\n\n theta_max = FT(ν * 0.5)\n theta_min = FT(ν * 0.4)\n T_max = FT(289.0)\n T_min = FT(288.0)\n\n c = FT(20.0)\n @. Y.soil.ϑ_l =\n theta_min +\n (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)\n Y.soil.θ_i .= FT(0.0)\n\n T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))\n\n θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)\n ρc_s =\n Soil.volumetric_heat_capacity.(\n θ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 72);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We use ClimaTimesteppers.jl for carrying out the time integration.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a timestepper and set up the ODE problem:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"dt = Float64(1000.0);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"saveat = collect(t0:FT(30000):tf)\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\ncb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Extract output","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z = parent(coords.subsurface.z)\nt = parent(sol.t)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nT = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Let's look at the initial and final times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(ϑ_l[1], z, xlabel = \"ϑ_l\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(ϑ_l[4], z, label = \"t = 1.5d\")\nplot!(ϑ_l[end], z, label = \"t = 3d\")\nsavefig(\"eq_moisture_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(T[1], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(T[4], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 1.5d\")\nplot!(T[end], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 3d\")\nsavefig(\"eq_temperature_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Analytic-Expectations","page":"Energy and Hydrology","title":"Analytic Expectations","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can determine a priori what we expect the final temperature to be in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"One could also solve the equation for ϑ_l specified by h z = 0 to determine the functional form of the equilibrium profile of the liquid water.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#References","page":"Energy and Hydrology","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.\nBalland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"using Plots\nimport ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing ClimaCore\nimport ClimaParams as CP\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nFT = Float64;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define simulation times","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"t0 = Float64(0)\ntf = Float64(60 * 60)\ndt = Float64(30);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define the domain","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"zmax = FT(0)\nzmin = FT(-1.1)\nnelems = 75\nΔ = FT((zmax - zmin) / nelems / 2)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();\nparameter_data = readdlm(data_file, ',');","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m\nksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s\nvgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).\nvgn = reverse(parameter_data[5, :])\nresidual_frac = reverse(parameter_data[2, :])\nporosity = reverse(parameter_data[3, :]);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Create fields corresponding to the parameter","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)\nK_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)\nθ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"hcm = SpaceVaryingInput(\n depth,\n (; α = vgα, n = vgn),\n soil_domain.space.subsurface,\n vanGenuchten{FT},\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The parameter struct:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"params = ClimaLand.Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"function top_flux_function(p, t)\n return -0.0001033\nend\ntop_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)\nbottom_bc = ClimaLand.Soil.FreeDrainage()\nboundary_fluxes = (; top = top_bc, bottom = bottom_bc)\nsoil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial the state vectors, and set initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"We also set the initial conditions of the auxiliary state here:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"stepper = CTS.ARS111()\n@assert FT in (Float32, Float64)\nerr = (FT == Float64) ? 1e-8 : 1e-4\nconvergence_cond = CTS.MaximumError(err)\nconv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 10,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n convergence_checker = conv_checker,\n ),\n)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = make_jacobian(soil)\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);\n\nz = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]\nplot(ϑ_l[1], z, label = \"initial\", color = \"grey\", aspect_ratio = 0.8)\nplot!(ϑ_l[2], z, label = \"8min\", color = \"orange\")\nplot!(ϑ_l[3], z, label = \"16min\", color = \"red\")\nplot!(ϑ_l[4], z, label = \"24min\", color = \"teal\")\nplot!(ϑ_l[5], z, label = \"32min\", color = \"blue\")\nplot!(ϑ_l[6], z, label = \"40min\", color = \"purple\")\nplot!(ϑ_l[7], z, label = \"60min\", color = \"green\")\nscatter!(porosity, depth, label = \"Porosity\")\nplot!(legend = :bottomright)\n\nplot!(xlim = [0, 0.7])\n\nplot!(\n ylim = [-1.1, 0],\n yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],\n)\n\nplot!(ylabel = \"Depth (m)\")\n\nplot!(xlabel = \"Volumeteric Water Content\")\n\nsavefig(\"./sv62_alpha_2_inf_updated_data_climaland.png\")","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy","page":"Canopy Models","title":"Canopy","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Structs","page":"Canopy Models","title":"Canopy Model Structs","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.CanopyModel\nClimaLand.Canopy.SharedCanopyParameters","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.CanopyModel","page":"Canopy Models","title":"ClimaLand.Canopy.CanopyModel","text":" CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}\n\nThe model struct for the canopy, which contains\n\nthe canopy model domain (a point for site-level simulations, or\n\nan extended surface (plane/spherical surface) for regional or global simulations.\n\nsubcomponent model type for radiative transfer. This is of type\n\nAbstractRadiationModel.\n\nsubcomponent model type for photosynthesis. This is of type\n\nAbstractPhotosynthesisModel, and currently only the FarquharModel is supported.\n\nsubcomponent model type for stomatal conductance. This is of type\n\nAbstractStomatalConductanceModel and currently only the MedlynModel is supported\n\nsubcomponent model type for plant hydraulics. This is of type\n\nAbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.\n\nsubcomponent model type for canopy energy. This is of type\n\nAbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.\n\nsubcomponent model type for canopy SIF. prognostically.\ncanopy model parameters, which include parameters that are shared\n\nbetween canopy model components or those needed to compute boundary fluxes.\n\nThe atmospheric conditions, which are either prescribed\n\n(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).\n\nThe radiative flux conditions, which are either prescribed\n\n(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).\n\nThe soil conditions, which are either prescribed (of type PrecribedSoil, for\n\nrunning the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)\n\nNote that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.\n\nautotrophic_respiration: Autotrophic respiration model, a canopy component model\nradiative_transfer: Radiative transfer model, a canopy component model\nphotosynthesis: Photosynthesis model, a canopy component model\nconductance: Stomatal conductance model, a canopy component model\nhydraulics: Plant hydraulics model, a canopy component model\nenergy: Energy balance model, a canopy component model\nsif: SIF model, a canopy component model\natmos: Atmospheric forcing: prescribed or coupled\nradiation: Radiative forcing: prescribed or coupled\nsoil_driver: Soil pressure: prescribed or prognostic\nparameters: Shared canopy parameters between component models\ndomain: Canopy model domain\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.SharedCanopyParameters","page":"Canopy Models","title":"ClimaLand.Canopy.SharedCanopyParameters","text":"SharedCanopyParameters{FT <: AbstractFloat, PSE}\n\nA place to store shared parameters that are required by multiple canopy components.\n\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nearth_param_set: Earth param set\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Fluxes","page":"Canopy Models","title":"Canopy Model Fluxes","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.DiagnosticTranspiration","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","page":"Canopy Models","title":"ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","text":"DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Soil-Drivers","page":"Canopy Models","title":"Canopy Model Soil Drivers","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.AbstractSoilDriver\nClimaLand.Canopy.PrescribedSoil","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.AbstractSoilDriver","page":"Canopy Models","title":"ClimaLand.Canopy.AbstractSoilDriver","text":"An abstract type of soil drivers of the canopy model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PrescribedSoil","page":"Canopy Models","title":"ClimaLand.Canopy.PrescribedSoil","text":" PrescribedSoil <: AbstractSoilDriver\n\nA container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.\n\nroot_depths: The depth of the root tips, in meters\nψ: Prescribed soil potential (m) in the root zone a function of time\nT: Prescribed soil surface temperature (K) as a function of time\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\nϵ: Soil emissivity\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-model","page":"Boundary conditions","title":"Boundary conditions for the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-Richards-equation","page":"Boundary conditions","title":"Boundary conditions for Richards equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.\nWaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).\nMoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).\nRichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-equation","page":"Boundary conditions","title":"Boundary conditions for the soil heat equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).\nTemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-water-equations-(EnergyHydrology-model)","page":"Boundary conditions","title":"Boundary conditions for the soil heat + water equations (EnergyHydrology model)","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"The full soil model requires boundary conditions for both Richards equation and the soil heat equation.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).\nAtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"This page was generated using Literate.jl.","category":"page"},{"location":"folderstructure/#ClimaLand-folder-structure","page":"Repository structure","title":"ClimaLand folder structure","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand home directory has 5 main folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"docs: contains files to generate the documentation website.\nexperiments: contains simple runs of ClimaLand models. \nparameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc. \nsrc: contains the code of ClimaLand models. \ntest: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR. \n.dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main). \n.github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"as well as 5 files:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git. \nLICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code. \nProject.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file. \nREADME.md: This markdown file contains the info that you can read on ClimaLand GitHub web page","category":"page"},{"location":"folderstructure/#/docs-folder","page":"Repository structure","title":"/docs folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Julia packages are recommended to have a \\docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The folder /docs contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation \"contribution guide\" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file. \na make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl. ","category":"page"},{"location":"folderstructure/#/experiments-folder","page":"Repository structure","title":"/experiments folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"For example, /experiments/LSM/ozark/ contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).\nozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.\nozark_parameters.jl: In this file, parameters values are defined. \nozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm. \nozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.","category":"page"},{"location":"folderstructure/#/src-folder","page":"Repository structure","title":"/src folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The /src folder contains the source code of ClimaLand models. It contains 3 folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.\nstandalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder. \nintegrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"As well as one file:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase. ","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Stomatal-Conductance","page":"Canopy Stomatal Conductance","title":"Stomatal Conductance","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Parameters","page":"Canopy Stomatal Conductance","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.MedlynConductanceParameters","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.MedlynConductanceParameters","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.MedlynConductanceParameters","text":"MedlynConductanceParameters{FT <: AbstractFloat}\n\nThe required parameters for the Medlyn stomatal conductance model.\n\nDrel: Relative diffusivity of water vapor (unitless)\ng0: Minimum stomatal conductance mol/m^2/s\ng1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/StomatalConductance/#Methods","page":"Canopy Stomatal Conductance","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.medlyn_term\nClimaLand.Canopy.medlyn_conductance\nClimaLand.Canopy.upscale_leaf_conductance\nClimaLand.Canopy.penman_monteith","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_term","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_term","text":"medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}\n\nComputes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).\n\nthermo_params is the Thermodynamics.jl parameter set.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_conductance","text":"medlyn_conductance(g0::FT,\n Drel::FT,\n medlyn_term::FT,\n An::FT,\n ca::FT) where {FT}\n\nComputes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).\n\nThis returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.upscale_leaf_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.upscale_leaf_conductance","text":"upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}\n\nThis currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.\n\nTODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.penman_monteith","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.penman_monteith","text":"penman_monteith(\n Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1) \n Rn::FT, # Net irradiance (W m−2)\n G::FT, # Ground heat flux (W m−2)\n ρa::FT, # Dry air density (kg m−3)\n cp::FT, # Specific heat capacity of air (J kg−1 K−1) \n VPD::FT, # vapor pressure deficit (Pa)\n ga::FT, # atmospheric conductance (m s−1)\n γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)\n gs::FT, # surface or stomatal conductance (m s−1)\n Lv::FT, # Volumetric latent heat of vaporization (J m-3)\n ) where {FT}\n\nComputes the evapotranspiration in m/s using the Penman-Monteith equation. \n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-Tutorial","page":"Intro to ClimaLand Domains","title":"Domain Tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/#Goals-of-the-tutorial","page":"Intro to ClimaLand Domains","title":"Goals of the tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Background","page":"Intro to ClimaLand Domains","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question \"only\" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"ClimaLand Domains were designed with this in mind. The domains are defined so that","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the user can easily switch geometries, e.g. single column to global model,\nindividual component models can be run by themselves, using a single domain,\nthe same domains can be used to set up multi-component models (LSMs),\ndifferent variables can exist on different parts of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#What-is-a-ClimaLand-Domain?","page":"Intro to ClimaLand Domains","title":"What is a ClimaLand Domain?","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-types","page":"Intro to ClimaLand Domains","title":"Domain types","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"0D: Domains.Point\n1D: Domains.Column\n2D: Domains.Plane, Domains.SphericalSurface\n3D: Domains.HybridBox, Domains.SphericalShell.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Domain Surface Domain Subsurface Domain\nColumn Point Column\nHybridBox Plane HybridBox\nSphericalShell SphericalSurface SphericalShell","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"There is a single key method which take a ClimaLand domain as an argument.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"coordinates(domain): under the hood, this function uses","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#How-variable-initialization-depends-on-domains","page":"Intro to ClimaLand Domains","title":"How variable initialization depends on domains","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Future-work","page":"Intro to ClimaLand Domains","title":"Future work","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into \"vertical\" and \"horizontal\" pieces.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[1]: finite differencing is used in the vertical, and spectral elements are used in the horizontal.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[2]: a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[3]: We also will support having an array-like type of variable.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Coupling-the-CliMA-Canopy-and-Soil-Hydraulics-Models","page":"Coupled Canopy and Soil","title":"Coupling the CliMA Canopy and Soil Hydraulics Models","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Preliminary-Setup","page":"Coupled Canopy and Soil","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load External Packages:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Soil.Biogeochemistry\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Setup the domain for the model:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4)\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Column(; zlim = (zmin, zmax), nelements = nelements);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"time_offset = 7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Site latitude and longitude","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"-92.2f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Height of the sensor at the site","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"atmos_h = FT(32) # m","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"32.0f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Setup-the-Coupled-Canopy-and-Soil-Physics-Model","page":"Coupled Canopy and Soil","title":"Setup the Coupled Canopy and Soil Physics Model","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the parameters for the soil model and provide them to the model parameters struct:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil_ν = FT(0.5) # m3/m3\nsoil_K_sat = FT(4e-7) # m/s\nsoil_S_s = FT(1e-3) # 1/m\nsoil_vg_n = FT(2.05) # unitless\nsoil_vg_α = FT(0.04) # inverse meters\nθ_r = FT(0.067); # m3/m3","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil heat transfer parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ν_ss_quartz = FT(0.1)\nν_ss_om = FT(0.1)\nν_ss_gravel = FT(0.0);\nz_0m_soil = FT(0.1)\nz_0b_soil = FT(0.1)\nsoil_ϵ = FT(0.98)\nsoil_α_PAR = FT(0.2)\nsoil_α_NIR = FT(0.4)\n\nsoil_domain = land_domain\nsoil_ps = Soil.EnergyHydrologyParameters(\n FT;\n ν = soil_ν,\n ν_ss_om = ν_ss_om,\n ν_ss_quartz = ν_ss_quartz,\n ν_ss_gravel = ν_ss_gravel,\n hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),\n K_sat = soil_K_sat,\n S_s = soil_S_s,\n θ_r = θ_r,\n earth_param_set = earth_param_set,\n z_0m = z_0m_soil,\n z_0b = z_0b_soil,\n emissivity = soil_ϵ,\n PAR_albedo = soil_α_PAR,\n NIR_albedo = soil_α_NIR,\n);\n\nsoil_args = (domain = soil_domain, parameters = soil_ps)\nsoil_model_type = Soil.EnergyHydrology{FT}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ClimaLand.Soil.EnergyHydrology{Float32}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}\n\nsoilco2_ps = SoilCO2ModelParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil microbes args","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))\n\nsoilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()\nsoilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);\nsoilco2_sources = (MicrobeProduction{FT}(),);\n\nsoilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);\n\nsoilco2_args = (;\n boundary_conditions = soilco2_boundary_conditions,\n sources = soilco2_sources,\n domain = soil_domain,\n parameters = soilco2_ps,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_types = (;\n autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},\n radiative_transfer = Canopy.TwoStreamModel{FT},\n photosynthesis = Canopy.FarquharModel{FT},\n conductance = Canopy.MedlynConductanceModel{FT},\n hydraulics = Canopy.PlantHydraulicsModel{FT},\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"autotrophic_respiration_args =\n (; parameters = AutotrophicRespirationParameters(FT))\n\nradiative_transfer_args = (;\n parameters = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = 0.1,\n α_NIR_leaf = 0.45,\n τ_PAR_leaf = 0.05,\n τ_NIR_leaf = 0.25,\n Ω = 0.69,\n )\n)\n\nconductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))\n\nphotosynthesis_args =\n (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));\n\nK_sat_plant = FT(1.8e-8)\nRAI = (SAI + maxLAI) * f_root_to_shoot;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Note: LAIfunction was determined from data in the script we included above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)\nfunction root_distribution(z::T; rooting_depth = FT(1.0)) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m\nend\n\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)\n\nplant_ν = FT(0.7)\nplant_S_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = plant_ν,\n S_s = plant_S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n)\n\nplant_hydraulics_args = (\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_midpoints = compartment_midpoints,\n compartment_surfaces = compartment_surfaces,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_args = (;\n autotrophic_respiration = autotrophic_respiration_args,\n radiative_transfer = radiative_transfer_args,\n photosynthesis = photosynthesis_args,\n conductance = conductance_args,\n hydraulics = plant_hydraulics_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We also need to provide the shared parameter struct to the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n)\ncanopy_domain = obtain_surface_domain(land_domain)\ncanopy_model_args = (; parameters = shared_params, domain = canopy_domain);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)\n\nland = SoilCanopyModel{FT}(;\n soilco2_type = soilco2_type,\n soilco2_args = soilco2_args,\n land_args = land_input,\n soil_model_type = soil_model_type,\n soil_args = soil_args,\n canopy_component_types = canopy_component_types,\n canopy_component_args = canopy_component_args,\n canopy_model_args = canopy_model_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y, p, coords = initialize(land);\nexp_tendency! = make_exp_tendency(land);\nimp_tendency! = make_imp_tendency(land);\njacobian! = make_jacobian(land);\njac_kwargs =\n (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We need to provide initial conditions for the soil and canopy hydraulics models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y.soil.ϑ_l = FT(0.4)\nY.soil.θ_i = FT(0.0)\nT_0 = FT(288.7)\nρc_s =\n volumetric_heat_capacity.(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n land.soil.parameters.ρc_ds,\n earth_param_set,\n )\nY.soil.ρe_int =\n volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)\n\nY.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air\n\nψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n plant_ν,\n plant_S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .=\n augmented_liquid_fraction.(plant_ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"t0 = Float64(150 * 3600 * 24)# start mid year\nN_days = 100\ntf = t0 + Float64(3600 * 24 * N_days)\ndt = Float64(30)\nn = 120\nsaveat = Array(t0:(n * dt):tf)\n\ntimestepper = CTS.ARS343()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now set the initial values for the cache variables for the combined soil and plant model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"set_initial_cache! = make_set_initial_cache(land)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Set the callbacks, which govern how often we save output, and how often we update the forcing data (\"drivers\")","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"sv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nmodel_drivers = ClimaLand.get_drivers(land)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\nupdateat = Array(t0:1800:tf)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Carry out the simulation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(\n prob,\n ode_algo;\n dt = dt,\n callback = cb,\n adaptive = false,\n saveat = saveat,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Plotting","page":"Coupled Canopy and Soil","title":"Plotting","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Transpiration plot:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Show the two plots together:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_canopy_flux_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"plt1 = Plots.plot(size = (500, 700));\nϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]\nplt1 = Plots.plot(\n daily,\n ϑ_l_10,\n label = \"10 cm\",\n xlabel = \"Days\",\n ylabel = \"SWC [m/m]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n color = \"blue\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],\n label = \"20cm\",\n color = \"red\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],\n label = \"30cm\",\n color = \"purple\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"root_stem_flux = [\n sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for\n k in 1:length(sol.t)\n]\nplt1 = Plots.plot(\n daily,\n root_stem_flux,\n label = \"soil-root-stem water flux\",\n ylabel = \"Water flux[mm/day]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And save the output","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_plant_flux.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"diagnostics/developers_diagnostics/#ClimaLand-Diagnostics:-why-and-how","page":"For developers","title":"ClimaLand Diagnostics: why and how","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in \"K\" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Diagnostics.jl defines the module,\ndiagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"compute methods are defined in a separate file, for example, bucket_compute_methods.jl.\nstandard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.\ndefault_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Compute-methods","page":"For developers","title":"Compute methods","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.α_sfc)\n else\n out .= p.bucket.α_sfc\n end\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@diagnostic_compute \"albedo\" BucketModel p.bucket.α\\_sfc","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Define-diagnostics","page":"For developers","title":"Define diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!(\n short_name = \"alpha\",\n long_name = \"Albedo\",\n standard_name = \"albedo\",\n units = \"\",\n compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),\n )","category":"page"},{"location":"diagnostics/developers_diagnostics/#Default-diagnostics","page":"For developers","title":"Default diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"function default_diagnostics(land_model::BucketModel, t_start; output_writer)\n\n define_diagnostics!(land_model)\n\n bucket_diagnostics = [\n \"alpha\",\n \"rn\",\n \"tsfc\",\n \"qsfc\",\n \"lhf\",\n \"rae\",\n \"shf\",\n \"vflux\",\n \"rhosfc\",\n \"t\",\n \"w\",\n \"ws\",\n \"sigmas\",\n ]\n\n default_outputs =\n hourly_averages(bucket_diagnostics...; output_writer, t_start)\n return [default_outputs...]\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = [\"gpp\", \"ct\", \"lai\", \"swc\", \"si\"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Standard-diagnostic-frequencies","page":"For developers","title":"Standard diagnostic frequencies","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(\n 60 * 60 * one(t_start),\n (+),\n output_writer,\n t_start,\n short_names...;\n pre_output_hook! = average_pre_output_hook!,\n)","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.","category":"page"},{"location":"","page":"Home","title":"Home","text":"using Pkg\nio = IOBuffer()\nv = Pkg.installed()[\"ClimaLand\"]\nprint(io, \"\"\"\n # ClimaLand.jl Documentation (v$(v))\n\n \"\"\")\nimport Markdown\nMarkdown.parse(String(take!(io)))","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand can be run coupled (or \"online\") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data (\"offline\").","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.","category":"page"},{"location":"#Important-Links","page":"Home","title":"Important Links","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CliMA Homepage\nCliMA GitHub Organisation\nClimaCoupler\nClimaAnalysis\nJulia Homepage","category":"page"},{"location":"#Documentation-for-Users-and-Developers","page":"Home","title":"Documentation for Users and Developers","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.","category":"page"},{"location":"#Physical-units","page":"Home","title":"Physical units","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Note that CliMA, in all its repositories, uses Standard Units, reminded below","category":"page"},{"location":"","page":"Home","title":"Home","text":"Quantity Unit Name SI Symbol SI Unit Equivalent\nLength Meter m 1 m\nMass Kilogram kg 1 kg\nTime Second s 1 s\nTemperature Kelvin K 1 K\nAmount of Substance Mole mol 1 mol\nEnergy Joule J 1 J = 1 N·m\nPower Watt W 1 W = 1 J/s\nPressure Pascal Pa 1 Pa = 1 N/m²\nFrequency Hertz Hz 1 Hz = 1 s⁻¹","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 270.0^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"T_air = FT(270.0)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 12\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sources = (PhaseChange{FT}(),);\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)\n Y.soil.θ_i .= 0\n T = FT(275.0)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 4)\ndt = Float64(5)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"5.0","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping functions:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = ClimaLand.make_jacobian(soil)\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\n\ntimestepper = CTS.ARS111()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsub = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n sv.saveval[k].soil.ice_frac,\n )[1] for k in 1:length(sol.t)\n]\n\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (400, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Rate (mm/d)\",\n title = \"Vapor Fluxes\",\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n sub .* (1000 * 3600 * 24),\n label = \"Sublimation\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Evaporation\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nsave(\"water_fluxes.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Temperature\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(260, 280)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 3, 4]\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax1,\n parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Ice\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax2,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Liquid Water\", xlabel = \"\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax3,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"profiles.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Soil/#Soil-Models","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"CurrentModule = ClimaLand.Soil","category":"page"},{"location":"APIs/Soil/#Soil-Models-2","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilModel\nClimaLand.Soil.RichardsModel\nClimaLand.Soil.EnergyHydrology","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilModel","text":"AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}\n\nThe abstract type for all soil models.\n\nCurrently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsModel","text":"RichardsModel\n\nA model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.\n\nA variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).\n\nIf you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrology","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrology","text":"EnergyHydrology <: AbstractSoilModel\n\nA model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.\n\nA variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.\n\nparameters: The parameter sets\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Parameter-Structs","page":"Soil Energy and Hydrology","title":"Soil Parameter Structs","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.RichardsParameters\nClimaLand.Soil.EnergyHydrologyParameters","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsParameters","text":"RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}\n\nA struct for storing parameters of the RichardsModel.\n\nν: The porosity of the soil (m^3/m^3)\nhydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrologyParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrologyParameters","text":"EnergyHydrologyParameters{\n FT <: AbstractFloat,\n F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n C,\n PSE,\n }\n\nA parameter structure for the integrated soil water and energy equation system.\n\nNote that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:\n\nSurface parameters: albedo in each wavelength band (SF)\nScalar parameters: emissivity, α, β, γ, γT_ref, Ω,\n\nroughness lengths z0, dds ) (FT)\n\nParameters defined in the interior: all else (F)\n\nκ_dry: The dry soil thermal conductivity, W/m/K\nκ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K\nκ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K\nρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)\nν: The porosity of the soil (m^3/m^3)\nν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)\nν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)\nν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)\nα: The parameter α used in computing Kersten number, unitless\nβ: The parameter β used in computing Kersten number, unitless\nhydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\nΩ: Ice impedance factor for the hydraulic conductivity\nγ: Coefficient of viscosity factor for the hydraulic conductivity\nγT_ref: Reference temperature for the viscosity factor\nPAR_albedo: Soil PAR Albedo\nNIR_albedo: Soil NIR Albedo\nemissivity: Soil Emissivity\nz_0m: Roughness length for momentum\nz_0b: Roughness length for scalars\nd_ds: Maximum dry soil layer thickness under evaporation (m)\nearth_param_set: Physical constants and clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Hydrology-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Hydrology Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_liquid_fraction\nClimaLand.Soil.pressure_head\nClimaLand.Soil.hydraulic_conductivity\nClimaLand.Soil.impedance_factor\nClimaLand.Soil.viscosity_factor\nClimaLand.Soil.effective_saturation\nClimaLand.Soil.matric_potential\nClimaLand.Soil.dψdϑ\nClimaLand.Soil.inverse_matric_potential\nClimaLand.Soil.AbstractSoilHydrologyClosure\nClimaLand.Soil.vanGenuchten\nClimaLand.Soil.BrooksCorey","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_liquid_fraction","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_liquid_fraction","text":"volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}\n\nA pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.pressure_head","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.pressure_head","text":"pressure_head(\n cm::vanGenuchten{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\npressure_head(\n cm::BrooksCorey{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.hydraulic_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.hydraulic_conductivity","text":" hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.\n\n\n\n\n\n hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.impedance_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.impedance_factor","text":"impedance_factor(\n f_i::FT,\n Ω::FT\n) where {FT}\n\nReturns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.viscosity_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.viscosity_factor","text":"viscosity_factor(\n T::FT,\n γ::FT,\n γT_ref::FT,\n) where {FT}\n\nReturns the multiplicative factor which accounts for the temperature dependence of the conductivity.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.effective_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.effective_saturation","text":"effective_saturation(porosity::FT, ϑ_l::FT, θr::FT) where {FT}\n\nA point-wise function computing the effective saturation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.matric_potential","text":" matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.dψdϑ","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dψdϑ","text":"dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.\n\n\n\n\n\ndψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.inverse_matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.inverse_matric_potential","text":" inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilHydrologyClosure","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilHydrologyClosure","text":"AbstractSoilHydrologyClosure{FT <: AbstractFloat}\n\nThe abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.\n\nTo add a new parameterization, methods are required for:\n\nmatric_potential,\ninversematricpotential,\npressure_head,\ndψdϑ,\nhydraulic_conductivity.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.vanGenuchten","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.vanGenuchten","text":"vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).\n\nα: The inverse of the air entry potential (1/m)\nn: The van Genuchten pore-size distribution index (unitless)\nm: The van Genuchten parameter m = 1 - 1/n (unitless)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.BrooksCorey","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.BrooksCorey","text":"BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).\n\nc: The pore-size distribution index (unitless)\nψb: The air entry matric potential, when S=1 (m)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Heat-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Heat Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_heat_capacity\nClimaLand.Soil.κ_solid\nClimaLand.Soil.κ_sat_frozen\nClimaLand.Soil.κ_sat_unfrozen\nClimaLand.Soil.κ_sat\nClimaLand.Soil.κ_dry\nClimaLand.Soil.kersten_number\nClimaLand.Soil.relative_saturation\nClimaLand.Soil.volumetric_internal_energy\nClimaLand.Soil.volumetric_internal_energy_liq\nClimaLand.Soil.temperature_from_ρe_int\nClimaLand.Soil.thermal_conductivity\nClimaLand.Soil.phase_change_source\nClimaLand.Soil.thermal_time","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_heat_capacity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_heat_capacity","text":"volumetric_heat_capacity(\n θ_l::FT,\n θ_i::FT,\n ρc_ds::FT,\n earth_param_set::EP,\n) where {FT,EP}\n\nCompute the expression for volumetric heat capacity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_solid","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_solid","text":"κ_solid(ν_ss_om::FT,\n ν_ss_quartz::FT,\n κ_om::FT,\n κ_quartz::FT,\n κ_minerals::FT) where {FT}\n\nComputes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_frozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_frozen","text":"function κ_sat_frozen(\n κ_solid::FT,\n ν::FT,\n κ_ice::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated frozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_unfrozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_unfrozen","text":"function κ_sat_unfrozen(\n κ_solid::FT,\n ν::FT,\n κ_l::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated unfrozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat","text":"κ_sat(\n θ_l::FT,\n θ_i::FT,\n κ_sat_unfrozen::FT,\n κ_sat_frozen::FT\n) where {FT}\n\nCompute the expression for saturated thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_dry","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_dry","text":"function κ_dry(ρp::FT,\n ν::FT,\n κ_solid::FT,\n κ_air::FT;\n a = FT(0.053)) where {FT}\n\nComputes the thermal conductivity of dry soil according to the model of Balland and Arp.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.kersten_number","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.kersten_number","text":"kersten_number(\n θ_i::FT,\n S_r::FT,\n α::FT,\n β::FT,\n ν_ss_om::FT,\n ν_ss_quartz::FT,\n ν_ss_gravel::FT,\n ) where {FT}\n\nCompute the expression for the Kersten number, using the Balland and Arp model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.relative_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.relative_saturation","text":"relative_saturation(\n θ_l::FT,\n θ_i::FT,\n ν::FT\n) where {FT}\n\nCompute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy","text":"volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy_liq","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy_liq","text":"volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.temperature_from_ρe_int","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.temperature_from_ρe_int","text":"temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_conductivity","text":"thermal_conductivity(\n κ_dry::FT,\n K_e::FT,\n κ_sat::FT\n) where {FT}\n\nCompute the expression for thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.phase_change_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.phase_change_source","text":"phase_change_source(\n θ_l::FT,\n θ_i::FT,\n T::FT,\n τ::FT,\n ν::FT,\n θ_r::FT,\n hydrology_cm::C,\n earth_param_set::EP,\n) where {FT, EP, C}\n\nReturns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.\n\nNote that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_time","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_time","text":"thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}\n\nReturns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Surface-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Surface Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.soil.soil_resistance\nClimaLand.Soil.dry_soil_layer_thickness\nClimaLand.Soil.soil_tortuosity","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.dry_soil_layer_thickness","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dry_soil_layer_thickness","text":"dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}\n\nReturns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_tortuosity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_tortuosity","text":"soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}\n\nComputes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.\n\nSee Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Runoff-Types-and-Methods","page":"Soil Energy and Hydrology","title":"Soil Runoff Types and Methods","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.NoRunoff\nClimaLand.Soil.SurfaceRunoff\nClimaLand.Soil.TOPMODELRunoff\nClimaLand.Soil.TOPMODELSubsurfaceRunoff\nClimaLand.Soil.subsurface_runoff_source\nClimaLand.Soil.update_runoff!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.NoRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.NoRunoff","text":"NoRunoff <: AbstractRunoffModel\n\nA concrete type of soil runoff model; the default choice, which does not include any runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.SurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.SurfaceRunoff","text":"SurfaceRunoff <: AbstractRunoffModel\n\nA simple model for runoff appropriate for single column runs.\n\nOnly surface runoff is computed, using a combination of Dunne and Hortonian runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELRunoff","text":"TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel\n\nThe TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.\n\nThe runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\nf_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.\nsubsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","text":"TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}\n\nThe TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.\n\nThe runoff flux is given by Equation 12 of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nR_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.subsurface_runoff_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.subsurface_runoff_source","text":"subsurface_runoff_source(runoff::AbstractRunoffModel)\n\nA helper function which returns the subsurface source of the runoff model runoff.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.update_runoff!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.update_runoff!","text":"update_runoff!(p, runoff::NoRunoff, _...)\n\nUpdates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.\n\n\n\n\n\nupdate_runoff!(\n p,\n runoff::SurfaceRunoff,\n Y,\n t,\n model::AbstractSoilModel,\n\n)\n\nThe update_runoff! function for the SurfaceRunoff model.\n\nUpdates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration\n\n\n\n\n\nupdate_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)\n\nUpdates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-BC-Methods-and-Types","page":"Soil Energy and Hydrology","title":"Soil BC Methods and Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.MoistureStateBC\nClimaLand.Soil.HeatFluxBC\nClimaLand.Soil.WaterFluxBC\nClimaLand.Soil.TemperatureStateBC\nClimaLand.Soil.FreeDrainage\nClimaLand.Soil.RichardsAtmosDrivenFluxBC\nClimaLand.Soil.AtmosDrivenFluxBC\nClimaLand.Soil.WaterHeatBC\nClimaLand.Soil.soil_boundary_fluxes!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.MoistureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.MoistureStateBC","text":"MoistureStateBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.HeatFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.HeatFluxBC","text":"HeatFluxBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterFluxBC","text":"WaterFluxBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.TemperatureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.TemperatureStateBC","text":"TemperatureStateBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.FreeDrainage","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.FreeDrainage","text":"FreeDrainage <: AbstractWaterBC\n\nA concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsAtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsAtmosDrivenFluxBC","text":"RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC\n\nA concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.\n\nIf you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.\n\nprecip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.AtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AtmosDrivenFluxBC","text":"AtmosDrivenFluxBC{\n A <: AbstractAtmosphericDrivers,\n B <: AbstractRadiativeDrivers,\n R <: AbstractRunoffModel\n} <: AbstractEnergyHydrologyBC\n\nA concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.\n\nThis choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.\n\natmos: The atmospheric conditions driving the model\nradiation: The radiative fluxes driving the model\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterHeatBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterHeatBC","text":"WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:\n AbstractEnergyHydrologyBC\n\nA general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_boundary_fluxes!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_boundary_fluxes!","text":"soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)\n\nupdates the boundary fluxes for ϑl and ρeint.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere,\n <:PrescribedRadiativeFluxes,\n },\n boundary::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz,\n Y,\n p,\n t,\n)\n\nReturns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.\n\nIf you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.\n\nThis function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},\n boundary::ClimaLand.TopBoundary,\n soil::EnergyHydrology{FT},\n Δz,\n Y,\n p,\n t,\n) where {FT}\n\nA method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Source-Types","page":"Soil Energy and Hydrology","title":"Soil Source Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilSource\nClimaLand.Soil.PhaseChange\nClimaLand.Soil.RootExtraction","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilSource","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilSource","text":"AbstractSoilSource{FT} <: ClimaLand.AbstractSource{FT}\n\nAn abstract type for types of source terms for the soil equations.\n\nIn standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.PhaseChange","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.PhaseChange","text":"PhaseChange{FT} <: AbstractSoilSource{FT}\n\nPhaseChange source type.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Jacobian-Structures","page":"Soil Energy and Hydrology","title":"Soil Jacobian Structures","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.ImplicitEquationJacobian","category":"page"},{"location":"APIs/Soil/#ClimaLand.ImplicitEquationJacobian","page":"Soil Energy and Hydrology","title":"ClimaLand.ImplicitEquationJacobian","text":"ImplicitEquationJacobian{M, S}\n\nA struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.\n\nmatrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.\n\nNote that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.\n\n\n\n\n\n","category":"type"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Radiative-transfer-scheme","page":"Beer model","title":"Radiative transfer scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Beer's-law","page":"Beer model","title":"Beer's law","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"APAR(PAR theta_s) = (PAR)(1 - rho_leaf)(1 - e^(-K(theta_s) LAI Omega))","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, ρ_leaf is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, θ_s is the zenith angle, and Ω is the clumping index following Braghiere (2021). K, Ω and ρ_leaf are all unitless. LAI is in m² m⁻². In order to compute K, we need θ_s in radians and the leaf angle distribution l_d (unitless). K is then defined as","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"K = l_dmax(cos(theta_s) epsilon)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"so that at night, when 3π/2 > θ_s > π/2, K is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Output Symbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Parameters Symbol Unit Range\nCanopy reflectance ρ_leaf - 0.0–1.0\nExtinction coefficient K - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Interactive-APAR(PAR,-LAI,-ρ_{leaf},-K,-Ω)","page":"Beer model","title":"Interactive APAR(PAR, LAI, ρ_leaf, K, Ω)","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Modeling-a-freezing-front-in-unsaturated-soil","page":"Phase Changes","title":"Modeling a freezing front in unsaturated soil","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν ) -fracF_Tρ_l","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac θ_i t = fracF_Tρ_i","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Here","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_i is the volumetric ice fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν denotes parameters relating to soil type, such as porosity, and","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T is the freeze-thaw term.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We solve these equations in an effectively 1-d domain with z -020, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z = 0) = 28 Wm^2K (T - 26715K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z= -02) = -3 Wm^2K (T - 27985K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T(t = 0 z) = 27985 K","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"-K h(t z = -02) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l(t = 0 z) = 033.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Import-necessary-modules","page":"Phase Changes","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS\nusing DelimitedFiles\nusing CairoMakie\n\nusing ClimaCore\nimport ClimaParams as CP\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Preliminary set-up","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"FT = Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν = FT(0.535)\nK_sat = FT(3.2e-6) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.48)\nvg_α = FT(1.11) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"You could also try the Brooks and Corey model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"#ψb = FT(-0.6)\n#c = FT(0.43)\n#hcm = BrooksCorey(;ψb = ψb, c = c);\nθ_r = FT(0.05)\nν_ss_om = FT(0.3)\nν_ss_quartz = FT(0.7)\nν_ss_gravel = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose the domain and discretization:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zmax = FT(0)\nzmin = FT(-0.2)\nnelems = 20\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)\nfunction top_heat_flux(p, t)\n FT = eltype(p.soil.T)\n p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))\n T_c = ClimaCore.Fields.level(p.soil.T, p_len)\n return @. FT(28 * (T_c - 267.15))\nend\nfunction bottom_heat_flux(p, t)\n FT = eltype(p.soil.T)\n T_c = ClimaCore.Fields.level(p.soil.T, 1)\n return @. FT(-3 * (T_c - 279.85))\nend\ntop_heat_flux_bc = HeatFluxBC(top_heat_flux)\nbottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)\nboundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),\n bottom = WaterHeatBC(;\n water = zero_water_flux_bc,\n heat = bottom_heat_flux_bc,\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sources = (PhaseChange{FT}(),);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can package this up in the EnergyHydrology model struct:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Running-a-simulation","page":"Phase Changes","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Once we have the model, we can initialize the state vectors and obtain the coordinates","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Y, p, coords = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"After which, we can specify the initial condition function, and initialze the variables:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"function init_soil!(Ysoil, z, params)\n ν = params.ν\n FT = eltype(Ysoil.soil.ϑ_l)\n Ysoil.soil.ϑ_l .= FT(0.33)\n Ysoil.soil.θ_i .= FT(0.0)\n T = FT(279.85)\n ρc_s = Soil.volumetric_heat_capacity(\n FT(0.33),\n FT(0.0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Ysoil.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n FT(0.0),\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 50);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the tendency function, and choose a timestep, and timestepper:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ndt = Float64(100)\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 3,\n update_j = CTS.UpdateEvery(CTS.NewTimeStep),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Comparison-to-data","page":"Phase Changes","title":"Comparison to data","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();\nds = readdlm(dataset_path, ',')\nhours = ds[:, 1][2:end]\nvwc = ds[:, 2][2:end] ./ 100.0\ndepth = ds[:, 3][2:end]\nmask_12h = hours .== 12\nmask_24h = hours .== 24\nmask_50h = hours .== 50;\n\nfig = Figure(size = (900, 300))\nax1 = Axis(\n fig[1, 1],\n title = \"12 hours\",\n xlabel = L\"θ_l + θ_i\",\n ylabel = \"Soil depth (m)\",\n)\nlimits!(ax1, 0.2, 0.5, -0.2, 0.0)\nax2 = Axis(\n fig[1, 2],\n title = \"24 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax2, 0.2, 0.5, -0.2, 0.0)\nax3 = Axis(\n fig[1, 3],\n title = \"50 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax3, 0.2, 0.5, -0.2, 0.0)\n\n\nz = parent(coords.subsurface.z)[:];\n\nscatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = \"\", color = \"purple\")\nlines!(\n ax1,\n parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\n\nscatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = \"\", color = \"purple\")\nlines!(\n ax2,\n parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\nscatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = \"Data\", color = \"purple\")\nlines!(\n ax3,\n parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],\n z,\n label = \"Simulation\",\n color = :green,\n)\naxislegend(ax3, position = :rb)\n\nsave(\"mizoguchi_data_comparison.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Discussion-and-Model-Explanation","page":"Phase Changes","title":"Discussion and Model Explanation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρ_l partial_tϑ_l + ρ_i partial_tθ_i = -F_T + F_T = 0","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing (T T_f)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"fracdp_lρ_l = L_f fracdTT","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"or","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l = p_l0 + L_f ρ_l fracT-T_fT_f mathcalH(T_f-T)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"What this implies is that above the freezing point, the pressure is equal to p_l0, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure p_l0 is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l0 = ρ_l g ψ(θ_l+ρ_iθ_iρ_l)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_l^* = (ν-θ_r) ψ^-1(p_l(ρ_l g)) + θ_r","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"For freezing, the freeze thaw function F_T is equal to","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T = frac1τ ρ_l (θ_l-θ_l^*) mathcalH(T_f-T) mathcalH(θ_l-θ_l^*)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"τ_LTE= c Δz²κ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#References","page":"Phase Changes","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.\nHansson et al., Vadose Zone Journal 3:693–704 (2004).\nM. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).\nKurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)\nWatanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58\nPainter and Karra, Vadose Zone Journal (2014) 13 (4)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/#Scraping-SNOTEL-Data","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"section"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We begin by importing all required packages:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"metadata = DataTools.snotel_metadata();\nmetacols = [\"id\", \"name\", \"state\", \"elev\", \"lat\", \"lon\"]\nDataFrames.rename!(metadata, Symbol.(metacols));","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"example_ID = 1030\nexample_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]\nexample_data = DataTools.scrape_site_paper(example_ID, example_state);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"const inch2meter = 0.0254\nconst kmphr2mps = 5.0 / 18.0\n\nfilter_val = Dict{Symbol, Tuple{Real, Real}}(\n :SWE => (0.0, 250.0),\n :z => (0.0, 420.0),\n :precip => (0.0, 250.0),\n :rel_hum_avg => (10.0, 100.0),\n :sol_rad_avg => (0.0, 1500.0),\n :wind_speed_avg => (0.0, 216.0),\n :air_temp_avg => (-40.0, 60.0),\n)\n\nscales = Dict{Symbol, Real}(\n :SWE => inch2meter,\n :z => inch2meter,\n :precip => inch2meter,\n :rel_hum_avg => 0.01,\n :wind_speed_avg => kmphr2mps,\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. \"start\" refers to 1850-01-01 or the first available date, while \"end\" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"* Indicates alternative handling of the rectify_daily_hourly() function.\n^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.\nA Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.\nT Requires a site that already has had the temperature bias correction at the portal level as of May 2024.\nX Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"good_stations = Dict{Int, Tuple{String, String}}(\n #306 => (\"start\", \"end\"), #*\n 316 => (\"start\", \"end\"),\n 344 => (\"start\", \"end\"),\n #=367 => (\"start\", \"end\"),\n 395 => (\"start\", \"end\"),\n 457 => (\"start\", \"end\"),\n 482 => (\"start\", \"end\"),\n 491 => (\"start\", \"end\"),\n 515 => (\"start\", \"2023-06-02\"), #X\n 532 => (\"start\", \"end\"),\n 551 => (\"start\", \"end\"),\n 571 => (\"start\", \"end\"),\n 599 => (\"start\", \"end\"),\n 608 => (\"start\", \"end\"),\n 613 => (\"start\", \"end\"),\n 641 => (\"start\", \"end\"), #A^\n 665 => (\"start\", \"end\"),\n 708 => (\"start\", \"end\"),\n 715 => (\"start\", \"end\"),\n 734 => (\"start\", \"end\"),\n 737 => (\"start\", \"end\"),\n 744 => (\"start\", \"end\"),\n 832 => (\"start\", \"end\"),\n 845 => (\"start\", \"end\"),\n 854 => (\"start\", \"end\"),\n 857 => (\"start\", \"end\"),\n 921 => (\"start\", \"end\"),\n 922 => (\"start\", \"end\"),\n 927 => (\"start\", \"end\"),\n 942 => (\"start\", \"end\"),\n 963 => (\"start\", \"end\"), #A^\n 969 => (\"start\", \"end\"),\n 974 => (\"start\", \"end\"),\n 978 => (\"start\", \"end\"), #*\n 1030 => (\"start\", \"end\"),\n 1035 => (\"start\", \"end\"), #A^\n 1053 => (\"start\", \"end\"),\n 1070 => (\"start\", \"end\"), #A^T\n 1083 => (\"start\", \"end\"),\n 1091 => (\"start\", \"end\"), #A^T\n 1092 => (\"start\", \"end\"), #A^T\n 1105 => (\"start\", \"end\"),\n 1122 => (\"start\", \"end\"), #*\n 1123 => (\"start\", \"end\"),\n 1159 => (\"start\", \"end\"),\n 1168 => (\"start\", \"end\"),\n 1170 => (\"start\", \"end\"),\n 1254 => (\"start\", \"end\"),\n 1286 => (\"start\", \"end\"),\n 2080 => (\"start\", \"end\"), #A^\n 2170 => (\"start\", \"end\"), #^\n =#\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We then loop through each site to scrape and follow an automated data pipeline, consisting of:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Extracting the daily and hourly timeseries from the site\nApplying the sensor bounds over each data timeseries (i.e. remove sensor error)\nConverting the hourly dataset into a daily dataset\nCoalescing the converted-hourly and daily data into one dataset\nScaling all data to the appropriate metric units\nRestricting data to complete cases\nMaking the differential variables ( fracdzdt, etc.)\nResetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip\nAttaching appropriate metadata","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"allsites = Any[];\nfor site in sort(collect(keys(good_stations)))\n state = metadata[metadata[!, :id] .== site, :state][1]\n start_date = good_stations[site][1]\n end_date = good_stations[site][2]\n\n hourly = DataTools.apply_bounds(\n DataTools.sitedata_hourly(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n hourly[!, :id] .= site\n #hourly = DataTools.bcqc_hourly(hourly)\n hourly_d = DataTools.hourly2daily(hourly)\n #DataFrames.allowmissing!(hourly_d)\n #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)\n #hourly_d[sflags, :sol_rad_avg] .= missing\n\n daily = DataTools.apply_bounds(\n DataTools.sitedata_daily(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n daily[!, :id] .= site\n gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)\n #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)\n #gap_daily = DataTools.d_impute(gap_daily)\n daily_scaled = DataTools.scale_cols(gap_daily, scales)\n daily_clean = daily_scaled[completecases(daily_scaled), :]\n daily_clean = DataTools.makediffs(daily_clean, Day(1))\n good_vals = daily_clean[!, :dprecipdt] .>= 0.0\n daily_clean[(!).(good_vals), :dprecipdt] .= 0.0\n daily_clean = daily_clean[!, Not(:precip)]\n #show(describe(daily_clean), allrows = true, allcols = true)\n #print(\"\\nSIZE: \", nrow(daily_clean), \"\\n\")\n\n daily_clean[!, :id] .= site\n daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]\n daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]\n daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]\n\n push!(allsites, daily_clean)\nend;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"With the sites complete, we condense all sites into a single DataFrame,","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"totaldata = deepcopy(allsites[1])\nfor site in allsites[2:end]\n append!(totaldata, site)\nend","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"and a final CSV.write(\"data.csv\", totaldata) call will save the file.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Setting-up-a-Coupled-Simulation","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"For more information about the bucket model, please see the bucket model tutorial.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Background","page":"Setting up a Coupled Simulation","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In our current setup, \"passed back to the land model via the coupler\" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Turbulent-Surface-Fluxes-and-Radiation","page":"Setting up a Coupled Simulation","title":"Turbulent Surface Fluxes and Radiation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we want different behavior. We have defined new coupled types to use instead of the \"prescribed\" types:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"similarily:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Surface-air-density","page":"Setting up a Coupled Simulation","title":"Surface air density","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Regridder/#Bucket","page":"Parameter Dataset Tools","title":"Bucket","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"CurrentModule = ClimaLand.Regridder","category":"page"},{"location":"APIs/Regridder/#Functions","page":"Parameter Dataset Tools","title":"Functions","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll\nClimaLand.Regridder.swap_space","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Hydrostatic-Equilibrium-test-for-Richards-Equation","page":"Richards Equation","title":"Hydrostatic Equilibrium test for Richards Equation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"frac ϑ_l t = K (ϑ_l ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We will solve this equation in a 1-d domain with z -50, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"-K h(t z = -5) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ(t = 0 z) = ν-0001","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"θ_i(t = 0 z) = 00","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"where nu is the porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Preliminary-setup","page":"Richards Equation","title":"Preliminary setup","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load external packages","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"import SciMLBase\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-soil-model","page":"Richards Equation","title":"Set up the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"First, we define the parameters: porosity \\nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K_sat = FT(0.0443 / (3600 * 100))\nS_s = FT(1e-3)\nν = FT(0.495)\nvg_α = FT(2.6)\nvg_n = FT(2)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0)\nparams = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"zmax = FT(0)\nzmin = FT(-5)\nnelems = 10\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nbottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nboundary_conditions = (; top = surface_flux, bottom = bottom_flux);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, in this case we don't have any sources, so we pass an empty tuple:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"soil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_conditions,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = ClimaLand.make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-simulation","page":"Richards Equation","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y.soil.ϑ_l .= FT(0.494);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 24 * 36);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"dt = Float64(1e3);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"stepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we set up the information used for our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Create-some-plots","page":"Richards Equation","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t = sol.t ./ (60 * 60 * 24);\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nz = parent(coords.subsurface.z)\nplot(\n ϑ_l[1],\n z,\n label = string(\"t = \", string(t[1]), \"days\"),\n xlim = [0.47, 0.501],\n ylabel = \"z\",\n xlabel = \"ϑ_l\",\n legend = :bottomleft,\n title = \"Equilibrium test\",\n);\nplot!(ϑ_l[end], z, label = string(\"t = \", string(t[end]), \"days\"));\nfunction hydrostatic_equilibrium(z, z_interface)\n ν = 0.495\n S_s = 1e-3\n α = 2.6\n n = 2.0\n m = 0.5\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nplot!(hydrostatic_equilibrium.(z, -0.56), z, label = \"equilibrium solution\");\n\nplot!(1e-3 .+ ϑ_l[1], z, label = \"porosity\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Save the output:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"savefig(\"equilibrium_test_ϑ_l.png\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#References","page":"Richards Equation","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#PlantHydraulics","page":"Plant Hydraulics","title":"PlantHydraulics","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"CurrentModule = ClimaLand.PlantHydraulics","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#Models","page":"Plant Hydraulics","title":"Models","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsModel","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","text":"PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}\n\nDefines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.\n\nThis model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can \"turn off\" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:\n\nn_leaf = 1\nn_stem = 0\nLAI = SAI = RAI = 0.\n\nA plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.\n\nFinally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.\n\nn_stem: The number of stem compartments for the plant; can be zero\nn_leaf: The number of leaf compartments for the plant; must be >=1\ncompartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters\ncompartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.\ncompartment_labels: The label (:stem or :leaf) of each compartment\nparameters: Parameters required by the Plant Hydraulics model\ntranspiration: The transpiration model, of type AbstractTranspiration\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Diagnostic-Variables","page":"Plant Hydraulics","title":"Plant Hydraulics Diagnostic Variables","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.effective_saturation\nClimaLand.PlantHydraulics.augmented_liquid_fraction\nClimaLand.PlantHydraulics.water_retention_curve\nClimaLand.PlantHydraulics.inverse_water_retention_curve\nClimaLand.PlantHydraulics.root_water_flux_per_ground_area!\nClimaLand.PlantHydraulics.flux\nClimaLand.PlantHydraulics.hydraulic_conductivity","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.effective_saturation","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.effective_saturation","text":"effective_saturation(\n ν::FT,\n ϑ_l::FT) where {FT}\n\nComputes the effective saturation given the augmented liquid fraction.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","text":"augmented_liquid_fraction(\n ν::FT,\n S_l::FT) where {FT}\n\nComputes the augmented liquid fraction from porosity and effective saturation.\n\nAugmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.water_retention_curve","text":"water_retention_curve(\n S_l::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","text":"inverse_water_retention_curve(\n ψ::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the effective saturation given the potential at a point, according to the linear retention curve model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","text":"PlantHydraulics.root_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrognosticSoil,\n model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nAn extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.\n\nIt is computed by summing the flux of water per ground area between roots and soil at each soil layer.\n\n\n\n\n\nroot_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.\n\nThe returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.flux","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.flux","text":"flux(\n z1,\n z2,\n ψ1,\n ψ2,\n K1,\n K2,\n) where {FT}\n\nComputes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.\n\nWe currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","text":"hydraulic_conductivity(conductivity_params::Weibull{FT}, ψ::FT) where {FT}\n\nComputes the hydraulic conductivity at a point, using the Weibull formulation, given the potential ψ.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Parameters","page":"Plant Hydraulics","title":"Plant Hydraulics Parameters","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsParameters\nClimaLand.PlantHydraulics.Weibull\nClimaLand.PlantHydraulics.LinearRetentionCurve","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","text":"PlantHydraulicsParameters\n\nA struct for holding parameters of the PlantHydraulics Model.\n\nai_parameterization: The area index model for LAI, SAI, RAI\nν: porosity (m3/m3)\nS_s: storativity (m3/m3)\nconductivity_model: Conductivity model and parameters\nretention_model: Water retention model and parameters\nroot_distribution: Root distribution function P(z)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.Weibull","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.Weibull","text":"Weibull{FT} <: AbstractConductivityModel{FT}\n\nA concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.\n\nFields\n\nK_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation\nψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).\nc: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","text":"LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}\n\nA concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.\n\nWhen ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.\n\nFields\n\na: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Methods-and-Types","page":"Plant Hydraulics","title":"Plant Hydraulics Methods and Types","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PrescribedSoilPressure\nClimaLand.PlantHydraulics.PrescribedTranspiration\nClimaLand.PlantHydraulics.AbstractRootExtraction","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","text":"PrescribedTranspiration{FT, F <: Function} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch when computing the transpiration from the leaves, in the case where transpiration is prescribed.\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's load the required modules:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"using ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Pond\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-I-Soil-Hydrology","page":"Intro to multi-component models","title":"The individual component models I - Soil Hydrology","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The RR equation for the volumetric water content of soil is given by","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S(xyz t)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In order to solve this, one must specify:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"boundary conditions,\nrelevant parameters (closure models for K and ψ),\na domain and a spatial discretization scheme,\nadditional source terms S, if applicable,\na time-stepping algorithm,\ninitial conditions.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"For the RR equation, we can create this as follows. First, we specify parameters:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ν = FT(0.495);\nK_sat = FT(0.0443 / 3600 / 100); # m/s\nS_s = FT(1e-3); #inverse meters\nvg_n = FT(2.0);\nvg_α = FT(2.6); # inverse meters\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0);\nsoil_ps = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next, let's define the spatial domain and discretization:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"zmax = FT(0);\nzmin = FT(-1);\nnelems = 20;\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And boundary conditions and source terms (none currently):","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"top_flux_bc = WaterFluxBC((p, t) -> 0.0);\nbot_flux_bc = WaterFluxBC((p, t) -> 0.0);\nsources = ()\nboundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(top = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#1#2\"}(Main.var\"##494\".var\"#1#2\"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#3#4\"}(Main.var\"##494\".var\"#3#4\"()))","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"With this information, we can make our model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil = Soil.RichardsModel{FT}(;\n parameters = soil_ps,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_soil, p_soil, coords_soil = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and we can set up the tendency function using the default as well,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_ode! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which computes, for the column domain,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-frac z (-Kfrac(ψ+z) z)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"for each value of ϑ on the mesh of our soil_domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function soil_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place\n update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place\n compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-II-Surface-Water","page":"Intro to multi-component models","title":"The individual component models II - Surface Water","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond model has a single variable, the pond height η, which satisfies the ODE:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"frac η t = -(P - I) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To write down the pond equations, we need to specify","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"P\nI","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s\n\ninfiltration(t) = -(1e-6) #m/s\npond_model =\n Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Again we can initialize the state vector and auxiliary vectors:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_pond, p_pond, coords_pond = initialize(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We can make the tendency function in the same way, for stepping the state forward in time:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"pond_ode! = make_exp_tendency(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond_ode! function works in the same way as for the soil model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function pond_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # falls back to default; does nothing\n update_boundary_fluxes!(p,Y,t) # p.surface_water.runoff in place\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#An-LSM-with-pond-and-soil:","page":"Intro to multi-component models","title":"An LSM with pond and soil:","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The LSM model must contain everything needed to write down the joint system of equations","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial etapartial t = -(P(t) - I(ϑ η P)) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmax z = I(ϑ η P)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmin z = 00","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"As in the standalone cases, defining our model requires specifying","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"parameters,\ndomains, discretizations\nprecipitation,\nboundary conditions,\nsources in the soil equation, if any.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's make our single column domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());\nsurface_water_args = NamedTuple();","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Atmospheric drivers don't \"belong\" to either component alone:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_args = (precip = precipitation,);\nland = LandHydrology{FT}(;\n land_args = land_args,\n soil_model_type = Soil.RichardsModel{FT},\n soil_args = soil_args,\n surface_water_model_type = Pond.PondModel{FT},\n surface_water_args = surface_water_args,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_aux(land)\n soil_update_aux! = make_update_aux(land.soil)\n surface_update_aux! = make_update_aux(land.surface_water)\n function update_aux!(p,Y,t)\n surface_update_aux!(p,Y,t) # does nothing to `p`\n soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ\n end\n return update_aux!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_boundary_fluxes(land)\n update_soil_bf! = make_update_boundary_fluxes(land.soil)\n update_pond_bf! = make_update_boundary_fluxes(land.surface_water)\n function update_boundary_fluxes!(p,Y,t)\n p.soil_infiltration = compute_infiltration(Y,p, t)\n update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration\n update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration\n end\n return update_boundary_fluxes!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and similarily for the compute_exp_tendency! functions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_compute_exp_tendency(land)\n soil_compute_exp_tendency! = make_update_aux(land.soil)\n surface_compute_exp_tendency! = make_update_aux(land.surface_water)\n function compute_exp_tendency!(dY,Y,p,t)\n surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η\n soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ\n end\n return compute_exp_tendency!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The exp_tendency! for the land model is then again just","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function exp_tendency!(dY, Y, p, t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"initialize(land.soil)\ninitialize(land.surface_water)\ninitializes additional auxiliary variables, like p.soil_infiltration\nappend these into Y, p, and coords:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y, p, coords = initialize(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We have volumetric liquid water fraction:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:ϑ_l,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and surface height of the pond:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:η,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"as well as auxiliary variables for the soil:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:K, :ψ, :top_bc, :bottom_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the runoff for surface water:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:runoff,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the additional variable required in the LSM is stored here as well:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:soil_infiltration, :soil, :surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and finally, coordinates - useful for visualization of solutions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.subsurface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the coordinates of the surface variables:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.surface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[0.0]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And we can make the tendency function as before:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_ode! = make_exp_tendency(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#Advantages-and-disadvantages","page":"Intro to multi-component models","title":"Advantages and disadvantages","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Some advantages to our interface design are as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.\nlikewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.\nthe exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).\nalthough we wrote it here in a hardwired fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.\nthe order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.\nthe code is also modular in terms of swapping out a simple component model for a more complex version.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Possible disadvantages to our interface design:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.\nTo accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.\nstandalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.\nwe need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"The same experiment is carried out 3 times","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"No evaporation (zero flux boundary conditions)\nWith evaporation but no drainage (Ksat = 0)\nWith evaporation and drainage","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Parameters","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"K_sat = FT(0.01 / 3600 / 24)\nvg_n = FT(1.45)\nvg_α = FT(1.5)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.4)\nθ_r = FT(0.04)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(0.3)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)# 10mm\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 294.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}(Main.var\"##317\".var\"#1#2\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}(Main.var\"##317\".var\"#3#4\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), nothing)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Atmos","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}(Main.var\"##317\".var\"#7#8\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}(Main.var\"##317\".var\"#9#10\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}(Main.var\"##317\".var\"#11#12\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}(Main.var\"##317\".var\"#13#14\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}(ClimaLand.var\"#21#24\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Simulation setup - no evaporation Boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"zero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nno_flux_boundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);\n\nt0 = Float64(0)\ntf = Float64(24 * 3600 * 15)\ndt = Float64(900.0)\nΔz = 0.01\nzmax = FT(0)\nzmin = FT(-1.6)\nnelems = Int((zmax - zmin) / Δz)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_flux_boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Y, p, cds = initialize(soil)\nfunction estimated_ic(z)\n 0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= estimated_ic.(z)\n Y.soil.θ_i .= 0\n T = FT(294.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\ncb = SciMLBase.CallbackSet(saving_cb);\n\nsol_no_evap =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Repeat with evaporation and drainage This requires different initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nevap_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n)\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = evap_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nsoil_exp_tendency! = make_exp_tendency(soil)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];\n\n# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"bottom_water_bc = MoistureStateBC((p, t) -> 0.35)\nno_drainage_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),\n)\nzmax = FT(0)\nzmin = FT(-0.16)\nnelems = Int((zmax - zmin) / Δz)\ndt = Float64(10.0)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_drainage_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z_no_evap, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol_no_drainage =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap_no_drainage = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"savepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (800, 400))\nax = Axis(fig[1, 1], xlabel = \"Day\", ylabel = \"Evaporation rate (mm/d)\")\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"With drainage\",\n color = :red,\n)\nCairoMakie.lines!(\n ax,\n sol_no_drainage.t ./ 3600 ./ 24,\n evap_no_drainage .* (1000 * 3600 * 24),\n label = \"No drainage\",\n color = :blue,\n)\n\nCairoMakie.axislegend(ax)\nax2 = Axis(fig[1, 2], xlabel = \"Day\", ylabel = \"Cumulative evaporation (mm)\")\nCairoMakie.lines!(\n ax2,\n sol.t ./ 3600 ./ 24,\n cumsum(evap) .* (1000 * 3600),\n color = :red,\n)\nCairoMakie.lines!(\n ax2,\n sol_no_drainage.t ./ 3600 ./ 24,\n cumsum(evap_no_drainage) .* (1000 * 3600),\n color = :blue,\n)\nsave(\"evaporation_lehmann2024_figS6.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Drainage only\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.4)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 10]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax1,\n parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Evap+Drainage\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.3, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 1, 2, 5, 13]\nfor i in 1:1:5\n CairoMakie.lines!(\n ax2,\n parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Evap only\", xlabel = \"Volumetric Water Content\")\nCairoMakie.ylims!(-0.15, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 2, 9, 14]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax3,\n parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n label = \"$(days[i]) days\",\n parent(z_no_evap)[:],\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"evaporation_gardner_fig1.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"Snow Model","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"CurrentModule = ClimaLand.Snow","category":"page"},{"location":"APIs/Snow/#Snow-Parameters","page":"Snow Model","title":"Snow Parameters","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"ClimaLand.Snow.SnowParameters","category":"page"},{"location":"APIs/Snow/#ClimaLand.Snow.SnowParameters","page":"Snow Model","title":"ClimaLand.Snow.SnowParameters","text":"SnowParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SnowModel.\n\nNote that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.\n\nρ_snow: Density of snow (kg/m^3)\nz_0m: Roughness length over snow for momentum (m)\nz_0b: Roughness length over snow for scalars (m)\nα_snow: Albedo of snow (unitless)\nϵ_snow: Emissivity of snow (unitless)\nθ_r: Volumetric holding capacity of water in snow (unitless)\nKsat: Hydraulic conductivity of wet snow (m/s)\nκ_ice: Thermal conductivity of ice (W/m/K)\nΔt: Timestep of the model (s)\nρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)\nearth_param_set: Clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Snow/#Snow-Functions-of-State","page":"Snow Model","title":"Snow Functions of State","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff\t ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Stomatal-conductance","page":"Medlyn model","title":"Stomatal conductance","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"This section describes multiple models of stomatal conductance implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Medlyn-Model","page":"Medlyn model","title":"Medlyn Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\nT = -rho_a g_mathrmeff leftq_a- q_v(T_mathrmleaf rho_mathrmsfc) right\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where T is the transpiration (mass flux of water vapor), q_a is the specific humidity at the lowest level of the atmosphere, q_v(T_mathrmT_leaf rho_mathrmsfc) is the saturated specific humidity over liquid water, given the temperature of the leave T_leaf and air density at the surface rho_sfc. We will approximate T_leaf = T_a and rho_mathrmsfc = rho_a.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"We also need the effective conductivity, given by","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n g_mathrmeff = frac1g_mathrmae^-1+g_mathrms^-1\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g_ae is the aerodynamic conductance, computed by the MOST solve, and g_s is the stomatal conductance to water vapor per unit ground area. The units of all conductances are ms.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginalign\ng_sm(PAR T VPD c_a) = g_0m + D_rel times m fracA_n(PAR T VPD c_a)c_anonumber \ng_s = fracg_smrho_m\nendalign","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where D_rel =16 (unitless) is the relative diffusivity of water vapor with respect to CO_2, rho_m is the molar density of water, and m is the Medlyn factor,","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n m = left( 1 + fracg_1sqrtVPD right) \nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A_n as the biochemical demand for CO_2 calculated using the photosynthesis model (Farquhar, 1980; Equation \\eqref{eq:an}; units of molar flux). The resulting units are ms. g{0,m}$ is a minimum molar conductivity. (subscript m indicates molar).","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Constants Symbol Unit Value\nRelative diffusivity of water vapor D_rel - 1.6\nMinimum stomatal conductance g_0 mol/m^2/s 1e-4\nSlope parameter g_1 sqrtPa 790","category":"page"},{"location":"standalone/README/#DynamicDocs","page":"DynamicDocs","title":"DynamicDocs","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Plant-Hydraulics","page":"Van Genuchten model","title":"Plant Hydraulics","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The plant hydraulics code solves for the volumetric water content in the stem and leaf (theta_stem and theta_leaf). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Van-Genuchten-Model","page":"Van Genuchten model","title":"Van Genuchten Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The volume flux of water q (m/s) between compartments with centers at two heights, z_1 and z_2, is given by Darcy's law as","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n q = -int_z_1^z_2 k(psi) dh\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where h = psi+z is the head (in meters), and k is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\\footnote{Double check this - the units of k in our code are ms.}","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\nq = -int_h_1^h_2 k(psi) dh approx -frack_1(psi_1) + k_2(psi_2)2 * (psi_2 - psi_1) + (z_2 - z_1)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"In order to close the set of equations, the user will have to specify k(psi) and a function psi(theta). In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of K_sat","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The change of water volume (m^3) V, in the compartments is then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd V_w stemdt = q_rootssigma_roots - q_stemsigma_stem nonumber \n fracd V_w leafdt = q_stemsigma_stem - tau sigma_leaf\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where tau is a transpiration volume flux per unit emitting area, and sigma is the total emitting/conducting area\\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals N. We can make use of the fact that NsigmaA, where A is the area of the ground those N individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Then we have:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd v_stemdt = q_rootsRAI - q_stemSAI nonumber \n fracd v_leafdt = q_stemSAI - tau LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where v now represents the volume of water in that compartment (of a bulk plant) per unit ground area.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We also need to convert from the variable v to psi, in order to compute root extraction with the soil. To do so, we can convert v to the volumetric water content, and from theta to psi using a van Genuchten relationship. To convert, let the volume of water per area of compartment be V_wstem, and H the typical ``length\" of the compartment. Then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n theta_stem=fracV_wstemA_ground times fracA_groundA_stem times frac1H_stem = fracv_stemH_stem times SAI \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Substituting in the volumetric water content, we have","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd theta_stemdt = fracq_rootsRAI - q_stemSAIH_stem SAI nonumber \n fracd theta_leafdt = fracq_stemSAI - tau LAIH_leaf LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction P(z), satisfying int P(z) dz = 1. Instead of having a single root at one discrete location, we can distribute the root system over different depths using P(z). The total flux from roots between z and z+dz is given by","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n dq_roots(z) = -P(z) dz int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"so that the net flux for the plant system would sum over this","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n q_roots = -int_z_min^z_sfc fracdq_roots(z)dzdz \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where z_min is the minimum soil layer of the simulation. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n S(z) = -(RAI) dq_roots(z)dz = (RAI) P(z) int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sign change occurs in the expression for S(z) because a positive value of q_roots indicates flow from the soil to the plant. This is a sink term for the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The model needs the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Drivers Symbol Unit Range\nA function of simulation time t given the leaf area index LAI m²/m² 0–10\nThe constant stem area index SAI m²/m² 0–10\nThe constant root area index RAI m²/m² 0–10","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Microbial-respiration","page":"DAMM model","title":"Microbial respiration","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Dual-Arrhenius-Michaelis-Menten","page":"DAMM model","title":"Dual Arrhenius Michaelis-Menten","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration (Rh) as a function of soil temperature (T_s) and soil moisture (theta).","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The rate of respiration, Rh, is expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n Rh = V_textmaxs_xMM_s_xMM_O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where V_textmaxs_x is the maximum potential rate of respiration, MM_s_x represents the availability of substrate, and MM_O_2 is used as the oxygen limitation factor. MM_s_x and MM_O_2 are between 0 (limiting) and 1 (non limiting). V_textmaxs_x, MM_s_x, and MM_O_2 are expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n V_textmaxs_x = alpha_s_x exp(frac-Ea_s_xRT_s)\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textsx = fracs_xkM_s_x+s_x\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textO_2 = fracO_2kM_O_2+O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where alpha_s_x is the pre-exponential factor, Ea_s_x is the activation energy of the reaction, R is the gas constant, and T_s is soil temperature. s_x is the concentration of all soluble substrate, and O_2 is the oxygen concentration. kM_s_x and kM_O_2 are the Michaelis constant for soil and oxygen, respectively.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, s_x is calculated as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n s_x = p_s_xtimesC_somtimes D_liqtimestheta^3\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where C_som is the total amount of soil organic carbon, and p_s_x is the fraction of C_som that is soluble. D_liq is the diffusion coefficient of the soluble carbon. theta is soil moisture.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of O_2 depends on the diffusion of gases within the soil, which is calculated as below:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n O_2 = D_Oatimes O_2a times porosity_air^43\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where D_Oa is the diffusion coefficient for O_2 in air, O_2a is the volume fraction of O_2 in air, and porosity_air is the air-filled porosity.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The air-filled porosity is calculated by subtracting the soil moisture from the total porosity (nu):","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n porosity_air = nu - theta\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"To sum up, the model has the following parameters:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Output Symbol Unit Range\nHeterotrophic respiration Rh mumol m^-2 s^-2 0–25","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Drivers Symbol Unit Range\nSoil temperature T_s C -20–50\nSoil moisture theta m^3 m^-3 0.0–1.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Parameters Symbol Unit Range\nSoil porosity nu m^3 m^-3 0.0–1.0\nPre-exponential factor alpha_s_x kg C m^-3 s^-1 100e3–300e3\nActivation energy Ea_s_x Jmol^-1 50e3–70e3\nMichaelis constant for soil kM_s_x kg C m^-3 1e-10–0.1\nMichaelis constant for O_2 kM_O_2 m^3 m^-3 1e-10–0.1\nVolumetric fraction of O_2 in the soil air content O_2_a - 0.005–0.5\nFraction of soil carbon that is considered soluble p_s_x - 0.005–0.5\nSoil organic C C_som kg C m^-3 1.0–10.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Constants Symbol Unit Value\nAir-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa) O_a100 - 0.1816\nDiffusivity of soil C substrate in liquid D_liq - 3.17\nDiffusion coefficient of oxygen in air D_Oa - 1.67","category":"page"},{"location":"Contributing/#Contributing","page":"Contribution guide","title":"Contributing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.","category":"page"},{"location":"Contributing/#Some-useful-tips","page":"Contribution guide","title":"Some useful tips","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"When you start working on a new feature branch, make sure you start from main by running: git checkout main.\nMake sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.\nWhen your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.","category":"page"},{"location":"Contributing/#Continuous-integration","page":"Contribution guide","title":"Continuous integration","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.","category":"page"},{"location":"Contributing/#Automated-testing","page":"Contribution guide","title":"Automated testing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Currently a number of checks are run per commit for a given PR.","category":"page"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.\nDocumentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.\nTests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Canopy-Energy-Model","page":"Canopy Energy","title":"Canopy Energy Model","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Methods","page":"Canopy Energy","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.canopy_temperature\nClimaLand.Canopy.root_energy_flux_per_ground_area!","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.canopy_temperature","page":"Canopy Energy","title":"ClimaLand.Canopy.canopy_temperature","text":"canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.\n\n\n\n\n\ncanopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.root_energy_flux_per_ground_area!","page":"Canopy Energy","title":"ClimaLand.Canopy.root_energy_flux_per_ground_area!","text":"root_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrognosticSoil{F},\n model::Canopy.AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT, F}\n\nA method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).\n\nNote that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\nroot_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.\n\nBackground information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#Types","page":"Canopy Energy","title":"Types","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.AbstractCanopyEnergyModel\nClimaLand.Canopy.PrescribedCanopyTempModel\nClimaLand.Canopy.BigLeafEnergyModel","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.PrescribedCanopyTempModel","page":"Canopy Energy","title":"ClimaLand.Canopy.PrescribedCanopyTempModel","text":"PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}\n\nA model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.\n\nNo equation for the energy of the canopy is solved.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.BigLeafEnergyModel","page":"Canopy Energy","title":"ClimaLand.Canopy.BigLeafEnergyModel","text":"BigLeafEnergyModel{FT} <: AbstractCanopyEnergyModel{FT}\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#SurfaceWater","page":"Surface Water Models","title":"SurfaceWater","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"CurrentModule = ClimaLand.Pond","category":"page"},{"location":"APIs/SurfaceWater/#Models","page":"Surface Water Models","title":"Models","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PondModel","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PondModel","page":"Surface Water Models","title":"ClimaLand.Pond.PondModel","text":"PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}\n\nA stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.\n\ndomain: The domain for the pond model\nrunoff: The runoff model for the pond model\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#Methods-and-Types","page":"Surface Water Models","title":"Methods and Types","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PrescribedRunoff\nClimaLand.Pond.surface_runoff","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PrescribedRunoff","page":"Surface Water Models","title":"ClimaLand.Pond.PrescribedRunoff","text":"PrescribedRunoff{F1 <: Function, F2 <: Function} <: AbstractSurfaceRunoff\n\nThe required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.surface_runoff","page":"Surface Water Models","title":"ClimaLand.Pond.surface_runoff","text":"function Pond.surface_runoff(\n runoff::PrognosticRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nExtension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#Autotrophic-Respiration","page":"Canopy Autotrophic Respiration","title":"Autotrophic Respiration","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#Parameters","page":"Canopy Autotrophic Respiration","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.AutotrophicRespirationParameters","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.AutotrophicRespirationParameters","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.AutotrophicRespirationParameters","text":"AutotrophicRespirationParameters{FT<:AbstractFloat}\n\nThe required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. \"The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics.\" Geoscientific Model Development 4.3 (2011): 701-722.\n\nne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)\nηsl: Live stem wood coefficient (kg C m-3)\nσl: Specific leaf density (kg C m-2 [leaf])\nμr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\nμs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1\nRel: Relative contribution or Rgrowth (-)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/AutotrophicRespiration/#Methods","page":"Canopy Autotrophic Respiration","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.nitrogen_content\nClimaLand.Canopy.plant_respiration_maintenance\nClimaLand.Canopy.plant_respiration_growth","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.nitrogen_content","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.nitrogen_content","text":"nitrogen_content(\n ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)\n Vcmax25::FT, #\n LAI::FT, # Leaf area index\n SAI::FT,\n RAI::FT,\n ηsl::FT, # live stem wood coefficient (kg C m-3) \n h::FT, # canopy height (m)\n σl::FT # Specific leaf density (kg C m-2 [leaf])\n μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\n μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 \n ) where {FT}\n\nComputes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_maintenance","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_maintenance","text":"plant_respiration_maintenance(\n Rd::FT, # Dark respiration\n β::FT, # Soil moisture factor\n Nl::FT, # Nitrogen content of leafs\n Nr::FT, # Nitrogen content of roots\n Ns::FT, # Nitrogen content of stems\n ) where {FT}\n\nComputes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_growth","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_growth","text":"plant_respiration_growth(\n Rel::FT, # Factor of relative contribution\n An::FT, # Net photosynthesis\n Rpm::FT # Plant maintenance respiration\n ) where {FT}\n\nComputes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.\n\n\n\n\n\n","category":"function"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Using-atmospheric-and-radiative-drivers","page":"Intro to forced site-level runs","title":"Using atmospheric and radiative drivers","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers\"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Types-of-forcing-data","page":"Intro to forced site-level runs","title":"Types of forcing data","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Creating-site-level-drivers-for-radiation","page":"Intro to forced site-level runs","title":"Creating site-level drivers for radiation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"using Dates\nusing Insolation # for computing zenith angle given lat, lon, time.\nusing ClimaLand\nimport ClimaLand.Parameters as LP\nimport ClimaParams","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the local_datetime array is read in from the data file.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Timezone (offset of local time from UTC in hrs)","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"time_offset = 7;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Site latitude and longitude","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"lat = 38.7441; # degree\nlong = -92.2000; # degree","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"ref_time = local_datetime[1] + Dates.Hour(time_offset);\ndata_dt = 3600.0;\nseconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));\nLW_d = 5.67 * 10^(-8) .* T .^ 4;\nSW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"LW_d = TimeVaryingInput(seconds, LW_d)\nSW_d = TimeVaryingInput(seconds, SW_d);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"earth_param_set = LP.LandParameters(Float64);\ninsol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation\nfunction zenith_angle(\n t,\n ref_time;\n latitude = lat,\n longitude = long,\n insol_params = insol_params,\n)\n current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC\n\n d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(\n current_datetime,\n ref_time,\n insol_params,\n ))\n\n\n return Insolation.instantaneous_zenith_angle(\n d,\n δ,\n η_UTC,\n longitude,\n latitude,\n )[1]\nend;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"radiation = ClimaLand.PrescribedRadiativeFluxes(\n Float64,\n SW_d,\n LW_d,\n ref_time;\n θs = zenith_angle,\n);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Updating-the-driver-variables-during-the-simulation","page":"Intro to forced site-level runs","title":"Updating the driver variables during the simulation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"In order to update them, we can make use of default update functions:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"update_radiation! = ClimaLand.make_update_drivers(radiation)\nt0 = seconds[1] # midnight local time\nupdate_radiation!(p, t0);\n@show(p.drivers);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])\n","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"During a simulation, the drivers are updated in place in p.drivers via a \"callback\", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"updateat = collect(seconds[1]:(3600 * 3):seconds[end]);\nupdatefunc = update_radiation!;\ncb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This callback must then be provided to the simulation solve function.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Photosynthesis","page":"Canopy Photosynthesis","title":"Photosynthesis","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Parameters","page":"Canopy Photosynthesis","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.SIFParameters\nClimaLand.Canopy.FarquharParameters\nClimaLand.Canopy.OptimalityFarquharParameters","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.SIFParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.SIFParameters","text":"SIFParameters{FT<:AbstractFloat}\n\nThe required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.\n\nkf: The rate coefficient for florescence, unitless\nkd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nmin_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkp: Rate coefficient for photochemical quenching\nkappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\nkappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.FarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.FarquharParameters","text":"FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}\n\nThe required parameters for the Farquhar photosynthesis model.\n\nVcmax25: Vcmax at 25 °C (mol CO2/m^2/s)\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercelluar O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]\npc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]\nmechanism: Photosynthesis mechanism: C3 or C4\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.OptimalityFarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.OptimalityFarquharParameters","text":"OptimalityFarquharParameters{FT<:AbstractFloat}\n\nThe required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.\n\nmechanism: Photosynthesis mechanism: C3 only\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercellular O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Fitting constant to compute the moisture stress factor (Pa^{-1})\npc: Fitting constant to compute the moisture stress factor (Pa)\nc: Constant describing cost of maintaining electron transport (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#Methods","page":"Canopy Photosynthesis","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.arrhenius_function\nClimaLand.Canopy.intercellular_co2\nClimaLand.Canopy.co2_compensation\nClimaLand.Canopy.rubisco_assimilation\nClimaLand.Canopy.light_assimilation\nClimaLand.Canopy.C3\nClimaLand.Canopy.C4\nClimaLand.Canopy.max_electron_transport\nClimaLand.Canopy.electron_transport\nClimaLand.Canopy.net_photosynthesis\nClimaLand.Canopy.optimality_max_photosynthetic_rates\nClimaLand.Canopy.moisture_stress\nClimaLand.Canopy.dark_respiration\nClimaLand.Canopy.compute_GPP\nClimaLand.Canopy.MM_Kc\nClimaLand.Canopy.MM_Ko\nClimaLand.Canopy.compute_Vcmax","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.arrhenius_function","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.arrhenius_function","text":"arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)\n\nComputes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.intercellular_co2","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.intercellular_co2","text":"intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}\n\nComputes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.co2_compensation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.co2_compensation","text":"co2_compensation(Γstar25::FT,\n ΔHΓstar::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.rubisco_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.rubisco_assimilation","text":"rubisco_assimilation(::C3,\n Vcmax::FT,\n ci::FT,\n Γstar::FT,\n Kc::FT,\n Ko::FT,\n oi::FT) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).\n\nThe empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nrubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.light_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.light_assimilation","text":"light_assimilation(::C3,\n J::FT,\n ci::FT,\n Γstar::FT) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nlight_assimilation(::C4, J::FT, _...) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C3","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C3","text":"C3 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C4","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C4","text":"C4 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.max_electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.max_electron_transport","text":"max_electron_transport(Vcmax::FT) where {FT}\n\nComputes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.electron_transport","text":"electron_transport(APAR::FT,\n Jmax::FT,\n θj::FT,\n ϕ::FT) where {FT}\n\nComputes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). \n\nSee Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.net_photosynthesis","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.net_photosynthesis","text":"net_photosynthesis(Ac::FT,\n Aj::FT,\n Rd::FT,\n β::FT) where {FT}\n\nComputes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). \n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.optimality_max_photosynthetic_rates","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.optimality_max_photosynthetic_rates","text":"optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)\n\nComputes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.\n\nSee Smith et al. 2019.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.moisture_stress","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.moisture_stress","text":"moisture_stress(pl::FT,\n sc::FT,\n pc::FT) where {FT}\n\nComputes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) . \n\nSee Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.dark_respiration","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.dark_respiration","text":"dark_respiration(Vcmax25::FT,\n β::FT,\n f::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_GPP","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_GPP","text":"compute_GPP(An::FT,\n K::FT,\n LAI::FT,\n Ω::FT) where {FT}\n\nComputes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Kc","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Kc","text":"MM_Kc(Kc25::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Ko","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Ko","text":"MM_Ko(Ko25::FT,\n ΔHko::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_Vcmax","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_Vcmax","text":"compute_Vcmax(Vcmax25::FT,\n T::FT,\n To::FT,\n R::FT,\n ep5::FT) where {FT}\n\nComputes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"getting_started/#Getting-Started","page":"Getting Started","title":"Getting Started","text":"","category":"section"},{"location":"getting_started/#For-Users","page":"Getting Started","title":"For Users","text":"","category":"section"},{"location":"getting_started/#Installation","page":"Getting Started","title":"Installation","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"julia> ] # Enter Package REPL mode\nPkg> add ClimaLand # Install ClimaLand\nPkg> # Go back to Julia REPL mode\nJulia> using ClimaLand","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.","category":"page"},{"location":"getting_started/#Parameterization","page":"Getting Started","title":"Parameterization","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Let's start with a basic example: compute canopy gross photosynthesis (GPP).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\n@doc ClimaLand.Canopy.compute_GPP","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"import ClimaLand.Canopy as canopy\ncanopy.compute_GPP(5.0, 0.5, 3.0, 0.7)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Et voilà!","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.","category":"page"},{"location":"getting_started/#ClimaLand-structure","page":"Getting Started","title":"ClimaLand structure","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ClimaLand contains multiple modules. They are listed below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using MethodAnalysis, ClimaLand\nchild_modules(ClimaLand)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To explore what modules, functions and types are exported in a particular module, you can use About.jl:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\nusing About\nabout(ClimaLand.Soil.Biogeochemistry)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/#Seasonal-Snow-Timeseries-Generation-with-a-Neural-Network","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"section"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The updates of the neural snow model follow the equation","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"fracdzdt = mathcalMleft(z SWE φ R v T_air P_snowright)","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"where","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"t is the time (s),\nz is the snow depth (m),\nmathcalM is the neural network,\nSWE is the Snow Water Equivalent, or the height of water if all the snow melted (m),\nφ is the relative humidity (0-1),\nR is the solar radiation (W/m²).\nv is the wind speed (W/m²).\nT_air is the air temperature (degrees C).\nP_snow is the water equivalent rate of snow precipitation (m/s).","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The model is a 1D model to permit utilization over any desired grid resolution and shape.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"┌ Warning: Package cuDNN not found in current path.\n│ - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia.\n│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.\n└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools\nModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\ncode_dir = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Snow\")\ninclude(joinpath(code_dir, \"display_tools.jl\"));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter n as outlined in the associated parameter, and the two loss function hyperparameters n_1, n_2.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"n = 4\nn1 = 2\nn2 = 4;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the fracdzdt column.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"pred_vars = [\n :z,\n :SWE,\n :rel_hum_avg,\n :sol_rad_avg,\n :wind_speed_avg,\n :air_temp_avg,\n :dprecipdt_snow,\n]\ntarget = :dzdt;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"nfeatures = length(pred_vars)\nz_idx = 1\np_idx = 7;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"training_data_download_link = \"https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv\"\ntesting_data_download_link = \"https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv\"\ndata_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)\nvaldata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)\nΔt = Second(86400)\nhole_thresh = 5;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"usedata = DataTools.prep_data(data_train);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of \"0\" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"out_scale = maximum(abs.(usedata[!, target]))\nin_scales = std.(eachcol(select(usedata, pred_vars)))\nx_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)\nps = ModelTools.get_model_ps(model);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)\nModelTools.setoutscale!(model, 1.0);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With that, training is as simple as calling the trainmodel! function:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"print(\"\\nTraining model!\\n\")\nModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"\nTraining model!\nEpoch: 10 | training loss: 0.002703763\nEpoch: 20 | training loss: 0.0026803392\nEpoch: 30 | training loss: 0.0026741433\nEpoch: 40 | training loss: 0.0025964214\nEpoch: 50 | training loss: 0.0025694624\nEpoch: 60 | training loss: 0.0026297325\nEpoch: 70 | training loss: 0.0025780287\nEpoch: 80 | training loss: 0.0025821207\nEpoch: 90 | training loss: 0.0025638747\nEpoch: 100 | training loss: 0.002566546\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.setoutscale!(model, out_scale)\nModelTools.settimescale!(model, Dates.value(Δt));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Note that gaps in the data are shown as shaded regions on the plotted timeseries.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = 1286\nsitedata = usedata[usedata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Slagamount Lakes, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot1.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = \"1070\" #string format for the testing ids is due to non-numerical testing site codes.\nsitedata = valdata[valdata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Anchorage Hillside, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot2.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This page was generated using Literate.jl.","category":"page"}] +} diff --git a/previews/PR753/siteinfo.js b/previews/PR753/siteinfo.js new file mode 100644 index 0000000000..003d43de80 --- /dev/null +++ b/previews/PR753/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR753"; diff --git a/previews/PR753/standalone/Project.toml b/previews/PR753/standalone/Project.toml new file mode 100644 index 0000000000..e0874fbf1e --- /dev/null +++ b/previews/PR753/standalone/Project.toml @@ -0,0 +1,7 @@ +[deps] +Bonito = "824d6782-a2ef-11e9-3a09-e5662e0c26f8" +ClimaLand = "08f4d4ce-cf43-44bb-ad95-9d2d5f413532" +ClimaLandSimulations = "348a0bd3-1299-4261-8002-d2cd97df6055" +ClimaParams = "5c42b081-d73a-476f-9059-fd94b934656c" +ParamViz = "9706fb73-1de9-418d-83da-fef8cd99bb4a" +Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" diff --git a/previews/PR753/standalone/README/index.html b/previews/PR753/standalone/README/index.html new file mode 100644 index 0000000000..71ad2a673f --- /dev/null +++ b/previews/PR753/standalone/README/index.html @@ -0,0 +1,2 @@ + +DynamicDocs · ClimaLand.jl
GitHub
diff --git a/previews/PR753/standalone/apps.jl b/previews/PR753/standalone/apps.jl new file mode 100644 index 0000000000..cdb1002497 --- /dev/null +++ b/previews/PR753/standalone/apps.jl @@ -0,0 +1,47 @@ +# Load packages +using ParamViz +using Bonito +using ClimaLand +using ClimaLand.Canopy +using ClimaLand.Soil.Biogeochemistry +import ClimaParams as CP +import ClimaLand.Parameters as LP +using ClimaLandSimulations +FT = Float64 +earth_param_set = LP.LandParameters(FT) +RTparams = BeerLambertParameters(FT) + +# Create server +IPa = "127.0.0.1" +port = 9385 +server = Server( + IPa, + port; + proxy_url = "https://clima.westus3.cloudapp.azure.com/jsserve/", +) + +# Load and create apps +include("apps/leaf_an.jl"); +An_app = An_app_f(); +An_app = An_app_f(); # need to run twice for unicode character... (bug) +include("apps/beer.jl"); +beer_app = Beer_app_f(); +beer_app = Beer_app_f(); +include("apps/hetero_resp.jl"); +Rh_app = Rh_app_f(); +Rh_app = Rh_app_f(); +include("apps/leaf_an_ci.jl"); +An_ci_app = An_ci_app_f(); +An_ci_app = An_ci_app_f(); +# Fluxnet DashBoard +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); + +# Route apps +route!(server, "/leaf_An" => An_app) +route!(server, "/beer_APAR" => beer_app) +route!(server, "/leaf_An_ci" => An_ci_app) +route!(server, "/Rh" => Rh_app) +route!(server, "/fluxnet" => fluxnet_app) + +wait() diff --git a/previews/PR753/standalone/apps/beer.jl b/previews/PR753/standalone/apps/beer.jl new file mode 100644 index 0000000000..266f3b0da7 --- /dev/null +++ b/previews/PR753/standalone/apps/beer.jl @@ -0,0 +1,35 @@ +using Unitful: m, s, mol, μmol + +function ParamViz.parameterisation(PAR, LAI, ρ_leaf, K, Ω, a, b) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + return APAR +end + +function Beer_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "LAI (m² m⁻²)"), + (FT.([0, 1500 * 1e-6]), FT.([0, 10])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (m^2 * m^-2, m^2 * m^-2)), + ) + + parameters = Parameters( + ( + "canopy reflectance, ρ_leaf", + "extinction coefficient, K", + "clumping index, Ω", + ), + (FT.([0, 1]), FT.([0, 1]), FT.([0, 1])), + ((m, m), (m, m), (m, m)), # dummy units, no conversion + ) + + constants = Constants(("a", "b"), (FT(1), FT(2))) # dummy constants + inputs = Inputs(drivers, parameters, constants) + output = Output( + "APAR (μmol m⁻² s⁻¹)", + [0, 1500 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + beer_app = webapp(ParamViz.parameterisation, inputs, output) + return beer_app +end diff --git a/previews/PR753/standalone/apps/hetero_resp.jl b/previews/PR753/standalone/apps/hetero_resp.jl new file mode 100644 index 0000000000..0a6ef56fe6 --- /dev/null +++ b/previews/PR753/standalone/apps/hetero_resp.jl @@ -0,0 +1,92 @@ +using Unitful: K, °C, mol, μmol, m, s, kg, J + +function ParamViz.parameterisation( + Tₛ, + θ, # drivers + ν, + α_sx, + Ea_sx, + kM_sx, + kM_O₂, + O₂_a, + p_sx, + Csom, # parameters + θ_a100, + D_liq, + D_oa, +) # constants + + params = SoilCO2ModelParameters(FT; ν) + # θ has to be lower than porosity + if θ > ν + θ = ν + end + + Rh = microbe_source(Tₛ, θ, Csom, params) + return Rh +end + +function Rh_app_f() + drivers = Drivers( + ("Tₛ (°C)", "θ (m³ m⁻³)"), # drivers.names + (FT.([273, 323]), FT.([0.0, 1.0])), # drivers.ranges + ((K, °C), (m^3 * m^-3, m^3 * m^-3)), + ) + + parameters = Parameters( + (# names + "Soil porosity, ν (m³ m⁻³)", + "Pre-exponential factor, α_sx (kg C m⁻³ s⁻¹)", + "Activation energy, Ea_sx (J mol⁻¹)", + "Michaelis constant for soil, kM_sx (kg C m⁻³)", + "Michaelis constant for O₂, kM_O₂ (m³ m⁻³)", + "Volumetric fraction of O₂ in the soil air, O₂_a (dimensionless)", + "Fraction of soil carbon that is considered soluble, p_sx (dimensionless)", + "Soil organic C, Csom (kg C m⁻³)", + ), + (# ranges + FT.([0.0, 1.0]), # porosity + FT.([100e3, 300e3]), # α_sx + FT.([50e3, 70e3]), # Ea_sx + FT.([1e-10, 0.1]), # kM_sx + FT.([1e-10, 0.1]), # kM_o2 + FT.([0.005, 0.5]), # O2_a + FT.([0.005, 0.5]), # p_sx + FT.([1.0, 10.0]), # Csom + ), + ( + (m^3 * m^-3, m^3 * m^-3), # porosity + (kg * m^-3 * s^-1, kg * m^-3 * s^-1), # α_sx + (J * mol^-1, J * mol^-1), # Ea_sx + (kg * m^-3, kg * m^-3), # kM_sx + (m^3 * m^-3, m^3 * m^-3), # kM_O2 + (m, m), # O2_a + (m, m), # p_sx + (kg * m^-3, kg * m^-3), # Csom + ), + ) + + constants = Constants( + (# names + "Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)", + "Diffusivity of soil C substrate in liquid (unitless)", + "Diffusion coefficient of oxygen in air, dimensionless", + ), + (# values + FT(0.1816), # θ_a100 + FT(3.17), # D_liq + FT(1.67), # D_oa + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "Rh (mg C m⁻³ s⁻¹)", # name #NEED TO CONVERT THE UNIT + [0, 20 * 1e-6], # range + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), # unit from, unit to --> actually need to fix this, should be g to mg C m-3 s-1 + ) + + Rh_app = webapp(ParamViz.parameterisation, inputs, output) + return Rh_app +end diff --git a/previews/PR753/standalone/apps/leaf_an.jl b/previews/PR753/standalone/apps/leaf_an.jl new file mode 100644 index 0000000000..5960996a6c --- /dev/null +++ b/previews/PR753/standalone/apps/leaf_an.jl @@ -0,0 +1,137 @@ +using Unitful: K, °C, mol, μmol, m, s, Pa, kPa + +function ParamViz.parameterisation( + PAR, + T, + β, + LAI, + ca, + VPD, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + Vcmax25, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + g1, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + medlynt = 1 + g1 / sqrt(VPD) + ci = intercellular_co2(ca, Γstar, medlynt) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "T (°C)"), + (FT.([0, 1500 * 1e-6]), FT.([273, 323])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (K, °C)), + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "CO2 concentration, ca (ppm)", + "Vapor pressure deficit, VPD (Pa)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([300 * 1e-6, 500 * 1e-6]), # ca + FT.([500, 10000]), # VPD, Pa + ), + ( + (m, m), # dummy units, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol / mol, μmol / mol), + (Pa, kPa), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "Vcmax25", # the maximum rate of carboxylation of Rubisco + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "g1", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd", + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(5e-5), # Vcmax25 - ozark model + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(141), # g1 - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + + An_app = webapp(ParamViz.parameterisation, inputs, output) + return An_app +end diff --git a/previews/PR753/standalone/apps/leaf_an_ci.jl b/previews/PR753/standalone/apps/leaf_an_ci.jl new file mode 100644 index 0000000000..351338a131 --- /dev/null +++ b/previews/PR753/standalone/apps/leaf_an_ci.jl @@ -0,0 +1,127 @@ +using Unitful: K, °C, mol, μmol, m, s + +function ParamViz.parameterisation( + ci, + T, + β, + LAI, + PAR, + Vcmax25, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_ci_app_f() + drivers = Drivers( + ("ci (ppm)", "T (°C)"), # names + (FT.([0.0001, 0.001]), FT.([273, 323])), # ranges + ((mol / mol, μmol / mol), (K, °C)), # units from, unit to + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "Photosynthetic radiation, PAR (μmol m⁻² s⁻¹)", + "Vcmax25, (μmol m⁻² s⁻¹)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([0, 1500 * 1e-6]), # PAR + FT.([1e-5, 1e-4]), # Vcmax25 + ), + ( + (m, m), # dummy unit, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd",# a constant + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + An_ci_app = webapp(ParamViz.parameterisation, inputs, output) + return An_ci_app +end diff --git a/previews/PR753/standalone/pages/snow/snow_model/index.html b/previews/PR753/standalone/pages/snow/snow_model/index.html new file mode 100644 index 0000000000..3e0cce5725 --- /dev/null +++ b/previews/PR753/standalone/pages/snow/snow_model/index.html @@ -0,0 +1,2 @@ + +Snow model · ClimaLand.jl
GitHub
diff --git a/previews/PR753/standalone/pages/soil/biogeochemistry/DAMM_model/index.html b/previews/PR753/standalone/pages/soil/biogeochemistry/DAMM_model/index.html new file mode 100644 index 0000000000..48ce81d498 --- /dev/null +++ b/previews/PR753/standalone/pages/soil/biogeochemistry/DAMM_model/index.html @@ -0,0 +1,18 @@ + +DAMM model · ClimaLand.jl
GitHub

Microbial respiration

This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand.

Dual Arrhenius Michaelis-Menten

The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration ($Rh$) as a function of soil temperature ($T_s$) and soil moisture ($\theta$).

The rate of respiration, $Rh$, is expressed as:

\[\begin{equation} + Rh = V_\text{maxs_x}MM_{s_x}MM_{O_2} +\end{equation}\]

where $V_\text{max}{s_x}$ is the maximum potential rate of respiration, $MM_{s_x}$ represents the availability of substrate, and $MM_{O_2}$ is used as the oxygen limitation factor. $MM_{s_x}$ and $MM_{O_2}$ are between 0 (limiting) and 1 (non limiting). $V_\text{max}{s_x}$, $MM_{s_x}$, and $MM_{O_2}$ are expressed as:

\[\begin{equation} + V_\text{max}{s_x} = \alpha_{s_x} \exp(\frac{-Ea_{s_x}}{RT_s}) +\end{equation}\]

\[\begin{equation} + MM_\text{sx} = \frac{[s_x]}{kM_{s_x}+[s_x]} +\end{equation}\]

\[\begin{equation} + MM_\text{O_2} = \frac{[O_2]}{kM_{O_2}+[O_2]} +\end{equation}\]

where $\alpha_{s_x}$ is the pre-exponential factor, $Ea_{s_x}$ is the activation energy of the reaction, $R$ is the gas constant, and $T_s$ is soil temperature. $[s_x]$ is the concentration of all soluble substrate, and $[O_2]$ is the oxygen concentration. $kM_{s_x}$ and $kM_{O_2}$ are the Michaelis constant for soil and oxygen, respectively.

The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, $[s_x]$ is calculated as:

\[\begin{equation} + [s_x] = p_{s_x}\times[C_{som}]\times D_{liq}\times\theta^3 +\end{equation}\]

where $[C_{som}]$ is the total amount of soil organic carbon, and $p_{s_x}$ is the fraction of $[C_{som}]$ that is soluble. $D_{liq}$ is the diffusion coefficient of the soluble carbon. $\theta$ is soil moisture.

The concentration of $O_2$ depends on the diffusion of gases within the soil, which is calculated as below:

\[\begin{equation} + [O_2] = D_{Oa}\times O_{2a} \times porosity_{air}^{4/3} +\end{equation}\]

where $D_{Oa}$ is the diffusion coefficient for $O_2$ in air, $O_{2a}$ is the volume fraction of $O_2$ in air, and $porosity_{air}$ is the air-filled porosity.

The air-filled porosity is calculated by subtracting the soil moisture from the total porosity ($\nu$):

\[\begin{equation} + porosity_{air} = \nu - \theta +\end{equation}\]

To sum up, the model has the following parameters:

OutputSymbolUnitRange
Heterotrophic respirationRh$\mu$mol $m^{-2}$ $s^{-2}$0–25
DriversSymbolUnitRange
Soil temperature$T_s$$°C$-20–50
Soil moisture$\theta$$m^3$ $m^{-3}$0.0–1.0
ParametersSymbolUnitRange
Soil porosity$\nu$$m^3$ $m^{-3}$0.0–1.0
Pre-exponential factor$\alpha_{s_x}$kg C $m^{-3}$ $s^{-1}$100e3–300e3
Activation energy$Ea_{s_x}$J$mol^{-1}$50e3–70e3
Michaelis constant for soil$kM_{s_x}$kg C $m^{-3}$1e-10–0.1
Michaelis constant for $O_2$$kM_{O_2}$$m^3$ $m^{-3}$1e-10–0.1
Volumetric fraction of $O_2$ in the soil air content$O_{2_a}$-0.005–0.5
Fraction of soil carbon that is considered soluble$p_{s_x}$-0.005–0.5
Soil organic C$C_{som}$kg C $m^{-3}$1.0–10.0
ConstantsSymbolUnitValue
Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)$O_{a100}$-0.1816
Diffusivity of soil C substrate in liquid$D_{liq}$-3.17
Diffusion coefficient of oxygen in air$D_{Oa}$-1.67
diff --git a/previews/PR753/standalone/pages/soil/energy/energy_model/index.html b/previews/PR753/standalone/pages/soil/energy/energy_model/index.html new file mode 100644 index 0000000000..40ddb16b8b --- /dev/null +++ b/previews/PR753/standalone/pages/soil/energy/energy_model/index.html @@ -0,0 +1,2 @@ + +Energy model · ClimaLand.jl
GitHub
diff --git a/previews/PR753/standalone/pages/soil/hydrology/richards_model/index.html b/previews/PR753/standalone/pages/soil/hydrology/richards_model/index.html new file mode 100644 index 0000000000..4d15781b8a --- /dev/null +++ b/previews/PR753/standalone/pages/soil/hydrology/richards_model/index.html @@ -0,0 +1,2 @@ + +Richards model · ClimaLand.jl
GitHub
diff --git a/previews/PR753/standalone/pages/surface_water/surface_water_model/index.html b/previews/PR753/standalone/pages/surface_water/surface_water_model/index.html new file mode 100644 index 0000000000..ec1b322337 --- /dev/null +++ b/previews/PR753/standalone/pages/surface_water/surface_water_model/index.html @@ -0,0 +1,2 @@ + +Surface water model · ClimaLand.jl
GitHub
diff --git a/previews/PR753/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html b/previews/PR753/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html new file mode 100644 index 0000000000..06ac1b26a5 --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html @@ -0,0 +1,36 @@ + +Farquhar model · ClimaLand.jl
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Farquhar Model

This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.

The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO$_2$ into the leaf, the assimilation of CO$_2$ during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO$_2$ concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.

The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:

An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)

An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)

Rubisco limited rate

\[\begin{equation} +a_1(T, c_a, VPD) = +\begin{cases} + V_{cmax}(T) \frac{(c_i(T, c_a, VPD) - \Gamma^*(T))}{(c_i(T, c_a, VPD) + K_c(T)*(1+o_i/K_o(T)))} & \text{for C3}\\ + V_{cmax}(T) & \text{for C4} +\end{cases} +\end{equation}\]

The dependence on the atmospheric CO$_2$ concentration $c_a$ (mol/mol) and vapor pressure deficit $VPD$ arise in the expression for $c_i$,

\[\begin{align} + c_i(T, c_a, VPD) = \max{(c_a(1-1/m(VPD)), \Gamma^*(T)}), +\end{align}\]

where and $m$ is the Medlyn factor (see Stomatal Conductance).

We also have

\[ \Gamma^*(T) = \Gamma^*_{25}\exp\left(\Delta H_{\Gamma^*}\frac{T - T_o}{T_o R T}\right),\]

where $\Delta H_{\Gamma^*}$ is the activation energy per mol for $\Gamma^*$.

Light limited rate

\[\begin{equation} +a_2 = +\begin{cases} + J(T, PAR) (c_i - \Gamma^*)/4(c_i + 2 \Gamma^*) & \text{for C3}\\ + J(T, PAR) & \text{for C4} +\end{cases} +\end{equation}\]

where J is the rate of electron transport, which has units of mol photon per m$^2$ per s. It depends on $PAR$ via $APAR$, as described below, and on $T$ via the dependence on $J_{max}$.

J is given by the root of the equation

\[\begin{align} + \theta_j J^2 - (I + J_{max}) J + I J_{max} &= 0 \nonumber \\ + I &= \frac{\phi}{2} (APAR) \nonumber \\ + J_{max}(T) &= V_{cmax}(T)\times e \exp\left(\Delta H_{J_{max}}\frac{T - T_o}{T_o R T}\right),\nonumber \\ +J(T, PAR) &= \frac{(I + J_{max} - \sqrt{(I + J_{max})^2 - 4\theta_j I \times J_{max}}}{2\theta_j}, +\end{align}\]

where $\phi = 0.6$ and $\theta_j = 0.9$ are the quantum yield of photosystem II and a curvature function (Bonan's book), and $\Delta H_{J_{max}}$ is the energy of activation of $J_{max}$.

The total net carbon assimilation (A$_n$, mol CO$_2$ m$^{-2}$ s$^{-1}$) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:

\[\begin{align} +A_n(T, PAR, VPD, c_a) = \text{max}(0, \text{min}(a_1 \beta, a_2) - R_d) +\end{align}\]

where $\beta$ is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R$_d$ is the leaf dark respiration calculated as

\[\begin{align} + R_{d,25}(\psi_l) &= f V_{cmax,25}\beta(\psi_l), \nonumber \\ + R_d (T, \psi_l) & = R_{d,25}(\psi_l)\exp\left(\Delta H_{R_{d}}\frac{T - T_o}{T_o R T}\right), +\end{align}\]

where $f = 0.015$ is a constant, $\Delta H_{R_d}$ is the energy of activation for $R_d$, and finally Vcmax is calculated as

\[\begin{equation} +V_{cmax}(T) = V_{cmax,25} \exp\left(\Delta H_{Vcmax}\frac{T - T_o}{T_o R T}\right)\\ +\end{equation}\]

with $V_{cmax,25}$ is a parameter (Vcmax at the reference temperature 25 C), and $\Delta H_{Vcmax} = 65,330 J/mol$.

The moisture stress factor is related to the leaf water potential $\psi_l$ as

\[\begin{align} + \beta = \frac{1+ \exp{(s_c \psi_c)}}{1+ \exp{(s_c(\psi_c - \psi_l))}}, +\end{align}\]

where $s_c = 4$MPa$^{-1}$, $\psi_c = -2$MPa, and $\psi_l$ is the leaf water potential computed by the plant hydraulics model.

GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:

\[\begin{align} +GPP(T, PAR, c_a, VPD, \theta_s) = A_n (1 - \exp(-K LAI \Omega))/K. +\end{align}\]

This is not currently needed by other components, but is used for offline validation of the model.

We need to supply the following parameters and “drivers"

  • $K_{c,25}$ and $K_{o,25}$, $V_{cmax, 25}$, $\Gamma^*_{25},\phi$, $\theta_j$, $o_i$, $s_c$, $\psi_c$
  • $\psi_l$, to compute $\beta$
  • Temperature $T$, $PAR$, $c_a$, VPD, $\theta_s$.
OutputSymbolUnitRange
Total net carbon assimilation$A_n$μmol CO$_2$ m$^{-2}$ s$^{-1}$0–25
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Temperature$T$°C0–50
ParametersSymbolUnitRange
Moisture stress$β$-0-1
Leaf Area IndexLAIm² m⁻²1–10
$CO_2$ concentration$c_a$ppm300e–500
Vapor pressure deficitVPDkPa1-10
ConstantsSymbolUnitValue
Zenith angle$θ_s$rad0.6
Leaf angle distribution$l_d$-0.5
Canopy reflectance$ρ_{leaf}$-0.1
Clumping index$Ω$-0.69
$CO_2$ compensation at 25°CΓ$^*_{25}$mol/mol4.275e-5
Energy of activation for $Γ^*$$ΔH_{Γ^*}$J/mol37830
Standard temperature$T_o$K298.15
Universal gas constant$R$J/mol8.314
The maximum rate of carboxylation of Rubisco$V_{cmax25}$mol CO$_2$ m$^{-2}$ s$^{-1}$5e-5
Energy of activation for $J_max$$ΔH_{J_max}$J/mol43540
Curvature parameter, a fitting constant to compute $J$$θ_j$-0.9
The quantum yied of photosystem II$\phi$-0.6
Energy of activation for $V_{cmax}$$ΔH_{V_{cmax}}$J/mol58520
Slope parameter for stomatal conductance models$g_1$-141
Michaelis Menten constant for $CO_2$ and at 25°C$K_{c25}$mol/mol4.049e-4
Energy of activation for $CO_2$$ΔH_{K_c}$J/mol79430
Michaelis Menten constant for $O_2$ at 25 °C$K_{o25}$mmol/mol0.2874
Energy of activation for $O_2$$ΔH_{K_o}$J/mol36380
Intercellular $O_2$ concentration$o_i$mol/mol0.209
Constant factor appearing the dark respiration term$f$-0.015
Energy of activation for $R_d$$ΔH_{R_d}$J/mol43390
diff --git a/previews/PR753/standalone/pages/vegetation/photosynthesis/optimality_model/index.html b/previews/PR753/standalone/pages/vegetation/photosynthesis/optimality_model/index.html new file mode 100644 index 0000000000..692eb77fe7 --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/photosynthesis/optimality_model/index.html @@ -0,0 +1,24 @@ + +Optimality model · ClimaLand.jl
GitHub

Optimality Model

Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation ($V_\text{cmax}$).

In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates $V_\text{cmax}$ and $J_\text{max}$ as a function of environmental variables as follows:

\[\begin{equation} + V_\text{cmax}^* = \varphi I \left(\frac{m}{m_c}\right)\left(\frac{\overline{\omega}^*}{8\theta}\right), +\end{equation}\]

where

\[\begin{equation} + \overline{\omega}^* = 1 + \overline{\omega} - \sqrt{(1 + \overline{\omega})^2 - 4\theta\overline{\omega}}, +\end{equation}\]

and

\[\begin{equation} + \overline{\omega} = -(1 - 2\theta) + \sqrt{(1 - \theta)\left(\frac{1}{\frac{4c}{m}\left(1 - \theta\frac{4c}{m}\right)} - 4\theta\right)}, +\end{equation}\]

and

\[\begin{equation} + c = \frac{m}{8\theta}\left(1 - \frac{\varphi I + J_\text{max} - 2\theta\varphi I}{\sqrt{(\varphi I + J_\text{max})^2 - 4\theta\varphi I J_\text{max}}}\right) +\end{equation}\]

and

\[\begin{equation} + J_\text{max} = \varphi I \overline{\omega} +\end{equation}\]

\[\begin{equation} + m = \frac{C'_i - \Gamma^*}{C'_i + 2\Gamma^*} +\end{equation}\]

\[\begin{equation} + C'_i = \Gamma^* + (C_a - \Gamma^*)\frac{\xi}{\xi + \sqrt{D_g}} +\end{equation}\]

\[\begin{equation} + \xi = \sqrt{\beta \frac{K + \Gamma^*}{1.6\eta^*}} +\end{equation}\]

\[\begin{equation} + K = K_c\left(1 + \frac{O_i}{K_o}\right) +\end{equation}\]

\[\begin{equation} + m_c = \frac{C'_i - \Gamma^*}{C'_i + K} +\end{equation}\]

\[\Gamma^*\]

is the CO$_2$ compensation point in the absence of mitochondrial respiration

\[\begin{equation} + \Gamma^* = \Gamma^*_0 f(T, \Delta H_a) p/p_0 +\end{equation}\]

where $\Gamma^*_0 = 4.332$ Pa, $p$ is the atmospheric pressure, $p_0 = 101325$ Pa, and $\Delta H_a = 37830$ J/mol.

The model has the following parameters:

  • $\varphi$ is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.
  • $\theta$ is the curvature of the light response curve (dimensionless). Estimated at 0.85.
  • $\beta$ is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.

For Smith et al. (2019) Vcmax model:

  • Altitude
  • $D_g$ is the vapor pressure deficit (VPD) at altitude
  • $C_a$ is the CO$_2$ partial pressure
  • $I$ is the incident photosynthetically active photon flux (PAR)
  • $T$ is the temperature
diff --git a/previews/PR753/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html b/previews/PR753/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html new file mode 100644 index 0000000000..e805246af3 --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html @@ -0,0 +1,23 @@ + +Van Genuchten model · ClimaLand.jl
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Plant Hydraulics

Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.

Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.

The plant hydraulics code solves for the volumetric water content in the stem and leaf ($\theta_{stem}$ and $\theta_{leaf}$). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment.

Van Genuchten Model

As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.

The volume flux of water $q$ (m/s) between compartments with centers at two heights, $z_1$ and $z_2$, is given by Darcy's law as

\[\begin{align} + q = -\int_{z_1}^{z_2} k(\psi) dh +\end{align}\]

where $h = \psi+z$ is the head (in meters), and $k$ is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\footnote{Double check this - the units of $k$ in our code are $m/s$.}

\[\begin{equation} +q = -\int_{h_1}^{h_2} k(\psi) dh \approx -\frac{k_1(\psi_1) + k_2(\psi_2)}{2} * [(\psi_2 - \psi_1) + (z_2 - z_1)]. +\end{equation}\]

In order to close the set of equations, the user will have to specify $k(\psi)$ and a function $\psi(\theta)$. In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of $K_{sat}.$

The change of water volume (m$^3), V$, in the compartments is then

\[\begin{align} + \frac{d V_{w, stem}}{dt} = q_{roots}\sigma_{roots} - q_{stem}\sigma_{stem} \nonumber \\ + \frac{d V_{w, leaf}}{dt} = q_{stem}\sigma_{stem} - \tau \sigma_{leaf}, +\end{align}\]

where $\tau$ is a transpiration volume flux per unit emitting area, and $\sigma$ is the total emitting/conducting area\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}.

This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals $N$. We can make use of the fact that $N\sigma/A$, where $A$ is the area of the ground those $N$ individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell.

Then we have:

\[\begin{align} + \frac{d v_{stem}}{dt} = q_{roots}RAI - q_{stem}SAI \nonumber \\ + \frac{d v_{leaf}}{dt} = q_{stem}SAI - \tau LAI, +\end{align}\]

where $v$ now represents the volume of water in that compartment (of a bulk plant) per unit ground area.

We also need to convert from the variable $v$ to $\psi$, in order to compute root extraction with the soil. To do so, we can convert $v$ to the volumetric water content, and from $\theta$ to $\psi$ using a van Genuchten relationship. To convert, let the volume of water per area of compartment be $V_{w,stem}$, and $H$ the typical ``length" of the compartment. Then

\[\begin{equation} + \theta_{stem}=\frac{V_{w,stem}}{A_{ground}} \times \frac{A_{ground}}{A_{stem}} \times \frac{1}{H_{stem}} = \frac{v_{stem}}{H_{stem} \times SAI }. +\end{equation}\]

Substituting in the volumetric water content, we have

\[\begin{align} + \frac{d \theta_{stem}}{dt} &= \frac{q_{roots}RAI - q_{stem}SAI}{H_{stem} SAI} \nonumber \\ + \frac{d \theta_{leaf}}{dt} &= \frac{q_{stem}SAI - \tau LAI}{H_{leaf} LAI}, +\end{align}\]

We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction $P(z)$, satisfying $\int P(z) dz = 1$. Instead of having a single root at one discrete location, we can distribute the root system over different depths using $P(z)$. The total flux from roots between $z$ and $z+dz$ is given by

\[\begin{equation} + dq_{roots}(z) = -P(z) dz \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh, +\end{equation}\]

so that the net flux for the plant system would sum over this

\[\begin{equation} + q_{roots} = -\int_{z_{min}}^{z_{sfc}} \frac{dq_{roots}(z)}{dz}dz , +\end{equation}\]

where $z_{min}$ is the minimum soil layer of the simulation.

The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with

\[\begin{equation} + S(z) = -(RAI) dq_{roots}(z)/dz = (RAI) P(z) \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh. +\end{equation}\]

The sign change occurs in the expression for $S(z)$ because a positive value of $q_{roots}$ indicates flow from the soil to the plant. This is a sink term for the soil.

The model needs the following parameters:

DriversSymbolUnitRange
A function of simulation time t given the leaf area indexLAIm²/m²0–10
The constant stem area indexSAIm²/m²0–10
The constant root area indexRAIm²/m²0–10
diff --git a/previews/PR753/standalone/pages/vegetation/radiative_transfer/beer_model/index.html b/previews/PR753/standalone/pages/vegetation/radiative_transfer/beer_model/index.html new file mode 100644 index 0000000000..9f6afd5636 --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/radiative_transfer/beer_model/index.html @@ -0,0 +1,4 @@ + +Beer model · ClimaLand.jl
GitHub

Radiative transfer scheme

This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand.

Beer's law

Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.

The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:

\[APAR(PAR, \theta_s) = (PAR)(1 - \rho_{leaf})(1 - e^{(-K(\theta_s) LAI \Omega)})\]

where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, $ρ_{leaf}$ is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, $θ_s$ is the zenith angle, and $Ω$ is the clumping index following Braghiere (2021). $K$, $Ω$ and $ρ_{leaf}$ are all unitless. LAI is in m² m⁻². In order to compute $K$, we need $θ_s$ in radians and the leaf angle distribution $l_d$ (unitless). K is then defined as

\[K = l_d/\max{(\cos{(\theta_s)}, \epsilon)}\]

so that at night, when 3π/2 > $θ_s$ > π/2, $K$ is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.

The model has the following parameters:

OutputSymbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy reflectance$ρ_{leaf}$-0.0–1.0
Extinction coefficient$K$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5

Interactive APAR(PAR, LAI, $ρ_{leaf}$, $K$, $Ω$)

diff --git a/previews/PR753/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html b/previews/PR753/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html new file mode 100644 index 0000000000..cb5330b9bb --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html @@ -0,0 +1,5 @@ + +Two-Stream model · ClimaLand.jl
GitHub

The Two-Stream Scheme

In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).

The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.

Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:

\[-\overline{\mu}(dI^{\uparrow})/dL + [1 - (1 - \beta)\omega]I^{\uparrow} - \omega \beta I^{\downarrow} = \omega \overline{\mu} K \beta_0 \exp{(-KL)},\\ +-\overline{\mu}(dI^{\downarrow})/dL + [1 - (1 - \beta)\omega]I^{\downarrow} - \omega \beta I^{\uparrow} = \omega \overline{\mu} K (1-\beta_0) \exp{(-KL)}\]

where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to

\[\int_{0}^{1}[\mu^{\prime}/G(\mu^{\prime})]d\mu^{\prime}\]

μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)

These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:

\[I^{\uparrow} = \frac{h_1\exp{(-KL)}}{\sigma} + h_2\exp{(-hL)} + h_3\exp{(hL)},\\ +I^{\downarrow} = \frac{h_4\exp{(-KL)}}{\sigma} + h_5\exp{(-hL)} + h_6\exp{(hL)}\]

For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:

\[I^{\uparrow} = h_7\exp{(-hL)} + h_8\exp{(hL)},\\ +I^{\downarrow} = h_9\exp{(-hL)} + h_{10}\exp{(hL)}\]

where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.

The model has the following parameters:

OutputSymmbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
Absorbed Near-Infrared RadiationANIRμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy PAR Reflectance$\alpha\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Reflectance$\alpha\_NIR\_{leaf}$-0.0–1.0
Canopy PAR Transmittance$\tau\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Transmittance$\tau\_NIR\_{leaf}$-0.0–1.0
Canopy Emissivity$ϵ\_canopy$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5
Typical wavelength per photon PAR$\lambda\_\gamma\_PAR$m5e-7
Typical wavelength per photon NIR$\lambda\_\gamma\_NIR$m1.65e-6
diff --git a/previews/PR753/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html b/previews/PR753/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html new file mode 100644 index 0000000000..9e00390a0e --- /dev/null +++ b/previews/PR753/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html @@ -0,0 +1,11 @@ + +Medlyn model · ClimaLand.jl
GitHub

Stomatal conductance

Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.

This section describes multiple models of stomatal conductance implemented in ClimaLand.

Medlyn Model

The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.

Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.

\[\begin{equation} +T = -\rho_a g_{\mathrm{eff}} \left[q_{a}- q_v(T_\mathrm{leaf}, \rho_\mathrm{sfc}) \right], +\end{equation}\]

where $T$ is the transpiration (mass flux of water vapor), $q_{a}$ is the specific humidity at the lowest level of the atmosphere, $q_v(T_\mathrm{T_{leaf}}, \rho_\mathrm{sfc})$ is the saturated specific humidity over liquid water, given the temperature of the leave $T_{leaf}$ and air density at the surface $\rho_{sfc}$. We will approximate $T_{leaf} = T_{a}$ and $\rho_{\mathrm{sfc}} = \rho_a$.

We also need the effective conductivity, given by

\[\begin{equation} + g_{\mathrm{eff}} = \frac{1}{g_{\mathrm{ae}}^{-1}+g_{\mathrm{s}}^{-1}}, +\end{equation}\]

where $g_{ae}$ is the aerodynamic conductance, computed by the MOST solve, and $g_s$ is the stomatal conductance to water vapor per unit ground area. The units of all conductances are $m/s$.

The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:

\[\begin{align} +g_{s,m}(PAR, T, VPD, c_a) &= g_{0,m} + D_{rel} \times m \frac{A_n(PAR, T, VPD, c_a)}{c_a}\nonumber \\ +g_s &= \frac{g_{s,m}}{\rho_m} +\end{align}\]

where $D_{rel} =1.6$ (unitless) is the relative diffusivity of water vapor with respect to CO$_2$, $\rho_m$ is the molar density of water, and $m$ is the Medlyn factor,

\[\begin{equation} + m = \left( 1 + \frac{g_1}{\sqrt{VPD}} \right), +\end{equation}\]

where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A$_n$ as the biochemical demand for CO$_2$ calculated using the photosynthesis model (Farquhar, 1980; Equation \eqref{eq:an}; units of molar flux). The resulting units are $m/s$. g{0,m}$ is a minimum molar conductivity. (subscript $m$ indicates molar).

The model has the following parameters:

ConstantsSymbolUnitValue
Relative diffusivity of water vapor$D_{rel}$-1.6
Minimum stomatal conductance$g_0$mol/$m^2$/s1e-4
Slope parameter$g_1$$\sqrt{Pa}$790
diff --git a/previews/PR754/.documenter-siteinfo.json b/previews/PR754/.documenter-siteinfo.json new file mode 100644 index 0000000000..4d855ffc53 --- /dev/null +++ b/previews/PR754/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-02T19:30:21","documenter_version":"1.6.0"}} \ No newline at end of file diff --git a/previews/PR754/APIs/Bucket/index.html b/previews/PR754/APIs/Bucket/index.html new file mode 100644 index 0000000000..1c435815af --- /dev/null +++ b/previews/PR754/APIs/Bucket/index.html @@ -0,0 +1,16 @@ + +Bucket Model · ClimaLand.jl
GitHub

Bucket

Types

ClimaLand.Bucket.BucketModelParameters — Type
struct BucketModelParameters{
+    FT <: AbstractFloat,
+    PSE,
+}

Container for holding the parameters of the bucket model.

  • κ_soil: Conductivity of the soil (W/K/m); constant

  • ρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant

  • albedo: Albedo Model

  • σS_c: Critical σSWE amount (m) where surface transitions from to snow-covered

  • f_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)

  • W_f: Capacity of the land bucket (m)

  • f_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)

  • p: Exponent used in β decay (unitless)

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • τc: τc timescale on which snow melts

  • earth_param_set: Earth Parameter set; physical constants, etc

source
ClimaLand.Bucket.PrescribedBaregroundAlbedo — Type
PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel

An albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).

source
ClimaLand.Bucket.PrescribedSurfaceAlbedo — Type
PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}
+                   <: AbstractBucketAlbedoModel

An albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.

Note that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.

source
ClimaLand.Bucket.BucketModel — Type
struct BucketModel{
+     FT,
+     PS <: BucketModelParameters{FT},
+     ATM <: AbstractAtmosphericDrivers{FT},
+     RAD <: AbstractRadiativeDrivers{FT},
+     D,
+ } <: AbstractBucketModel{FT}

Concrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.

  • parameters: Parameters required by the bucket model

  • atmos: The atmospheric drivers: Prescribed or Coupled

  • radiation: The radiation drivers: Prescribed or Coupled

  • domain: The domain of the model

source

Misc Functions

ClimaLand.surface_albedo — Function
ClimaLand.surface_albedo(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+) where {FT}

Returns the surface albedo field of the EnergyHydrology soil model.

source
surface_albedo(model::SnowModel, Y, p)

A helper function which computes and returns the snow albedo.

source
surface_albedo(model::BucketModel, Y, p)

Returns the bulk surface albedo, which gets updated in update_aux via next_albedo.

source
surface_albedo(model::AbstractModel, Y, p)

A helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.Bucket.beta_factor — Function
beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}

Computes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:

β = (x/xc)^p x < xc 1 otherwise

where x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.

source
diff --git a/previews/PR754/APIs/ClimaLand/index.html b/previews/PR754/APIs/ClimaLand/index.html new file mode 100644 index 0000000000..424d766061 --- /dev/null +++ b/previews/PR754/APIs/ClimaLand/index.html @@ -0,0 +1,13 @@ + +ClimaLand · ClimaLand.jl
GitHub

ClimaLand

LSM Model Types and methods

Missing docstring.

Missing docstring for ClimaLand.SoilPlantHydrologyModel. Check Documenter's build log for details.

ClimaLand.LandSoilBiogeochemistry — Type
struct LandSoilBiogeochemistry{
+    FT,
+    SEH <: Soil.EnergyHydrology{FT},
+    SB <: Soil.Biogeochemistry.SoilCO2Model{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.

  • soil: The soil model

  • soilco2: The biochemistry model

source
ClimaLand.LandHydrology — Type
struct LandHydrology{
+    FT,
+    SM <: Soil.AbstractSoilModel{FT},
+    SW <: Pond.AbstractSurfaceWaterModel{FT},
+} <: AbstractLandModel{FT}

A concrete type of land model used for simulating systems with a soil and surface water component.

  • soil: The soil model

  • surface_water: The surface water model

source
Missing docstring.

Missing docstring for ClimaLand.make_interactions_update_aux. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.initialize_interactions. Check Documenter's build log for details.

ClimaLand.land_components — Function
land_components(land::AbstractLandModel)

Returns the component names of the land model, by calling propertynames(land).

source
ClimaLand.lsm_aux_vars — Function

lsmauxvars(m::AbstractLandModel)

Returns the additional aux variable symbols for the model in the form of a tuple.

source
lsm_aux_vars(m::SoilCanopyModel)

The names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_types — Function

lsmauxtypes(m::AbstractLandModel)

Returns the shared additional aux variable types for the model in the form of a tuple.

source
lsm_aux_types(m::SoilCanopyModel)

The types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
ClimaLand.lsm_aux_domain_names — Function

lsmauxdomain_names(m::AbstractLandModel)

Returns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.

This is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.

source
lsm_aux_domain_names(m::SoilCanopyModel)

The domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.

source
Missing docstring.

Missing docstring for ClimaLand.domain_name. Check Documenter's build log for details.

Land Hydrology

ClimaLand.infiltration_capacity — Function
function infiltration_capacity(
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+)

Function which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.

Defined such that positive means into soil.

source
ClimaLand.infiltration_at_point — Function
infiltration_at_point(η::FT, i_c::FT, P::FT)

Returns the infiltration given pond height η, infiltration capacity, and precipitation.

This is defined such that positive means into soil.

source
ClimaLand.PrognosticRunoff — Type
PrognosticRunoff <: Pond.AbstractSurfaceRunoff

Concrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.

This is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source
ClimaLand.RunoffBC — Type
RunoffBC <: Soil.AbstractSoilBC

Concrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.

This is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.

source

SoilCanopyModel

ClimaLand.PrognosticSoil — Type
 PrognosticSoil{FT} <: AbstractSoilDriver

Concrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

source
ClimaLand.RootExtraction — Type
RootExtraction{FT} <: Soil.AbstractSoilSource{FT}

Concrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.

This is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.

source

LandSoilBiogeochemistry

Missing docstring.

Missing docstring for ClimaLand.PrognosticMet. Check Documenter's build log for details.

diff --git a/previews/PR754/APIs/Regridder/index.html b/previews/PR754/APIs/Regridder/index.html new file mode 100644 index 0000000000..4cc5a375c6 --- /dev/null +++ b/previews/PR754/APIs/Regridder/index.html @@ -0,0 +1,2 @@ + +Parameter Dataset Tools · ClimaLand.jl
GitHub

Bucket

Functions

Missing docstring.

Missing docstring for ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.Regridder.swap_space. Check Documenter's build log for details.

diff --git a/previews/PR754/APIs/Snow/index.html b/previews/PR754/APIs/Snow/index.html new file mode 100644 index 0000000000..c2c752aaac --- /dev/null +++ b/previews/PR754/APIs/Snow/index.html @@ -0,0 +1,2 @@ + +Snow Model · ClimaLand.jl
GitHub

Snow Model

Snow Parameters

ClimaLand.Snow.SnowParameters — Type
SnowParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SnowModel.

Note that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.

  • ρ_snow: Density of snow (kg/m^3)

  • z_0m: Roughness length over snow for momentum (m)

  • z_0b: Roughness length over snow for scalars (m)

  • α_snow: Albedo of snow (unitless)

  • ϵ_snow: Emissivity of snow (unitless)

  • θ_r: Volumetric holding capacity of water in snow (unitless)

  • Ksat: Hydraulic conductivity of wet snow (m/s)

  • κ_ice: Thermal conductivity of ice (W/m/K)

  • Δt: Timestep of the model (s)

  • ρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)

  • earth_param_set: Clima-wide parameters

source

Snow Functions of State

```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe

diff --git a/previews/PR754/APIs/Soil/index.html b/previews/PR754/APIs/Soil/index.html new file mode 100644 index 0000000000..7bc9a71f0d --- /dev/null +++ b/previews/PR754/APIs/Soil/index.html @@ -0,0 +1,109 @@ + +Soil Energy and Hydrology · ClimaLand.jl
GitHub

Soil Models

Soil Models

ClimaLand.Soil.AbstractSoilModel — Type
AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}

The abstract type for all soil models.

Currently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.

source
ClimaLand.Soil.RichardsModel — Type
RichardsModel

A model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.

A variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).

If you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water

source
ClimaLand.Soil.EnergyHydrology — Type
EnergyHydrology <: AbstractSoilModel

A model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.

A variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.

  • parameters: The parameter sets

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions

  • sources: A tuple of sources, each of type AbstractSoilSource

  • lateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat

source

Soil Parameter Structs

ClimaLand.Soil.RichardsParameters — Type
RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}

A struct for storing parameters of the RichardsModel.

  • ν: The porosity of the soil (m^3/m^3)

  • hydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

source
ClimaLand.Soil.EnergyHydrologyParameters — Type
EnergyHydrologyParameters{
+        FT <: AbstractFloat,
+        F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},
+        C,
+        PSE,
+    }

A parameter structure for the integrated soil water and energy equation system.

Note that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:

  • Surface parameters: albedo in each wavelength band (SF)
  • Scalar parameters: emissivity, α, β, γ, γT_ref, Ω,

roughness lengths z0, dds ) (FT)

  • Parameters defined in the interior: all else (F)

  • κ_dry: The dry soil thermal conductivity, W/m/K

  • κ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K

  • κ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K

  • ρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)

  • ν: The porosity of the soil (m^3/m^3)

  • ν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)

  • ν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)

  • ν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)

  • α: The parameter α used in computing Kersten number, unitless

  • β: The parameter β used in computing Kersten number, unitless

  • hydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey

  • K_sat: The saturated hydraulic conductivity (m/s)

  • S_s: The specific storativity (1/m)

  • θ_r: The residual water fraction (m^3/m^3

  • Ω: Ice impedance factor for the hydraulic conductivity

  • γ: Coefficient of viscosity factor for the hydraulic conductivity

  • γT_ref: Reference temperature for the viscosity factor

  • PAR_albedo: Soil PAR Albedo

  • NIR_albedo: Soil NIR Albedo

  • emissivity: Soil Emissivity

  • z_0m: Roughness length for momentum

  • z_0b: Roughness length for scalars

  • d_ds: Maximum dry soil layer thickness under evaporation (m)

  • earth_param_set: Physical constants and clima-wide parameters

source

Soil Hydrology Parameterizations

ClimaLand.Soil.volumetric_liquid_fraction — Function
volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}

A pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.

source
ClimaLand.Soil.pressure_head — Function
pressure_head(
+    cm::vanGenuchten{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
pressure_head(
+    cm::BrooksCorey{FT},
+    θ_r::FT,
+    ϑ_l::FT,
+    ν_eff::FT,
+    S_s::FT,
+) where {FT}

A point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.

source
ClimaLand.Soil.hydraulic_conductivity — Function
 hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.

source
 hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}

A point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.

source
ClimaLand.Soil.impedance_factor — Function
impedance_factor(
+    f_i::FT,
+    Ω::FT
+) where {FT}

Returns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.viscosity_factor — Function
viscosity_factor(
+    T::FT,
+    γ::FT,
+    γT_ref::FT,
+) where {FT}

Returns the multiplicative factor which accounts for the temperature dependence of the conductivity.

Only for use with the EnergyHydrology model.

source
ClimaLand.Soil.matric_potential — Function
 matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the van Genuchten formulation.

source
 matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}

A point-wise function returning the matric potential, using the Brooks and Corey formulation.

source
ClimaLand.Soil.dψdϑ — Function

dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.

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dψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)

Computes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.

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ClimaLand.Soil.inverse_matric_potential — Function
 inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.

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 inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}

A point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.

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ClimaLand.Soil.AbstractSoilHydrologyClosure — Type
AbstractSoilHydrologyClosure{FT <: AbstractFloat}

The abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.

To add a new parameterization, methods are required for:

  • matric_potential,
  • inversematricpotential,
  • pressure_head,
  • dψdϑ,
  • hydraulic_conductivity.
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ClimaLand.Soil.vanGenuchten — Type
vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}

The van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).

  • α: The inverse of the air entry potential (1/m)

  • n: The van Genuchten pore-size distribution index (unitless)

  • m: The van Genuchten parameter m = 1 - 1/n (unitless)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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ClimaLand.Soil.BrooksCorey — Type

BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}

The Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).

  • c: The pore-size distribution index (unitless)

  • ψb: The air entry matric potential, when S=1 (m)

  • S_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface

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Soil Heat Parameterizations

ClimaLand.Soil.κ_solid — Function
κ_solid(ν_ss_om::FT,
+        ν_ss_quartz::FT,
+        κ_om::FT,
+        κ_quartz::FT,
+        κ_minerals::FT) where {FT}

Computes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.

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ClimaLand.Soil.κ_sat_frozen — Function
function κ_sat_frozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_ice::FT
+) where {FT}

Computes the thermal conductivity for saturated frozen soil.

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ClimaLand.Soil.κ_sat_unfrozen — Function
function κ_sat_unfrozen(
+    κ_solid::FT,
+    ν::FT,
+    κ_l::FT
+) where {FT}

Computes the thermal conductivity for saturated unfrozen soil.

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ClimaLand.Soil.κ_sat — Function
κ_sat(
+    θ_l::FT,
+    θ_i::FT,
+    κ_sat_unfrozen::FT,
+    κ_sat_frozen::FT
+) where {FT}

Compute the expression for saturated thermal conductivity of soil matrix.

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ClimaLand.Soil.κ_dry — Function
function κ_dry(ρp::FT,
+               ν::FT,
+               κ_solid::FT,
+               κ_air::FT;
+               a = FT(0.053)) where {FT}

Computes the thermal conductivity of dry soil according to the model of Balland and Arp.

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ClimaLand.Soil.kersten_number — Function
kersten_number(
+    θ_i::FT,
+    S_r::FT,
+    α::FT,
+    β::FT,
+    ν_ss_om::FT,
+    ν_ss_quartz::FT,
+    ν_ss_gravel::FT,
+    ) where {FT}

Compute the expression for the Kersten number, using the Balland and Arp model.

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ClimaLand.Soil.relative_saturation — Function
relative_saturation(
+        θ_l::FT,
+        θ_i::FT,
+        ν::FT
+) where {FT}

Compute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.

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ClimaLand.Soil.volumetric_internal_energy — Function
volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,
+                             earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.

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ClimaLand.Soil.volumetric_internal_energy_liq — Function
volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}

A pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.

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ClimaLand.Soil.temperature_from_ρe_int — Function
temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT
+                        earth_param_set::EP) where {FT, EP}

A pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.

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ClimaLand.Soil.phase_change_source — Function
phase_change_source(
+    θ_l::FT,
+    θ_i::FT,
+    T::FT,
+    τ::FT,
+    ν::FT,
+    θ_r::FT,
+    hydrology_cm::C,
+    earth_param_set::EP,
+) where {FT, EP, C}

Returns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.

Note that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.

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ClimaLand.Soil.thermal_time — Function
thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}

Returns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.

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Soil Surface Parameterizations

Missing docstring.

Missing docstring for ClimaLand.soil.soil_resistance. Check Documenter's build log for details.

ClimaLand.Soil.dry_soil_layer_thickness — Function
dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}

Returns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).

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ClimaLand.Soil.soil_tortuosity — Function
soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}

Computes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.

See Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.

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Soil Runoff Types and Methods

ClimaLand.Soil.Runoff.SurfaceRunoff — Type
SurfaceRunoff <: AbstractRunoffModel

A simple model for runoff appropriate for single column runs.

Only surface runoff is computed, using a combination of Dunne and Hortonian runoff.

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ClimaLand.Soil.Runoff.TOPMODELRunoff — Type
TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel

The TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.

The runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

  • f_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.

  • subsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.

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ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff — Type
TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}

The TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.

The runoff flux is given by Equation 12 of Niu et al. (2005), "A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models".

  • R_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated

  • f_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)

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ClimaLand.Soil.Runoff.update_runoff! — Function
update_runoff!(p, runoff::NoRunoff, _...)

Updates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.

source
update_runoff!(
+    p,
+    runoff::SurfaceRunoff,
+    Y,
+    t,
+    model::AbstractSoilModel,

)

The update_runoff! function for the SurfaceRunoff model.

Updates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration

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update_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)

Updates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration

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Soil BC Methods and Types

ClimaLand.Soil.MoistureStateBC — Type

MoistureStateBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.HeatFluxBC — Type

HeatFluxBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.WaterFluxBC — Type

WaterFluxBC <: AbstractWaterBC

A simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.TemperatureStateBC — Type

TemperatureStateBC <: AbstractHeatBC

A simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.

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ClimaLand.Soil.FreeDrainage — Type
FreeDrainage <: AbstractWaterBC

A concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.

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ClimaLand.Soil.RichardsAtmosDrivenFluxBC — Type

RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC

A concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.

If you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.

  • precip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.

  • runoff: The runoff model. The default is no runoff.

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ClimaLand.Soil.AtmosDrivenFluxBC — Type
AtmosDrivenFluxBC{
+    A <: AbstractAtmosphericDrivers,
+    B <: AbstractRadiativeDrivers,
+    R <: AbstractRunoffModel
+} <: AbstractEnergyHydrologyBC

A concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.

This choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.

A runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.

  • atmos: The atmospheric conditions driving the model

  • radiation: The radiative fluxes driving the model

  • runoff: The runoff model. The default is no runoff.

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ClimaLand.Soil.WaterHeatBC — Type
WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:
+   AbstractEnergyHydrologyBC

A general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.

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ClimaLand.Soil.soil_boundary_fluxes! — Function
soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)

updates the boundary fluxes for ϑl and ρeint.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere,
+        <:PrescribedRadiativeFluxes,
+    },
+    boundary::ClimaLand.TopBoundary,
+    model::EnergyHydrology,
+    Δz,
+    Y,
+    p,
+    t,
+)

Returns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.

If you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.

This function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.

source
soil_boundary_fluxes!(
+    bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},
+    boundary::ClimaLand.TopBoundary,
+    soil::EnergyHydrology{FT},
+    Δz,
+    Y,
+    p,
+    t,
+) where {FT}

A method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.

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Soil Source Types

ClimaLand.Soil.AbstractSoilSource — Type
AbstractSoilSource{FT} <:  ClimaLand.AbstractSource{FT}

An abstract type for types of source terms for the soil equations.

In standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).

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Missing docstring.

Missing docstring for ClimaLand.Soil.RootExtraction. Check Documenter's build log for details.

Soil Jacobian Structures

ClimaLand.ImplicitEquationJacobian — Type
ImplicitEquationJacobian{M, S}

A struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.

matrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.

Note that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.

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diff --git a/previews/PR754/APIs/SoilBiogeochemistry/index.html b/previews/PR754/APIs/SoilBiogeochemistry/index.html new file mode 100644 index 0000000000..7119bbf366 --- /dev/null +++ b/previews/PR754/APIs/SoilBiogeochemistry/index.html @@ -0,0 +1,17 @@ + +Soil Biogeochemistry · ClimaLand.jl
GitHub

Soil Biogeochemistry

Model Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2Model — Type
SoilCO2Model

A model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.

  • parameters: the parameter set

  • domain: the soil domain, using ClimaCore.Domains

  • boundary_conditions: the boundary conditions, of type NamedTuple

  • sources: A tuple of sources, each of type AbstractSource

  • drivers: Drivers

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Parameter Structure

ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters — Type
SoilCO2ModelParameters{FT <: AbstractFloat, PSE}

A struct for storing parameters of the SoilCO2Model.

All of these parameters are currently treated as global constants.

  • D_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)

  • D_liq: Diffusivity of soil C substrate in liquid (unitless)

  • α_sx: Pre-exponential factor (kg C m-3 s-1)

  • Ea_sx: Activation energy (J mol-1)

  • kM_sx: Michaelis constant (kg C m-3)

  • kM_o2: Michaelis constant for O2 (m3 m-3)

  • O2_a: Volumetric fraction of O₂ in the soil air, dimensionless

  • D_oa: Diffusion coefficient of oxygen in air, dimensionless

  • p_sx: Fraction of soil carbon that is considered soluble, dimensionless

  • earth_param_set: Physical constants used Clima-wide

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Model-specific Types

ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver — Type
AbstractSoilDriver

An abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).

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ClimaLand.Soil.Biogeochemistry.SoilDrivers — Type
SoilDrivers

A container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).

  • met: Soil temperature and moisture drivers - Prescribed or Prognostic

  • soc: Soil SOM driver - Prescribed only

  • atmos: Prescribed atmospheric variables

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ClimaLand.Soil.Biogeochemistry.PrescribedMet — Type
PrescribedMet <: AbstractSoilDriver

A container which holds the prescribed functions for soil temperature and moisture.

This is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.

  • temperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat

  • volumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat

  • ν: Soil porosity (m³ m⁻³)

  • θ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)

  • b: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)

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Functions of State

ClimaLand.Soil.Biogeochemistry.co2_diffusivity — Function
co2_diffusivity(
+                T_soil::FT,
+                θ_w::FT,
+                P_sfc::FT,
+                θ_a100::FT,
+                b::FT,
+                ν::FT,
+                params::SoilCO2ModelParameters{FT},
+                ) where {FT}

Computes the diffusivity of CO₂ within the soil (D).

First, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.

This parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.

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ClimaLand.Soil.Biogeochemistry.microbe_source — Function
microbe_source(T_soil::FT,
+               θ_l::FT,
+               Csom::FT,
+               ν::FT,
+               params::SoilCO2ModelParameters{FT}
+               ) where {FT}

Computes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).

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Extendible Functions

ClimaLand.Soil.Biogeochemistry.soil_moisture — Function
soil_moisture(driver::PrognosticSoil, p, Y, t, z)

Returns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.

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soil_moisture(driver::PrescribedMet, p, Y, t, z)

Returns the soil moisture at location (z) and time (t) for the prescribed soil case.

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ClimaLand.Soil.Biogeochemistry.soil_temperature — Function
soil_temperature(driver::PrognosticSoil, p, Y, t, z)

Returns the prognostic soil temperature.

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soil_temperature(driver::PrescribedMet, p, Y, t, z)

Returns the soil temperature at location (z) and time (t) for the prescribed soil case.

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Missing docstring.

Missing docstring for ClimaLand.Soil.Biogeochemistry.soil_SOM_C. Check Documenter's build log for details.

diff --git a/previews/PR754/APIs/SurfaceWater/index.html b/previews/PR754/APIs/SurfaceWater/index.html new file mode 100644 index 0000000000..90672092da --- /dev/null +++ b/previews/PR754/APIs/SurfaceWater/index.html @@ -0,0 +1,7 @@ + +Surface Water Models · ClimaLand.jl
GitHub

SurfaceWater

Models

ClimaLand.Pond.PondModel — Type
PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}

A stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.

  • domain: The domain for the pond model

  • runoff: The runoff model for the pond model

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Methods and Types

ClimaLand.Pond.PrescribedRunoff — Type
PrescribedRunoff{F1 <: Function, F2 <: Function} <:  AbstractSurfaceRunoff

The required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.

source
ClimaLand.Pond.surface_runoff — Function
function Pond.surface_runoff(
+    runoff::PrognosticRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

Extension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.

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diff --git a/previews/PR754/APIs/canopy/AutotrophicRespiration/index.html b/previews/PR754/APIs/canopy/AutotrophicRespiration/index.html new file mode 100644 index 0000000000..61f6c81337 --- /dev/null +++ b/previews/PR754/APIs/canopy/AutotrophicRespiration/index.html @@ -0,0 +1,23 @@ + +Canopy Autotrophic Respiration · ClimaLand.jl
GitHub

Autotrophic Respiration

Parameters

ClimaLand.Canopy.AutotrophicRespirationParameters — Type
AutotrophicRespirationParameters{FT<:AbstractFloat}

The required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. "The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics." Geoscientific Model Development 4.3 (2011): 701-722.

  • ne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)

  • ηsl: Live stem wood coefficient (kg C m-3)

  • σl: Specific leaf density (kg C m-2 [leaf])

  • μr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0

  • μs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1

  • Rel: Relative contribution or Rgrowth (-)

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Methods

ClimaLand.Canopy.nitrogen_content — Function
nitrogen_content(
+                 ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)
+                 Vcmax25::FT, #
+                 LAI::FT, # Leaf area index
+                 SAI::FT,
+                 RAI::FT,
+                 ηsl::FT, # live stem  wood coefficient (kg C m-3) 
+                 h::FT, # canopy height (m)
+                 σl::FT # Specific leaf density (kg C m-2 [leaf])
+                 μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0
+                 μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 
+                ) where {FT}

Computes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).

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ClimaLand.Canopy.plant_respiration_maintenance — Function
plant_respiration_maintenance(
+    Rd::FT, # Dark respiration
+    β::FT, # Soil moisture factor
+    Nl::FT, # Nitrogen content of leafs
+    Nr::FT, # Nitrogen content of roots
+    Ns::FT, # Nitrogen content of stems
+    ) where {FT}

Computes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).

source
ClimaLand.Canopy.plant_respiration_growth — Function
plant_respiration_growth(
+    Rel::FT, # Factor of relative contribution
+    An::FT, # Net photosynthesis
+    Rpm::FT # Plant maintenance respiration
+    ) where {FT}

Computes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.

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diff --git a/previews/PR754/APIs/canopy/Canopy/index.html b/previews/PR754/APIs/canopy/Canopy/index.html new file mode 100644 index 0000000000..93a3d442a8 --- /dev/null +++ b/previews/PR754/APIs/canopy/Canopy/index.html @@ -0,0 +1,2 @@ + +Canopy Models · ClimaLand.jl
GitHub

Canopy

Canopy Model Structs

ClimaLand.Canopy.CanopyModel — Type
 CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}

The model struct for the canopy, which contains

  • the canopy model domain (a point for site-level simulations, or

an extended surface (plane/spherical surface) for regional or global simulations.

  • subcomponent model type for radiative transfer. This is of type

AbstractRadiationModel.

  • subcomponent model type for photosynthesis. This is of type

AbstractPhotosynthesisModel, and currently only the FarquharModel is supported.

  • subcomponent model type for stomatal conductance. This is of type

AbstractStomatalConductanceModel and currently only the MedlynModel is supported

  • subcomponent model type for plant hydraulics. This is of type

AbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.

  • subcomponent model type for canopy energy. This is of type

AbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.

  • subcomponent model type for canopy SIF. prognostically.
  • canopy model parameters, which include parameters that are shared

between canopy model components or those needed to compute boundary fluxes.

  • The atmospheric conditions, which are either prescribed

(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).

  • The radiative flux conditions, which are either prescribed

(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).

  • The soil conditions, which are either prescribed (of type PrecribedSoil, for

running the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)

Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.

  • autotrophic_respiration: Autotrophic respiration model, a canopy component model

  • radiative_transfer: Radiative transfer model, a canopy component model

  • photosynthesis: Photosynthesis model, a canopy component model

  • conductance: Stomatal conductance model, a canopy component model

  • hydraulics: Plant hydraulics model, a canopy component model

  • energy: Energy balance model, a canopy component model

  • sif: SIF model, a canopy component model

  • atmos: Atmospheric forcing: prescribed or coupled

  • radiation: Radiative forcing: prescribed or coupled

  • soil_driver: Soil pressure: prescribed or prognostic

  • parameters: Shared canopy parameters between component models

  • domain: Canopy model domain

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ClimaLand.Canopy.SharedCanopyParameters — Type
SharedCanopyParameters{FT <: AbstractFloat, PSE}

A place to store shared parameters that are required by multiple canopy components.

  • z_0m: Roughness length for momentum (m)

  • z_0b: Roughness length for scalars (m)

  • earth_param_set: Earth param set

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Canopy Model Fluxes

ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration — Type
DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}

A concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.

source

Canopy Model Soil Drivers

ClimaLand.Canopy.PrescribedSoil — Type
 PrescribedSoil <: AbstractSoilDriver

A container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.

  • root_depths: The depth of the root tips, in meters

  • ψ: Prescribed soil potential (m) in the root zone a function of time

  • T: Prescribed soil surface temperature (K) as a function of time

  • α_PAR: Soil albedo for PAR

  • α_NIR: Soil albedo for NIR

  • ϵ: Soil emissivity

source
diff --git a/previews/PR754/APIs/canopy/CanopyEnergy/index.html b/previews/PR754/APIs/canopy/CanopyEnergy/index.html new file mode 100644 index 0000000000..dffd675549 --- /dev/null +++ b/previews/PR754/APIs/canopy/CanopyEnergy/index.html @@ -0,0 +1,16 @@ + +Canopy Energy · ClimaLand.jl
GitHub

Canopy Energy Model

Methods

ClimaLand.Canopy.canopy_temperature — Function
canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)

Returns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.

source
canopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)

Returns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.

source
ClimaLand.Canopy.root_energy_flux_per_ground_area! — Function
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrognosticSoil{F},
+    model::Canopy.AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT, F}

A method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).

Note that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source
root_energy_flux_per_ground_area!(
+    fa_energy::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::AbstractCanopyEnergyModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.

Background information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.

source

Types

Missing docstring.

Missing docstring for ClimaLand.Canopy.AbstractCanopyEnergyModel. Check Documenter's build log for details.

ClimaLand.Canopy.PrescribedCanopyTempModel — Type
PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}

A model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.

No equation for the energy of the canopy is solved.

source
diff --git a/previews/PR754/APIs/canopy/Photosynthesis/index.html b/previews/PR754/APIs/canopy/Photosynthesis/index.html new file mode 100644 index 0000000000..4f560bf2ae --- /dev/null +++ b/previews/PR754/APIs/canopy/Photosynthesis/index.html @@ -0,0 +1,44 @@ + +Canopy Photosynthesis · ClimaLand.jl
GitHub

Photosynthesis

Parameters

ClimaLand.Canopy.SIFParameters — Type
SIFParameters{FT<:AbstractFloat}

The required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.

  • kf: The rate coefficient for florescence, unitless

  • kd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • min_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless

  • kn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)

  • kp: Rate coefficient for photochemical quenching

  • kappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

  • kappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.

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ClimaLand.Canopy.FarquharParameters — Type
FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}

The required parameters for the Farquhar photosynthesis model.

  • Vcmax25: Vcmax at 25 °C (mol CO2/m^2/s)

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercelluar O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]

  • pc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]

  • mechanism: Photosynthesis mechanism: C3 or C4

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ClimaLand.Canopy.OptimalityFarquharParameters — Type
OptimalityFarquharParameters{FT<:AbstractFloat}

The required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.

  • mechanism: Photosynthesis mechanism: C3 only

  • Γstar25: Γstar at 25 °C (mol/mol)

  • Kc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)

  • Ko25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)

  • ΔHkc: Energy of activation for CO2 (J/mol)

  • ΔHko: Energy of activation for oxygen (J/mol)

  • ΔHVcmax: Energy of activation for Vcmax (J/mol)

  • ΔHΓstar: Energy of activation for Γstar (J/mol)

  • ΔHJmax: Energy of activation for Jmax (J/mol)

  • ΔHRd: Energy of activation for Rd (J/mol)

  • To: Reference temperature equal to 25 degrees Celsius (K)

  • oi: Intercellular O2 concentration (mol/mol); taken to be constant

  • ϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)

  • θj: Curvature parameter, a fitting constant to compute J, unitless

  • f: Constant factor appearing the dark respiration term, equal to 0.015.

  • sc: Fitting constant to compute the moisture stress factor (Pa^{-1})

  • pc: Fitting constant to compute the moisture stress factor (Pa)

  • c: Constant describing cost of maintaining electron transport (unitless)

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Methods

ClimaLand.Canopy.arrhenius_function — Function
arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)

Computes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.intercellular_co2 — Function
intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}

Computes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).

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ClimaLand.Canopy.co2_compensation — Function
co2_compensation(Γstar25::FT,
+                 ΔHΓstar::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.rubisco_assimilation — Function
rubisco_assimilation(::C3,
+                     Vcmax::FT,
+                     ci::FT,
+                     Γstar::FT,
+                     Kc::FT,
+                     Ko::FT,
+                     oi::FT) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).

The empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
rubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}

Computes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).

source
ClimaLand.Canopy.light_assimilation — Function
light_assimilation(::C3,
+                   J::FT,
+                   ci::FT,
+                   Γstar::FT) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
light_assimilation(::C4, J::FT, _...) where {FT}

Computes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).

source
ClimaLand.Canopy.C3 — Type
C3 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

source
ClimaLand.Canopy.C4 — Type
C4 <: AbstractPhotosynthesisMechanism

Helper struct for dispatching between C3 and C4 photosynthesis.

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ClimaLand.Canopy.max_electron_transport — Function
max_electron_transport(Vcmax::FT) where {FT}

Computes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.electron_transport — Function
electron_transport(APAR::FT,
+                   Jmax::FT,
+                   θj::FT,
+                   ϕ::FT) where {FT}

Computes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ).

See Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

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ClimaLand.Canopy.net_photosynthesis — Function
net_photosynthesis(Ac::FT,
+                   Aj::FT,
+                   Rd::FT,
+                   β::FT) where {FT}

Computes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.optimality_max_photosynthetic_rates — Function

optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)

Computes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical "curvature parameter" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.

See Smith et al. 2019.

source
ClimaLand.Canopy.moisture_stress — Function
moisture_stress(pl::FT,
+                sc::FT,
+                pc::FT) where {FT}

Computes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) .

See Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.dark_respiration — Function
dark_respiration(Vcmax25::FT,
+                 β::FT,
+                 f::FT,
+                 ΔHkc::FT,
+                 T::FT,
+                 To::FT,
+                 R::FT) where {FT}

Computes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.compute_GPP — Function
compute_GPP(An::FT,
+         K::FT,
+         LAI::FT,
+         Ω::FT) where {FT}

Computes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).

source
ClimaLand.Canopy.MM_Kc — Function
MM_Kc(Kc25::FT,
+      ΔHkc::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.MM_Ko — Function
MM_Ko(Ko25::FT,
+      ΔHko::FT,
+      T::FT,
+      To::FT,
+      R::FT) where {FT}

Computes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
ClimaLand.Canopy.compute_Vcmax — Function
compute_Vcmax(Vcmax25::FT,
+       T::FT,
+       To::FT,
+       R::FT,
+       ep5::FT) where {FT}

Computes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).

See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).

source
diff --git a/previews/PR754/APIs/canopy/PlantHydraulics/index.html b/previews/PR754/APIs/canopy/PlantHydraulics/index.html new file mode 100644 index 0000000000..14747dda9b --- /dev/null +++ b/previews/PR754/APIs/canopy/PlantHydraulics/index.html @@ -0,0 +1,35 @@ + +Plant Hydraulics · ClimaLand.jl
GitHub

PlantHydraulics

Models

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel — Type
PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}

Defines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.

This model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can "turn off" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:

  • n_leaf = 1
  • n_stem = 0
  • LAI = SAI = RAI = 0.

A plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.

Finally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.

  • n_stem: The number of stem compartments for the plant; can be zero

  • n_leaf: The number of leaf compartments for the plant; must be >=1

  • compartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters

  • compartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.

  • compartment_labels: The label (:stem or :leaf) of each compartment

  • parameters: Parameters required by the Plant Hydraulics model

  • transpiration: The transpiration model, of type AbstractTranspiration

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Plant Hydraulics Diagnostic Variables

ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction — Function
augmented_liquid_fraction(
+    ν::FT,
+    S_l::FT) where {FT}

Computes the augmented liquid fraction from porosity and effective saturation.

Augmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.

source
ClimaLand.Canopy.PlantHydraulics.water_retention_curve — Function
water_retention_curve(
+    S_l::FT,
+    b::FT,
+    ν::FT,
+    S_s::FT) where {FT}

Returns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.

source
ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area! — Function
PlantHydraulics.root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrognosticSoil,
+    model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)

An extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.

It is computed by summing the flux of water per ground area between roots and soil at each soil layer.

source
root_water_flux_per_ground_area!(
+    fa::ClimaCore.Fields.Field,
+    s::PrescribedSoil,
+    model::PlantHydraulicsModel{FT},
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+) where {FT}

A method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.

The returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.

source
ClimaLand.Canopy.PlantHydraulics.flux — Function
flux(
+    z1,
+    z2,
+    ψ1,
+    ψ2,
+    K1,
+    K2,
+) where {FT}

Computes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.

We currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.

source

Plant Hydraulics Parameters

ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters — Type
PlantHydraulicsParameters

A struct for holding parameters of the PlantHydraulics Model.

  • ai_parameterization: The area index model for LAI, SAI, RAI

  • ν: porosity (m3/m3)

  • S_s: storativity (m3/m3)

  • conductivity_model: Conductivity model and parameters

  • retention_model: Water retention model and parameters

  • root_distribution: Root distribution function P(z)

source
ClimaLand.Canopy.PlantHydraulics.Weibull — Type
Weibull{FT} <: AbstractConductivityModel{FT}

A concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.

Fields

  • K_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation

  • ψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).

  • c: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).

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ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve — Type
LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}

A concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.

When ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.

Fields

  • a: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.
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Plant Hydraulics Methods and Types

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.PrescribedSoilPressure. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ClimaLand.PlantHydraulics.AbstractRootExtraction. Check Documenter's build log for details.

diff --git a/previews/PR754/APIs/canopy/RadiativeTransfer/index.html b/previews/PR754/APIs/canopy/RadiativeTransfer/index.html new file mode 100644 index 0000000000..b00cb8e088 --- /dev/null +++ b/previews/PR754/APIs/canopy/RadiativeTransfer/index.html @@ -0,0 +1,33 @@ + +Canopy RT · ClimaLand.jl
GitHub

Radiative Transfer

Parameters

ClimaLand.Canopy.BeerLambertParameters — Type
BeerLambertParameters{FT <: AbstractFloat}

The required parameters for the Beer-Lambert radiative transfer model.

  • α_PAR_leaf: PAR leaf reflectance (unitless)

  • α_NIR_leaf: NIR leaf reflectance

  • ϵ_canopy: Emissivity of the canopy

  • Ω: Clumping index following Braghiere (2021) (unitless)

  • λ_γ_PAR: Typical wavelength per PAR photon (m)

  • λ_γ_NIR: Typical wavelength per NIR photon (m)

  • G_Function: Leaf angle distribution function

source

Methods

Missing docstring.

Missing docstring for ClimaLand.Canopy.compute_absorbances. Check Documenter's build log for details.

ClimaLand.Canopy.plant_absorbed_pfd — Function
plant_absorbed_pfd(
+    RT::BeerLambertModel{FT},
+    SW_IN:FT,
+    α_leaf::FT,
+    LAI::FT,
+    K::FT,
+    α_soil::FT
+)

Computes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil).

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

source
plant_absorbed_pfd(
+    RT::TwoStreamModel{FT},
+    α_leaf,
+    SW_IN::FT,
+    LAI::FT,
+    K::FT,
+    τ_leaf,
+    θs::FT,
+    α_soil::FT,
+)

Computes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band.

This applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.

Returns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.

source
ClimaLand.Canopy.extinction_coeff — Function
extinction_coeff(ld::FT,
+                 θs::FT) where {FT}

Computes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).

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Missing docstring.

Missing docstring for ClimaLand.Canopy.extinction_coeff. Check Documenter's build log for details.

ClimaLand.Canopy.canopy_radiant_energy_fluxes! — Function
Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,
+                                     s::PrognosticSoil{F},
+                                     canopy,
+                                     radiation::PrescribedRadiativeFluxes,
+                                     earth_param_set::PSE,
+                                     Y::ClimaCore.Fields.FieldVector,
+                                     t,
+                                    ) where {FT, PSE}

In standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.

In integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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canopy_radiant_energy_fluxes!(p::NamedTuple,
+                              s::PrescribedSoil,
+                              canopy,
+                              radiation::PrescribedRadiativeFluxes,
+                              earth_param_set::PSE,
+                              Y::ClimaCore.Fields.FieldVector,
+                              t,
+                             ) where {PSE}

Computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.

LW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.

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diff --git a/previews/PR754/APIs/canopy/StomatalConductance/index.html b/previews/PR754/APIs/canopy/StomatalConductance/index.html new file mode 100644 index 0000000000..1d6560bfd4 --- /dev/null +++ b/previews/PR754/APIs/canopy/StomatalConductance/index.html @@ -0,0 +1,17 @@ + +Canopy Stomatal Conductance · ClimaLand.jl
GitHub

Stomatal Conductance

Parameters

ClimaLand.Canopy.MedlynConductanceParameters — Type
MedlynConductanceParameters{FT <: AbstractFloat}

The required parameters for the Medlyn stomatal conductance model.

  • Drel: Relative diffusivity of water vapor (unitless)

  • g0: Minimum stomatal conductance mol/m^2/s

  • g1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})

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Methods

ClimaLand.Canopy.medlyn_term — Function
medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}

Computes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).

thermo_params is the Thermodynamics.jl parameter set.

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ClimaLand.Canopy.medlyn_conductance — Function
medlyn_conductance(g0::FT,
+                   Drel::FT,
+                   medlyn_term::FT,
+                   An::FT,
+                   ca::FT) where {FT}

Computes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).

This returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.

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ClimaLand.Canopy.upscale_leaf_conductance — Function
upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}

This currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.

TODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.

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ClimaLand.Canopy.penman_monteith — Function
penman_monteith(
+    Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1)  
+    Rn::FT, # Net irradiance (W m−2)
+    G::FT, # Ground heat flux (W m−2)
+    ρa::FT, # Dry air density (kg m−3)
+    cp::FT, # Specific heat capacity of air (J kg−1 K−1) 
+    VPD::FT, # vapor pressure deficit (Pa)
+    ga::FT, # atmospheric conductance (m s−1)
+    γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)
+    gs::FT, # surface or stomatal conductance (m s−1)
+    Lv::FT, # Volumetric latent heat of vaporization (J m-3)
+    ) where {FT}

Computes the evapotranspiration in m/s using the Penman-Monteith equation.

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diff --git a/previews/PR754/APIs/shared_utilities/index.html b/previews/PR754/APIs/shared_utilities/index.html new file mode 100644 index 0000000000..619336265a --- /dev/null +++ b/previews/PR754/APIs/shared_utilities/index.html @@ -0,0 +1,249 @@ + +Shared Utilities · ClimaLand.jl
GitHub

Shared Utilities

Domains

ClimaLand.Domains.AbstractDomain — Type
AbstractDomain{FT <:AbstractFloat}

An abstract type for domains.

The domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.

Additionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.

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Missing docstring.

Missing docstring for ClimaLand.Domains.AbstractLSMDomain. Check Documenter's build log for details.

ClimaLand.Domains.SphericalShell — Type
struct SphericalShell{FT} <: AbstractDomain{FT}
+    radius::FT
+    depth::FT
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • radius: The radius of the shell

  • depth: The radial extent of the shell

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: The number of elements to be used in the non-radial and radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.SphericalSurface — Type
struct SphericalSurface{FT} <: AbstractDomain{FT}
+    radius::FT
+    nelements::Tuple{Int, Int}
+    npolynomial::Int
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.

space is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).

Fields

  • radius: The radius of the surface

  • nelements: The number of elements to be used in the non-radial directions

  • npolynomial: The polynomial order to be used in the non-radial directions

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space

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ClimaLand.Domains.HybridBox — Type
struct HybridBox{FT} <: AbstractDomain{FT}
+    xlim::Tuple{FT, FT}
+    ylim::Tuple{FT, FT}
+    zlim::Tuple{FT, FT}
+    longlat::Union{Nothing, Tuple{FT, FT}},
+    dz_tuple::Union{Tuple{FT, FT}, Nothing}
+    nelements::Tuple{Int, Int, Int}
+    npolynomial::Int
+    periodic::Tuple{Bool, Bool}
+end

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.

When longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • zlim: Domain interval limits along z axis, in meters

  • longlat: When not nothing, a Tuple that contains the center long and lat.

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • nelements: Number of elements to discretize interval, (nx, ny,nz)

  • npolynomial: Polynomial order for the horizontal directions

  • periodic: Flag indicating periodic boundaries in horizontal

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Column — Type
Column{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.

space is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.

Fields

  • zlim: Domain interval limits, (zmin, zmax), in meters

  • nelements: Number of elements used to discretize the interval

  • dz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.

  • boundary_names: Boundary face identifiers

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space

  • fields: Fields associated with the coordinates of the domain that are useful to store

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ClimaLand.Domains.Plane — Type
Plane{FT} <: AbstractDomain{FT}

A struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.

When longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.

longlat are in degrees, with longitude going from -180 to 180.

:warning: Only independent columns are supported! (No lateral flow).

space is a NamedTuple holding the surface space (in this case, the entire Plane space).

Fields

  • xlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)

  • ylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)

  • longlat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).

  • nelements: Number of elements to discretize interval, (nx, ny)

  • periodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.

  • npolynomial: Polynomial order for both x and y

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space

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ClimaLand.Domains.Point — Type
Point{FT} <: AbstractDomain{FT}

A domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.

space is a NamedTuple holding the surface space (in this case, the Point space).

Fields

  • z_sfc: Surface elevation relative to a reference (m)

  • space: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space

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ClimaLand.Domains.coordinates — Function
coordinates(domain::AbstractDomain)

Returns the coordinate fields for the domain as a NamedTuple.

The returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.

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Domains.coordinates(model::AbstractLandModel)

Returns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.

For example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.

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ClimaLand.Domains.obtain_face_space — Function
obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the face space, if applicable, for the center space cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the face space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_space — Function
obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)

Returns the surface space, if applicable, for the center space cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)

Returns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.

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obtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)

Returns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.

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ClimaLand.Domains.obtain_surface_domain — Function
obtain_surface_domain(d::AbstractDomain) where {FT}

Default method throwing an error; any domain with a corresponding domain should define a new method of this function.

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obtain_surface_domain(c::Column{FT}) where {FT}

Returns the Point domain corresponding to the top face (surface) of the Column domain c.

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obtain_surface_domain(b::HybridBox{FT}) where {FT}

Returns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.

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obtain_surface_domain(s::SphericalShell{FT}) where {FT}

Returns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.

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ClimaLand.Domains.top_center_to_surface — Function
top_center_to_surface(center_field::ClimaCore.Fields.Field)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.

For example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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top_center_to_surface(val)

When val is a scalar (e.g. a single float or struct), returns val.

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ClimaLand.Domains.top_face_to_surface — Function
top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)

Creates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.

Given a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.

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ClimaLand.Domains.linear_interpolation_to_surface! — Function
linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)

Linearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.

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ClimaLand.Domains.get_Δz — Function
get_Δz(z::ClimaCore.Fields.Field)

A function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.

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Models

ClimaLand.AbstractImExModel — Type
AbstractImExModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.

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ClimaLand.AbstractExpModel — Type
AbstractExpModel{FT} <: AbstractModel{FT}

An abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.

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ClimaLand.make_exp_tendency — Function
make_exp_tendency(model::AbstractModel)

Returns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_imp_tendency — Function
make_imp_tendency(model::AbstractImExModel)

Returns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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make_imp_tendency(model::AbstractModel)

Returns an imp_tendency that does nothing. This model type is not stepped explicity.

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ClimaLand.make_compute_exp_tendency — Function
make_explicit_tendency(model::Soil.RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

Construct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.

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make_compute_exp_tendency(model::EnergyHydrology)

An extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.

This function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::BucketModel{FT}) where {FT}

Creates the computeexptendency! function for the bucket model.

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make_compute_exp_tendency(model::AbstractModel)

Return a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_exp_tendency! should be compatible with SciMLBase.jl solvers.

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 ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)

Creates the computeexptendency!(dY,Y,p,t) function for the canopy component.

Since component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.

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make_compute_exp_tendency(canopy::CanopyModel)

Creates and returns the computeexptendency! for the CanopyModel.

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make_compute_exp_tendency(model::SoilCO2Model)

An extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_exp_tendency(model::PlantHydraulicsModel, _)

A function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.

Below, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).

Note that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.

To prevent dividing by zero, we change AI/(AI x dz)" to "AI/max(AI x dz, eps(FT))"

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ClimaLand.make_compute_imp_tendency — Function
make_compute_imp_tendency(model::RichardsModel)

An extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.

This function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.

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make_compute_imp_tendency(model::EnergyHydrology)

An extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.

This version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.

This has been written so as to work with Differential Equations.jl.

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make_compute_imp_tendency(model::AbstractModel)

Return a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.

compute_imp_tendency! should be compatible with SciMLBase.jl solvers.

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ClimaLand.make_update_aux — Function
make_update_aux(model::RichardsModel)

An extension of the function make_update_aux, for the Richardson- Richards equation.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::EnergyHydrology)

An extension of the function make_update_aux, for the integrated soil hydrology and energy model.

This function creates and returns a function which updates the auxiliary variables p.soil.variable in place.

This has been written so as to work with Differential Equations.jl.

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make_update_aux(model::BucketModel{FT}) where {FT}

Creates the update_aux! function for the BucketModel.

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make_update_aux(model::AbstractModel)

Return an update_aux! function that updates auxiliary parameters p.

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 ClimaLand.make_update_aux(canopy::CanopyModel{FT,
+                                              <:AutotrophicRespirationModel,
+                                              <:Union{BeerLambertModel, TwoStreamModel},
+                                              <:FarquharModel,
+                                              <:MedlynConductanceModel,
+                                              <:PlantHydraulicsModel,},
+                          ) where {FT}

Creates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.

Please note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.

The other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.

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make_update_aux(model::SoilCO2Model)

An extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.

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ClimaLand.make_update_boundary_fluxes — Function
make_update_boundary_fluxes(model::AbstractModel)

Return an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..

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make_update_boundary_fluxes(
+    land::LandHydrology{FT, SM, SW},
+) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}

A method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.

This function is called each ode function evaluation.

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make_update_boundary_fluxes(
+    land::SoilCanopyModel{FT, MM, SM, RM},
+) where {
+    FT,
+    MM <: Soil.Biogeochemistry.SoilCO2Model{FT},
+    SM <: Soil.RichardsModel{FT},
+    RM <: Canopy.CanopyModel{FT}
+    }

A method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models.

This function is called each ode function evaluation, prior to the tendency function evaluation.

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ClimaLand.make_set_initial_cache — Function
make_set_initial_cache(model::AbstractModel)

Returns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.

In principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.

Furthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.

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ClimaLand.make_update_drivers — Function
make_update_drivers(::AbstractClimaLandDrivers)

Creates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.

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make_update_drivers(driver_tuple)

Creates and returns a function which updates the forcing variables ("drivers"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.

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make_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.

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make_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.

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make_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.

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make_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}

Creates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.

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ClimaLand.prognostic_vars — Function
prognostic_vars(soil::RichardsModel)

A function which returns the names of the prognostic variables of RichardsModel.

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prognostic_vars(soil::EnergyHydrology)

A function which returns the names of the prognostic variables of EnergyHydrology.

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prognostic_vars(::SnowModel)

Returns the prognostic variable names of the snow model.

For this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.

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prognostic_vars(m::AbstractModel)

Returns the prognostic variable symbols for the model in the form of a tuple.

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_vars(::AbstractCanopyComponent)

Returns the prognostic vars of the canopy component passed in as an argument.

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prognostic_vars(canopy::CanopyModel)

Returns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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prognostic_vars(model::PlantHydraulicsModel)

A function which returns the names of the prognostic variables of the PlantHydraulicsModel.

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ClimaLand.prognostic_types — Function
prognostic_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the prognostic variables of EnergyHydrology.

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prognostic_types(::SnowModel{FT})

Returns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.

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prognostic_types(m::AbstractModel{FT}) where {FT}

Returns the prognostic variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

Here, the coordinate points are those returned by coordinates(model).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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ClimaLand.prognostic_types(::AbstractCanopyComponent)

Returns the prognostic types of the canopy component passed in as an argument.

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prognostic_types(canopy::CanopyModel)

Returns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the prognostic types for the PlantHydraulicsModel.

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ClimaLand.prognostic_domain_names — Function
prognostic_domain_names(::SnowModel)

Returns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is ":surface".

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prognosticdomainnames(m::AbstractModel)

Returns the domain names for the prognostic variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

Note that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.

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prognosticdomainnames(m::AbstractCanopyComponent)

Returns the domain names for the prognostic variables in the form of a tuple.

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ClimaLand.auxiliary_vars — Function
auxiliary_vars(soil::RichardsModel)

A function which returns the names of the auxiliary variables of RichardsModel.

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auxiliary_vars(soil::EnergyHydrology)

A function which returns the names of the auxiliary variables of EnergyHydrology.

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auxiliary_vars(::SnowModel)

Returns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.

Since the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.

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auxiliary_vars(m::AbstractModel)

Returns the auxiliary variable symbols for the model in the form of a tuple.

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ClimaLand.auxiliary_vars(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_vars(canopy::CanopyModel)

Returns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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auxiliary_vars(model::PlantHydraulicsModel)

A function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.

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ClimaLand.auxiliary_types — Function
auxiliary_types(soil::RichardsModel)

A function which returns the names of the auxiliary types of RichardsModel.

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auxiliary_types(soil::EnergyHydrology{FT}) where {FT}

A function which returns the types of the auxiliary variables of EnergyHydrology.

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auxiliary_types(m::AbstractModel{FT}) where {FT}

Returns the auxiliary variable types for the model in the form of a tuple.

Types provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include

  • Float64, Float32 for scalar variables (a scalar value at each

coordinate point)

  • SVector{k,Float64} for a mutable but statically sized array of

length k at each coordinate point.

  • Note that Arrays, MVectors are not isbits and cannot be used.

Here, the coordinate points are those returned by coordinates(model).

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ClimaLand.auxiliary_types(::AbstractCanopyComponent)

Returns the auxiliary types of the canopy component passed in as an argument.

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auxiliary_types(canopy::CanopyModel)

Returns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.

This relies on the propertynames of CanopyModel being the same as those returned by canopy_components.

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ClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}

Defines the auxiliary types for the PlantHydraulicsModel.

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ClimaLand.auxiliary_domain_names — Function
auxiliary_domain_names(soil::RichardsModel)

A function which returns the names of the auxiliary domain names of RichardsModel.

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auxiliarydomainnames(m::AbstractModel)

Returns the domain names for the auxiliary variables in the form of a tuple.

Examples: (:surface, :surface, :subsurface).

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auxiliarydomainnames(m::AbstractCanopyComponent)

Returns the domain names for the auxiliary variables in the form of a tuple.

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ClimaLand.initialize_prognostic — Function
initialize_prognostic(model::AbstractModel, state::NamedTuple)

Returns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.

If a model has no prognostic variables, the returned FieldVector contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.

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initialize_prognostic(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_prognostic(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input state is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.

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ClimaLand.initialize_auxiliary — Function
initialize_auxiliary(model::AbstractModel, state::NamedTuple)

Returns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.

If a model has no auxiliary variables, the returned NamedTuple contains only an empty array.

The input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.

Adjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.

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initialize_auxiliary(
+    component::AbstractCanopyComponent,
+    state,
+)

Creates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.

The input state is usually a ClimaCore Field object.

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initialize_auxiliary(
+    model::CanopyModel{FT},
+    coords,
+) where {FT}

Creates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.

The input coords is usually a ClimaCore Field object.

This function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.

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ClimaLand.initialize — Function
initialize(model::AbstractModel)

Creates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.

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ClimaLand.name — Function
name(model::AbstractModel)

Returns a symbol of the model component name, e.g. :soil or :vegetation.

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ClimaLand.AbstractBC — Type
AbstractBC

An abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.

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ClimaLand.source! — Function
 source!(dY::ClimaCore.Fields.FieldVector,
+         src::PhaseChange{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Computes the source terms for phase change.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::SoilSublimation{FT},
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple,
+         model
+         )

Updates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.

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 source!(dY::ClimaCore.Fields.FieldVector,
+         src::AbstractSource,
+         Y::ClimaCore.Fields.FieldVector,
+         p::NamedTuple
+         )::ClimaCore.Fields.Field

A stub function, which is extended by ClimaLand.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                 src::RootExtraction,
+                 Y::ClimaCore.Fields.FieldVector,
+                 p::NamedTuple
+                 model::EnergyHydrology)

An extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.

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ClimaLand.source!(dY::ClimaCore.Fields.FieldVector,
+                      src::MicrobeProduction,
+                      Y::ClimaCore.Fields.FieldVector,
+                      p::NamedTuple,
+                      params)

A method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.

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ClimaLand.source!(
+    dY::ClimaCore.Fields.FieldVector,
+    src::TOPMODELSubsurfaceRunoff,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    model::AbstractSoilModel{FT},
+) where {FT}

Adjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.

The sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).

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ClimaLand.AbstractBoundary — Type
AbstractBoundary

An abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).

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ClimaLand.TopBoundary — Type
TopBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the top boundary.

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ClimaLand.BottomBoundary — Type
BottomBoundary{} <: AbstractBoundary{}

A simple object which should be passed into a function to indicate that we are considering the bottom boundary.

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ClimaLand.boundary_flux — Function
boundary_flux(bc::WaterFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(bc::RichardsAtmosDrivenFluxBC,
+                       boundary::ClimaLand.AbstractBoundary,
+                       model::RichardsModel{FT},
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field where {FT}

A method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.

If model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.

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boundary_flux(rre_bc::MoistureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.

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boundary_flux(bc::FreeDrainage,
+                       boundary::ClimaLand.BottomBoundary,
+                       model::AbstractSoilModel,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which enforces free drainage at the bottom of the domain.

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boundary_flux(bc::HeatFluxBC,  _...)::ClimaCore.Fields.Field

A method of boundary fluxes which returns the desired flux.

We add a field of zeros in order to convert the bc (float) into a field.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.TopBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.

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boundary_flux(heat_bc::TemperatureStateBC,
+                       ::ClimaLand.BottomBoundary,
+                       model::EnergyHydrology,
+                       Δz::ClimaCore.Fields.Field,
+                       Y::ClimaCore.Fields.FieldVector,
+                       p::NamedTuple,
+                       t,
+                       )::ClimaCore.Fields.Field

A method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.

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boundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field

A function which returns the correct boundary flux given any boundary condition (BC).

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function ClimaLand.boundary_flux(
+    bc::RunoffBC,
+    ::TopBoundary,
+    model::Soil.RichardsModel,
+    Δz::FT,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+    params,
+)::ClimaCore.Fields.Field

Extension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).

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ClimaLand.boundary_flux(
+    bc::SoilCO2FluxBC,
+    boundary::ClimaLand.AbstractBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.

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ClimaLand.boundary_flux(
+bc::SoilCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.

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ClimaLand.boundary_flux(
+    bc::SoilCO2StateBC,
+    boundary::ClimaLand.BottomBoundary,
+    Δz::ClimaCore.Fields.Field,
+    Y::ClimaCore.Fields.FieldVector,
+    p::NamedTuple,
+    t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.

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ClimaLand.boundary_flux(
+bc::AtmosCO2StateBC,
+boundary::ClimaLand.TopBoundary,
+Δz::ClimaCore.Fields.Field,
+Y::ClimaCore.Fields.FieldVector,
+p::NamedTuple,
+t,
+)::ClimaCore.Fields.Field

A method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.

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ClimaLand.diffusive_flux — Function
diffusive_flux(K, x_2, x_1, Δz)

Calculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.

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ClimaLand.boundary_vars — Function
boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.

These variables are updated in place in boundary_flux.

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boundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                <:AbstractRadiativeDrivers,
+                                <:AbstractRunoffModel,
+                                }, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.

These variables are updated in place in soil_boundary_fluxes!.

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boundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_vars method for MoistureStateBC at the top boundary.

These variables are updated in place in boundary_flux.

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boundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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boundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)

The list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

For the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.

Use this function in the exact same way you would use auxiliary_vars.

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ClimaLand.boundary_var_domain_names — Function
boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                          <:Runoff.AbstractRunoffModel,
+                                          },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,
+                                              <:AbstractRadiativeDrivers,
+                                              <:AbstractRunoffModel,
+                                              },
+                          ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.

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boundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)

An extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.

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boundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)

The list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names.

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ClimaLand.boundary_var_types — Function
boundary_var_types(::RichardsModel{FT},
+                    ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,
+                                                <: Runoff.AbstractRunoffModel,
+                                              },
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.

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boundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.

Because we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.

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boundary_var_types(
+    ::EnergyHydrology{FT},
+    ::AtmosDrivenFluxBC{
+        <:PrescribedAtmosphere{FT},
+        <:AbstractRadiativeDrivers{FT},
+        <:AbstractRunoffModel,
+    }, ::ClimaLand.TopBoundary,
+) where {FT}

An extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.

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boundary_var_types(::RichardsModel{FT},
+                    ::MoistureStateBC,
+                    ::ClimaLand.TopBoundary,
+                    ) where {FT}

An extension of the boundary_var_types method for MoistureStateBC at the top boundary.

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boundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}

The list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.

Use in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.

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ClimaLand.make_jacobian — Function

make_jacobian(model::AbstractModel)

Creates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.

The default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.

Note that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.

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ClimaLand.make_compute_jacobian — Function
ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}

Creates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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ClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}

Creates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.

Using this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).

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make_compute_jacobian(model::AbstractModel)

Creates and returns a function which computes the entries of the Jacobian matrix W in place.

If the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.

The default is that no updates are required, but this function must be extended for models that use implicit timestepping.

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ClimaLand.set_dfluxBCdY! — Function
ClimaLand.set_dfluxBCdY!(
+    model::RichardsModel,
+    ::MoistureStateBC,
+    boundary::ClimaLand.TopBoundary,
+    Δz,
+    Y,
+    p,
+    t,

)

Computes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.

For Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.

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set_dfluxBCdY!(::AbstractModel,
+              ::AbstractBC,
+              ::AbstractBoundary,
+              _...)::Union{ClimaCore.Fields.FieldVector, Nothing}

A function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.

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ClimaLand.get_drivers — Function
ClimaLand.get_drivers(model::RichardsModel)

Returns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.

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ClimaLand.get_drivers(model::SnowModel)

Returns the driver variable symbols for the SnowModel.

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get_drivers(model::AbstractModel)

Returns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned

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Drivers

ClimaLand.PrescribedAtmosphere — Type
PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.

The default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.

Since not all models require co2 concentration, the default for that is nothing.

  • liquid_precip: Precipitation (m/s) function of time: positive by definition

  • snow_precip: Snow precipitation (m/s) function of time: positive by definition

  • T: Prescribed atmospheric temperature (function of time) at the reference height (K)

  • u: Prescribed wind speed (function of time) at the reference height (m/s)

  • q: Prescribed specific humidity (function of time) at the reference height (_)

  • P: Prescribed air pressure (function of time) at the reference height (Pa)

  • c_co2: CO2 concentration in atmosphere (mol/mol)

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • h: Reference height (m), relative to surface elevation

  • gustiness: Minimum wind speed (gustiness; m/s)

  • thermo_params: Thermodynamic parameters

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ClimaLand.PrescribedPrecipitation — Type
PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}

Container for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).

  • liquid_precip: Precipitation (m/s) function of time: positive by definition
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ClimaLand.PrescribedRadiativeFluxes — Type
PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}

Container for the prescribed radiation functions needed to drive land models in standalone mode.

  • SW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface

  • LW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface

  • ref_time: Reference time - the datetime corresponding to t=0 for the simulation

  • θs: Sun zenith angle, in radians

source
ClimaLand.turbulent_fluxes — Function
turbulent_fluxes(atmos::PrescribedAtmosphere,
+               model::AbstractModel,
+               Y::ClimaCore.Fields.FieldVector,
+               p::NamedTuple,
+               t
+               )

Computes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.

It solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.

source
turbulent_fluxes(atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                t)

Computes the turbulent surface fluxes terms at the ground for a coupled simulation.

source
ClimaLand.turbulent_fluxes_at_a_point — Function
turbulent_fluxes_at_a_point(T_sfc::FT,
+                            q_sfc::FT,
+                            ρ_sfc::FT,
+                            β_sfc::FT,
+                            h_sfc::FT,
+                            r_sfc::FT,
+                            d_sfc::FT,
+                            ts_in,
+                            u::FT,
+                            h::FT,
+                            gustiness::FT,
+                            z_0m::FT,
+                            z_0b::FT,
+                            earth_param_set::EP,
+                           ) where {FT <: AbstractFloat, P}

Computes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc

This returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.

source
Missing docstring.

Missing docstring for ClimaLand.radiative_fluxes_at_a_point. Check Documenter's build log for details.

ClimaLand.set_atmos_ts! — Function
set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)

Fill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.

source
ClimaLand.surface_air_density — Function
surface_air_density(
+                    atmos::PrescribedAtmosphere,
+                    model::AbstractModel,
+                    Y,
+                    p,
+                    t,
+                    T_sfc,
+                    )

A helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.

We additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.

Extending this function for your model is only necessary if you need to compute the air density in a different way.

source
ClimaLand.surface_air_density(
+                atmos::CoupledAtmosphere,
+                model::AbstractModel,
+                Y,
+                p,
+                _...,
+            )

Returns the air density at the surface in the case of a coupled simulation.

This requires the field ρ_sfc to be present in the cache p under the name of the model.

source
ClimaLand.surface_temperature — Function
ClimaLand.surface_temperature(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface temperature field of the EnergyHydrology soil model.

The assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.

source
ClimaLand.surface_temperature(model::SnowModel, Y, p)

a helper function which returns the surface temperature for the snow model, which is stored in the aux state.

source
ClimaLand.surface_temperature(model::BucketModel, Y, p)

a helper function which returns the surface temperature for the bucket model, which is stored in the aux state.

source
surface_temperature(model::AbstractModel, Y, p, t)

A helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_temperature(model::CanopyModel, Y, p, t)

A helper function which returns the temperature for the canopy model.

source
ClimaLand.surface_resistance — Function
ClimaLand.surface_resistance(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the surface resistance field of the EnergyHydrology soil model.

source
surface_resistance(model::AbstractModel, Y, p, t)

A helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

The default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.

source
ClimaLand.surface_resistance(
+    model::CanopyModel{FT},
+    Y,
+    p,
+    t,
+) where {FT}

Returns the stomatal resistance field of the CanopyModel canopy.

source
ClimaLand.surface_specific_humidity — Function
ClimaLand.surface_specific_humidity(
+    model::EnergyHydrology{FT},
+    Y,
+    p,
+    T_sfc,
+    ρ_sfc
+) where {FT}

Returns the surface specific humidity field of the EnergyHydrology soil model.

This models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.

Over the soil ice, the specific humidity is the saturated value.

The total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

Computes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.

source
ClimaLand.surface_specific_humidity(model::BucketModel, Y, p)

a helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.

source
surface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)

A helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.

Extending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.

source
ClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)

A helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.

source
Missing docstring.

Missing docstring for ClimaLand.make_update_drivers. Check Documenter's build log for details.

diff --git a/previews/PR754/Contributing/index.html b/previews/PR754/Contributing/index.html new file mode 100644 index 0000000000..6b2dc8257d --- /dev/null +++ b/previews/PR754/Contributing/index.html @@ -0,0 +1,2 @@ + +Contribution guide · ClimaLand.jl
GitHub

Contributing

Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.

Some useful tips

  • When you start working on a new feature branch, make sure you start from main by running: git checkout main.
  • Make sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.
  • When your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.

Continuous integration

After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.

Automated testing

Currently a number of checks are run per commit for a given PR.

  • JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.
  • Documentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.
  • Tests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.
diff --git a/previews/PR754/assets/documenter.js b/previews/PR754/assets/documenter.js new file mode 100644 index 0000000000..2e7840cea0 --- /dev/null +++ b/previews/PR754/assets/documenter.js @@ -0,0 +1,1074 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'mathjax': 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/MathJax.js?config=TeX-AMS_HTML', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "mathjax": { + "exports": "MathJax" + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['mathjax'], function(MathJax) { +MathJax.Hub.Config({ + "jax": [ + "input/TeX", + "output/HTML-CSS", + "output/NativeMML" + ], + "TeX": { + "equationNumbers": { + "autoNumber": "AMS" + }, + "Macros": {} + }, + "tex2jax": { + "inlineMath": [ + [ + "$", + "$" + ], + [ + "\\(", + "\\)" + ] + ], + "processEscapes": true + }, + "config": [ + "MMLorHTML.js" + ], + "extensions": [ + "MathMenu.js", + "MathZoom.js", + "TeX/AMSmath.js", + "TeX/AMSsymbols.js", + "TeX/autobold.js", + "TeX/autoload-all.js" + ] +} +); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on( + "click", + ".docstring .docstring-article-toggle-button", + function () { + let articleToggleTitle = "Expand docstring"; + const parent = $(this).parent(); + + debounce(() => { + if (parent.siblings("section").is(":visible")) { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + parent + .find("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + parent + .children(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + parent.siblings("section").slideToggle(); + }); + } +); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $("a.docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * RegX escape function from MDN + * Refer: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ + function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + searchstring = escapeRegExp(querystring); + let textindex = new RegExp(`${searchstring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(searchstring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + combineWith: "AND", + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` +
+ + Ctrl + + / to search + + esc to close +
+ `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/previews/PR754/assets/logo.svg b/previews/PR754/assets/logo.svg new file mode 100644 index 0000000000..3717cae0a7 --- /dev/null +++ b/previews/PR754/assets/logo.svg @@ -0,0 +1,55 @@ + + + + + + + + + + + + diff --git a/previews/PR754/assets/themes/catppuccin-frappe.css b/previews/PR754/assets/themes/catppuccin-frappe.css new file mode 100644 index 0000000000..54a5b77ea0 --- /dev/null +++ b/previews/PR754/assets/themes/catppuccin-frappe.css @@ -0,0 +1 @@ +html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis,html.theme--catppuccin-frappe .file-cta,html.theme--catppuccin-frappe .file-name,html.theme--catppuccin-frappe .select select,html.theme--catppuccin-frappe .textarea,html.theme--catppuccin-frappe .input,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-frappe .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid 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.is-focused.pagination-next,html.theme--catppuccin-frappe .is-focused.pagination-link,html.theme--catppuccin-frappe .is-focused.pagination-ellipsis,html.theme--catppuccin-frappe .is-focused.file-cta,html.theme--catppuccin-frappe .is-focused.file-name,html.theme--catppuccin-frappe .select select.is-focused,html.theme--catppuccin-frappe .is-focused.textarea,html.theme--catppuccin-frappe .is-focused.input,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--catppuccin-frappe .is-focused.button,html.theme--catppuccin-frappe .pagination-previous:active,html.theme--catppuccin-frappe .pagination-next:active,html.theme--catppuccin-frappe .pagination-link:active,html.theme--catppuccin-frappe .pagination-ellipsis:active,html.theme--catppuccin-frappe .file-cta:active,html.theme--catppuccin-frappe .file-name:active,html.theme--catppuccin-frappe .select select:active,html.theme--catppuccin-frappe .textarea:active,html.theme--catppuccin-frappe 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html.theme--catppuccin-frappe .content kbd.button.is-outlined{background-color:transparent;border-color:#414559;box-shadow:none;color:#414559}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#414559}html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-frappe .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #414559 #414559 !important}html.theme--catppuccin-frappe 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.button.is-primary.is-inverted.is-outlined.is-focused,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#8caaee}html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--catppuccin-frappe .button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-frappe 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.button.is-primary.is-light.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-hovered.docs-sourcelink{background-color:#e2eafb;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-primary.is-light:active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.docs-sourcelink:active,html.theme--catppuccin-frappe .button.is-primary.is-light.is-active,html.theme--catppuccin-frappe .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d7e1f9;border-color:transparent;color:#153a8e}html.theme--catppuccin-frappe .button.is-link{background-color:#8caaee;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:hover,html.theme--catppuccin-frappe .button.is-link.is-hovered{background-color:#81a2ec;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-link:focus,html.theme--catppuccin-frappe 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.button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-outlined:focus,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-focused{background-color:#8caaee;border-color:#8caaee;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #8caaee #8caaee !important}html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-link.is-outlined{background-color:transparent;border-color:#8caaee;box-shadow:none;color:#8caaee}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-link.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe 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.button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #81c8be #81c8be !important}html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-info.is-light{background-color:#f1f9f8;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:hover,html.theme--catppuccin-frappe .button.is-info.is-light.is-hovered{background-color:#e8f5f3;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe .button.is-info.is-light:active,html.theme--catppuccin-frappe .button.is-info.is-light.is-active{background-color:#dff1ef;border-color:transparent;color:#2d675f}html.theme--catppuccin-frappe 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.button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6d189 #a6d189 !important}html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-success.is-light{background-color:#f4f9f0;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:hover,html.theme--catppuccin-frappe .button.is-success.is-light.is-hovered{background-color:#edf6e7;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-success.is-light:active,html.theme--catppuccin-frappe .button.is-success.is-light.is-active{background-color:#e6f2de;border-color:transparent;color:#446a29}html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:hover,html.theme--catppuccin-frappe .button.is-warning.is-hovered{background-color:#e3c386;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus,html.theme--catppuccin-frappe .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning:focus:not(:active),html.theme--catppuccin-frappe .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(229,200,144,0.25)}html.theme--catppuccin-frappe .button.is-warning:active,html.theme--catppuccin-frappe .button.is-warning.is-active{background-color:#e0be7b;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning{background-color:#e5c890;border-color:#e5c890;box-shadow:none}html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-focused{background-color:#e5c890;border-color:#e5c890;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-frappe .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-outlined{background-color:transparent;border-color:#e5c890;box-shadow:none;color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#e5c890}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e5c890 #e5c890 !important}html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-frappe .button.is-warning.is-light{background-color:#fbf7ee;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:hover,html.theme--catppuccin-frappe .button.is-warning.is-light.is-hovered{background-color:#f9f2e4;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-warning.is-light:active,html.theme--catppuccin-frappe .button.is-warning.is-light.is-active{background-color:#f6edda;border-color:transparent;color:#78591c}html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:hover,html.theme--catppuccin-frappe .button.is-danger.is-hovered{background-color:#e57779;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus,html.theme--catppuccin-frappe .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger:focus:not(:active),html.theme--catppuccin-frappe .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(231,130,132,0.25)}html.theme--catppuccin-frappe .button.is-danger:active,html.theme--catppuccin-frappe .button.is-danger.is-active{background-color:#e36d6f;border-color:transparent;color:#fff}html.theme--catppuccin-frappe .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger{background-color:#e78284;border-color:#e78284;box-shadow:none}html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-frappe .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-focused{background-color:#e78284;border-color:#e78284;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-frappe .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-outlined{background-color:transparent;border-color:#e78284;box-shadow:none;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#e78284}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #e78284 #e78284 !important}html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-frappe .button.is-danger.is-light{background-color:#fceeee;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:hover,html.theme--catppuccin-frappe .button.is-danger.is-light.is-hovered{background-color:#fae3e4;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-danger.is-light:active,html.theme--catppuccin-frappe .button.is-danger.is-light.is-active{background-color:#f8d8d9;border-color:transparent;color:#9a1e20}html.theme--catppuccin-frappe .button.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-frappe .button.is-small:not(.is-rounded),html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .button.is-normal{font-size:1rem}html.theme--catppuccin-frappe .button.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .button.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .button[disabled],fieldset[disabled] html.theme--catppuccin-frappe .button{background-color:#737994;border-color:#626880;box-shadow:none;opacity:.5}html.theme--catppuccin-frappe .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-frappe .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-frappe .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-frappe .button.is-static{background-color:#292c3c;border-color:#626880;color:#838ba7;box-shadow:none;pointer-events:none}html.theme--catppuccin-frappe .button.is-rounded,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-frappe .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-frappe .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-frappe .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-frappe .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-frappe .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-frappe .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-frappe .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-frappe .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-frappe .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-frappe .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-frappe .buttons.has-addons .button:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-frappe .buttons.has-addons .button:focus,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused,html.theme--catppuccin-frappe .buttons.has-addons .button:active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-frappe .buttons.has-addons .button:focus:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-frappe .buttons.has-addons .button:active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-frappe .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-frappe .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .buttons.is-centered{justify-content:center}html.theme--catppuccin-frappe .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-frappe .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-frappe .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-frappe .button.is-responsive.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-frappe .button.is-responsive,html.theme--catppuccin-frappe .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-frappe .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-frappe .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-frappe .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-frappe .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-frappe .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-frappe .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-frappe .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-frappe .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-frappe .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-frappe .content li+li{margin-top:0.25em}html.theme--catppuccin-frappe .content p:not(:last-child),html.theme--catppuccin-frappe .content dl:not(:last-child),html.theme--catppuccin-frappe .content ol:not(:last-child),html.theme--catppuccin-frappe .content ul:not(:last-child),html.theme--catppuccin-frappe .content blockquote:not(:last-child),html.theme--catppuccin-frappe .content pre:not(:last-child),html.theme--catppuccin-frappe .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-frappe .content h1,html.theme--catppuccin-frappe .content h2,html.theme--catppuccin-frappe .content h3,html.theme--catppuccin-frappe .content h4,html.theme--catppuccin-frappe .content h5,html.theme--catppuccin-frappe .content h6{color:#c6d0f5;font-weight:600;line-height:1.125}html.theme--catppuccin-frappe .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-frappe .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-frappe .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-frappe .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-frappe .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-frappe .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-frappe .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-frappe .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-frappe .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-frappe .content blockquote{background-color:#292c3c;border-left:5px solid #626880;padding:1.25em 1.5em}html.theme--catppuccin-frappe .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-frappe .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-frappe .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-frappe .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-frappe .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-frappe .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-frappe .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-frappe .content ul ul ul{list-style-type:square}html.theme--catppuccin-frappe .content dd{margin-left:2em}html.theme--catppuccin-frappe .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-frappe .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-frappe .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-frappe .content figure img{display:inline-block}html.theme--catppuccin-frappe .content figure figcaption{font-style:italic}html.theme--catppuccin-frappe .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-frappe .content sup,html.theme--catppuccin-frappe .content sub{font-size:75%}html.theme--catppuccin-frappe .content table{width:100%}html.theme--catppuccin-frappe .content table td,html.theme--catppuccin-frappe .content table th{border:1px solid #626880;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-frappe .content table th{color:#b0bef1}html.theme--catppuccin-frappe .content table th:not([align]){text-align:inherit}html.theme--catppuccin-frappe .content table thead td,html.theme--catppuccin-frappe .content table thead th{border-width:0 0 2px;color:#b0bef1}html.theme--catppuccin-frappe .content table tfoot td,html.theme--catppuccin-frappe .content table tfoot th{border-width:2px 0 0;color:#b0bef1}html.theme--catppuccin-frappe .content table tbody tr:last-child td,html.theme--catppuccin-frappe .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-frappe .content .tabs li+li{margin-top:0}html.theme--catppuccin-frappe .content.is-small,html.theme--catppuccin-frappe #documenter 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select:not([multiple]){padding-right:2.5em}html.theme--catppuccin-frappe .select select[multiple]{height:auto;padding:0}html.theme--catppuccin-frappe .select select[multiple] option{padding:0.5em 1em}html.theme--catppuccin-frappe .select:not(.is-multiple):not(.is-loading):hover::after{border-color:#99d1db}html.theme--catppuccin-frappe .select.is-white:not(:hover)::after{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select{border-color:#fff}html.theme--catppuccin-frappe .select.is-white select:hover,html.theme--catppuccin-frappe .select.is-white select.is-hovered{border-color:#f2f2f2}html.theme--catppuccin-frappe .select.is-white select:focus,html.theme--catppuccin-frappe .select.is-white select.is-focused,html.theme--catppuccin-frappe .select.is-white select:active,html.theme--catppuccin-frappe .select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-frappe 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select.is-focused,html.theme--catppuccin-frappe .select.is-dark select:active,html.theme--catppuccin-frappe .content kbd.select select:active,html.theme--catppuccin-frappe .select.is-dark select.is-active,html.theme--catppuccin-frappe .content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(65,69,89,0.25)}html.theme--catppuccin-frappe .select.is-primary:not(:hover)::after,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-primary select:hover,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:hover,html.theme--catppuccin-frappe .select.is-primary select.is-hovered,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-primary select:focus,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:focus,html.theme--catppuccin-frappe .select.is-primary select.is-focused,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-focused,html.theme--catppuccin-frappe .select.is-primary select:active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select:active,html.theme--catppuccin-frappe .select.is-primary select.is-active,html.theme--catppuccin-frappe .docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-link:not(:hover)::after{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select{border-color:#8caaee}html.theme--catppuccin-frappe .select.is-link select:hover,html.theme--catppuccin-frappe .select.is-link select.is-hovered{border-color:#769aeb}html.theme--catppuccin-frappe .select.is-link select:focus,html.theme--catppuccin-frappe .select.is-link select.is-focused,html.theme--catppuccin-frappe .select.is-link select:active,html.theme--catppuccin-frappe .select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(140,170,238,0.25)}html.theme--catppuccin-frappe .select.is-info:not(:hover)::after{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select{border-color:#81c8be}html.theme--catppuccin-frappe .select.is-info select:hover,html.theme--catppuccin-frappe .select.is-info select.is-hovered{border-color:#6fc0b5}html.theme--catppuccin-frappe .select.is-info select:focus,html.theme--catppuccin-frappe .select.is-info select.is-focused,html.theme--catppuccin-frappe .select.is-info select:active,html.theme--catppuccin-frappe .select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(129,200,190,0.25)}html.theme--catppuccin-frappe 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a.navbar-item:focus,html.theme--catppuccin-frappe a.navbar-item:focus-within,html.theme--catppuccin-frappe a.navbar-item:hover,html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link:focus,html.theme--catppuccin-frappe .navbar-link:focus-within,html.theme--catppuccin-frappe .navbar-link:hover,html.theme--catppuccin-frappe .navbar-link.is-active{background-color:rgba(0,0,0,0);color:#8caaee}html.theme--catppuccin-frappe .navbar-item{flex-grow:0;flex-shrink:0}html.theme--catppuccin-frappe .navbar-item img{max-height:1.75rem}html.theme--catppuccin-frappe .navbar-item.has-dropdown{padding:0}html.theme--catppuccin-frappe .navbar-item.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-item.is-tab{border-bottom:1px solid transparent;min-height:4rem;padding-bottom:calc(0.5rem - 1px)}html.theme--catppuccin-frappe .navbar-item.is-tab:focus,html.theme--catppuccin-frappe .navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee}html.theme--catppuccin-frappe .navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#8caaee;border-bottom-style:solid;border-bottom-width:3px;color:#8caaee;padding-bottom:calc(0.5rem - 3px)}html.theme--catppuccin-frappe .navbar-content{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless){padding-right:2.5em}html.theme--catppuccin-frappe .navbar-link:not(.is-arrowless)::after{border-color:#fff;margin-top:-0.375em;right:1.125em}html.theme--catppuccin-frappe .navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}html.theme--catppuccin-frappe .navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}html.theme--catppuccin-frappe .navbar-divider{background-color:rgba(0,0,0,0.2);border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){html.theme--catppuccin-frappe 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a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#838ba7}html.theme--catppuccin-frappe .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#8caaee}.navbar.is-spaced html.theme--catppuccin-frappe .navbar-dropdown,html.theme--catppuccin-frappe .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--catppuccin-frappe .navbar-dropdown.is-right{left:auto;right:0}html.theme--catppuccin-frappe .navbar-divider{display:block}html.theme--catppuccin-frappe .navbar>.container .navbar-brand,html.theme--catppuccin-frappe .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--catppuccin-frappe .navbar>.container .navbar-menu,html.theme--catppuccin-frappe .container>.navbar 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body.has-spaced-navbar-fixed-bottom{padding-bottom:6rem}html.theme--catppuccin-frappe a.navbar-item.is-active,html.theme--catppuccin-frappe .navbar-link.is-active{color:#8caaee}html.theme--catppuccin-frappe a.navbar-item.is-active:not(:focus):not(:hover),html.theme--catppuccin-frappe .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--catppuccin-frappe .navbar-item.has-dropdown:focus .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown:hover .navbar-link,html.theme--catppuccin-frappe .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--catppuccin-frappe .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--catppuccin-frappe .pagination{font-size:1rem;margin:-.25rem}html.theme--catppuccin-frappe .pagination.is-small,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}html.theme--catppuccin-frappe .pagination.is-medium{font-size:1.25rem}html.theme--catppuccin-frappe .pagination.is-large{font-size:1.5rem}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-previous,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--catppuccin-frappe .pagination.is-rounded .pagination-next,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:9999px}html.theme--catppuccin-frappe .pagination.is-rounded .pagination-link,html.theme--catppuccin-frappe #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:9999px}html.theme--catppuccin-frappe .pagination,html.theme--catppuccin-frappe .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link{border-color:#626880;color:#8caaee;min-width:2.5em}html.theme--catppuccin-frappe .pagination-previous:hover,html.theme--catppuccin-frappe .pagination-next:hover,html.theme--catppuccin-frappe .pagination-link:hover{border-color:#737994;color:#99d1db}html.theme--catppuccin-frappe .pagination-previous:focus,html.theme--catppuccin-frappe .pagination-next:focus,html.theme--catppuccin-frappe .pagination-link:focus{border-color:#737994}html.theme--catppuccin-frappe .pagination-previous:active,html.theme--catppuccin-frappe .pagination-next:active,html.theme--catppuccin-frappe .pagination-link:active{box-shadow:inset 0 1px 2px 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768px){html.theme--catppuccin-frappe .pagination{flex-wrap:wrap}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next{flex-grow:1;flex-shrink:1}html.theme--catppuccin-frappe .pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{html.theme--catppuccin-frappe .pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}html.theme--catppuccin-frappe .pagination-previous,html.theme--catppuccin-frappe .pagination-next,html.theme--catppuccin-frappe .pagination-link,html.theme--catppuccin-frappe .pagination-ellipsis{margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination-previous{order:2}html.theme--catppuccin-frappe .pagination-next{order:3}html.theme--catppuccin-frappe .pagination{justify-content:space-between;margin-bottom:0;margin-top:0}html.theme--catppuccin-frappe .pagination.is-centered .pagination-previous{order:1}html.theme--catppuccin-frappe .pagination.is-centered 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rgba(10,10,10,0.25)}html.theme--catppuccin-latte .button.is-black:active,html.theme--catppuccin-latte .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-black[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:hover,html.theme--catppuccin-latte .button.is-light.is-hovered{background-color:#eee;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus,html.theme--catppuccin-latte .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light:focus:not(:active),html.theme--catppuccin-latte .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}html.theme--catppuccin-latte .button.is-light:active,html.theme--catppuccin-latte .button.is-light.is-active{background-color:#e8e8e8;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none}html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-outlined.is-focused{background-color:#f5f5f5;border-color:#f5f5f5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f5f5f5}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark,html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:hover,html.theme--catppuccin-latte .content kbd.button:hover,html.theme--catppuccin-latte .button.is-dark.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-hovered{background-color:#c5c9d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus,html.theme--catppuccin-latte .content kbd.button:focus,html.theme--catppuccin-latte .button.is-dark.is-focused,html.theme--catppuccin-latte .content kbd.button.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark:focus:not(:active),html.theme--catppuccin-latte .content kbd.button:focus:not(:active),html.theme--catppuccin-latte .button.is-dark.is-focused:not(:active),html.theme--catppuccin-latte .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(204,208,218,0.25)}html.theme--catppuccin-latte .button.is-dark:active,html.theme--catppuccin-latte .content kbd.button:active,html.theme--catppuccin-latte .button.is-dark.is-active,html.theme--catppuccin-latte .content kbd.button.is-active{background-color:#bdc2cf;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark[disabled],html.theme--catppuccin-latte .content kbd.button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button{background-color:#ccd0da;border-color:#ccd0da;box-shadow:none}html.theme--catppuccin-latte .button.is-dark.is-inverted,html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-focused{background-color:#ccd0da;border-color:#ccd0da;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-latte .button.is-dark.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-outlined{background-color:transparent;border-color:#ccd0da;box-shadow:none;color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ccd0da}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ccd0da #ccd0da !important}html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined[disabled],html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--catppuccin-latte .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-latte .button.is-primary,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:hover,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-focused,html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary:focus:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--catppuccin-latte .button.is-primary.is-focused:not(:active),html.theme--catppuccin-latte .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-primary:active,html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink:active,html.theme--catppuccin-latte .button.is-primary.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-active.docs-sourcelink{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-primary[disabled],html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.docs-sourcelink{background-color:#1e66f5;border-color:#1e66f5;box-shadow:none}html.theme--catppuccin-latte .button.is-primary.is-inverted,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-inverted:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-inverted.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-primary.is-inverted[disabled],html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-primary.is-inverted,fieldset[disabled] html.theme--catppuccin-latte .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-loading::after,html.theme--catppuccin-latte .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-primary.is-outlined,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#1e66f5;color:#1e66f5}html.theme--catppuccin-latte .button.is-primary.is-outlined:hover,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-hovered,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--catppuccin-latte .button.is-primary.is-outlined:focus,html.theme--catppuccin-latte .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--catppuccin-latte .button.is-primary.is-outlined.is-focused,html.theme--catppuccin-latte 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.button.is-primary.is-light.is-active,html.theme--catppuccin-latte .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d3e1fd;border-color:transparent;color:#0a52e1}html.theme--catppuccin-latte .button.is-link{background-color:#1e66f5;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:hover,html.theme--catppuccin-latte .button.is-link.is-hovered{background-color:#125ef4;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus,html.theme--catppuccin-latte .button.is-link.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-link:focus:not(:active),html.theme--catppuccin-latte .button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(30,102,245,0.25)}html.theme--catppuccin-latte .button.is-link:active,html.theme--catppuccin-latte .button.is-link.is-active{background-color:#0b57ef;border-color:transparent;color:#fff}html.theme--catppuccin-latte 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.button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-outlined{background-color:transparent;border-color:#40a02b;box-shadow:none;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#40a02b}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #40a02b #40a02b !important}html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-success.is-light{background-color:#f1fbef;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:hover,html.theme--catppuccin-latte .button.is-success.is-light.is-hovered{background-color:#e8f8e5;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-success.is-light:active,html.theme--catppuccin-latte .button.is-success.is-light.is-active{background-color:#e0f5db;border-color:transparent;color:#40a12b}html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:hover,html.theme--catppuccin-latte .button.is-warning.is-hovered{background-color:#d4871c;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus,html.theme--catppuccin-latte .button.is-warning.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning:focus:not(:active),html.theme--catppuccin-latte .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(223,142,29,0.25)}html.theme--catppuccin-latte .button.is-warning:active,html.theme--catppuccin-latte .button.is-warning.is-active{background-color:#c8801a;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning{background-color:#df8e1d;border-color:#df8e1d;box-shadow:none}html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-focused{background-color:#df8e1d;border-color:#df8e1d;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-outlined{background-color:transparent;border-color:#df8e1d;box-shadow:none;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#df8e1d}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #df8e1d #df8e1d !important}html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-warning.is-light{background-color:#fdf6ed;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:hover,html.theme--catppuccin-latte .button.is-warning.is-light.is-hovered{background-color:#fbf1e2;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-warning.is-light:active,html.theme--catppuccin-latte .button.is-warning.is-light.is-active{background-color:#faebd6;border-color:transparent;color:#9e6515}html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:hover,html.theme--catppuccin-latte .button.is-danger.is-hovered{background-color:#c60e36;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus,html.theme--catppuccin-latte .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger:focus:not(:active),html.theme--catppuccin-latte .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(210,15,57,0.25)}html.theme--catppuccin-latte .button.is-danger:active,html.theme--catppuccin-latte .button.is-danger.is-active{background-color:#ba0d33;border-color:transparent;color:#fff}html.theme--catppuccin-latte .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger{background-color:#d20f39;border-color:#d20f39;box-shadow:none}html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-latte .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-focused{background-color:#d20f39;border-color:#d20f39;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-latte .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-outlined{background-color:transparent;border-color:#d20f39;box-shadow:none;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#d20f39}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #d20f39 #d20f39 !important}html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-latte .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-latte .button.is-danger.is-light{background-color:#feecf0;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:hover,html.theme--catppuccin-latte .button.is-danger.is-light.is-hovered{background-color:#fde0e6;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-danger.is-light:active,html.theme--catppuccin-latte .button.is-danger.is-light.is-active{background-color:#fcd4dd;border-color:transparent;color:#e9113f}html.theme--catppuccin-latte .button.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-latte .button.is-small:not(.is-rounded),html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .button.is-normal{font-size:1rem}html.theme--catppuccin-latte .button.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .button.is-large{font-size:1.5rem}html.theme--catppuccin-latte .button[disabled],fieldset[disabled] html.theme--catppuccin-latte .button{background-color:#9ca0b0;border-color:#acb0be;box-shadow:none;opacity:.5}html.theme--catppuccin-latte .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-latte .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-latte .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-latte .button.is-static{background-color:#e6e9ef;border-color:#acb0be;color:#8c8fa1;box-shadow:none;pointer-events:none}html.theme--catppuccin-latte .button.is-rounded,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-latte .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-latte .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-latte .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-latte .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-latte .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-latte .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-latte .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-latte .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-latte .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-latte .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-latte .buttons.has-addons .button:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-latte .buttons.has-addons .button:focus,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused,html.theme--catppuccin-latte .buttons.has-addons .button:active,html.theme--catppuccin-latte .buttons.has-addons .button.is-active,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-latte .buttons.has-addons .button:focus:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-latte .buttons.has-addons .button:active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-latte .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-latte .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .buttons.is-centered{justify-content:center}html.theme--catppuccin-latte .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-latte .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-latte .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-latte .button.is-responsive.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-latte .button.is-responsive,html.theme--catppuccin-latte .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-latte .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-latte .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-latte .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-latte .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-latte .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-latte .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-latte .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-latte .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-latte .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-latte .content li+li{margin-top:0.25em}html.theme--catppuccin-latte .content p:not(:last-child),html.theme--catppuccin-latte .content dl:not(:last-child),html.theme--catppuccin-latte .content ol:not(:last-child),html.theme--catppuccin-latte .content ul:not(:last-child),html.theme--catppuccin-latte .content blockquote:not(:last-child),html.theme--catppuccin-latte .content pre:not(:last-child),html.theme--catppuccin-latte .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-latte .content h1,html.theme--catppuccin-latte .content h2,html.theme--catppuccin-latte .content h3,html.theme--catppuccin-latte .content h4,html.theme--catppuccin-latte .content h5,html.theme--catppuccin-latte .content h6{color:#4c4f69;font-weight:600;line-height:1.125}html.theme--catppuccin-latte .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-latte .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-latte .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-latte .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-latte .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-latte .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-latte .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-latte .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-latte .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-latte .content blockquote{background-color:#e6e9ef;border-left:5px solid #acb0be;padding:1.25em 1.5em}html.theme--catppuccin-latte .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-latte .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-latte .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-latte .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-latte .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-latte .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-latte .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-latte .content ul ul ul{list-style-type:square}html.theme--catppuccin-latte .content dd{margin-left:2em}html.theme--catppuccin-latte .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-latte .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-latte .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-latte .content figure img{display:inline-block}html.theme--catppuccin-latte .content figure figcaption{font-style:italic}html.theme--catppuccin-latte .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-latte .content sup,html.theme--catppuccin-latte .content sub{font-size:75%}html.theme--catppuccin-latte .content table{width:100%}html.theme--catppuccin-latte .content table td,html.theme--catppuccin-latte .content table th{border:1px solid #acb0be;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-latte .content table th{color:#41445a}html.theme--catppuccin-latte .content table 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#documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}html.theme--catppuccin-latte .file.is-normal{font-size:1rem}html.theme--catppuccin-latte .file.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .file.is-medium .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-large{font-size:1.5rem}html.theme--catppuccin-latte .file.is-large .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .file.has-name.is-empty .file-cta{border-radius:.4em}html.theme--catppuccin-latte .file.has-name.is-empty .file-name{display:none}html.theme--catppuccin-latte .file.is-boxed .file-label{flex-direction:column}html.theme--catppuccin-latte .file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}html.theme--catppuccin-latte .file.is-boxed .file-name{border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-boxed .file-icon{height:1.5em;width:1.5em}html.theme--catppuccin-latte .file.is-boxed .file-icon .fa{font-size:21px}html.theme--catppuccin-latte .file.is-boxed.is-small .file-icon .fa,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .file.is-boxed.is-medium .file-icon .fa{font-size:28px}html.theme--catppuccin-latte .file.is-boxed.is-large .file-icon .fa{font-size:35px}html.theme--catppuccin-latte .file.is-boxed.has-name .file-cta{border-radius:.4em .4em 0 0}html.theme--catppuccin-latte .file.is-boxed.has-name .file-name{border-radius:0 0 .4em .4em;border-width:0 1px 1px}html.theme--catppuccin-latte .file.is-centered{justify-content:center}html.theme--catppuccin-latte .file.is-fullwidth .file-label{width:100%}html.theme--catppuccin-latte .file.is-fullwidth .file-name{flex-grow:1;max-width:none}html.theme--catppuccin-latte .file.is-right{justify-content:flex-end}html.theme--catppuccin-latte .file.is-right .file-cta{border-radius:0 .4em .4em 0}html.theme--catppuccin-latte .file.is-right .file-name{border-radius:.4em 0 0 .4em;border-width:1px 0 1px 1px;order:-1}html.theme--catppuccin-latte .file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}html.theme--catppuccin-latte .file-label:hover .file-cta{background-color:#c5c9d5;color:#41445a}html.theme--catppuccin-latte .file-label:hover .file-name{border-color:#a5a9b8}html.theme--catppuccin-latte .file-label:active .file-cta{background-color:#bdc2cf;color:#41445a}html.theme--catppuccin-latte .file-label:active .file-name{border-color:#9ea2b3}html.theme--catppuccin-latte .file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}html.theme--catppuccin-latte .file-cta,html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-radius:.4em;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}html.theme--catppuccin-latte .file-cta{background-color:#ccd0da;color:#4c4f69}html.theme--catppuccin-latte .file-name{border-color:#acb0be;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:inherit;text-overflow:ellipsis}html.theme--catppuccin-latte .file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:.5em;width:1em}html.theme--catppuccin-latte .file-icon .fa{font-size:14px}html.theme--catppuccin-latte .label{color:#41445a;display:block;font-size:1rem;font-weight:700}html.theme--catppuccin-latte .label:not(:last-child){margin-bottom:0.5em}html.theme--catppuccin-latte .label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}html.theme--catppuccin-latte .label.is-medium{font-size:1.25rem}html.theme--catppuccin-latte .label.is-large{font-size:1.5rem}html.theme--catppuccin-latte .help{display:block;font-size:.75rem;margin-top:0.25rem}html.theme--catppuccin-latte .help.is-white{color:#fff}html.theme--catppuccin-latte .help.is-black{color:#0a0a0a}html.theme--catppuccin-latte .help.is-light{color:#f5f5f5}html.theme--catppuccin-latte .help.is-dark,html.theme--catppuccin-latte .content kbd.help{color:#ccd0da}html.theme--catppuccin-latte .help.is-primary,html.theme--catppuccin-latte .docstring>section>a.help.docs-sourcelink{color:#1e66f5}html.theme--catppuccin-latte .help.is-link{color:#1e66f5}html.theme--catppuccin-latte .help.is-info{color:#179299}html.theme--catppuccin-latte .help.is-success{color:#40a02b}html.theme--catppuccin-latte .help.is-warning{color:#df8e1d}html.theme--catppuccin-latte .help.is-danger{color:#d20f39}html.theme--catppuccin-latte .field:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.has-addons{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.has-addons .control:not(:last-child){margin-right:-1px}html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .button,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .button,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,html.theme--catppuccin-latte .field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]),html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]){z-index:3}html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .button:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .button.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):focus:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-focused:not([disabled]):hover,html.theme--catppuccin-latte .field.has-addons .control .select select:not([disabled]):active:hover,html.theme--catppuccin-latte .field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}html.theme--catppuccin-latte .field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.has-addons.has-addons-centered{justify-content:center}html.theme--catppuccin-latte .field.has-addons.has-addons-right{justify-content:flex-end}html.theme--catppuccin-latte .field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped{display:flex;justify-content:flex-start}html.theme--catppuccin-latte .field.is-grouped>.control{flex-shrink:0}html.theme--catppuccin-latte .field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--catppuccin-latte .field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-latte .field.is-grouped.is-grouped-centered{justify-content:center}html.theme--catppuccin-latte .field.is-grouped.is-grouped-right{justify-content:flex-end}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline{flex-wrap:wrap}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:last-child,html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}html.theme--catppuccin-latte .field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field.is-horizontal{display:flex}}html.theme--catppuccin-latte .field-label .label{font-size:inherit}@media screen and (max-width: 768px){html.theme--catppuccin-latte .field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}html.theme--catppuccin-latte .field-label.is-small,html.theme--catppuccin-latte #documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-normal{padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-medium{font-size:1.25rem;padding-top:0.375em}html.theme--catppuccin-latte .field-label.is-large{font-size:1.5rem;padding-top:0.375em}}html.theme--catppuccin-latte .field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--catppuccin-latte .field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}html.theme--catppuccin-latte .field-body .field{margin-bottom:0}html.theme--catppuccin-latte .field-body>.field{flex-shrink:1}html.theme--catppuccin-latte .field-body>.field:not(.is-narrow){flex-grow:1}html.theme--catppuccin-latte .field-body>.field:not(:last-child){margin-right:.75rem}}html.theme--catppuccin-latte .control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:inherit}html.theme--catppuccin-latte .control.has-icons-left .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-left .select:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,html.theme--catppuccin-latte .control.has-icons-right .select:focus~.icon{color:#ccd0da}html.theme--catppuccin-latte .control.has-icons-left .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-small~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-small~.icon{font-size:.75rem}html.theme--catppuccin-latte .control.has-icons-left .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-left .select.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,html.theme--catppuccin-latte .control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}html.theme--catppuccin-latte .control.has-icons-left 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kbd.button.is-outlined{background-color:transparent;border-color:#363a4f;box-shadow:none;color:#363a4f}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .content kbd.button.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--catppuccin-macchiato .content 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.button.is-info.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-focused{background-color:#8bd5ca;border-color:#8bd5ca;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-outlined{background-color:transparent;border-color:#8bd5ca;box-shadow:none;color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#8bd5ca}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #8bd5ca #8bd5ca !important}html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-info.is-light{background-color:#f0faf8;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:hover,html.theme--catppuccin-macchiato .button.is-info.is-light.is-hovered{background-color:#e7f6f4;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-info.is-light:active,html.theme--catppuccin-macchiato .button.is-info.is-light.is-active{background-color:#ddf3f0;border-color:transparent;color:#276d62}html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:hover,html.theme--catppuccin-macchiato .button.is-success.is-hovered{background-color:#9ed78c;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus,html.theme--catppuccin-macchiato .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success:focus:not(:active),html.theme--catppuccin-macchiato .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,218,149,0.25)}html.theme--catppuccin-macchiato .button.is-success:active,html.theme--catppuccin-macchiato .button.is-success.is-active{background-color:#96d382;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success{background-color:#a6da95;border-color:#a6da95;box-shadow:none}html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-focused{background-color:#a6da95;border-color:#a6da95;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-outlined{background-color:transparent;border-color:#a6da95;box-shadow:none;color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6da95}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6da95 #a6da95 !important}html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-success.is-light{background-color:#f2faf0;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:hover,html.theme--catppuccin-macchiato .button.is-success.is-light.is-hovered{background-color:#eaf6e6;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-success.is-light:active,html.theme--catppuccin-macchiato .button.is-success.is-light.is-active{background-color:#e2f3dd;border-color:transparent;color:#386e26}html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:hover,html.theme--catppuccin-macchiato .button.is-warning.is-hovered{background-color:#eccf94;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus,html.theme--catppuccin-macchiato .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning:focus:not(:active),html.theme--catppuccin-macchiato .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(238,212,159,0.25)}html.theme--catppuccin-macchiato .button.is-warning:active,html.theme--catppuccin-macchiato .button.is-warning.is-active{background-color:#eaca89;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning{background-color:#eed49f;border-color:#eed49f;box-shadow:none}html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-focused{background-color:#eed49f;border-color:#eed49f;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-macchiato .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-outlined{background-color:transparent;border-color:#eed49f;box-shadow:none;color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#eed49f}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #eed49f #eed49f !important}html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-macchiato .button.is-warning.is-light{background-color:#fcf7ee;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:hover,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-hovered{background-color:#faf2e3;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-warning.is-light:active,html.theme--catppuccin-macchiato .button.is-warning.is-light.is-active{background-color:#f8eed8;border-color:transparent;color:#7e5c16}html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:hover,html.theme--catppuccin-macchiato .button.is-danger.is-hovered{background-color:#eb7c8c;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus,html.theme--catppuccin-macchiato .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger:focus:not(:active),html.theme--catppuccin-macchiato .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(237,135,150,0.25)}html.theme--catppuccin-macchiato .button.is-danger:active,html.theme--catppuccin-macchiato .button.is-danger.is-active{background-color:#ea7183;border-color:transparent;color:#fff}html.theme--catppuccin-macchiato .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger{background-color:#ed8796;border-color:#ed8796;box-shadow:none}html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-macchiato .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-focused{background-color:#ed8796;border-color:#ed8796;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-macchiato .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-outlined{background-color:transparent;border-color:#ed8796;box-shadow:none;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ed8796}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ed8796 #ed8796 !important}html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-macchiato .button.is-danger.is-light{background-color:#fcedef;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:hover,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-hovered{background-color:#fbe2e6;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-danger.is-light:active,html.theme--catppuccin-macchiato .button.is-danger.is-light.is-active{background-color:#f9d7dc;border-color:transparent;color:#971729}html.theme--catppuccin-macchiato .button.is-small,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-macchiato .button.is-small:not(.is-rounded),html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-macchiato .button.is-normal{font-size:1rem}html.theme--catppuccin-macchiato .button.is-medium{font-size:1.25rem}html.theme--catppuccin-macchiato .button.is-large{font-size:1.5rem}html.theme--catppuccin-macchiato .button[disabled],fieldset[disabled] html.theme--catppuccin-macchiato .button{background-color:#6e738d;border-color:#5b6078;box-shadow:none;opacity:.5}html.theme--catppuccin-macchiato .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-macchiato .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-macchiato .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-macchiato .button.is-static{background-color:#1e2030;border-color:#5b6078;color:#8087a2;box-shadow:none;pointer-events:none}html.theme--catppuccin-macchiato .button.is-rounded,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-macchiato .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-macchiato .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-macchiato .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-macchiato .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-macchiato .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-macchiato .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-macchiato .buttons.are-small 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.container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-macchiato .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-macchiato .content li+li{margin-top:0.25em}html.theme--catppuccin-macchiato .content p:not(:last-child),html.theme--catppuccin-macchiato .content dl:not(:last-child),html.theme--catppuccin-macchiato .content ol:not(:last-child),html.theme--catppuccin-macchiato .content ul:not(:last-child),html.theme--catppuccin-macchiato .content blockquote:not(:last-child),html.theme--catppuccin-macchiato .content pre:not(:last-child),html.theme--catppuccin-macchiato .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-macchiato .content h1,html.theme--catppuccin-macchiato .content h2,html.theme--catppuccin-macchiato .content h3,html.theme--catppuccin-macchiato .content h4,html.theme--catppuccin-macchiato .content h5,html.theme--catppuccin-macchiato .content 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ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-macchiato .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-macchiato .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-macchiato .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-macchiato .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-macchiato .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-macchiato .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-macchiato .content ul ul ul{list-style-type:square}html.theme--catppuccin-macchiato .content dd{margin-left:2em}html.theme--catppuccin-macchiato .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-macchiato 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form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--catppuccin-macchiato .is-black.textarea,html.theme--catppuccin-macchiato .is-black.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}html.theme--catppuccin-macchiato .is-black.textarea:focus,html.theme--catppuccin-macchiato .is-black.input:focus,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:focus,html.theme--catppuccin-macchiato .is-black.is-focused.textarea,html.theme--catppuccin-macchiato .is-black.is-focused.input,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--catppuccin-macchiato .is-black.textarea:active,html.theme--catppuccin-macchiato .is-black.input:active,html.theme--catppuccin-macchiato #documenter .docs-sidebar form.docs-search>input.is-black:active,html.theme--catppuccin-macchiato 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html.theme--catppuccin-mocha .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-link.is-light{background-color:#ebf3fe;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:hover,html.theme--catppuccin-mocha .button.is-link.is-light.is-hovered{background-color:#dfebfe;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-link.is-light:active,html.theme--catppuccin-mocha .button.is-link.is-light.is-active{background-color:#d3e3fd;border-color:transparent;color:#063c93}html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:hover,html.theme--catppuccin-mocha .button.is-info.is-hovered{background-color:#8adfd1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus,html.theme--catppuccin-mocha .button.is-info.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info:focus:not(:active),html.theme--catppuccin-mocha .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(148,226,213,0.25)}html.theme--catppuccin-mocha .button.is-info:active,html.theme--catppuccin-mocha .button.is-info.is-active{background-color:#80ddcd;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info{background-color:#94e2d5;border-color:#94e2d5;box-shadow:none}html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-focused{background-color:#94e2d5;border-color:#94e2d5;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-outlined{background-color:transparent;border-color:#94e2d5;box-shadow:none;color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#94e2d5}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #94e2d5 #94e2d5 !important}html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-info.is-light{background-color:#effbf9;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:hover,html.theme--catppuccin-mocha .button.is-info.is-light.is-hovered{background-color:#e5f8f5;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-info.is-light:active,html.theme--catppuccin-mocha .button.is-info.is-light.is-active{background-color:#dbf5f1;border-color:transparent;color:#207466}html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:hover,html.theme--catppuccin-mocha .button.is-success.is-hovered{background-color:#9de097;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus,html.theme--catppuccin-mocha .button.is-success.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success:focus:not(:active),html.theme--catppuccin-mocha .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(166,227,161,0.25)}html.theme--catppuccin-mocha .button.is-success:active,html.theme--catppuccin-mocha .button.is-success.is-active{background-color:#93dd8d;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success{background-color:#a6e3a1;border-color:#a6e3a1;box-shadow:none}html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-focused{background-color:#a6e3a1;border-color:#a6e3a1;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-outlined{background-color:transparent;border-color:#a6e3a1;box-shadow:none;color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#a6e3a1}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #a6e3a1 #a6e3a1 !important}html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-success.is-light{background-color:#f0faef;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:hover,html.theme--catppuccin-mocha .button.is-success.is-light.is-hovered{background-color:#e7f7e5;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-success.is-light:active,html.theme--catppuccin-mocha .button.is-success.is-light.is-active{background-color:#def4dc;border-color:transparent;color:#287222}html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:hover,html.theme--catppuccin-mocha .button.is-warning.is-hovered{background-color:#f8dea3;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus,html.theme--catppuccin-mocha .button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning:focus:not(:active),html.theme--catppuccin-mocha .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(249,226,175,0.25)}html.theme--catppuccin-mocha .button.is-warning:active,html.theme--catppuccin-mocha .button.is-warning.is-active{background-color:#f7d997;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning{background-color:#f9e2af;border-color:#f9e2af;box-shadow:none}html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-focused{background-color:#f9e2af;border-color:#f9e2af;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--catppuccin-mocha .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-outlined{background-color:transparent;border-color:#f9e2af;box-shadow:none;color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f9e2af}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f9e2af #f9e2af !important}html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--catppuccin-mocha .button.is-warning.is-light{background-color:#fef8ec;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:hover,html.theme--catppuccin-mocha .button.is-warning.is-light.is-hovered{background-color:#fdf4e0;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-warning.is-light:active,html.theme--catppuccin-mocha .button.is-warning.is-light.is-active{background-color:#fcf0d4;border-color:transparent;color:#8a620a}html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:hover,html.theme--catppuccin-mocha .button.is-danger.is-hovered{background-color:#f27f9f;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus,html.theme--catppuccin-mocha .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger:focus:not(:active),html.theme--catppuccin-mocha .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(243,139,168,0.25)}html.theme--catppuccin-mocha .button.is-danger:active,html.theme--catppuccin-mocha .button.is-danger.is-active{background-color:#f17497;border-color:transparent;color:#fff}html.theme--catppuccin-mocha .button.is-danger[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger{background-color:#f38ba8;border-color:#f38ba8;box-shadow:none}html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--catppuccin-mocha .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-focused{background-color:#f38ba8;border-color:#f38ba8;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--catppuccin-mocha .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-outlined{background-color:transparent;border-color:#f38ba8;box-shadow:none;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:hover,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined:focus,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#f38ba8}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f38ba8 #f38ba8 !important}html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--catppuccin-mocha .button.is-danger.is-light{background-color:#fdedf1;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:hover,html.theme--catppuccin-mocha .button.is-danger.is-light.is-hovered{background-color:#fce1e8;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-danger.is-light:active,html.theme--catppuccin-mocha .button.is-danger.is-light.is-active{background-color:#fbd5e0;border-color:transparent;color:#991036}html.theme--catppuccin-mocha .button.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--catppuccin-mocha .button.is-small:not(.is-rounded),html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .button.is-normal{font-size:1rem}html.theme--catppuccin-mocha .button.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .button.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .button[disabled],fieldset[disabled] html.theme--catppuccin-mocha .button{background-color:#6c7086;border-color:#585b70;box-shadow:none;opacity:.5}html.theme--catppuccin-mocha .button.is-fullwidth{display:flex;width:100%}html.theme--catppuccin-mocha .button.is-loading{color:transparent !important;pointer-events:none}html.theme--catppuccin-mocha .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--catppuccin-mocha .button.is-static{background-color:#181825;border-color:#585b70;color:#7f849c;box-shadow:none;pointer-events:none}html.theme--catppuccin-mocha .button.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--catppuccin-mocha .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--catppuccin-mocha .buttons .button{margin-bottom:0.5rem}html.theme--catppuccin-mocha .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--catppuccin-mocha .buttons:last-child{margin-bottom:-0.5rem}html.theme--catppuccin-mocha .buttons:not(:last-child){margin-bottom:1rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--catppuccin-mocha .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--catppuccin-mocha .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--catppuccin-mocha .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--catppuccin-mocha .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--catppuccin-mocha .buttons.has-addons .button:last-child{margin-right:0}html.theme--catppuccin-mocha .buttons.has-addons .button:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-hovered{z-index:2}html.theme--catppuccin-mocha .buttons.has-addons .button:focus,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused,html.theme--catppuccin-mocha .buttons.has-addons .button:active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected{z-index:3}html.theme--catppuccin-mocha .buttons.has-addons .button:focus:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-focused:hover,html.theme--catppuccin-mocha .buttons.has-addons .button:active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-active:hover,html.theme--catppuccin-mocha .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--catppuccin-mocha .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--catppuccin-mocha .buttons.is-centered{justify-content:center}html.theme--catppuccin-mocha .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--catppuccin-mocha .buttons.is-right{justify-content:flex-end}html.theme--catppuccin-mocha .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--catppuccin-mocha .button.is-responsive.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--catppuccin-mocha .button.is-responsive,html.theme--catppuccin-mocha .button.is-responsive.is-normal{font-size:.75rem}html.theme--catppuccin-mocha .button.is-responsive.is-medium{font-size:1rem}html.theme--catppuccin-mocha .button.is-responsive.is-large{font-size:1.25rem}}html.theme--catppuccin-mocha .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--catppuccin-mocha .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--catppuccin-mocha .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--catppuccin-mocha .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--catppuccin-mocha .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--catppuccin-mocha .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--catppuccin-mocha .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--catppuccin-mocha .content li+li{margin-top:0.25em}html.theme--catppuccin-mocha .content p:not(:last-child),html.theme--catppuccin-mocha .content dl:not(:last-child),html.theme--catppuccin-mocha .content ol:not(:last-child),html.theme--catppuccin-mocha .content ul:not(:last-child),html.theme--catppuccin-mocha .content blockquote:not(:last-child),html.theme--catppuccin-mocha .content pre:not(:last-child),html.theme--catppuccin-mocha .content table:not(:last-child){margin-bottom:1em}html.theme--catppuccin-mocha .content h1,html.theme--catppuccin-mocha .content h2,html.theme--catppuccin-mocha .content h3,html.theme--catppuccin-mocha .content h4,html.theme--catppuccin-mocha .content h5,html.theme--catppuccin-mocha .content h6{color:#cdd6f4;font-weight:600;line-height:1.125}html.theme--catppuccin-mocha .content h1{font-size:2em;margin-bottom:0.5em}html.theme--catppuccin-mocha .content h1:not(:first-child){margin-top:1em}html.theme--catppuccin-mocha .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--catppuccin-mocha .content h2:not(:first-child){margin-top:1.1428em}html.theme--catppuccin-mocha .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--catppuccin-mocha .content h3:not(:first-child){margin-top:1.3333em}html.theme--catppuccin-mocha .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--catppuccin-mocha .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--catppuccin-mocha .content h6{font-size:1em;margin-bottom:1em}html.theme--catppuccin-mocha .content blockquote{background-color:#181825;border-left:5px solid #585b70;padding:1.25em 1.5em}html.theme--catppuccin-mocha .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ol:not([type]){list-style-type:decimal}html.theme--catppuccin-mocha .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--catppuccin-mocha .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--catppuccin-mocha .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--catppuccin-mocha .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--catppuccin-mocha .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--catppuccin-mocha .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--catppuccin-mocha .content ul ul ul{list-style-type:square}html.theme--catppuccin-mocha .content dd{margin-left:2em}html.theme--catppuccin-mocha .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--catppuccin-mocha .content figure:not(:first-child){margin-top:2em}html.theme--catppuccin-mocha .content figure:not(:last-child){margin-bottom:2em}html.theme--catppuccin-mocha .content figure img{display:inline-block}html.theme--catppuccin-mocha .content figure figcaption{font-style:italic}html.theme--catppuccin-mocha .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--catppuccin-mocha .content sup,html.theme--catppuccin-mocha .content sub{font-size:75%}html.theme--catppuccin-mocha .content table{width:100%}html.theme--catppuccin-mocha .content table td,html.theme--catppuccin-mocha .content table th{border:1px solid #585b70;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--catppuccin-mocha .content table th{color:#b8c5ef}html.theme--catppuccin-mocha .content table th:not([align]){text-align:inherit}html.theme--catppuccin-mocha .content table thead td,html.theme--catppuccin-mocha .content table thead th{border-width:0 0 2px;color:#b8c5ef}html.theme--catppuccin-mocha .content table tfoot td,html.theme--catppuccin-mocha .content table tfoot th{border-width:2px 0 0;color:#b8c5ef}html.theme--catppuccin-mocha .content table tbody tr:last-child td,html.theme--catppuccin-mocha .content table tbody tr:last-child th{border-bottom-width:0}html.theme--catppuccin-mocha .content .tabs li+li{margin-top:0}html.theme--catppuccin-mocha .content.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--catppuccin-mocha .content.is-normal{font-size:1rem}html.theme--catppuccin-mocha .content.is-medium{font-size:1.25rem}html.theme--catppuccin-mocha .content.is-large{font-size:1.5rem}html.theme--catppuccin-mocha .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--catppuccin-mocha .icon.is-small,html.theme--catppuccin-mocha #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--catppuccin-mocha .icon.is-medium{height:2rem;width:2rem}html.theme--catppuccin-mocha .icon.is-large{height:3rem;width:3rem}html.theme--catppuccin-mocha .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--catppuccin-mocha .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--catppuccin-mocha .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--catppuccin-mocha .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--catppuccin-mocha div.icon-text{display:flex}html.theme--catppuccin-mocha .image,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--catppuccin-mocha .image img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--catppuccin-mocha .image img.is-rounded,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--catppuccin-mocha .image.is-fullwidth,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--catppuccin-mocha .image.is-square img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--catppuccin-mocha .image.is-square .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--catppuccin-mocha .image.is-1by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar 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.has-ratio,html.theme--catppuccin-mocha .image.is-5by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--catppuccin-mocha .image.is-5by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--catppuccin-mocha .image.is-16by9 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--catppuccin-mocha .image.is-16by9 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--catppuccin-mocha .image.is-2by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--catppuccin-mocha .image.is-2by1 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--catppuccin-mocha .image.is-3by1 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--catppuccin-mocha 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img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--catppuccin-mocha .image.is-3by5 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--catppuccin-mocha .image.is-9by16 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--catppuccin-mocha .image.is-9by16 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--catppuccin-mocha .image.is-1by2 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--catppuccin-mocha .image.is-1by2 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--catppuccin-mocha .image.is-1by3 img,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--catppuccin-mocha .image.is-1by3 .has-ratio,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}html.theme--catppuccin-mocha .image.is-square,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-square,html.theme--catppuccin-mocha .image.is-1by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}html.theme--catppuccin-mocha .image.is-5by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}html.theme--catppuccin-mocha .image.is-4by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--catppuccin-mocha .image.is-3by2,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--catppuccin-mocha .image.is-5by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--catppuccin-mocha .image.is-16by9,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--catppuccin-mocha .image.is-2by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--catppuccin-mocha .image.is-3by1,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--catppuccin-mocha .image.is-4by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--catppuccin-mocha .image.is-3by4,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--catppuccin-mocha .image.is-2by3,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--catppuccin-mocha .image.is-3by5,html.theme--catppuccin-mocha #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--catppuccin-mocha .image.is-9by16,html.theme--catppuccin-mocha #documenter 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.button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark 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.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark 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.button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-link.is-light{background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:hover,html.theme--documenter-dark .button.is-link.is-light.is-hovered{background-color:#e2fbf6;border-color:transparent;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:active,html.theme--documenter-dark 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.button.is-info.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#3c5dcd}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #3c5dcd #3c5dcd !important}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark 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.button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;color:#259a12}html.theme--documenter-dark .button.is-success.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-outlined.is-focused{background-color:#259a12;border-color:#259a12;color:#fff}html.theme--documenter-dark .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #259a12 #259a12 !important}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-success.is-light{background-color:#effded;color:#2ec016}html.theme--documenter-dark .button.is-success.is-light:hover,html.theme--documenter-dark 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rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#f4c72f;box-shadow:none;color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f4c72f #f4c72f !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fefaec;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fdf7e0;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fdf3d3;border-color:transparent;color:#8c6e07}html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#c13930;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(203,60,51,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#b7362e;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:#cb3c33;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#cb3c33;color:#cb3c33}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark 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html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fbefef;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#f8e6e5;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#f6dcda;border-color:transparent;color:#c03930}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 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.buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark 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img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 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.docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar 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Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. 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The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/previews/PR754/assets/warner.js b/previews/PR754/assets/warner.js new file mode 100644 index 0000000000..3f6f5d0083 --- /dev/null +++ b/previews/PR754/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. 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Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/previews/PR754/diagnostics/available_diagnostics/index.html b/previews/PR754/diagnostics/available_diagnostics/index.html new file mode 100644 index 0000000000..b140a14c3e --- /dev/null +++ b/previews/PR754/diagnostics/available_diagnostics/index.html @@ -0,0 +1,281 @@ + +Available diagnostics · ClimaLand.jl
GitHub

Available diagnostic variables

Autogenerate table of available diagnostics:

include("make_diagnostic_table.jl")
┌────────────┬─────────────────┬──────────┬──────────────────────────────────┬─────────────────┐
+│ Short name │ Long name       │ Units    │ Comments                         │ Standard name   │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ airp       │ Air pressure    │ Pa       │ The air pressure.                │ air_pressure    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ alpha      │ Albedo          │          │ The fraction of incoming         │ albedo          │
+│            │                 │          │ radiation reflected by the land  │                 │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ an         │ Leaf Net        │ mol CO2  │ Net photosynthesis (carbon       │ leaf_net_photos │
+│            │ Photosynthesis  │ m^-2     │ assimilation) of a leaf,         │ ynthesis        │
+│            │                 │ s^-1     │ computed for example by the      │                 │
+│            │                 │          │ Farquhar model.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ anir       │ Absorbed Near   │ mol      │ The amount of near infrared      │ absorbed_near_i │
+│            │ Infrared        │ photons  │ radiation absorbed by the        │ nfrared_radiati │
+│            │ Radiation       │ m^-2     │ canopy.                          │ on              │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ apar       │ Absorbed        │ mol      │ The amount of                    │ absorbed_photos │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ ynthetically_ac │
+│            │ lly Active      │ m^-2     │ radiation absorbed by the leaf.  │ tive_radiation  │
+│            │ Radiation       │ s^-1     │ The rest if reflected or         │                 │
+│            │                 │          │ transmitted.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ clhf       │ Canopy Latent   │ W m^-2   │ The energy used for canopy       │ canopy_latent_h │
+│            │ Heat Flux       │          │ transpiration.                   │ eat_flux        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ crae       │ Canopy          │ s m^-1   │ The canopy aerodynamic           │ canopy_aerodyna │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ mic_resistance  │
+│            │ Resistance      │          │ resistance (r_a) is a measure    │                 │
+│            │                 │          │ of how much the air near the     │                 │
+│            │                 │          │ Earth's surface resists the      │                 │
+│            │                 │          │ movement of water vapor and      │                 │
+│            │                 │          │ heat from the surface into the   │                 │
+│            │                 │          │ air.                             │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ cshf       │ Canopy          │ W m^-2   │ The energy used for canopy       │ canopy_sensible │
+│            │ Sensible Heat   │          │ temperature change.              │ _heat_flux      │
+│            │ Flux            │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ct         │ Canopy          │ K        │ Canopy temperature.              │ canopy_temperat │
+│            │ Temperature     │          │                                  │ ure             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ far        │ Root flux per   │ m s^-1   │ Flux of water volume per m^2 of  │ root_flux_per_g │
+│            │ ground area     │          │ root per second, multiplied by   │ round_area      │
+│            │                 │          │ the area index (root             │                 │
+│            │                 │          │ area/ground area).               │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gpp        │ Gross Primary   │ mol CO2  │ Net photosynthesis (carbon       │ gross_primary_p │
+│            │ Productivity    │ m^-2     │ assimilation) of the canopy.     │ roductivity     │
+│            │                 │ s^-1     │ This is equivalent to leaf net   │                 │
+│            │                 │          │ assimilation scaled to the       │                 │
+│            │                 │          │ canopy level.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ gs         │ Stomatal        │ mol H2O  │ The conductance of leaves. This  │ stomatal_conduc │
+│            │ Conductance     │ m^-2     │ depends on stomatal opening. It  │ tance           │
+│            │                 │ s^-1     │ varies with factors such as      │                 │
+│            │                 │          │ soil moisture or atmospheric     │                 │
+│            │                 │          │ water demand.                    │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ hr         │ Heterotrophic   │ mol      │ The CO2 efflux at the soil       │ heterotrophic_r │
+│            │ Respiration     │ m^-2     │ surface due to microbial         │ espiration      │
+│            │                 │ s^-1     │ decomposition of soil organic    │                 │
+│            │                 │          │ matter. This is not necessarily  │                 │
+│            │                 │          │ equal to CO2 production by       │                 │
+│            │                 │          │ microbes, as co2 diffusion       │                 │
+│            │                 │          │ through the soil pores takes     │                 │
+│            │                 │          │ time.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ infil      │ Infiltration    │ m s^-1   │ The flux of liquid water volume  │ infiltration    │
+│            │                 │          │ into the soil (m^3 of water per  │                 │
+│            │                 │          │ m^2 of ground per second).       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lai        │ Leaf area Index │ m^2 m^-2 │ The area index of leaves,        │ leaf_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ leaves per surface area of       │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lhf        │ Latent Heat     │ W m^-2   │ Exchange of energy at the        │ latent_heat_flu │
+│            │ Flux            │          │ land-atmosphere interface due    │ x               │
+│            │                 │          │ to water evaporation or          │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwd        │ Down Longwave   │ W m^-2   │ The down (in) longwave           │ down_longwave_r │
+│            │ Radiation       │          │ radiation at the surface.        │ adiation        │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ lwn        │ Net Longwave    │ W m^-2   │ The net (in minus out) longwave  │ net_longwave_ra │
+│            │ Radiation       │          │ radiation at the surface.        │ diation         │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ msf        │ Moisture        │          │ Sensitivity of plants            │ moisture_stress │
+│            │ Stress Factor   │          │ conductance to soil water        │ _factor         │
+│            │                 │          │ content. Unitless                │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ nir        │ Near Infrared   │ mol      │ The amount of near infrared      │ near_infrared_r │
+│            │ Radiation       │ photons  │ radiation reaching the canopy.   │ adiation        │
+│            │                 │ m^-2     │                                  │                 │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ par        │ Photosynthetica │ mol      │ The subset of total radiation    │ photosynthetica │
+│            │ lly Active      │ photons  │ that activates photosynthesis    │ lly_active_radi │
+│            │ Radiation       │ m^-2     │ reaching the canopy.             │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ qsfc       │ Surface         │          │ Ratio of water vapor mass to     │ surface_specifi │
+│            │ Specific        │          │ total moist air parcel mass.     │ c_humidity      │
+│            │ Humidity        │          │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ra         │ Autotrophic     │ mol CO2  │ The canopy autotrophic           │ autotrophic_res │
+│            │ Respiration     │ m^-2     │ respiration, the sum of leaves,  │ piration        │
+│            │                 │ s^-1     │ stems and roots respiration.     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rae        │ Aerodynamic     │ m s^-1   │ Effiency of turbulent transport  │ aerodynamic_res │
+│            │ Resistance      │          │ controlling the land-atmosphere  │ istance         │
+│            │                 │          │ exchange of sensible and latent  │                 │
+│            │                 │          │ heat.                            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rai        │ Root area Index │ m^2 m^-2 │ The area index of roots,         │ root_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ roots per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rain       │ Rainfall        │ m s^-1   │ Precipitation of liquid water    │ rainfall        │
+│            │                 │          │ volume (m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground per second).              │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rd         │ Leaf            │ mol CO2  │ Leaf respiration, called dark    │ leaf_dark_respi │
+│            │ Respiration     │ m^-2     │ respiration because usually      │ ration          │
+│            │                 │ s^-1     │ measured in the abscence of      │                 │
+│            │                 │          │ radiation.                       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rhosfc     │ Surface Air     │ kg m^−3  │ Density of air at the            │ surface_air_den │
+│            │ Density         │          │ land-atmosphere interface.       │ sity            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rn         │ Net Radiation   │ W m^-2   │ Difference between incoming and  │ net_radiation   │
+│            │                 │          │ outgoing shortwave and longwave  │                 │
+│            │                 │          │ radiation at the land surface.   │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rnir       │ Reflected Near  │ mol      │ The amount of near infrared      │ reflected_near_ │
+│            │ Infrared        │ photons  │ radiation reflected by the       │ infrared_radiat │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ion             │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ rpar       │ Reflected       │ mol      │ The amount of                    │ reflected_photo │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ synthetically_a │
+│            │ lly Active      │ m^-2     │ radiation reflected by leaves.   │ ctive_radiation │
+│            │ Radiation       │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sai        │ Stem area Index │ m^2 m^-2 │ The area index of stems,         │ stem_area_index │
+│            │                 │          │ expressed in surface area of     │                 │
+│            │                 │          │ stems per surface area of        │                 │
+│            │                 │          │ ground.                          │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scd        │ Soil CO2        │ m^2 s^-1 │ The diffusivity of CO2 in the    │ soil_co2_diffus │
+│            │ Diffusivity     │          │ porous phase of the soil.        │ ivity           │
+│            │                 │          │ Depends on soil texture,         │                 │
+│            │                 │          │ moisture, and temperature.       │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ scms       │ Soil CO2        │ kg C     │ The production of CO2 by         │ soil_co2_microb │
+│            │ Microbial       │ m^-3     │ microbes in the soil. Vary by    │ ial_source      │
+│            │ Source          │ s^-1     │ layers of soil depth. (depth     │                 │
+│            │                 │          │ resolved)                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sco2       │ Soil CO2        │ kg C m^3 │ Concentration of CO2 in the      │ soil_co2        │
+│            │                 │          │ porous air space of the soil.    │                 │
+│            │                 │          │ (depth resolved)                 │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shc        │ Soil Hydraulic  │ m s^-1   │ Soil hydraulic conductivity.     │ soil_hydraulic_ │
+│            │ Conductivity    │          │ (depth resolved)                 │ conductivity    │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ shf        │ Sensible Heat   │ W m^-2   │ Exchange of energy at the        │ sensible_heat_f │
+│            │ Flux            │          │ land-atmosphere interface due    │ lux             │
+│            │                 │          │ to temperature difference.       │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ si         │ Soil Ice        │ m^3 m^-3 │ The volume of soil ice per       │ soil_ice        │
+│            │                 │          │ volume of soil. (depth resolved) │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sie        │ Soil Internal   │ W m^-2   │ The energy per volume of soil.   │ soil_internal_e │
+│            │ Energy          │          │ (depth resolved)                 │ nergy           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sif        │ Solar Induced   │ W m^-2   │ The fluorescence of leaves       │ solar_induced_f │
+│            │ Fluorescence    │          │ induced by solar radiation at    │ luorescence     │
+│            │                 │          │ 755nm. This quantity is          │                 │
+│            │                 │          │ correlated with photosynthesis   │                 │
+│            │                 │          │ activity.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ snow       │ Snowfall        │ m s^-1   │ The precipitation of snow in     │ snowfall        │
+│            │                 │          │ liquid water volume (m^3 of      │                 │
+│            │                 │          │ water per m^2 of ground per      │                 │
+│            │                 │          │ second).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soc        │ Soil organic    │ kg C     │ Mass of organic carbon per       │ soil_organic_ca │
+│            │ carbon          │ m^-3     │ volume of soil. (depth resolved) │ rbon            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soillhf    │ Soil Latent     │ W m^-2   │ Soil latent heat flux, the       │ soil_Latent_Hea │
+│            │ Heat Flux       │          │ amount of liquid water           │ t_Flux          │
+│            │                 │          │ evaporated by the soil,          │                 │
+│            │                 │          │ expressed in energy units (W     │                 │
+│            │                 │          │ m^-2).                           │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrae    │ Soil            │ s m^-1   │ The soil aerodynamic             │ soil_aerodynami │
+│            │ Aerodynamic     │          │ resistance. Aerodynamic          │ c_resistance    │
+│            │ Resistance      │          │ resistance is a measure of how   │                 │
+│            │                 │          │ much the air near the Earth's    │                 │
+│            │                 │          │ surface resists the movement of  │                 │
+│            │                 │          │ water vapor and heat from the    │                 │
+│            │                 │          │ surface into the air.            │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilrn     │ Soil Net        │ W m^-2   │ Net radiation at the soil        │ soil_net_radiat │
+│            │ Radiation       │          │ surface.                         │ ion             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ soilshf    │ Soil Sensible   │ W m^-2   │ Soil sensible heat flux, the     │ soil_sensible_H │
+│            │ Heat Flux       │          │ amount of energy exchanged       │ eat_Flux        │
+│            │                 │          │ between the soil and atmosphere  │                 │
+│            │                 │          │ to change the temperature of     │                 │
+│            │                 │          │ the soil.                        │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ssfc       │ Snow Water      │ m        │ Snow at the soil surface,        │ snow_water_equi │
+│            │ Equivalent      │          │ expressed in water equivalent.   │ valent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ stc        │ Soil Thermal    │ W m^-1   │ Soil thermal conductivity.       │ soil_thermal_co │
+│            │ Conductivity    │ K^-1     │ (depth resolved)                 │ nductivity      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swc        │ Soil Water      │ m^3 m^-3 │ The volume of soil water per     │ soil_water_cont │
+│            │ Content         │          │ volume of soil. (depth resolved) │ ent             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swd        │ Short Longwave  │ W m^-2   │ The short (in) longwave          │ short_longwave_ │
+│            │ Radiation       │          │ radiation at the surface.        │ radiation       │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swn        │ Net Shortwave   │ W m^-2   │ The net (in minus out)           │ net_shortwave_r │
+│            │ Radiation       │          │ shortwave radiation at the       │ adiation        │
+│            │                 │          │ surface.                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ swp        │ Soil Water      │ Pa       │ Soil water potential. (depth     │ soil_water_pote │
+│            │ Potential       │          │ resolved)                        │ ntial           │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ sza        │ Solar Zenith    │          │ Solar zenith angle.              │ solar_zenith_an │
+│            │ Angle           │          │                                  │ gle             │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tnir       │ Transmitted     │ mol      │ The amount of near infrared      │ transmitted_nea │
+│            │ Near Infrared   │ photons  │ radiation transmitted by the     │ r_infrared_radi │
+│            │ Radiation       │ m^-2     │ canopy.                          │ ation           │
+│            │                 │ s^-1     │                                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tpar       │ Transmitted     │ mol      │ The amount of                    │ transmitted_pho │
+│            │ Photosynthetica │ photons  │ photosynthetically active        │ tosynthetically │
+│            │ lly Active      │ m^-2     │ radiation transmitted by leaves. │ _active_radiati │
+│            │ Radiation       │ s^-1     │                                  │ on              │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ trans      │ Canopy          │ m s^-1   │ The water evaporated from the    │ canopy_transpir │
+│            │ Transpiration   │          │ canopy due to leaf               │ ation           │
+│            │                 │          │ transpiration (flux of water     │                 │
+│            │                 │          │ volume, m^3 of water per m^2 of  │                 │
+│            │                 │          │ ground).                         │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsfc       │ Surface         │ K        │ Temperature of the land surface. │ surface_tempera │
+│            │ Temperature     │          │                                  │ ture            │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ tsoil      │ Soil            │ K        │ Soil temperature at multiple     │ soil_temperatur │
+│            │ temperature     │          │ soil depth. (depth resolved)     │ e               │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vcmax25    │ Vcmax25         │ mol CO2  │ The parameter vcmax at 25        │ vcmax25         │
+│            │                 │ m^-2     │ degree celsius. Important for    │                 │
+│            │                 │ s^-1     │ the Farquhar model of leaf       │                 │
+│            │                 │          │ photosynthesis.                  │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ vflux      │ Liquid water    │ m s^-1   │ Flux of water from the land      │ vapor_flux      │
+│            │ evaporation     │          │ surface to the atmosphere.       │                 │
+│            │                 │          │ E.g., evaporation or             │                 │
+│            │                 │          │ sublimation.                     │                 │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ ws         │ Wind Speed      │ m s^-1   │ The average wind speed.          │ wind_speed      │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsfc       │ Surface Water   │ m        │ Water at the soil surface.       │ surface_water_c │
+│            │ Content         │          │                                  │ ontent          │
+├────────────┼─────────────────┼──────────┼──────────────────────────────────┼─────────────────┤
+│ wsoil      │ subsurface      │ m        │ Soil water content.              │ subsurface_wate │
+│            │ Water Storage   │          │                                  │ r_storage       │
+└────────────┴─────────────────┴──────────┴──────────────────────────────────┴─────────────────┘
diff --git a/previews/PR754/diagnostics/developers_diagnostics/index.html b/previews/PR754/diagnostics/developers_diagnostics/index.html new file mode 100644 index 0000000000..27dc65540a --- /dev/null +++ b/previews/PR754/diagnostics/developers_diagnostics/index.html @@ -0,0 +1,44 @@ + +For developers · ClimaLand.jl
GitHub

ClimaLand Diagnostics: why and how

ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in "K" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.

Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,

  • Diagnostics.jl defines the module,
  • diagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function

add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.

  • define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.

add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).

  • compute methods are defined in a separate file, for example, bucket_compute_methods.jl.
  • standard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.
  • default_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).

will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.

The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.

Compute methods

Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:

@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.α_sfc)
+    else
+        out .= p.bucket.α_sfc
+    end
+end

It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:

@diagnostic_compute "albedo" BucketModel p.bucket.α\_sfc

The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.

Define diagnostics

Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:

add_diagnostic_variable!(
+        short_name = "alpha",
+        long_name = "Albedo",
+        standard_name = "albedo",
+        units = "",
+        compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),
+    )

Default diagnostics

For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,

function default_diagnostics(land_model::BucketModel, t_start; output_writer)
+
+    define_diagnostics!(land_model)
+
+    bucket_diagnostics = [
+        "alpha",
+        "rn",
+        "tsfc",
+        "qsfc",
+        "lhf",
+        "rae",
+        "shf",
+        "vflux",
+        "rhosfc",
+        "t",
+        "w",
+        "ws",
+        "sigmas",
+    ]
+
+    default_outputs =
+        hourly_averages(bucket_diagnostics...; output_writer, t_start)
+    return [default_outputs...]
+end

is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).

For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = ["gpp", "ct", "lai", "swc", "si"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.

Standard diagnostic frequencies

We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example

hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(
+    60 * 60 * one(t_start),
+    (+),
+    output_writer,
+    t_start,
+    short_names...;
+    pre_output_hook! = average_pre_output_hook!,
+)

will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.

diff --git a/previews/PR754/diagnostics/make_diagnostic_table.jl b/previews/PR754/diagnostics/make_diagnostic_table.jl new file mode 100644 index 0000000000..4839efd307 --- /dev/null +++ b/previews/PR754/diagnostics/make_diagnostic_table.jl @@ -0,0 +1,35 @@ +import ClimaLand as CL +using PrettyTables + +# Print all available diagnostics to an ASCII table + +CL.Diagnostics.define_diagnostics!(nothing) +short_names = [] +long_names = [] +units = [] +comments = [] +standard_names = [] +for d in values(CL.Diagnostics.ALL_DIAGNOSTICS) + push!(short_names, d.short_name) + push!(long_names, d.long_name) + push!(units, d.units) + push!(comments, d.comments) + push!(standard_names, d.standard_name) +end +i = sortperm(short_names) # indices of short_names sorted alphabetically +data = hcat( + short_names[i], + long_names[i], + units[i], + comments[i], + standard_names[i], +) +pretty_table( + data; + autowrap = true, + linebreaks = true, + columns_width = [10, 15, 8, 32, 15], # Width = 80 + body_hlines = collect(1:size(data)[1]), + header = ["Short name", "Long name", "Units", "Comments", "Standard name"], + alignment = :l, +) diff --git a/previews/PR754/diagnostics/users_diagnostics/index.html b/previews/PR754/diagnostics/users_diagnostics/index.html new file mode 100644 index 0000000000..f7c277ba3f --- /dev/null +++ b/previews/PR754/diagnostics/users_diagnostics/index.html @@ -0,0 +1,58 @@ + +For users · ClimaLand.jl
GitHub

Using ClimaLand Diagnostics when running a simulation

When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.

This is where ClimaLand Diagnostics comes in for users.

In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.

Default Diagnostics

Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:

define an output folder

output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path("base_output_dir/")

define a space

Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:

space = bucket_domain.space.subsurface

define your writter

Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:

nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)

providing the space and output_dir defined in steps 1. and 2.

make your diagnostics on your model, using your writter, and define a callback

Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:

t0 = 0 # the starting time of your simulation
+
+reference_date = DateTime(2024) # reference_date is the DateTime of your starting time
+
+diags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)
+
+diagnostic_handler =
+    ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)
+
+diag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)

Your diagnostics have now been written in netcdf files in your output folder.

Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.

Custom Diagnostics

When defining a custom diagnostic, follow these steps:

Define how to compute your diagnostic variable from your model state and cache.

For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.

function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)
+    if isnothing(out)
+        return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)
+    else
+        out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf
+    end
+end

Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,

@diagnostic_compute "latent_heat_flux" BucketModel p.bucket.turbulent_fluxes.lhf

Add that diagnostic(s) variable to your list of variables

 add_diagnostic_variable!(
+    short_name = "bor",
+    long_name = "Bowen ratio",
+    standard_name = "bowen_ratio",
+    units = "",
+    comments = "Ratio of sensible to latent heat flux.",
+    compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),
+)
+
+add_diagnostic_variable!(
+    short_name = "lhf",
+    long_name = "Latent Heat Flux",
+    standard_name = "latent_heat_flux",
+    units = "W m^-2",
+    comments = "Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.",
+    compute! = (out, Y, p, t) ->
+    compute_latent_heat_flux!(out, Y, p, t, land_model),
+)

Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.

seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(
+    90 * 24 * 60 * 60 * one(t_start),
+    max,
+    output_writer,
+    t_start,
+    short_names...,
+)

Define a function to return your ScheduledDiagnostics

Now, you can call your schedule with your variables.

my_custom_diagnostics = ["lhf", "bor"]
+
+diags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)

Analyze your simulation output

Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.

Here is an example of how to plot a variable:

import ClimaAnalysis
+
+import ClimaAnalysis.Visualize as viz
+
+import CairoMakie # the plotting package used by ClimaAnalysis
+
+simdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.
+
+var = get(simdir; "lhf") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.
+
+fig = CairoMakie.Figure() # creates an empty figure object
+
+viz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.
+
+CairoMakie.save(fig) # saves the figure in current working directory
diff --git a/previews/PR754/folderstructure/index.html b/previews/PR754/folderstructure/index.html new file mode 100644 index 0000000000..0f71934a98 --- /dev/null +++ b/previews/PR754/folderstructure/index.html @@ -0,0 +1,2 @@ + +Repository structure · ClimaLand.jl
GitHub

ClimaLand folder structure

ClimaLand home directory has 5 main folders:

  • docs: contains files to generate the documentation website.
  • experiments: contains simple runs of ClimaLand models.
  • parameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc.
  • src: contains the code of ClimaLand models.
  • test: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.

and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push.

  • .buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR.
  • .dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main).
  • .github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand.

as well as 5 files:

  • .gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git.
  • LICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code.
  • Project.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file.
  • README.md: This markdown file contains the info that you can read on ClimaLand GitHub web page

/docs folder

Julia packages are recommended to have a \docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.

The folder /docs contains:

  • a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation "contribution guide" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file.
  • a make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch.

Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl.

/experiments folder

The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified.

For example, /experiments/LSM/ozark/ contains:

  • ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).
  • ozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.
  • ozark_parameters.jl: In this file, parameters values are defined.
  • ozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm.
  • ozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.

/src folder

The /src folder contains the source code of ClimaLand models. It contains 3 folders:

  • sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.
  • standalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder.
  • integrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.

As well as one file:

  • ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase.
diff --git a/previews/PR754/generated/integrated/ozark_canopy_flux_test.png b/previews/PR754/generated/integrated/ozark_canopy_flux_test.png new file mode 100644 index 0000000000..3545594673 Binary files /dev/null and b/previews/PR754/generated/integrated/ozark_canopy_flux_test.png differ diff --git a/previews/PR754/generated/integrated/ozark_soil_plant_flux.png b/previews/PR754/generated/integrated/ozark_soil_plant_flux.png new file mode 100644 index 0000000000..5df1f84d8b Binary files /dev/null and b/previews/PR754/generated/integrated/ozark_soil_plant_flux.png differ diff --git a/previews/PR754/generated/integrated/ozark_soil_test.png b/previews/PR754/generated/integrated/ozark_soil_test.png new file mode 100644 index 0000000000..6589f9d47b Binary files /dev/null and b/previews/PR754/generated/integrated/ozark_soil_test.png differ diff --git a/previews/PR754/generated/integrated/soil_canopy_tutorial/index.html b/previews/PR754/generated/integrated/soil_canopy_tutorial/index.html new file mode 100644 index 0000000000..9ed7c43a4f --- /dev/null +++ b/previews/PR754/generated/integrated/soil_canopy_tutorial/index.html @@ -0,0 +1,338 @@ + +Coupled Canopy and Soil · ClimaLand.jl
GitHub

Coupling the CliMA Canopy and Soil Hydraulics Models

In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.

In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Soil.Biogeochemistry
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the domain for the model:

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4)
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Column(; zlim = (zmin, zmax), nelements = nelements);
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32) # m
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Setup the Coupled Canopy and Soil Physics Model

We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.

For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.

Define the parameters for the soil model and provide them to the model parameters struct:

Soil parameters

soil_ν = FT(0.5) # m3/m3
+soil_K_sat = FT(4e-7) # m/s
+soil_S_s = FT(1e-3) # 1/m
+soil_vg_n = FT(2.05) # unitless
+soil_vg_α = FT(0.04) # inverse meters
+θ_r = FT(0.067); # m3/m3

Soil heat transfer parameters

ν_ss_quartz = FT(0.1)
+ν_ss_om = FT(0.1)
+ν_ss_gravel = FT(0.0);
+z_0m_soil = FT(0.1)
+z_0b_soil = FT(0.1)
+soil_ϵ = FT(0.98)
+soil_α_PAR = FT(0.2)
+soil_α_NIR = FT(0.4)
+
+soil_domain = land_domain
+soil_ps = Soil.EnergyHydrologyParameters(
+    FT;
+    ν = soil_ν,
+    ν_ss_om = ν_ss_om,
+    ν_ss_quartz = ν_ss_quartz,
+    ν_ss_gravel = ν_ss_gravel,
+    hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),
+    K_sat = soil_K_sat,
+    S_s = soil_S_s,
+    θ_r = θ_r,
+    earth_param_set = earth_param_set,
+    z_0m = z_0m_soil,
+    z_0b = z_0b_soil,
+    emissivity = soil_ϵ,
+    PAR_albedo = soil_α_PAR,
+    NIR_albedo = soil_α_NIR,
+);
+
+soil_args = (domain = soil_domain, parameters = soil_ps)
+soil_model_type = Soil.EnergyHydrology{FT}
ClimaLand.Soil.EnergyHydrology{Float32}

For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.

soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}
+
+soilco2_ps = SoilCO2ModelParameters(FT);

soil microbes args

Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))
+
+soilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()
+soilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);
+soilco2_sources = (MicrobeProduction{FT}(),);
+
+soilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);
+
+soilco2_args = (;
+    boundary_conditions = soilco2_boundary_conditions,
+    sources = soilco2_sources,
+    domain = soil_domain,
+    parameters = soilco2_ps,
+);

Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.

Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.

canopy_component_types = (;
+    autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},
+    radiative_transfer = Canopy.TwoStreamModel{FT},
+    photosynthesis = Canopy.FarquharModel{FT},
+    conductance = Canopy.MedlynConductanceModel{FT},
+    hydraulics = Canopy.PlantHydraulicsModel{FT},
+);

Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.

autotrophic_respiration_args =
+    (; parameters = AutotrophicRespirationParameters(FT))
+
+radiative_transfer_args = (;
+    parameters = TwoStreamParameters(
+        FT;
+        G_Function = ConstantGFunction(FT(0.5)),
+        α_PAR_leaf = 0.1,
+        α_NIR_leaf = 0.45,
+        τ_PAR_leaf = 0.05,
+        τ_NIR_leaf = 0.25,
+        Ω = 0.69,
+    )
+)
+
+conductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))
+
+photosynthesis_args =
+    (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));
+
+K_sat_plant = FT(1.8e-8)
+RAI = (SAI + maxLAI) * f_root_to_shoot;

Note: LAIfunction was determined from data in the script we included above.

ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)
+function root_distribution(z::T; rooting_depth = FT(1.0)) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m
+end
+
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)
+
+plant_ν = FT(0.7)
+plant_S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = plant_ν,
+    S_s = plant_S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+)
+
+plant_hydraulics_args = (
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_midpoints = compartment_midpoints,
+    compartment_surfaces = compartment_surfaces,
+);

We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.

canopy_component_args = (;
+    autotrophic_respiration = autotrophic_respiration_args,
+    radiative_transfer = radiative_transfer_args,
+    photosynthesis = photosynthesis_args,
+    conductance = conductance_args,
+    hydraulics = plant_hydraulics_args,
+);

We also need to provide the shared parameter struct to the canopy.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+)
+canopy_domain = obtain_surface_domain(land_domain)
+canopy_model_args = (; parameters = shared_params, domain = canopy_domain);

We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.

land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)
+
+land = SoilCanopyModel{FT}(;
+    soilco2_type = soilco2_type,
+    soilco2_args = soilco2_args,
+    land_args = land_input,
+    soil_model_type = soil_model_type,
+    soil_args = soil_args,
+    canopy_component_types = canopy_component_types,
+    canopy_component_args = canopy_component_args,
+    canopy_model_args = canopy_model_args,
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.

Y, p, coords = initialize(land);
+exp_tendency! = make_exp_tendency(land);
+imp_tendency! = make_imp_tendency(land);
+jacobian! = make_jacobian(land);
+jac_kwargs =
+    (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);

We need to provide initial conditions for the soil and canopy hydraulics models:

Y.soil.ϑ_l = FT(0.4)
+Y.soil.θ_i = FT(0.0)
+T_0 = FT(288.7)
+ρc_s =
+    volumetric_heat_capacity.(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        land.soil.parameters.ρc_ds,
+        earth_param_set,
+    )
+Y.soil.ρe_int =
+    volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)
+
+Y.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air
+
+ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        plant_ν,
+        plant_S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .=
+        augmented_liquid_fraction.(plant_ν, S_l_ini[i])
+end;

Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.

t0 = Float64(150 * 3600 * 24)# start mid year
+N_days = 100
+tf = t0 + Float64(3600 * 24 * N_days)
+dt = Float64(30)
+n = 120
+saveat = Array(t0:(n * dt):tf)
+
+timestepper = CTS.ARS343()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Now set the initial values for the cache variables for the combined soil and plant model.

set_initial_cache! = make_set_initial_cache(land)
+set_initial_cache!(p, Y, t0);

Set the callbacks, which govern how often we save output, and how often we update the forcing data ("drivers")

sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+model_drivers = ClimaLand.get_drivers(land)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+updateat = Array(t0:1800:tf)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Carry out the simulation

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(
+    prob,
+    ode_algo;
+    dt = dt,
+    callback = cb,
+    adaptive = false,
+    saveat = saveat,
+);

Plotting

Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_canopy_flux_test.png");

Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.

plt1 = Plots.plot(size = (500, 700));
+ϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]
+plt1 = Plots.plot(
+    daily,
+    ϑ_l_10,
+    label = "10 cm",
+    xlabel = "Days",
+    ylabel = "SWC [m/m]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+    color = "blue",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],
+    label = "20cm",
+    color = "red",
+);
+
+plot!(
+    plt1,
+    daily,
+    [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],
+    label = "30cm",
+    color = "purple",
+);

Save the output:

savefig("ozark_soil_test.png");

And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:

root_stem_flux = [
+    sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for
+    k in 1:length(sol.t)
+]
+plt1 = Plots.plot(
+    daily,
+    root_stem_flux,
+    label = "soil-root-stem water flux",
+    ylabel = "Water flux[mm/day]",
+    xlim = [minimum(daily), maximum(daily)],
+    size = (500, 700),
+    margins = 10Plots.mm,
+);

And save the output

savefig("ozark_soil_plant_flux.png");

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/shared_utilities/driver_tutorial/index.html b/previews/PR754/generated/shared_utilities/driver_tutorial/index.html new file mode 100644 index 0000000000..0a9f5bca4e --- /dev/null +++ b/previews/PR754/generated/shared_utilities/driver_tutorial/index.html @@ -0,0 +1,49 @@ + +Intro to forced site-level runs · ClimaLand.jl
GitHub

Using atmospheric and radiative drivers

The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.

Types of forcing data

We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.

The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).

The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.

Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.

Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.

Creating site-level drivers for radiation

First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.

using Dates
+using Insolation # for computing zenith angle given lat, lon, time.
+using ClimaLand
+import ClimaLand.Parameters as LP
+import ClimaParams

Assume the local_datetime array is read in from the data file.

local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data

Timezone (offset of local time from UTC in hrs)

time_offset = 7;

Site latitude and longitude

lat = 38.7441; # degree
+long = -92.2000; # degree

Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time

ref_time = local_datetime[1] + Dates.Hour(time_offset);
+data_dt = 3600.0;
+seconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);

Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:

T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));
+LW_d = 5.67 * 10^(-8) .* T .^ 4;
+SW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);

Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.

LW_d = TimeVaryingInput(seconds, LW_d)
+SW_d = TimeVaryingInput(seconds, SW_d);

Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:

earth_param_set = LP.LandParameters(Float64);
+insol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation
+function zenith_angle(
+    t,
+    ref_time;
+    latitude = lat,
+    longitude = long,
+    insol_params = insol_params,
+)
+    current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC
+
+    d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(
+        current_datetime,
+        ref_time,
+        insol_params,
+    ))
+
+
+    return Insolation.instantaneous_zenith_angle(
+        d,
+        δ,
+        η_UTC,
+        longitude,
+        latitude,
+    )[1]
+end;

Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.

radiation = ClimaLand.PrescribedRadiativeFluxes(
+    Float64,
+    SW_d,
+    LW_d,
+    ref_time;
+    θs = zenith_angle,
+);

Updating the driver variables during the simulation

The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:

p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));

In order to update them, we can make use of default update functions:

update_radiation! = ClimaLand.make_update_drivers(radiation)
+t0 = seconds[1] # midnight local time
+update_radiation!(p, t0);
+@show(p.drivers);
p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])
+

During a simulation, the drivers are updated in place in p.drivers via a "callback", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:

updateat = collect(seconds[1]:(3600 * 3):seconds[end]);
+updatefunc = update_radiation!;
+cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);

This callback must then be provided to the simulation solve function.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Bucket/bucket_tutorial/index.html b/previews/PR754/generated/standalone/Bucket/bucket_tutorial/index.html new file mode 100644 index 0000000000..909cef7c4a --- /dev/null +++ b/previews/PR754/generated/standalone/Bucket/bucket_tutorial/index.html @@ -0,0 +1,145 @@ + +Introduction to the Land Bucket Model · ClimaLand.jl
GitHub

Introduction to the Land Bucket Model

The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.

This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.

At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.

In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.

All equation variables are defined immediately below. We have:

$\frac{d W}{dt} = -I,$

$\frac{d Ws}{dt} = -(P_{liq} + σM + (1-σ) E_{soil} - I),$

$\frac{d σS}{dt} = -(P_{snow} + σE_{snow} - σM),$

$ρc \frac{\partial T}{\partial t} = κ_{soil} \frac{\partial T}{\partial z}$

$F_{bot} = 0.0 = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{bot}}$

$(1-σ) (R_n+ SHF + LHF)_{soil} + σG_{undersnow} = -κ_{soil} \frac{\partial T}{\partial z}|_{z = z_{sfc}}$

$G_{undersnow} = (R_n+ SHF + LHF)_{snow} - F_{intosnow}$

$F_{intosnow} = -ρ_l L_{f,0} (P_{snow} + E_{snow}-M)$

$R_n = -(1-α)*SW↓ -LW↓ + σ_{SB} T_{sfc}^4$

where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.

For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.

Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.

Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.

As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have

$E_{soil} = β(W, W_f) E(q_{sat}(T_{sfc}, ρ_{sfc}; liquid),$

where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have

$E_{snow} = E(q_{sat}(T_{sfc}, ρ_{sfc}; ice).$

Simulating a standalone bucket model

First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.

import SciMLBase
+import ClimaTimeSteppers as CTS

We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.

using ClimaCore

We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.

import ClimaParams as CP

We also use Insolation to calculate solar zenith angle and solar insolation.

Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.

using ClimaLand.Bucket:
+    BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo
+using ClimaLand.Domains: coordinates, Column
+using ClimaLand:
+    initialize,
+    make_update_aux,
+    make_exp_tendency,
+    make_set_initial_cache,
+    PrescribedAtmosphere,
+    PrescribedRadiativeFluxes
+using ClimaUtilities.TimeVaryingInputs: TimeVaryingInput

We also want to plot the solution

using Plots

And we need to use the DateTime type to store reference times

using Dates
+
+FT = Float32;

As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:

import ClimaLand
+import ClimaLand.Parameters as LP
+earth_param_set = LP.LandParameters(FT);

Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.

soil_depth = FT(3.5);
+bucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);
+surface_space = bucket_domain.space.surface
ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}
+  Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])

Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.

α_bareground_func = (coordinate_point) -> 0.2;
+α_snow = FT(0.8);
+albedo =
+    PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);

The critical snow level setting the scale for when we interpolate between snow and surface albedo

σS_c = FT(0.2);

The field capacity of the soil

W_f = FT(0.15);

Roughness lengths (meters)

z_0m = FT(1e-2);
+z_0b = FT(1e-3);

Thermal parameters of soil

κ_soil = FT(0.7);
+ρc_soil = FT(2e6);

Snow melt timescale

τc = FT(3600);

Simulation start time, end time, and timestep

t0 = 0.0;
+tf = 7 * 86400;
+Δt = 3600.0;
+
+bucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);

The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.

ref_time = DateTime(2005);

To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Here we define the model drivers Prescribed atmospheric variables

Precipitation:

precip = (t) -> 0;
+snow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);

Diurnal temperature variations:

T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);

Constant otherwise:

u_atmos = (t) -> 3.0;
+q_atmos = (t) -> 0.005;
+h_atmos = FT(2);
+P_atmos = (t) -> 101325;

We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)

bucket_atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(snow_precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set,
+);

Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:

SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));
+LW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;
+bucket_rad = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:

model = BucketModel(
+    parameters = bucket_parameters,
+    domain = bucket_domain,
+    atmosphere = bucket_atmos,
+    radiation = bucket_rad,
+);

Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.

Like all ClimaLand models, we set up the state vector using initialize:

Y, p, coords = initialize(model);

We can inspect the prognostic and auxiliary variables of the model:

ClimaLand.prognostic_vars(model)
+Y.bucket |> propertynames
(:W, :T, :Ws, :σS)

The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.

ClimaLand.auxiliary_vars(model)
+p.bucket |> propertynames
(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)

Next is to set initial conditions.

Y.bucket.T .= FT(270);
+Y.bucket.W .= FT(0.05);
+Y.bucket.Ws .= FT(0.0);
+Y.bucket.σS .= FT(0.08);

We also set the initial values of the cache here:

set_initial_cache! = make_set_initial_cache(model);
+set_initial_cache!(p, Y, t0);

Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:

exp_tendency! = make_exp_tendency(model);

Now we choose our timestepping algorithm.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

Then we can set up the simulation and solve it:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)

saveat = collect(t0:Δt:tf);
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+saving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);
+updateat = copy(saveat)
+model_drivers = ClimaLand.get_drivers(model)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+
+sol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);

Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.

W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];
+Ws = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];
+σS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];
+T_sfc =
+    [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];
+evaporation = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]
+    for k in 1:length(sol.t)
+];
+R_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];

The turbulent energy flux is the sum of latent and sensible heat fluxes.

LHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for
+    k in 1:length(sol.t)
+];
+SHF = [
+    parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for
+    k in 1:length(sol.t)
+];
+turbulent_energy_flux = SHF .+ LHF
+
+plot(
+    sol.t ./ 86400,
+    W,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "W (m)",
+    title = "Land water storage (m)",
+)
+savefig("w.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png"

plot(
+    sol.t ./ 86400,
+    σS,
+    label = "",
+    xlabel = "time (days)",
+    ylabel = "σS (m)",
+    title = "Area weighted SWE (m) ",
+)
+savefig("swe.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png"

plot(
+    sol.t ./ 86400,
+    snow_precip.(sol.t),
+    label = "Net precipitation",
+    xlabel = "time (days)",
+    ylabel = "Flux (m/s)",
+    title = "Surface water fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, evaporation, label = "Sublimation/Evaporation")
+savefig("water_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png"

plot(
+    sol.t ./ 86400,
+    T_sfc,
+    title = "Surface Temperatures",
+    label = "Ground temperature",
+    xlabel = "time (days)",
+    ylabel = "T_sfc (K)",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, T_atmos.(sol.t), label = "Atmospheric Temperature")
+savefig("t.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png"

plot(
+    sol.t ./ 86400,
+    R_n,
+    label = "Net radiative flux",
+    xlabel = "time (days)",
+    ylabel = "Flux (W/m^2)",
+    title = "Surface energy fluxes",
+    legend = :bottomright,
+)
+plot!(sol.t ./ 86400, turbulent_energy_flux, label = "Turbulent fluxes")
+plot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = "Net flux")
+savefig("energy_f.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png"

References

[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Bucket/coupled_bucket/index.html b/previews/PR754/generated/standalone/Bucket/coupled_bucket/index.html new file mode 100644 index 0000000000..8666de14a3 --- /dev/null +++ b/previews/PR754/generated/standalone/Bucket/coupled_bucket/index.html @@ -0,0 +1,2 @@ + +Setting up a Coupled Simulation · ClimaLand.jl
GitHub

Setting up a Coupled Simulation

For more information about the bucket model, please see the bucket model tutorial.

This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.

Background

Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).

Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.

In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.

In our current setup, "passed back to the land model via the coupler" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.

In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.

Turbulent Surface Fluxes and Radiation

Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:

prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)

These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.

In the coupled case, we want different behavior. We have defined new $coupled$ types to use instead of the "prescribed" types:

struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end

Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end

similarily:

function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end

These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!

Surface air density

Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.

In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end

Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Bucket/energy_f.png b/previews/PR754/generated/standalone/Bucket/energy_f.png new file mode 100644 index 0000000000..2fe9f0d606 Binary files /dev/null and b/previews/PR754/generated/standalone/Bucket/energy_f.png differ diff --git a/previews/PR754/generated/standalone/Bucket/swe.png b/previews/PR754/generated/standalone/Bucket/swe.png new file mode 100644 index 0000000000..9201c070e6 Binary files /dev/null and b/previews/PR754/generated/standalone/Bucket/swe.png differ diff --git a/previews/PR754/generated/standalone/Bucket/t.png b/previews/PR754/generated/standalone/Bucket/t.png new file mode 100644 index 0000000000..387db53ad8 Binary files /dev/null and b/previews/PR754/generated/standalone/Bucket/t.png differ diff --git a/previews/PR754/generated/standalone/Bucket/w.png b/previews/PR754/generated/standalone/Bucket/w.png new file mode 100644 index 0000000000..b11d3a746a Binary files /dev/null and b/previews/PR754/generated/standalone/Bucket/w.png differ diff --git a/previews/PR754/generated/standalone/Bucket/water_f.png b/previews/PR754/generated/standalone/Bucket/water_f.png new file mode 100644 index 0000000000..a5ee08bbf4 Binary files /dev/null and b/previews/PR754/generated/standalone/Bucket/water_f.png differ diff --git a/previews/PR754/generated/standalone/Canopy/canopy_tutorial/index.html b/previews/PR754/generated/standalone/Canopy/canopy_tutorial/index.html new file mode 100644 index 0000000000..b5d7f8f1b4 --- /dev/null +++ b/previews/PR754/generated/standalone/Canopy/canopy_tutorial/index.html @@ -0,0 +1,202 @@ + +Standalone Canopy · ClimaLand.jl
GitHub

Introduction to the Canopy Model

This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.

The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.

ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.

The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.

The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.

Preliminary Setup

Load External Packages:

import SciMLBase
+using Plots
+using Statistics
+using Dates
+using Insolation

Load CliMA Packages and ClimaLand Modules:

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using StaticArrays
+using ClimaLand
+using ClimaLand.Domains: Point
+using ClimaLand.Canopy
+using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand
+import ClimaLand.Parameters as LP

Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:

const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Setup the Canopy Model

We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.

First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.

nelements = 10
+zmin = FT(-2)
+zmax = FT(0)
+f_root_to_shoot = FT(3.5)
+SAI = FT(0.00242)
+maxLAI = FT(4.2)
+plant_ν = FT(2.46e-4) # kg/m^2
+n_stem = Int64(1)
+n_leaf = Int64(1)
+h_stem = FT(9)
+h_leaf = FT(9.5)
+compartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]
+compartment_surfaces = [zmax, h_stem, h_stem + h_leaf]
+land_domain = Point(; z_sfc = FT(0.0))
ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}
+  Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))
  • We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also

read in the observed LAI and let that vary in time in a prescribed manner.

Use the data tools for reading FLUXNET data sets

include(
+    joinpath(pkgdir(ClimaLand), "experiments/integrated/fluxnet/data_tools.jl"),
+);

First provide some information about the site Timezone (offset from UTC in hrs)

time_offset = 7
7

Site latitude and longitude

lat = FT(38.7441) # degree
+long = FT(-92.2000) # degree
-92.2f0

Height of the sensor at the site

atmos_h = FT(32)
32.0f0

Provide the site site ID and the path to the data file:

site_ID = "US-MOz"
+data_link = "https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv"
+
+include(
+    joinpath(
+        pkgdir(ClimaLand),
+        "experiments/integrated/fluxnet/met_drivers_FLUXNET.jl",
+    ),
+);
[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.
+[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag
+[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present
+[ Info: Information: Data for LE_CORR is complete and no QC flag present
+[ Info: Information: Data for H_CORR is complete and no QC flag present
+[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value
+[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag
+[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag
+

Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.

z0_m = FT(2)
+z0_b = FT(0.2)
+
+shared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(
+    z0_m,
+    z0_b,
+    earth_param_set,
+);

For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:

ψ_soil0 = FT(0.0)
+
+soil_driver = PrescribedSoil(
+    FT;
+    root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),
+    ψ = t -> ψ_soil0,
+    α_PAR = FT(0.2),
+    α_NIR = FT(0.4),
+    T = t -> 298.0,
+    ϵ = FT(0.99),
+);

Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:

rt_params = TwoStreamParameters(
+    FT;
+    G_Function = ConstantGFunction(FT(0.5)),
+    α_PAR_leaf = FT(0.1),
+    α_NIR_leaf = FT(0.45),
+    τ_PAR_leaf = FT(0.05),
+    τ_NIR_leaf = FT(0.25),
+    Ω = FT(0.69),
+    λ_γ_PAR = FT(5e-7),
+    λ_γ_NIR = FT(1.65e-6),
+)
+
+rt_model = TwoStreamModel{FT}(rt_params);

Arguments for conductance model:

cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))
+
+stomatal_model = MedlynConductanceModel{FT}(cond_params);

Arguments for photosynthesis model:

photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))
+
+photosynthesis_model = FarquharModel{FT}(photo_params);

Arguments for autotrophic respiration model:

AR_params = AutotrophicRespirationParameters(FT)
+AR_model = AutotrophicRespirationModel{FT}(AR_params);

Arguments for plant hydraulics model are more complicated.

Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.

LAI = 4.2
+LAIfunction = (t) -> LAI
+SAI = FT(0.00242)
+f_root_to_shoot = FT(3.5)
+RAI = FT((SAI + LAI) * f_root_to_shoot)
+ai_parameterization =
+    PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)
+rooting_depth = FT(1.0);

Define the root distribution function p(z):

function root_distribution(z::T; rooting_depth = rooting_depth) where {T}
+    return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))
+end;

Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.

K_sat_plant = FT(1.8e-8)
+ψ63 = FT(-4 / 0.0098)
+Weibull_param = FT(4)
+a = FT(0.05 * 0.0098)
+
+conductivity_model =
+    PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)
+
+retention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);

Use these values to populate the parameters of the PlantHydraulics model:

ν = FT(0.7)
+S_s = FT(1e-2 * 0.0098)
+
+plant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;
+    ai_parameterization = ai_parameterization,
+    ν = ν,
+    S_s = S_s,
+    root_distribution = root_distribution,
+    conductivity_model = conductivity_model,
+    retention_model = retention_model,
+);

Define the remaining variables required for the plant hydraulics model.

plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;
+    parameters = plant_hydraulics_ps,
+    n_stem = n_stem,
+    n_leaf = n_leaf,
+    compartment_surfaces = compartment_surfaces,
+    compartment_midpoints = compartment_midpoints,
+);

Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.

canopy = ClimaLand.Canopy.CanopyModel{FT}(;
+    parameters = shared_params,
+    domain = land_domain,
+    autotrophic_respiration = AR_model,
+    radiative_transfer = rt_model,
+    photosynthesis = photosynthesis_model,
+    conductance = stomatal_model,
+    hydraulics = plant_hydraulics,
+    soil_driver = soil_driver,
+    atmos = atmos,
+    radiation = radiation,
+);
[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature
+

Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.

Y, p, coords = ClimaLand.initialize(canopy)
+exp_tendency! = make_exp_tendency(canopy);

Provide initial conditions for the canopy hydraulics model

ψ_stem_0 = FT(-1e5 / 9800)
+ψ_leaf_0 = FT(-2e5 / 9800)
+
+S_l_ini =
+    inverse_water_retention_curve.(
+        retention_model,
+        [ψ_stem_0, ψ_leaf_0],
+        ν,
+        S_s,
+    )
+
+for i in 1:2
+    Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])
+end;

Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

t0 = 0.0
+N_days = 364
+tf = t0 + 3600 * 24 * N_days
+dt = 225.0;

Initialize the cache variables for the canopy using the initial conditions and initial time.

set_initial_cache! = make_set_initial_cache(canopy)
+set_initial_cache!(p, Y, t0);

Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.

n = 16
+saveat = Array(t0:(n * dt):tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);

Create the callback function which updates the forcing variables, or drivers.

updateat = Array(t0:1800:tf)
+model_drivers = ClimaLand.get_drivers(canopy)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Select a timestepping algorithm and setup the ODE problem.

timestepper = CTS.RK4();
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),
+    Y,
+    (t0, tf),
+    p,
+);

Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Create some plots

We can now plot the data produced in the simulation. For example, GPP:

daily = sol.t ./ 3600 ./ 24
+model_GPP = [
+    parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
+    k in 1:length(sv.saveval)
+]
+
+plt1 = Plots.plot(size = (600, 700));
+Plots.plot!(
+    plt1,
+    daily,
+    model_GPP .* 1e6,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "GPP [μmol/mol]",
+);

Transpiration plot:

T = [
+    parent(sv.saveval[k].canopy.conductance.transpiration)[1] for
+    k in 1:length(sv.saveval)
+]
+T = T .* (1e3 * 24 * 3600)
+
+plt2 = Plots.plot(size = (500, 700));
+Plots.plot!(
+    plt2,
+    daily,
+    T,
+    label = "Model",
+    xlim = [minimum(daily), maximum(daily)],
+    xlabel = "days",
+    ylabel = "Vapor Flux [mm/day]",
+);

Show the two plots together:

Plots.plot(plt1, plt2, layout = (2, 1));

Save the output:

savefig("ozark_standalone_canopy_test.png");

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Canopy/ozark_standalone_canopy_test.png b/previews/PR754/generated/standalone/Canopy/ozark_standalone_canopy_test.png new file mode 100644 index 0000000000..7a17facc9c Binary files /dev/null and b/previews/PR754/generated/standalone/Canopy/ozark_standalone_canopy_test.png differ diff --git a/previews/PR754/generated/standalone/Snow/base_tutorial/index.html b/previews/PR754/generated/standalone/Snow/base_tutorial/index.html new file mode 100644 index 0000000000..627cb9604b --- /dev/null +++ b/previews/PR754/generated/standalone/Snow/base_tutorial/index.html @@ -0,0 +1,74 @@ + +Seasonal Snow Timeseries Generation with a Neural Network · ClimaLand.jl
GitHub

Seasonal Snow Timeseries Generation with a Neural Network

This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.

The updates of the neural snow model follow the equation

$\frac{dz}{dt} = \mathcal{M}\left(z, SWE, φ, R, v, T_{air}, P_{snow}\right),$

where

  • $t$ is the time (s),

  • $z$ is the snow depth (m),

  • $\mathcal{M}$ is the neural network,

  • $SWE$ is the Snow Water Equivalent, or the height of water if all the snow melted (m),

  • $φ$ is the relative humidity (0-1),

  • $R$ is the solar radiation (W/m²).

  • $v$ is the wind speed (W/m²).

  • $T_{air}$ is the air temperature (degrees C).

  • $P_{snow}$ is the water equivalent rate of snow precipitation (m/s).

The model is a 1D model to permit utilization over any desired grid resolution and shape.

We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.

We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN
┌ Warning: Package cuDNN not found in current path.
+│ - Run `import Pkg; Pkg.add("cuDNN")` to install the cuDNN package, then restart julia.
+│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.
+└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57
+

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools
+ModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;

and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.

using ClimaLand
+code_dir = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Snow")
+include(joinpath(code_dir, "display_tools.jl"));

Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter $n$ as outlined in the associated parameter, and the two loss function hyperparameters $n_1$, $n_2$.

n = 4
+n1 = 2
+n2 = 4;

We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the $\frac{dz}{dt}$ column.

pred_vars = [
+    :z,
+    :SWE,
+    :rel_hum_avg,
+    :sol_rad_avg,
+    :wind_speed_avg,
+    :air_temp_avg,
+    :dprecipdt_snow,
+]
+target = :dzdt;

Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.

nfeatures = length(pred_vars)
+z_idx = 1
+p_idx = 7;

We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.

training_data_download_link = "https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv"
+testing_data_download_link = "https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv"
+data_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)
+valdata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)
+Δt = Second(86400)
+hole_thresh = 5;

With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):

usedata = DataTools.prep_data(data_train);

After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of "0" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.

out_scale = maximum(abs.(usedata[!, target]))
+in_scales = std.(eachcol(select(usedata, pred_vars)))
+x_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);

We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.

model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)
+ps = ModelTools.get_model_ps(model);

As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.

ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)
+ModelTools.setoutscale!(model, 1.0);

With that, training is as simple as calling the trainmodel! function:

print("\nTraining model!\n")
+ModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);

+Training model!
+Epoch: 10 | training loss: 0.0028616574
+Epoch: 20 | training loss: 0.0029524094
+Epoch: 30 | training loss: 0.0027160626
+Epoch: 40 | training loss: 0.0026204009
+Epoch: 50 | training loss: 0.0026128984
+Epoch: 60 | training loss: 0.0025816862
+Epoch: 70 | training loss: 0.0026889986
+Epoch: 80 | training loss: 0.002563585
+Epoch: 90 | training loss: 0.0026516444
+Epoch: 100 | training loss: 0.0025791847
+

To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.

ModelTools.setoutscale!(model, out_scale)
+ModelTools.settimescale!(model, Dates.value(Δt));

For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):

Note that gaps in the data are shown as shaded regions on the plotted timeseries.

site_id = 1286
+sitedata = usedata[usedata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Slagamount Lakes, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot1.png",
+    display_plot = false,
+);

Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:

site_id = "1070" #string format for the testing ids is due to non-numerical testing site codes.
+sitedata = valdata[valdata[!, :id] .== site_id, :]
+true_series = sitedata[!, :z]
+pred_series, _, _ =
+    ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)
+ptitle = "Anchorage Hillside, Snow Depth (m)"
+siteplot(
+    ptitle,
+    sitedata[!, :date],
+    [true_series, pred_series],
+    ["Data", "Neural Model"],
+    [:black, :red],
+    savename = "base_tutorial_plot2.png",
+    display_plot = false,
+);

Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Snow/base_tutorial_plot1.png b/previews/PR754/generated/standalone/Snow/base_tutorial_plot1.png new file mode 100644 index 0000000000..7635fbd8ba Binary files /dev/null and b/previews/PR754/generated/standalone/Snow/base_tutorial_plot1.png differ diff --git a/previews/PR754/generated/standalone/Snow/base_tutorial_plot2.png b/previews/PR754/generated/standalone/Snow/base_tutorial_plot2.png new file mode 100644 index 0000000000..204339445e Binary files /dev/null and b/previews/PR754/generated/standalone/Snow/base_tutorial_plot2.png differ diff --git a/previews/PR754/generated/standalone/Snow/data_tutorial/index.html b/previews/PR754/generated/standalone/Snow/data_tutorial/index.html new file mode 100644 index 0000000000..4d36553df6 --- /dev/null +++ b/previews/PR754/generated/standalone/Snow/data_tutorial/index.html @@ -0,0 +1,132 @@ + +Scraping SNOTEL Data · ClimaLand.jl
GitHub

Scraping SNOTEL Data

This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.

We begin by importing all required packages:

using ClimaLand
+using DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN

The code lives in an extenson that we have to manually load. The extension can be loaded only if "CSV", "HTTP", "Flux", "StatsBase", "cuDNN" and "ClimaLand" are loaded.

DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;

We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.

metadata = DataTools.snotel_metadata();
+metacols = ["id", "name", "state", "elev", "lat", "lon"]
+DataFrames.rename!(metadata, Symbol.(metacols));

At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:

example_ID = 1030
+example_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]
+example_data = DataTools.scrape_site_paper(example_ID, example_state);

And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.

We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.

const inch2meter = 0.0254
+const kmphr2mps = 5.0 / 18.0
+
+filter_val = Dict{Symbol, Tuple{Real, Real}}(
+    :SWE => (0.0, 250.0),
+    :z => (0.0, 420.0),
+    :precip => (0.0, 250.0),
+    :rel_hum_avg => (10.0, 100.0),
+    :sol_rad_avg => (0.0, 1500.0),
+    :wind_speed_avg => (0.0, 216.0),
+    :air_temp_avg => (-40.0, 60.0),
+)
+
+scales = Dict{Symbol, Real}(
+    :SWE => inch2meter,
+    :z => inch2meter,
+    :precip => inch2meter,
+    :rel_hum_avg => 0.01,
+    :wind_speed_avg => kmphr2mps,
+);

We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. "start" refers to 1850-01-01 or the first available date, while "end" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:

  • * Indicates alternative handling of the rectify_daily_hourly() function.

  • ^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.

  • A Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.

  • T Requires a site that already has had the temperature bias correction at the portal level as of May 2024.

  • X Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.

good_stations = Dict{Int, Tuple{String, String}}(
+    #306 => ("start", "end"), #*
+    316 => ("start", "end"),
+    344 => ("start", "end"),
+    #=367 => ("start", "end"),
+    395 => ("start", "end"),
+    457 => ("start", "end"),
+    482 => ("start", "end"),
+    491 => ("start", "end"),
+    515 => ("start", "2023-06-02"), #X
+    532 => ("start", "end"),
+    551 => ("start", "end"),
+    571 => ("start", "end"),
+    599 => ("start", "end"),
+    608 => ("start", "end"),
+    613 => ("start", "end"),
+    641 => ("start", "end"), #A^
+    665 => ("start", "end"),
+    708 => ("start", "end"),
+    715 => ("start", "end"),
+    734 => ("start", "end"),
+    737 => ("start", "end"),
+    744 => ("start", "end"),
+    832 => ("start", "end"),
+    845 => ("start", "end"),
+    854 => ("start", "end"),
+    857 => ("start", "end"),
+    921 => ("start", "end"),
+    922 => ("start", "end"),
+    927 => ("start", "end"),
+    942 => ("start", "end"),
+    963 => ("start", "end"), #A^
+    969 => ("start", "end"),
+    974 => ("start", "end"),
+    978 => ("start", "end"), #*
+    1030 => ("start", "end"),
+    1035 => ("start", "end"), #A^
+    1053 => ("start", "end"),
+    1070 => ("start", "end"), #A^T
+    1083 => ("start", "end"),
+    1091 => ("start", "end"), #A^T
+    1092 => ("start", "end"), #A^T
+    1105 => ("start", "end"),
+    1122 => ("start", "end"), #*
+    1123 => ("start", "end"),
+    1159 => ("start", "end"),
+    1168 => ("start", "end"),
+    1170 => ("start", "end"),
+    1254 => ("start", "end"),
+    1286 => ("start", "end"),
+    2080 => ("start", "end"), #A^
+    2170 => ("start", "end"), #^
+    =#
+);

We then loop through each site to scrape and follow an automated data pipeline, consisting of:

  • Extracting the daily and hourly timeseries from the site
  • Applying the sensor bounds over each data timeseries (i.e. remove sensor error)
  • Converting the hourly dataset into a daily dataset
  • Coalescing the converted-hourly and daily data into one dataset
  • Scaling all data to the appropriate metric units
  • Restricting data to complete cases
  • Making the differential variables ( $\frac{dz}{dt}$, etc.)
  • Resetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip
  • Attaching appropriate metadata

A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.

allsites = Any[];
+for site in sort(collect(keys(good_stations)))
+    state = metadata[metadata[!, :id] .== site, :state][1]
+    start_date = good_stations[site][1]
+    end_date = good_stations[site][2]
+
+    hourly = DataTools.apply_bounds(
+        DataTools.sitedata_hourly(
+             site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+       filter_val,
+    )
+    hourly[!, :id] .= site
+    #hourly = DataTools.bcqc_hourly(hourly)
+    hourly_d = DataTools.hourly2daily(hourly)
+    #DataFrames.allowmissing!(hourly_d)
+    #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)
+   #hourly_d[sflags, :sol_rad_avg] .= missing
+
+    daily = DataTools.apply_bounds(
+        DataTools.sitedata_daily(
+            site,
+            state,
+            start = start_date,
+            finish = end_date,
+        ),
+        filter_val,
+    )
+    daily[!, :id] .= site
+    gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)
+    #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)
+    #gap_daily = DataTools.d_impute(gap_daily)
+    daily_scaled = DataTools.scale_cols(gap_daily, scales)
+    daily_clean = daily_scaled[completecases(daily_scaled), :]
+    daily_clean = DataTools.makediffs(daily_clean, Day(1))
+    good_vals = daily_clean[!, :dprecipdt] .>= 0.0
+    daily_clean[(!).(good_vals), :dprecipdt] .= 0.0
+    daily_clean = daily_clean[!, Not(:precip)]
+    #show(describe(daily_clean), allrows = true, allcols = true)
+    #print("\nSIZE: ", nrow(daily_clean), "\n")
+
+    daily_clean[!, :id] .= site
+    daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]
+    daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]
+    daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]
+
+    push!(allsites, daily_clean)
+end;

With the sites complete, we condense all sites into a single DataFrame,

totaldata = deepcopy(allsites[1])
+for site in allsites[2:end]
+    append!(totaldata, site)
+end

and a final CSV.write("data.csv", totaldata) call will save the file.

Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/boundary_conditions/index.html b/previews/PR754/generated/standalone/Soil/boundary_conditions/index.html new file mode 100644 index 0000000000..95e4248305 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/boundary_conditions/index.html @@ -0,0 +1,2 @@ + +Boundary conditions · ClimaLand.jl
GitHub

Boundary conditions for the soil model

In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.

Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.

Boundary conditions for Richards equation

  1. FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.

  2. WaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).

  3. MoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).

  4. RichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.

Boundary conditions for the soil heat equation

  1. HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).

  2. TemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).

Boundary conditions for the soil heat + water equations (EnergyHydrology model)

The full soil model requires boundary conditions for both Richards equation and the soil heat equation.

  1. WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).

  2. AtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/eq_moisture_plot.png b/previews/PR754/generated/standalone/Soil/eq_moisture_plot.png new file mode 100644 index 0000000000..df15e83379 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/eq_moisture_plot.png differ diff --git a/previews/PR754/generated/standalone/Soil/eq_temperature_plot.png b/previews/PR754/generated/standalone/Soil/eq_temperature_plot.png new file mode 100644 index 0000000000..f50685a410 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/eq_temperature_plot.png differ diff --git "a/previews/PR754/generated/standalone/Soil/equilibrium_test_\317\221_l.png" "b/previews/PR754/generated/standalone/Soil/equilibrium_test_\317\221_l.png" new file mode 100644 index 0000000000..e19d4c660e Binary files /dev/null and "b/previews/PR754/generated/standalone/Soil/equilibrium_test_\317\221_l.png" differ diff --git a/previews/PR754/generated/standalone/Soil/evaporation/index.html b/previews/PR754/generated/standalone/Soil/evaporation/index.html new file mode 100644 index 0000000000..187a200159 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/evaporation/index.html @@ -0,0 +1,229 @@ + +Coarse Sand Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.

Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.

ref_time = DateTime(2005) # required argument, but not used in this case
+SW_d = (t) -> 0
+LW_d = (t) -> 301.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 5
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)
+    Y.soil.θ_i .= 0
+    T = FT(296.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 13)
+dt = Float64(900.0)
900.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Define the tendency functions

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+evaporation_data =
+    ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();
+ref_soln_E = readdlm(evaporation_data, ',')
+ref_soln_E_350mm = ref_soln_E[2:end, 1:2]
+data_dates = ref_soln_E_350mm[:, 1]
+data_e = ref_soln_E_350mm[:, 2];
+
+fig = Figure(size = (800, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Evaporation rate (mm/d)",
+    title = "Bare soil evaporation",
+)
+CairoMakie.xlims!(minimum(data_dates), maximum(data_dates))
+CairoMakie.lines!(
+    ax,
+    FT.(data_dates),
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+ax = Axis(
+    fig[1, 2],
+    xlabel = "Mass (g)",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+A_col = π * (0.027)^2
+mass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col
+mass_loss =
+    [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]
+CairoMakie.lines!(
+    ax,
+    cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,
+    FT.(data_e),
+    label = "Data",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    mass_loss,
+    evap .* (1000 * 3600 * 24),
+    label = "Model",
+    color = :black,
+)
+save("evaporation_lehmann2008_fig8b.png", fig);

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/evaporation_gardner_fig1.png b/previews/PR754/generated/standalone/Soil/evaporation_gardner_fig1.png new file mode 100644 index 0000000000..4634fa4cf2 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/evaporation_gardner_fig1.png differ diff --git a/previews/PR754/generated/standalone/Soil/evaporation_gilat_loess/index.html b/previews/PR754/generated/standalone/Soil/evaporation_gilat_loess/index.html new file mode 100644 index 0000000000..ef31b3bb6f --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/evaporation_gilat_loess/index.html @@ -0,0 +1,374 @@ + +Gilat Loess Evaporation · ClimaLand.jl
GitHub

This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.

For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations

The same experiment is carried out 3 times

  1. No evaporation (zero flux boundary conditions)
  2. With evaporation but no drainage (Ksat = 0)
  3. With evaporation and drainage
using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);

Parameters

K_sat = FT(0.01 / 3600 / 24)
+vg_n = FT(1.45)
+vg_α = FT(1.5)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.4)
+θ_r = FT(0.04)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(0.3)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)# 10mm
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 294.15^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+)
PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#1#2"}(Main.var"##317".var"#1#2"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#3#4"}(Main.var"##317".var"#3#4"()), Dates.DateTime("2005-01-01T00:00:00"), nothing)

Atmos

T_air = FT(301.15)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+)
PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#5#6"}(Main.var"##317".var"#5#6"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#7#8"}(Main.var"##317".var"#7#8"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#9#10"}(Main.var"##317".var"#9#10"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#11#12"}(Main.var"##317".var"#11#12"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var"##317".var"#13#14"}(Main.var"##317".var"#13#14"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var"#21#24"}(ClimaLand.var"#21#24"()), Dates.DateTime("2005-01-01T00:00:00"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))

Simulation setup - no evaporation Boundary conditions

zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+no_flux_boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
+
+t0 = Float64(0)
+tf = Float64(24 * 3600 * 15)
+dt = Float64(900.0)
+Δz = 0.01
+zmax = FT(0)
+zmin = FT(-1.6)
+nelems = Int((zmax - zmin) / Δz)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_flux_boundary_fluxes,
+    sources = (),
+);

Initial conditions

Y, p, cds = initialize(soil)
+function estimated_ic(z)
+    0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= estimated_ic.(z)
+    Y.soil.θ_i .= 0
+    T = FT(294.15)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        Y.soil.θ_i,
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+cb = SciMLBase.CallbackSet(saving_cb);
+
+sol_no_evap =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Repeat with evaporation and drainage This requires different initial conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+evap_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+)
+
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = evap_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+soil_exp_tendency! = make_exp_tendency(soil)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];
+
+# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.

bottom_water_bc = MoistureStateBC((p, t) -> 0.35)
+no_drainage_boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),
+)
+zmax = FT(0)
+zmin = FT(-0.16)
+nelems = Int((zmax - zmin) / Δz)
+dt = Float64(10.0)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)
+z_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = no_drainage_boundary_fluxes,
+    sources = (),
+)
+Y, p, cds = initialize(soil)
+init_soil!(Y, z_no_evap, soil.parameters)
+set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
+sol_no_drainage =
+    SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)
+evap_no_drainage = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+];

Figures

savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (800, 400))
+ax = Axis(fig[1, 1], xlabel = "Day", ylabel = "Evaporation rate (mm/d)")
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "With drainage",
+    color = :red,
+)
+CairoMakie.lines!(
+    ax,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    evap_no_drainage .* (1000 * 3600 * 24),
+    label = "No drainage",
+    color = :blue,
+)
+
+CairoMakie.axislegend(ax)
+ax2 = Axis(fig[1, 2], xlabel = "Day", ylabel = "Cumulative evaporation (mm)")
+CairoMakie.lines!(
+    ax2,
+    sol.t ./ 3600 ./ 24,
+    cumsum(evap) .* (1000 * 3600),
+    color = :red,
+)
+CairoMakie.lines!(
+    ax2,
+    sol_no_drainage.t ./ 3600 ./ 24,
+    cumsum(evap_no_drainage) .* (1000 * 3600),
+    color = :blue,
+)
+save("evaporation_lehmann2024_figS6.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Drainage only")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.4)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 10]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax1,
+        parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Evap+Drainage", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.3, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 1, 2, 5, 13]
+for i in 1:1:5
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Evap only", xlabel = "Volumetric Water Content")
+CairoMakie.ylims!(-0.15, 0)
+CairoMakie.xlims!(0.0, 0.4)
+days = [0, 2, 9, 14]
+for i in 1:1:4
+    CairoMakie.lines!(
+        ax3,
+        parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],
+        label = "$(days[i]) days",
+        parent(z_no_evap)[:],
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("evaporation_gardner_fig1.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png b/previews/PR754/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png new file mode 100644 index 0000000000..95012f78db Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/evaporation_lehmann2008_fig8b.png differ diff --git a/previews/PR754/generated/standalone/Soil/evaporation_lehmann2024_figS6.png b/previews/PR754/generated/standalone/Soil/evaporation_lehmann2024_figS6.png new file mode 100644 index 0000000000..f6f7ed2cc8 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/evaporation_lehmann2024_figS6.png differ diff --git a/previews/PR754/generated/standalone/Soil/freezing_front/index.html b/previews/PR754/generated/standalone/Soil/freezing_front/index.html new file mode 100644 index 0000000000..c0c9d473c2 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/freezing_front/index.html @@ -0,0 +1,177 @@ + +Phase Changes · ClimaLand.jl
GitHub

Modeling a freezing front in unsaturated soil

Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...) -\frac{F_T}{ρ_l}$

$\frac{ ∂ θ_i}{∂ t} = \frac{F_T}{ρ_i}$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity, and

$F_T$ is the freeze-thaw term.

To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.

We solve these equations in an effectively 1-d domain with $z ∈ [-0.2,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 28 W/m^2/K (T - 267.15K) ẑ$

$- κ ∇T(t, z= -0.2) = -3 W/m^2/K (T - 279.85K) ẑ$

$T(t = 0, z) = 279.85 K$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -0.2) = 0 ẑ$

$ϑ_l(t = 0, z) = 0.33$.

The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.

Import necessary modules

import SciMLBase
+import ClimaTimeSteppers as CTS
+using DelimitedFiles
+using CairoMakie
+
+using ClimaCore
+import ClimaParams as CP
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Preliminary set-up

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
Float32

Set the values of other parameters required by the model:

ν = FT(0.535)
+K_sat = FT(3.2e-6) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.48)
+vg_α = FT(1.11) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);

You could also try the Brooks and Corey model:

#ψb = FT(-0.6)
+#c = FT(0.43)
+#hcm = BrooksCorey(;ψb = ψb, c = c);
+θ_r = FT(0.05)
+ν_ss_om = FT(0.3)
+ν_ss_quartz = FT(0.7)
+ν_ss_gravel = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat,
+    S_s,
+    θ_r,
+);

Choose the domain and discretization:

zmax = FT(0)
+zmin = FT(-0.2)
+nelems = 20
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Set the boundary conditions:

zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)
+function top_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))
+    T_c = ClimaCore.Fields.level(p.soil.T, p_len)
+    return @. FT(28 * (T_c - 267.15))
+end
+function bottom_heat_flux(p, t)
+    FT = eltype(p.soil.T)
+    T_c = ClimaCore.Fields.level(p.soil.T, 1)
+    return @. FT(-3 * (T_c - 279.85))
+end
+top_heat_flux_bc = HeatFluxBC(top_heat_flux)
+bottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)
+boundary_fluxes = (;
+    top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),
+    bottom = WaterHeatBC(;
+        water = zero_water_flux_bc,
+        heat = bottom_heat_flux_bc,
+    ),
+);

Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.

sources = (PhaseChange{FT}(),);

Now we can package this up in the EnergyHydrology model struct:

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Running a simulation

Once we have the model, we can initialize the state vectors and obtain the coordinates

Y, p, coords = initialize(soil);

After which, we can specify the initial condition function, and initialze the variables:

function init_soil!(Ysoil, z, params)
+    ν = params.ν
+    FT = eltype(Ysoil.soil.ϑ_l)
+    Ysoil.soil.ϑ_l .= FT(0.33)
+    Ysoil.soil.θ_i .= FT(0.0)
+    T = FT(279.85)
+    ρc_s = Soil.volumetric_heat_capacity(
+        FT(0.33),
+        FT(0.0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Ysoil.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            FT(0.0),
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 50);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Create the tendency function, and choose a timestep, and timestepper:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+dt = Float64(100)
+
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 3,
+        update_j = CTS.UpdateEvery(CTS.NewTimeStep),
+    ),
+);

Problem definition and callbacks

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);

Comparison to data

This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.

dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();
+ds = readdlm(dataset_path, ',')
+hours = ds[:, 1][2:end]
+vwc = ds[:, 2][2:end] ./ 100.0
+depth = ds[:, 3][2:end]
+mask_12h = hours .== 12
+mask_24h = hours .== 24
+mask_50h = hours .== 50;
+
+fig = Figure(size = (900, 300))
+ax1 = Axis(
+    fig[1, 1],
+    title = "12 hours",
+    xlabel = L"θ_l + θ_i",
+    ylabel = "Soil depth (m)",
+)
+limits!(ax1, 0.2, 0.5, -0.2, 0.0)
+ax2 = Axis(
+    fig[1, 2],
+    title = "24 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax2, 0.2, 0.5, -0.2, 0.0)
+ax3 = Axis(
+    fig[1, 3],
+    title = "50 hours",
+    xlabel = L"θ_l + θ_i",
+    yticksvisible = false,
+    yticklabelsvisible = false,
+)
+limits!(ax3, 0.2, 0.5, -0.2, 0.0)
+
+
+z = parent(coords.subsurface.z)[:];
+
+scatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = "", color = "purple")
+lines!(
+    ax1,
+    parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+
+scatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = "", color = "purple")
+lines!(
+    ax2,
+    parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],
+    z,
+    label = "",
+    color = :green,
+)
+
+scatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = "Data", color = "purple")
+lines!(
+    ax3,
+    parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],
+    z,
+    label = "Simulation",
+    color = :green,
+)
+axislegend(ax3, position = :rb)
+
+save("mizoguchi_data_comparison.png", fig);

Discussion and Model Explanation

To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies

$ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0$

Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing ($T < T_f$)

$\frac{dp_l}{ρ_l} = L_f \frac{dT}{T},$

or

$p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)$

where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).

What this implies is that above the freezing point, the pressure is equal to $p_{l,0}$, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure $p_{l,0}$ is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):

$p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)$

where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for

$θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.$

For freezing, the freeze thaw function F_T is equal to

$F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)$

which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).

Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).

This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time

$τ_{LTE}= c̃ Δz²/κ,$

which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).

References

  • Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.
  • Hansson et al., Vadose Zone Journal 3:693–704 (2004).
  • M. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).
  • Kurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)
  • Watanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58
  • Painter and Karra, Vadose Zone Journal (2014) 13 (4)

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/layered_soil/index.html b/previews/PR754/generated/standalone/Soil/layered_soil/index.html new file mode 100644 index 0000000000..b1c5a54473 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/layered_soil/index.html @@ -0,0 +1,105 @@ + +Layered Soil · ClimaLand.jl
GitHub

This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.

using Plots
+import ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using ClimaCore
+import ClimaParams as CP
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+FT = Float64;

Define simulation times

t0 = Float64(0)
+tf = Float64(60 * 60)
+dt = Float64(30);

Define the domain

zmax = FT(0)
+zmin = FT(-1.1)
+nelems = 75
+Δ = FT((zmax - zmin) / nelems / 2)
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169183 doi:10.4141/CJSS09118

data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();
+parameter_data = readdlm(data_file, ',');

Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.

depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m
+ksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s
+vgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).
+vgn = reverse(parameter_data[5, :])
+residual_frac = reverse(parameter_data[2, :])
+porosity = reverse(parameter_data[3, :]);

Create fields corresponding to the parameter

ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)
+K_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)
+θ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);

The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:

S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;

The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:

hcm = SpaceVaryingInput(
+    depth,
+    (; α = vgα, n = vgn),
+    soil_domain.space.subsurface,
+    vanGenuchten{FT},
+);

The parameter struct:

params = ClimaLand.Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.

function top_flux_function(p, t)
+    return -0.0001033
+end
+top_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)
+bottom_bc = ClimaLand.Soil.FreeDrainage()
+boundary_fluxes = (; top = top_bc, bottom = bottom_bc)
+soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = (),
+);

Initial the state vectors, and set initial conditions

Y, p, cds = initialize(soil);

Initial conditions

Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.

We also set the initial conditions of the auxiliary state here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping:

stepper = CTS.ARS111()
+@assert FT in (Float32, Float64)
+err = (FT == Float64) ? 1e-8 : 1e-4
+convergence_cond = CTS.MaximumError(err)
+conv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 10,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+        convergence_checker = conv_checker,
+    ),
+)
+exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = make_jacobian(soil)
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);
+
+z = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]
+plot(ϑ_l[1], z, label = "initial", color = "grey", aspect_ratio = 0.8)
+plot!(ϑ_l[2], z, label = "8min", color = "orange")
+plot!(ϑ_l[3], z, label = "16min", color = "red")
+plot!(ϑ_l[4], z, label = "24min", color = "teal")
+plot!(ϑ_l[5], z, label = "32min", color = "blue")
+plot!(ϑ_l[6], z, label = "40min", color = "purple")
+plot!(ϑ_l[7], z, label = "60min", color = "green")
+scatter!(porosity, depth, label = "Porosity")
+plot!(legend = :bottomright)
+
+plot!(xlim = [0, 0.7])
+
+plot!(
+    ylim = [-1.1, 0],
+    yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],
+)
+
+plot!(ylabel = "Depth (m)")
+
+plot!(xlabel = "Volumeteric Water Content")
+
+savefig("./sv62_alpha_2_inf_updated_data_climaland.png")
"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png"

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/mizoguchi_data_comparison.png b/previews/PR754/generated/standalone/Soil/mizoguchi_data_comparison.png new file mode 100644 index 0000000000..a7b91672fd Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/mizoguchi_data_comparison.png differ diff --git a/previews/PR754/generated/standalone/Soil/profiles.png b/previews/PR754/generated/standalone/Soil/profiles.png new file mode 100644 index 0000000000..0e27696c63 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/profiles.png differ diff --git a/previews/PR754/generated/standalone/Soil/richards_equation/index.html b/previews/PR754/generated/standalone/Soil/richards_equation/index.html new file mode 100644 index 0000000000..af06349517 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/richards_equation/index.html @@ -0,0 +1,90 @@ + +Richards Equation · ClimaLand.jl
GitHub

Hydrostatic Equilibrium test for Richards Equation

This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.

The equations are:

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (ϑ_l; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in a 1-d domain with $z ∈ [-5,0]$, and with the following boundary and initial conditions:

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -5) = 0 ẑ$

$ϑ(t = 0, z) = ν-0.001$

$θ_i(t = 0, z) = 0.0.$

where $\nu$ is the porosity.

When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.

Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).

Preliminary setup

  • Load external packages
import SciMLBase
+using Plots
  • Load CliMA packages and ClimaLand modules
using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP
  • Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:
const FT = Float32;
+earth_param_set = LP.LandParameters(FT);

Set up the soil model

We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.

First, we define the parameters: porosity \nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.

K_sat = FT(0.0443 / (3600 * 100))
+S_s = FT(1e-3)
+ν = FT(0.495)
+vg_α = FT(2.6)
+vg_n = FT(2)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0)
+params = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:

zmax = FT(0)
+zmin = FT(-5)
+nelems = 10
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:

surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+bottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)
+boundary_conditions = (; top = surface_flux, bottom = bottom_flux);

Lastly, in this case we don't have any sources, so we pass an empty tuple:

sources = ();

Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.

soil = Soil.RichardsModel{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_conditions,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = ClimaLand.make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.

Y.soil.ϑ_l .= FT(0.494);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 24 * 36);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.

dt = Float64(1e3);

Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.

stepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    stepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);

Here we set up the information used for our Jacobian.

jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);

And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);
+sol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);

Create some plots

We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).

t = sol.t ./ (60 * 60 * 24);
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+z = parent(coords.subsurface.z)
+plot(
+    ϑ_l[1],
+    z,
+    label = string("t = ", string(t[1]), "days"),
+    xlim = [0.47, 0.501],
+    ylabel = "z",
+    xlabel = "ϑ_l",
+    legend = :bottomleft,
+    title = "Equilibrium test",
+);
+plot!(ϑ_l[end], z, label = string("t = ", string(t[end]), "days"));
+function hydrostatic_equilibrium(z, z_interface)
+    ν = 0.495
+    S_s = 1e-3
+    α = 2.6
+    n = 2.0
+    m = 0.5
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+plot!(hydrostatic_equilibrium.(z, -0.56), z, label = "equilibrium solution");
+
+plot!(1e-3 .+ ϑ_l[1], z, label = "porosity");

Save the output:

savefig("equilibrium_test_ϑ_l.png");

References

  • Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/soil_energy_hydrology/index.html b/previews/PR754/generated/standalone/Soil/soil_energy_hydrology/index.html new file mode 100644 index 0000000000..d30eb531e0 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/soil_energy_hydrology/index.html @@ -0,0 +1,125 @@ + +Energy and Hydrology · ClimaLand.jl
GitHub

Coupled heat and water equations tending towards equilibrium

The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.

Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.

The equations are:

$\frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)$

$\frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...).$

Here

$t$ is the time (s),

$z$ is the location in the vertical (m),

$ρe_{int}$ is the volumetric internal energy of the soil (J/m^3),

$T$ is the temperature of the soil (K),

$κ$ is the thermal conductivity (W/m/K),

$ρe_{int_{liq}}$ is the volumetric internal energy of liquid water (J/m^3),

$K$ is the hydraulic conductivity (m/s),

$h$ is the hydraulic head (m),

$ϑ_l$ is the augmented volumetric liquid water fraction,

$θ_i$ is the volumetric ice fraction, and

$ν, ...$ denotes parameters relating to soil type, such as porosity.

We will solve this equation in an effectively 1-d domain with $z ∈ [-1,0]$, and with the following boundary and initial conditions:

$- κ ∇T(t, z = 0) = 0 ẑ$

$-κ ∇T(t, z = -1) = 0 ẑ$

$T(t = 0, z) = T_{min} + (T_{max}-T_{min}) e^{Cz}$

$- K ∇h(t, z = 0) = 0 ẑ$

$-K ∇h(t, z = -1) = 0 ẑ$

$ϑ(t = 0, z) = ϑ_{min} + (ϑ_{max}-ϑ_{min}) e^{Cz},$

where $C, T_{min}, T_{max}, ϑ_{min},$ and $ϑ_{max}$ are constants.

If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy $∂h/∂z = 0$ and $∂T/∂z = 0$. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.

We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.

Import necessary modules

External (non - CliMA) modules

import SciMLBase
+using Statistics
+using Plots

CliMA packages and ClimaLand modules

using ClimaCore
+import ClimaParams as CP
+import ClimaTimeSteppers as CTS
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+
+import ClimaLand
+import ClimaLand.Parameters as LP

Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:

FT = Float32
+earth_param_set = LP.LandParameters(FT);

Create the model

Set the values of other parameters required by the model:

ν = FT(0.395)
0.395f0

Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).

ν_ss_quartz = FT(0.92)
+ν_ss_om = FT(0.0)
+ν_ss_gravel = FT(0.0)
0.0f0

Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.

Ksat = FT(4.42 / 3600 / 100) # m/s
+S_s = FT(1e-3) #inverse meters
+vg_n = FT(1.89)
+vg_α = FT(7.5) # inverse meters
+hydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+θ_r = FT(0.0)
+params = Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm,
+    K_sat = Ksat,
+    S_s,
+    θ_r,
+);

We also need to pick a domain on which to solve the equations:

zmax = FT(0)
+zmin = FT(-1.0)
+nelems = 50
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.

Water boundary conditions:

surface_water_flux = WaterFluxBC((p, t) -> 0.0)
+bottom_water_flux = WaterFluxBC((p, t) -> 0.0);

The boundary conditions for the heat equation:

surface_heat_flux = HeatFluxBC((p, t) -> 0.0)
+bottom_heat_flux = HeatFluxBC((p, t) -> 0.0);

We wrap up all of those in a WaterHeatBC struct:

boundary_fluxes = (;
+    top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),
+    bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),
+);

We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.

sources = ();

Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.

soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.

exp_tendency! = make_exp_tendency(soil);
+imp_tendency! = make_imp_tendency(soil);
+jacobian! = ClimaLand.make_jacobian(soil);

Set up the simulation

We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:

Y, p, coords = initialize(soil);
+Y.soil |> propertynames
+
+p.soil |> propertynames
+
+coords |> propertynames
(:surface, :subsurface)

Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.

function init_soil!(Y, z, params)
+    ν = params.ν
+    θ_r = params.θ_r
+    FT = eltype(Y.soil.ϑ_l)
+    zmax = FT(0)
+    zmin = FT(-1)
+
+    theta_max = FT(ν * 0.5)
+    theta_min = FT(ν * 0.4)
+    T_max = FT(289.0)
+    T_min = FT(288.0)
+
+    c = FT(20.0)
+    @. Y.soil.ϑ_l =
+        theta_min +
+        (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)
+    Y.soil.θ_i .= FT(0.0)
+
+    T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))
+
+    θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)
+    ρc_s =
+        Soil.volumetric_heat_capacity.(
+            θ_l,
+            Y.soil.θ_i,
+            params.ρc_ds,
+            params.earth_param_set,
+        )
+    Y.soil.ρe_int .=
+        Soil.volumetric_internal_energy.(
+            Y.soil.θ_i,
+            ρc_s,
+            T,
+            params.earth_param_set,
+        )
+end
+
+init_soil!(Y, coords.subsurface.z, soil.parameters);

We choose the initial and final simulation times:

t0 = Float64(0)
+tf = Float64(60 * 60 * 72);

We set the cache values corresponding to the initial conditions of the state Y:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, t0);

We use ClimaTimesteppers.jl for carrying out the time integration.

Choose a timestepper and set up the ODE problem:

dt = Float64(1000.0);
+timestepper = CTS.ARS111();
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+);
+
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);
+
+prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+);

By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:

saveat = collect(t0:FT(30000):tf)
+saved_values = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+);
+cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);

Now we can solve the problem.

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);

Extract output

z = parent(coords.subsurface.z)
+t = parent(sol.t)
+ϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]
+T = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];

Let's look at the initial and final times:

plot(ϑ_l[1], z, xlabel = "ϑ_l", ylabel = "z (m)", label = "t = 0d")
+plot!(ϑ_l[4], z, label = "t = 1.5d")
+plot!(ϑ_l[end], z, label = "t = 3d")
+savefig("eq_moisture_plot.png");

plot(T[1], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 0d")
+plot!(T[4], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 1.5d")
+plot!(T[end], z, xlabel = "T (K)", ylabel = "z (m)", label = "t = 3d")
+savefig("eq_temperature_plot.png");

Analytic Expectations

We can determine a priori what we expect the final temperature to be in equilibrium.

Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).

Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.

One could also solve the equation for ϑ_l specified by $∂ h/∂ z = 0$ to determine the functional form of the equilibrium profile of the liquid water.

References

  • Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.
  • Balland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/sublimation/index.html b/previews/PR754/generated/standalone/Soil/sublimation/index.html new file mode 100644 index 0000000000..e72da162d5 --- /dev/null +++ b/previews/PR754/generated/standalone/Soil/sublimation/index.html @@ -0,0 +1,255 @@ + +Bare soil site · ClimaLand.jl
GitHub

Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.

using CairoMakie
+import SciMLBase
+import ClimaTimeSteppers as CTS
+using Thermodynamics
+
+using ClimaCore
+import ClimaParams as CP
+using SurfaceFluxes
+using StaticArrays
+using Dates
+using DelimitedFiles: readdlm
+
+using ClimaLand
+using ClimaLand.Domains: Column
+using ClimaLand.Soil
+import ClimaLand
+import ClimaLand.Parameters as LP
+import SurfaceFluxes.Parameters as SFP
+
+FT = Float64;
+earth_param_set = LP.LandParameters(FT)
+thermo_params = LP.thermodynamic_parameters(earth_param_set);
+
+ref_time = DateTime(2005)
+SW_d = (t) -> 0
+LW_d = (t) -> 270.0^4 * 5.67e-8
+radiation = PrescribedRadiativeFluxes(
+    FT,
+    TimeVaryingInput(SW_d),
+    TimeVaryingInput(LW_d),
+    ref_time,
+);

Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.

T_air = FT(270.0)
+rh = FT(0.38)
+esat = Thermodynamics.saturation_vapor_pressure(
+    thermo_params,
+    T_air,
+    Thermodynamics.Liquid(),
+)
+e = rh * esat
+q = FT(0.622 * e / (101325 - 0.378 * e))
+precip = (t) -> 0.0
+T_atmos = (t) -> T_air
+u_atmos = (t) -> 1.0
+q_atmos = (t) -> q
+h_atmos = FT(0.1)
+P_atmos = (t) -> 101325
+gustiness = FT(1e-2)
+atmos = PrescribedAtmosphere(
+    TimeVaryingInput(precip),
+    TimeVaryingInput(precip),
+    TimeVaryingInput(T_atmos),
+    TimeVaryingInput(u_atmos),
+    TimeVaryingInput(q_atmos),
+    TimeVaryingInput(P_atmos),
+    ref_time,
+    h_atmos,
+    earth_param_set;
+    gustiness = gustiness,
+);

Define the boundary conditions

top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)
+zero_water_flux = WaterFluxBC((p, t) -> 0)
+zero_heat_flux = HeatFluxBC((p, t) -> 0)
+boundary_fluxes = (;
+    top = top_bc,
+    bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),
+);
[ Info: Warning: No runoff model was provided; zero runoff generated.
+

Define the parameters n and alpha estimated by matching vG curve.

K_sat = FT(225.1 / 3600 / 24 / 1000)
+vg_n = FT(10.0)
+vg_α = FT(6.0)
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)
+ν = FT(0.43)
+θ_r = FT(0.045)
+S_s = FT(1e-3)
+ν_ss_om = FT(0.0)
+ν_ss_quartz = FT(1.0)
+ν_ss_gravel = FT(0.0)
+emissivity = FT(1.0)
+PAR_albedo = FT(0.2)
+NIR_albedo = FT(0.4)
+z_0m = FT(1e-3)
+z_0b = FT(1e-4)
+d_ds = FT(0.01)
+params = ClimaLand.Soil.EnergyHydrologyParameters(
+    FT;
+    ν,
+    ν_ss_om,
+    ν_ss_quartz,
+    ν_ss_gravel,
+    hydrology_cm = hcm,
+    K_sat,
+    S_s,
+    θ_r,
+    PAR_albedo,
+    NIR_albedo,
+    emissivity,
+    z_0m,
+    z_0b,
+    earth_param_set,
+    d_ds,
+);

Domain - single column

zmax = FT(0)
+zmin = FT(-0.35)
+nelems = 12
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);
+z = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;

Soil model, and create the prognostic vector Y and cache p:

sources = (PhaseChange{FT}(),);
+soil = Soil.EnergyHydrology{FT}(;
+    parameters = params,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+)
+
+Y, p, cds = initialize(soil);

Set initial conditions

function hydrostatic_equilibrium(z, z_interface, params)
+    (; ν, S_s, hydrology_cm) = params
+    (; α, n, m) = hydrology_cm
+    if z < z_interface
+        return -S_s * (z - z_interface) + ν
+    else
+        return ν * (1 + (α * (z - z_interface))^n)^(-m)
+    end
+end
+function init_soil!(Y, z, params)
+    FT = eltype(Y.soil.ϑ_l)
+    Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)
+    Y.soil.θ_i .= 0
+    T = FT(275.0)
+    ρc_s = @. Soil.volumetric_heat_capacity(
+        Y.soil.ϑ_l,
+        FT(0),
+        params.ρc_ds,
+        params.earth_param_set,
+    )
+    Y.soil.ρe_int =
+        Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)
+end
+init_soil!(Y, z, soil.parameters);

Timestepping:

t0 = Float64(0)
+tf = Float64(24 * 3600 * 4)
+dt = Float64(5)
5.0

We also set the initial conditions of the cache here:

set_initial_cache! = make_set_initial_cache(soil)
+set_initial_cache!(p, Y, t0);

Timestepping functions:

exp_tendency! = make_exp_tendency(soil)
+imp_tendency! = make_imp_tendency(soil)
+jacobian! = ClimaLand.make_jacobian(soil)
+jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)
+
+timestepper = CTS.ARS111()
+ode_algo = CTS.IMEXAlgorithm(
+    timestepper,
+    CTS.NewtonsMethod(
+        max_iters = 1,
+        update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),
+    ),
+)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))

Define the problem and callbacks:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(
+        T_exp! = exp_tendency!,
+        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
+        dss! = ClimaLand.dss!,
+    ),
+    Y,
+    (t0, tf),
+    p,
+)
+saveat = Array(t0:3600.0:tf)
+sv = (;
+    t = Array{Float64}(undef, length(saveat)),
+    saveval = Array{NamedTuple}(undef, length(saveat)),
+)
+saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
+updateat = deepcopy(saveat)
+model_drivers = ClimaLand.get_drivers(soil)
+updatefunc = ClimaLand.make_update_drivers(model_drivers)
+driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
+cb = SciMLBase.CallbackSet(driver_cb, saving_cb);

Solve

sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);

Figures

Extract the evaporation at each saved step

evap = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        (1 .- sv.saveval[k].soil.ice_frac),
+    )[1] for k in 1:length(sol.t)
+]
+sub = [
+    parent(
+        sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*
+        sv.saveval[k].soil.ice_frac,
+    )[1] for k in 1:length(sol.t)
+]
+
+savepath = joinpath(pkgdir(ClimaLand), "docs/tutorials/standalone/Soil/")
+
+fig = Figure(size = (400, 400))
+ax = Axis(
+    fig[1, 1],
+    xlabel = "Day",
+    ylabel = "Rate (mm/d)",
+    title = "Vapor Fluxes",
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    sub .* (1000 * 3600 * 24),
+    label = "Sublimation",
+    color = :blue,
+)
+CairoMakie.lines!(
+    ax,
+    sol.t ./ 3600 ./ 24,
+    evap .* (1000 * 3600 * 24),
+    label = "Evaporation",
+    color = :black,
+)
+CairoMakie.axislegend(ax)
+
+save("water_fluxes.png", fig);

fig2 = Figure(size = (800, 1200))
+ax1 = Axis(fig2[1, 1], title = "Temperature")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(260, 280)
+linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]
+days = [0, 1, 2, 3, 4]
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax1,
+        parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax2 = Axis(fig2[2, 1], title = "Ice", ylabel = "Depth(cm)")
+
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax2,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+ax3 = Axis(fig2[3, 1], title = "Liquid Water", xlabel = "")
+CairoMakie.ylims!(-0.35, 0)
+CairoMakie.xlims!(0.0, 0.5)
+for i in 1:length(days)
+    CairoMakie.lines!(
+        ax3,
+        parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],
+        parent(z)[:],
+        label = "$(days[i]) days",
+        color = :black,
+        linestyle = linestyles[i],
+    )
+end
+
+CairoMakie.axislegend(ax3, position = :lt)
+CairoMakie.axislegend(ax2, position = :lt)
+CairoMakie.axislegend(ax1, position = :lt)
+save("profiles.png", fig2);

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png b/previews/PR754/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png new file mode 100644 index 0000000000..1d25aedb18 Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png differ diff --git a/previews/PR754/generated/standalone/Soil/water_fluxes.png b/previews/PR754/generated/standalone/Soil/water_fluxes.png new file mode 100644 index 0000000000..9a54a6023d Binary files /dev/null and b/previews/PR754/generated/standalone/Soil/water_fluxes.png differ diff --git a/previews/PR754/generated/standalone/Usage/LSM_single_column_tutorial/index.html b/previews/PR754/generated/standalone/Usage/LSM_single_column_tutorial/index.html new file mode 100644 index 0000000000..55db098cfe --- /dev/null +++ b/previews/PR754/generated/standalone/Usage/LSM_single_column_tutorial/index.html @@ -0,0 +1,82 @@ + +Intro to multi-component models · ClimaLand.jl
GitHub

The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.

The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.

We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.

First, let's load the required modules:

using ClimaLand
+using ClimaLand.Domains: Column, obtain_surface_domain
+using ClimaLand.Soil
+using ClimaLand.Pond
+
+FT = Float32;

The individual component models I - Soil Hydrology

The RR equation for the volumetric water content of soil is given by

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S(x,y,z, t)$

In order to solve this, one must specify:

  • boundary conditions,
  • relevant parameters (closure models for K and ψ),
  • a domain and a spatial discretization scheme,
  • additional source terms S, if applicable,
  • a time-stepping algorithm,
  • initial conditions.

We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).

Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.

For the RR equation, we can create this as follows. First, we specify parameters:

ν = FT(0.495);
+K_sat = FT(0.0443 / 3600 / 100); # m/s
+S_s = FT(1e-3); #inverse meters
+vg_n = FT(2.0);
+vg_α = FT(2.6); # inverse meters
+hcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);
+θ_r = FT(0);
+soil_ps = Soil.RichardsParameters(;
+    ν = ν,
+    hydrology_cm = hcm,
+    K_sat = K_sat,
+    S_s = S_s,
+    θ_r = θ_r,
+);

Next, let's define the spatial domain and discretization:

zmax = FT(0);
+zmin = FT(-1);
+nelems = 20;
+soil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

And boundary conditions and source terms (none currently):

top_flux_bc = WaterFluxBC((p, t) -> 0.0);
+bot_flux_bc = WaterFluxBC((p, t) -> 0.0);
+sources = ()
+boundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)
(top = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#1#2"}(Main.var"##494".var"#1#2"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var"##494".var"#3#4"}(Main.var"##494".var"#3#4"()))

With this information, we can make our model:

soil = Soil.RichardsModel{FT}(;
+    parameters = soil_ps,
+    domain = soil_domain,
+    boundary_conditions = boundary_fluxes,
+    sources = sources,
+);

We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,

Y_soil, p_soil, coords_soil = initialize(soil);

and we can set up the tendency function using the default as well,

soil_ode! = make_exp_tendency(soil);

which computes, for the column domain,

$-\frac{∂ }{∂z} (-K\frac{∂(ψ+z)}{∂ z})$

for each value of ϑ on the mesh of our soil_domain.

Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:

function soil_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place
+         update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place
+         compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.
+end

It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.

Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.

Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.

The individual component models II - Surface Water

The pond model has a single variable, the pond height η, which satisfies the ODE:

$\frac{∂ η}{∂ t} = -(P - I) = R,$

where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.

To write down the pond equations, we need to specify

  • P
  • I

which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:

precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s
+
+infiltration(t) = -(1e-6) #m/s
+pond_model =
+    Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));

Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.

Again we can initialize the state vector and auxiliary vectors:

Y_pond, p_pond, coords_pond = initialize(pond_model);

We can make the tendency function in the same way, for stepping the state forward in time:

pond_ode! = make_exp_tendency(pond_model);

The pond_ode! function works in the same way as for the soil model:

function pond_ode!(dY, Y, p, t)
+         update_aux!(p,Y,t) # falls back to default; does nothing
+         update_boundary_fluxes!(p,Y,t)  # p.surface_water.runoff in place
+         compute_exp_tendency!(dY, Y, p, t)
+end

An LSM with pond and soil:

The LSM model must contain everything needed to write down the joint system of equations

$\frac{\partial \eta}{\partial t} = -(P(t) - I(ϑ, η, P)) = R,$

$\frac{\partial ϑ}{\partial t} = -∇ ⋅ (-K∇(ψ+z)) + S$

$-K ∇(ψ+z)|_{z = zmax} ⋅ ẑ = I(ϑ, η, P)$

$-K ∇(ψ+z)|_{z = zmin} ⋅ ẑ = 0.0.$

These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.

As in the standalone cases, defining our model requires specifying

  • parameters,
  • domains, discretizations
  • precipitation,
  • boundary conditions,
  • sources in the soil equation, if any.

First, let's make our single column domain.

lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);

Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.

soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());
+surface_water_args = NamedTuple();

Atmospheric drivers don't "belong" to either component alone:

land_args = (precip = precipitation,);
+land = LandHydrology{FT}(;
+    land_args = land_args,
+    soil_model_type = Soil.RichardsModel{FT},
+    soil_args = soil_args,
+    surface_water_model_type = Pond.PondModel{FT},
+    surface_water_args = surface_water_args,
+);

Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).

Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?

Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.

To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:

function make_update_aux(land)
+         soil_update_aux! = make_update_aux(land.soil)
+         surface_update_aux! = make_update_aux(land.surface_water)
+         function update_aux!(p,Y,t)
+                  surface_update_aux!(p,Y,t) # does nothing to `p`
+                  soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ
+         end
+         return update_aux!
+end
function make_update_boundary_fluxes(land)
+         update_soil_bf! = make_update_boundary_fluxes(land.soil)
+         update_pond_bf! = make_update_boundary_fluxes(land.surface_water)
+         function update_boundary_fluxes!(p,Y,t)
+                  p.soil_infiltration = compute_infiltration(Y,p, t)
+                  update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration
+                  update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration
+         end
+         return update_boundary_fluxes!
+end

and similarily for the compute_exp_tendency! functions:

function make_compute_exp_tendency(land)
+         soil_compute_exp_tendency! = make_update_aux(land.soil)
+         surface_compute_exp_tendency! = make_update_aux(land.surface_water)
+         function compute_exp_tendency!(dY,Y,p,t)
+                  surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η
+                  soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ
+         end
+         return compute_exp_tendency!
+end

The exp_tendency! for the land model is then again just

function exp_tendency!(dY, Y, p, t)
+         update_aux!(p,Y,t)
+         update_boundary_fluxes!(p,Y,t)
+         compute_exp_tendency!(dY, Y, p, t)
+end

In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.

A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:

  • initialize(land.soil)
  • initialize(land.surface_water)
  • initializes additional auxiliary variables, like p.soil_infiltration
  • append these into Y, p, and coords:
Y, p, coords = initialize(land);

We have volumetric liquid water fraction:

propertynames(Y.soil)
(:ϑ_l,)

and surface height of the pond:

propertynames(Y.surface_water)
(:η,)

as well as auxiliary variables for the soil:

propertynames(p.soil)
(:K, :ψ, :top_bc, :bottom_bc)

and the runoff for surface water:

propertynames(p.surface_water)
(:runoff,)

and the additional variable required in the LSM is stored here as well:

propertynames(p)
(:soil_infiltration, :soil, :surface_water)

and finally, coordinates - useful for visualization of solutions:

coords.subsurface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]

and the coordinates of the surface variables:

coords.surface
ClimaCore.Geometry.ZPoint{Float32}-valued Field:
+  z: Float32[0.0]

And we can make the tendency function as before:

land_ode! = make_exp_tendency(land);

Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.

Advantages and disadvantages

Some advantages to our interface design are as follows:

  • a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.
  • likewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.
  • the exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).
  • although we wrote it here in a $hardwired$ fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.
  • the order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.
  • the code is also modular in terms of swapping out a simple component model for a more complex version.

Possible disadvantages to our interface design:

  • Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.
  • To accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.
  • standalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.
  • we need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.

This page was generated using Literate.jl.

diff --git a/previews/PR754/generated/standalone/Usage/domain_tutorial/index.html b/previews/PR754/generated/standalone/Usage/domain_tutorial/index.html new file mode 100644 index 0000000000..6f7314f2c6 --- /dev/null +++ b/previews/PR754/generated/standalone/Usage/domain_tutorial/index.html @@ -0,0 +1,2 @@ + +Intro to ClimaLand Domains · ClimaLand.jl
GitHub

Domain Tutorial

Goals of the tutorial

The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.

Background

In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question "only" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).

For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.

In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.

ClimaLand Domains were designed with this in mind. The domains are defined so that

  1. the user can easily switch geometries, e.g. single column to global model,
  2. individual component models can be run by themselves, using a single domain,
  3. the same domains can be used to set up multi-component models (LSMs),
  4. different variables can exist on different parts of the domain.

What is a ClimaLand Domain?

A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.

Domain types

All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:

As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.

DomainSurface DomainSubsurface Domain
ColumnPointColumn
HybridBoxPlaneHybridBox
SphericalShellSphericalSurfaceSphericalShell

There is a single key method which take a ClimaLand domain as an argument.

the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.

It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.

How variable initialization depends on domains

Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.

When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.

They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.

The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].

How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.

This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.

Future work

Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into "vertical" and "horizontal" pieces.

We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.

This page was generated using Literate.jl.

  • 1finite differencing is used in the vertical, and spectral elements are used in the horizontal.
  • 2a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.
  • 3We also will support having an array-like type of variable.
diff --git a/previews/PR754/generated/standalone/Usage/model_tutorial/index.html b/previews/PR754/generated/standalone/Usage/model_tutorial/index.html new file mode 100644 index 0000000000..9ac05f8d86 --- /dev/null +++ b/previews/PR754/generated/standalone/Usage/model_tutorial/index.html @@ -0,0 +1,133 @@ + +Intro to standalone models · ClimaLand.jl
GitHub

The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.

This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.

General setup

We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.

Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.

Your model defines a system of equations of the following form:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in $\vec{Y}$. Generically, we will speak of the functions $\vec{f}$ as tendencies; these can be functions of the prognostic state, of space $\vec{x}$, and of time $t$, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions

The cache ("auxiliary variables")

There are typically quantities, which depend on the state vector $\vec{Y}$, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require "a lot" of memory to store (e.g., most variables in global runs). Allocating memory "on-the-fly" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.

Denoting the cache as $\vec{p}$, your equations may be rewritten as:

$\frac{d \vec{Y}}{d t} = \vec{f}(\vec{Y}, \vec{p}, \vec{x}, t; \mbox{params} \ldots)$

$\vec{p}(\vec{x}, t) = \vec{g}(\vec{Y}, \vec{x}, t; \mbox{params} \ldots)$

The variables $\vec{p}$ at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported ($\vec{Y}$ cannot be zero dimensional).

In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.

The Model

All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.

The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.

The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.

As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.

Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to "imex" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as "explicit" or "implicit" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.

Let's first import some needed packages.

import ClimaTimeSteppers as CTS
+using SciMLBase
+using Plots
+using ClimaCore
+using ClimaLand

Import the functions we are extending for our model:

import ClimaLand:
+    name,
+    make_exp_tendency,
+    make_compute_exp_tendency,
+    make_update_aux,
+    make_update_boundary_fluxes,
+    prognostic_vars,
+    prognostic_types,
+    prognostic_domain_names,
+    auxiliary_vars,
+    auxiliary_types,
+    auxiliary_domain_names

The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).

struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}
+    "van Genuchten model parameters"
+    vGmodel::ClimaLand.Soil.vanGenuchten{FT}
+    "Porosity [unitless]"
+    ν::FT
+    "Residual water fraction [unitless]"
+    θ_r::FT
+    "Saturated hydraulic conductiity [m/s]"
+    Ksat::FT
+    "Surface flux, used as boundary condition [m/s]"
+    F_sfc::FT
+    "Domain of the model"
+    domain::D
+end;

For reasons that will be clear momentarily, let's also define the name of the model:

ClimaLand.name(model::RichardsTutorialModel) = :soil;

Explicit tendency

Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:

$\frac{\partial ϑ_l}{\partial t} = - ∇⋅[-K(θ) ∇(ψ(θ)+z)],$

where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:

function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)
+    function compute_exp_tendency!(dY, Y, p, t)
+        gradc2f = ClimaCore.Operators.GradientC2F()
+        interpc2f = ClimaCore.Operators.InterpolateC2F()
+        FT = FTfromY(Y)
+        divf2c = ClimaCore.Operators.DivergenceF2C(
+            top = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(model.F_sfc),
+            ),
+            bottom = ClimaCore.Operators.SetValue(
+                ClimaCore.Geometry.WVector.(FT(0)),
+            ),
+        )
+
+        @. dY.soil.θ =
+            -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))
+    end
+    return compute_exp_tendency!
+end;

A couple of notes: the vector $\vec{dY}$ contains the evaluation of the tendency function for each variable in $\vec{Y}$. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.

The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.

The prognostic state vector $\vec{Y}$ and cache $\vec{p}$

We have given the state vector $\vec{Y}$ a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.

ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);
+ClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);
+ClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);

The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).

ClimaLand.auxiliary_vars(::RichardsTutorialModel) =
+    (:K, :ψ, :top_flux, :bottom_flux, :z)
+ClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =
+    (FT, FT, FT, FT, FT);
+ClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =
+    (:subsurface, :subsurface, :surface, :surface, :subsurface);

Updating the cache

We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.

function ClimaLand.make_update_aux(model::RichardsTutorialModel)
+    function update_aux!(p, Y, t)
+        p.soil.z .=
+            ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step
+        @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(
+            model.vGmodel,
+            model.Ksat,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+        @. p.soil.ψ = ClimaLand.Soil.matric_potential(
+            model.vGmodel,
+            ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),
+        )
+    end
+    return update_aux!
+end;
+
+function ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)
+    function update_boundary_fluxes!(p, Y, t)
+        FT = ClimaLand.FTfromY(Y)
+        p.soil.top_flux .= model.F_sfc
+        p.soil.bottom_flux .= FT(0)
+    end
+    return update_boundary_fluxes!
+end;

The default tendency function in ClimaLand for any AbstractModel carries out the following:

function make_exp_tendency(model::AbstractModel)
+    update_aux! = make_update_aux(model)
+    update_boundary_fluxes! = make_update_boundary_fluxes(model)
+    compute_exp_tendency! = make_compute_exp_tendency(model)
+    function exp_tendency!(dY,Y,p,t)
+        update_aux!(p,Y,t)
+        update_boundary_fluxes!(p,Y,t)
+        compute_exp_tendency!(dY,Y,p,t)
+    end
+    return exp_tendency!
+end;

Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.

Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone "component" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.

More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.

Running a simulation

Create a model instance.

FT = Float32
+vGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)
+Ksat = FT(4.0e-7)
+ν = 0.5f0
+θ_r = 0.0f0
+F_sfc = FT(-3.0e-8)
+domain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)
+soil = RichardsTutorialModel{Float32, typeof(domain)}(
+    vGmodel,
+    ν,
+    θ_r,
+    Ksat,
+    F_sfc,
+    domain,
+);

Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.

Y, p, cds = initialize(soil);

Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,

Y.soil
1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 0.0

The same is true for p:

p.soil
(K = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:
+  Float32[0.0], bottom_flux = Float32-valued Field:
+  Float32[0.0], z = Float32-valued Field:
+  Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

Here we now update Y in place with initial conditions of our choosing.

Y.soil.θ = 0.25f0;

Set initial cache variable values, and inspect values:

set_initial_cache! = make_set_initial_cache(soil);
+set_initial_cache!(p, Y, 0.0);
+@show p.soil.K
+
+@show p.soil.ψ
+
+@show p.soil.top_flux
Float32-valued Field:
+  Float32[-3.0f-8]

Next up is to create the exp_tendency! function:

exp_tendency! = make_exp_tendency(soil);

Running the simulation

Set the initial and end times, timestep:

t0 = 0.0;
+tf = 7 * 24 * 3600.0;
+dt = 1800.0;

Select the timestepping algorithm we want to use from CTS.jl.

timestepper = CTS.RK4()
+ode_algo = CTS.ExplicitAlgorithm(timestepper)
ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)

SciMLBase problem statement using CTS.jl internals:

prob = SciMLBase.ODEProblem(
+    CTS.ClimaODEFunction(T_exp! = exp_tendency!),
+    Y,
+    (t0, tf),
+    p,
+);

Solve command:

sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);

The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.

This page was generated using Literate.jl.

diff --git a/previews/PR754/getting_started/index.html b/previews/PR754/getting_started/index.html new file mode 100644 index 0000000000..1d9603449e --- /dev/null +++ b/previews/PR754/getting_started/index.html @@ -0,0 +1,32 @@ + +Getting Started · ClimaLand.jl
GitHub

Getting Started

For Users

Installation

First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:

julia> ] # Enter Package REPL mode
+Pkg> add ClimaLand # Install ClimaLand
+Pkg> # Go back to Julia REPL mode
+Julia> using ClimaLand

A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.

Parameterization

Let's start with a basic example: compute canopy gross photosynthesis (GPP).

julia> using ClimaLand
julia> @doc ClimaLand.Canopy.compute_GPP  compute_GPP(An::FT,
+           K::FT,
+           LAI::FT,
+           Ω::FT) where {FT}
+
+  Computes the total canopy photosynthesis (GPP) as a function of the total
+  net carbon assimilation (An), the extinction coefficient (K), leaf area
+  index (LAI) and the clumping index (Ω).

As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:

julia> import ClimaLand.Canopy as canopy
julia> canopy.compute_GPP(5.0, 0.5, 3.0, 0.7)9.28660358412635

Et voilà!

Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.

ClimaLand structure

ClimaLand contains multiple modules. They are listed below:

julia> using MethodAnalysis, ClimaLand
julia> child_modules(ClimaLand)13-element Vector{Module}:
+ ClimaLand
+ ClimaLand.Artifacts
+ ClimaLand.Bucket
+ ClimaLand.Canopy
+ ClimaLand.Canopy.PlantHydraulics
+ ClimaLand.Diagnostics
+ ClimaLand.Domains
+ ClimaLand.Parameters
+ ClimaLand.Pond
+ ClimaLand.Snow
+ ClimaLand.Soil
+ ClimaLand.Soil.Biogeochemistry
+ ClimaLand.Soil.Runoff

To explore what modules, functions and types are exported in a particular module, you can use About.jl:

julia> using ClimaLand
julia> using About
julia> about(ClimaLand.Soil.Biogeochemistry)Module ClimaLand.Soil.Biogeochemistry
+
+Re-exports 13 names (from ClimaLand):
+• Biogeochemistry         • AtmosCO2StateBC    • SoilCO2ModelParameters
+• co2_diffusivity         • MicrobeProduction  • SoilCO2StateBC        
+• microbe_source          • PrescribedMet      • SoilDrivers           
+• volumetric_air_content  • SoilCO2FluxBC    
+• AbstractSoilDriver      • SoilCO2Model

To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.

diff --git a/previews/PR754/index.html b/previews/PR754/index.html new file mode 100644 index 0000000000..a59b91a9b0 --- /dev/null +++ b/previews/PR754/index.html @@ -0,0 +1,2 @@ + +Home · ClimaLand.jl
GitHub

ClimaLand.jl Documentation (v0.14.3)

Introduction

ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).

ClimaLand can be run coupled (or "online") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data ("offline").

ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.

Documentation for Users and Developers

ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.

Physical units

Note that CliMA, in all its repositories, uses Standard Units, reminded below

QuantityUnit NameSI SymbolSI Unit Equivalent
LengthMeterm1 m
MassKilogramkg1 kg
TimeSeconds1 s
TemperatureKelvinK1 K
Amount of SubstanceMolemol1 mol
EnergyJouleJ1 J = 1 N·m
PowerWattW1 W = 1 J/s
PressurePascalPa1 Pa = 1 N/m²
FrequencyHertzHz1 Hz = 1 s⁻¹
diff --git a/previews/PR754/objects.inv b/previews/PR754/objects.inv new file mode 100644 index 0000000000..101874932f Binary files /dev/null and b/previews/PR754/objects.inv differ diff --git a/previews/PR754/search_index.js b/previews/PR754/search_index.js new file mode 100644 index 0000000000..e1e39fe6d5 --- /dev/null +++ b/previews/PR754/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"APIs/canopy/RadiativeTransfer/#Radiative-Transfer","page":"Canopy RT","title":"Radiative Transfer","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#Parameters","page":"Canopy RT","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.BeerLambertParameters","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.BeerLambertParameters","page":"Canopy RT","title":"ClimaLand.Canopy.BeerLambertParameters","text":"BeerLambertParameters{FT <: AbstractFloat}\n\nThe required parameters for the Beer-Lambert radiative transfer model.\n\nα_PAR_leaf: PAR leaf reflectance (unitless)\nα_NIR_leaf: NIR leaf reflectance\nϵ_canopy: Emissivity of the canopy\nΩ: Clumping index following Braghiere (2021) (unitless)\nλ_γ_PAR: Typical wavelength per PAR photon (m)\nλ_γ_NIR: Typical wavelength per NIR photon (m)\nG_Function: Leaf angle distribution function\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/RadiativeTransfer/#Methods","page":"Canopy RT","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/RadiativeTransfer/","page":"Canopy RT","title":"Canopy RT","text":"ClimaLand.Canopy.compute_absorbances\nClimaLand.Canopy.plant_absorbed_pfd\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.extinction_coeff\nClimaLand.Canopy.canopy_radiant_energy_fluxes!","category":"page"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.plant_absorbed_pfd","page":"Canopy RT","title":"ClimaLand.Canopy.plant_absorbed_pfd","text":"plant_absorbed_pfd(\n RT::BeerLambertModel{FT},\n SW_IN:FT,\n α_leaf::FT,\n LAI::FT,\n K::FT,\n α_soil::FT\n)\n\nComputes the absorbed, reflected, and transmitted photon flux density in terms of mol photons per m^2 per second for a radiation band.\n\nThis applies the Beer-Lambert law, which is a function of incident radiation (SW_IN; moles of photons/m^2/), leaf reflectance (α_leaf), the extinction coefficient (K), leaf area index (LAI), and the albedo of the soil (α_soil). \n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\nplant_absorbed_pfd(\n RT::TwoStreamModel{FT},\n α_leaf,\n SW_IN::FT,\n LAI::FT,\n K::FT,\n τ_leaf,\n θs::FT,\n α_soil::FT,\n)\n\nComputes the absorbed, transmitted, and reflected photon flux density in terms of mol photons per m^2 per second for a radiation band. \n\nThis applies the two-stream radiative transfer solution which takes into account the impacts of scattering within the canopy. The function takes in all parameters from the parameter struct of a TwoStreamModel, along with the incident radiation, LAI, extinction coefficient K, soil albedo from the canopy soil_driver, solar zenith angle, and τ.\n\nReturns a tuple of reflected, absorbed, and transmitted radiation in mol photons/m^2/s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.extinction_coeff","page":"Canopy RT","title":"ClimaLand.Canopy.extinction_coeff","text":"extinction_coeff(ld::FT,\n θs::FT) where {FT}\n\nComputes the vegetation extinction coefficient (K), as a function of the sun zenith angle (θs), and the leaf angle distribution (ld).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/RadiativeTransfer/#ClimaLand.Canopy.canopy_radiant_energy_fluxes!","page":"Canopy RT","title":"ClimaLand.Canopy.canopy_radiant_energy_fluxes!","text":"Canopy.canopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrognosticSoil{F},\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {FT, PSE}\n\nIn standalone mode, this function computes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy.\n\nIn integrated mode, we have already computed those quantities in lsm_radiant_energy_fluxes!, so this method does nothing additional.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\ncanopy_radiant_energy_fluxes!(p::NamedTuple,\n s::PrescribedSoil,\n canopy,\n radiation::PrescribedRadiativeFluxes,\n earth_param_set::PSE,\n Y::ClimaCore.Fields.FieldVector,\n t,\n ) where {PSE}\n\nComputes and stores the net long and short wave radition, in W/m^2, absorbed by the canopy when the canopy is run in standalone mode, with a PrescribedSoil conditions.\n\nLW and SW net radiation are stored in p.canopy.radiative_transfer.LW_n and p.canopy.radiative_transfer.SW_n.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/available_diagnostics/#Available-diagnostic-variables","page":"Available diagnostics","title":"Available diagnostic variables","text":"","category":"section"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"Autogenerate table of available diagnostics:","category":"page"},{"location":"diagnostics/available_diagnostics/","page":"Available diagnostics","title":"Available diagnostics","text":"include(\"make_diagnostic_table.jl\")","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This sets up the simulation that mimicks the coarse sand lab experiment presented in Figures 7 and 8a of Lehmann, Assouline, Or (Phys Rev E 77, 2008).","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We model evaporation using Monin-Obukhov surface theory. In our soil model, it is not possible to set the initial condition corresponding to MOST fluxes, but not include radiative fluxes. This is because for land surface models does not make sense to include atmospheric forcing but not radiative forcing.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Because of this, we need to supply downward welling short and long wave radiation. We chose SW = 0 and LW = σT^4, in order to approximately balance out the blackbody emission of the soil which is accounted for by our model. Our assumption is that in the lab experiment there was no radiative heating or cooling of the soil.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"ref_time = DateTime(2005) # required argument, but not used in this case\nSW_d = (t) -> 0\nLW_d = (t) -> 301.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 5\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.001), params)\n Y.soil.θ_i .= 0\n T = FT(296.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 13)\ndt = Float64(900.0)","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"900.0","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the tendency functions","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\nevaporation_data =\n ClimaLand.Artifacts.lehmann_assouline_or2008_evaporation_data();\nref_soln_E = readdlm(evaporation_data, ',')\nref_soln_E_350mm = ref_soln_E[2:end, 1:2]\ndata_dates = ref_soln_E_350mm[:, 1]\ndata_e = ref_soln_E_350mm[:, 2];\n\nfig = Figure(size = (800, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Evaporation rate (mm/d)\",\n title = \"Bare soil evaporation\",\n)\nCairoMakie.xlims!(minimum(data_dates), maximum(data_dates))\nCairoMakie.lines!(\n ax,\n FT.(data_dates),\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nax = Axis(\n fig[1, 2],\n xlabel = \"Mass (g)\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nA_col = π * (0.027)^2\nmass_0 = sum(sol.u[1].soil.ϑ_l) * 1e6 * A_col\nmass_loss =\n [mass_0 - sum(sol.u[k].soil.ϑ_l) * 1e6 * A_col for k in 1:length(sol.t)]\nCairoMakie.lines!(\n ax,\n cumsum(FT.(data_e)) ./ (1000 * 24) .* A_col .* 1e6,\n FT.(data_e),\n label = \"Data\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n mass_loss,\n evap .* (1000 * 3600 * 24),\n label = \"Model\",\n color = :black,\n)\nsave(\"evaporation_lehmann2008_fig8b.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation/","page":"Coarse Sand Evaporation","title":"Coarse Sand Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The AbstractModel framework allows users to define land component models (e.g. for snow, soil, vegetation, carbon...) which can be run in standalone mode, or as part of a land surface model with many components. In order to achieve this flexibility, we require a standard interface, which is what AbstractModels provide. The interface is designed to work with an external package for the time-stepping of ODEs, ClimaTimesteppers.jl, with ClimaCore.jl, for the spatial discretization of PDEs, and with ClimaLand.jl, for designing and running multi-component land surface models. For a developer of a new land model component, using AbstractModels as shown below is the first step towards building a model which can be run in standalone or with other components in an integrated land surface model.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This tutorial introduces some of the functionality of the AbstractModel interface functions and types. We demonstrate how to use a Model <: AbstractModel structure to define a set of equations, and explain a few core methods which must be defined for your Model type in order to run a simulation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#General-setup","page":"Intro to standalone models","title":"General setup","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We assume you are solving a system of the form of a set of PDEs or ODEs. Additional algebraic equations can be accomodated as well, but only in addition to variables advanced using differential equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Spatially discretized PDEs reduce to a system of ODEs, so we can assume an ODE system in what follows without a loss of generality. When using AbstractModels, you should use ClimaCore to discretize your PDE, as applicable.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Your model defines a system of equations of the following form:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables that are stepped forward via a differential equation are referred to as prognostic variables, and are stored in vecY. Generically, we will speak of the functions vecf as tendencies; these can be functions of the prognostic state, of space vecx, and of time t, as well as of other parameters. Note that quantities such as boundary conditions, source terms, etc, will appear within these tendency functions","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-cache-(\"auxiliary-variables\")","page":"Intro to standalone models","title":"The cache (\"auxiliary variables\")","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"There are typically quantities, which depend on the state vector vecY, location, time, and other parameters, which are expensive to compute, needed multiple times in the tendency computation, or require \"a lot\" of memory to store (e.g., most variables in global runs). Allocating memory \"on-the-fly\" is typically time-consuming. In these cases, it is far better to compute a quantity once and store in a variable where memory has been pre-allocated. The location where memory is allocated is called the model cache.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Denoting the cache as vecp, your equations may be rewritten as:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracd vecYd t = vecf(vecY vecp vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"vecp(vecx t) = vecg(vecY vecx t mboxparams ldots)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The variables vecp at the current timestep are functions of the prognostic state, space, time, and parameters. These variables are referred to as auxiliary variables (or cache variables). Their main purpose is for storing the value of a quantity in a pre-allocated spot in memory, to avoid computing something expensive many times per time-step, or to avoid allocating memory each timestep. From a mathematical point of view, they represent intermediate quantities computed in each tendency. A model purely consisting of algebraic equations, with no prognostic variables, is not supported (vecY cannot be zero dimensional).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"In order to define this set of equations, in a manner which is consistent with the AbstractModel interface (used by ClimaLand.jl) and time-stepping algorithms (OrdinaryDiffEq.jl for the present), the following must be provided.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-Model","page":"Intro to standalone models","title":"The Model","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"All ClimaLand component models are concrete instances of AbstractModels. The reason for grouping them in such a way is because they all have shared required functionality, as we will see, and can make use of common default behavior.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model structure holds all of the information needed to create the full right hand side function, including parameters (which can be functions of space and time), boundary conditions, and physical equations.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The purpose of our AbstractModel interface is that it allows you to run land component models in standalone mode and in an LSM mode without a change in interface.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"As a simple demonstration of use, we'll build a model now which solves Richards Equation assuming a prescribed flux at the surface, and zero flux at the bottom of the column.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that some model equations are stiff and require a very small timestep if stepped explicitly in time. Some model equations are amenable to \"imex\" timestepping, where some tendency functions are stepped implicitly, and some are stepped explicitly. Tagging a tendency function as \"explicit\" or \"implicit\" hardcodes something about the timestepping, and as such, conflates the idea of the model (which defines the equations) and the independent idea of a simulation (which solves the equations). However, we decided we did not need to support the flexibility of solving any set of equations in any way, as we are focused on land surface modeling in particular. In this example, we will tag the tendency as an explicitly time-stepped tendency. A follow-on tutorial will explain how to define an implicit tendency and tendency Jacobian.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Let's first import some needed packages.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaTimeSteppers as CTS\nusing SciMLBase\nusing Plots\nusing ClimaCore\nusing ClimaLand","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Import the functions we are extending for our model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"import ClimaLand:\n name,\n make_exp_tendency,\n make_compute_exp_tendency,\n make_update_aux,\n make_update_boundary_fluxes,\n prognostic_vars,\n prognostic_types,\n prognostic_domain_names,\n auxiliary_vars,\n auxiliary_types,\n auxiliary_domain_names","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The model should contain everything you need to create the tendency function. In this case, that is some parameters, the surface flux boundary value, the floating point precision, and the domain of the model (single column, global run, etc..).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"struct RichardsTutorialModel{FT, D} <: AbstractModel{FT}\n \"van Genuchten model parameters\"\n vGmodel::ClimaLand.Soil.vanGenuchten{FT}\n \"Porosity [unitless]\"\n ν::FT\n \"Residual water fraction [unitless]\"\n θ_r::FT\n \"Saturated hydraulic conductiity [m/s]\"\n Ksat::FT\n \"Surface flux, used as boundary condition [m/s]\"\n F_sfc::FT\n \"Domain of the model\"\n domain::D\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"For reasons that will be clear momentarily, let's also define the name of the model:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.name(model::RichardsTutorialModel) = :soil;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Explicit-tendency","page":"Intro to standalone models","title":"Explicit tendency","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here is where we need to specify the equations of motion. The prognostic variables for Richards equation consist of the volumetric water content at each location in the domain, θ. The differential equations are:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"fracpartial ϑ_lpartial t = - -K(θ) (ψ(θ)+z)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"where K(θ) is the hydraulic conductivity, and ψ(θ) is the matric potential. We now create the function which makes the compute_exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_compute_exp_tendency(model::RichardsTutorialModel)\n function compute_exp_tendency!(dY, Y, p, t)\n gradc2f = ClimaCore.Operators.GradientC2F()\n interpc2f = ClimaCore.Operators.InterpolateC2F()\n FT = FTfromY(Y)\n divf2c = ClimaCore.Operators.DivergenceF2C(\n top = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(model.F_sfc),\n ),\n bottom = ClimaCore.Operators.SetValue(\n ClimaCore.Geometry.WVector.(FT(0)),\n ),\n )\n\n @. dY.soil.θ =\n -(divf2c(-interpc2f(p.soil.K) * gradc2f(p.soil.ψ + p.soil.z)))\n end\n return compute_exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"A couple of notes: the vector vecdY contains the evaluation of the tendency function for each variable in vecY. It is updated in place (so no extra allocations are needed). Note that Y is not a simple array. It is a ClimaCore FieldVector, which allow us to impose some organizational structure on the state while still behaving like arrays in some ways. We use the symbol returned by name(model) to create the naming hierarchy in Y, dY, p. This is useful for multi-component models.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The arguments of compute_exp_tendency! are generic for any time-stepping algorithm. The compute_exp_tendency! function is only created once. If there are time-varying forcing terms appearing, for example, the forcing functions must be stored in model and passed in that way.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#The-prognostic-state-vector-\\vec{Y}-and-cache-\\vec{p}","page":"Intro to standalone models","title":"The prognostic state vector vecY and cache vecp","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We have given the state vector vecY a particular structure, and don't expect the user to build this themselves. In order to have the structure Y (and p) correctly created, the model developer needs to define the names of the prognostic and auxiliary variables, as well as their types (often a floating point scalar), and where in the domain they are defined. For example, the volumetric water content is a scalar (type FT), with name θ, and it is defined throughout the subsurface of the domain.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.prognostic_vars(::RichardsTutorialModel) = (:θ,);\nClimaLand.prognostic_types(::RichardsTutorialModel{FT}) where {FT} = (FT,);\nClimaLand.prognostic_domain_names(::RichardsTutorialModel) = (:subsurface,);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The auxiliary variables for this model are the hydraulic conductivity, matric potential, boundary fluxes, and heights of each level in the domain. All of these are scalars, and some are defined throughout the soil volume, or subsurface, while some are defined only on a surface (at the top or bottom of the domain).","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaLand.auxiliary_vars(::RichardsTutorialModel) =\n (:K, :ψ, :top_flux, :bottom_flux, :z)\nClimaLand.auxiliary_types(::RichardsTutorialModel{FT}) where {FT} =\n (FT, FT, FT, FT, FT);\nClimaLand.auxiliary_domain_names(::RichardsTutorialModel) =\n (:subsurface, :subsurface, :surface, :surface, :subsurface);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Updating-the-cache","page":"Intro to standalone models","title":"Updating the cache","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"We next need to define how we update the auxiliary variables. These are split between two functions, update_aux!, and update_boundary_fluxes!. For standalone component models, these could be combined into a single function, and indeed they could also be part of the tendency function itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function ClimaLand.make_update_aux(model::RichardsTutorialModel)\n function update_aux!(p, Y, t)\n p.soil.z .=\n ClimaCore.Fields.coordinate_field(model.domain.space.subsurface).z # technically this does not need to update each step\n @. p.soil.K = ClimaLand.Soil.hydraulic_conductivity(\n model.vGmodel,\n model.Ksat,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n @. p.soil.ψ = ClimaLand.Soil.matric_potential(\n model.vGmodel,\n ClimaLand.Soil.effective_saturation(model.ν, Y.soil.θ, model.θ_r),\n )\n end\n return update_aux!\nend;\n\nfunction ClimaLand.make_update_boundary_fluxes(model::RichardsTutorialModel)\n function update_boundary_fluxes!(p, Y, t)\n FT = ClimaLand.FTfromY(Y)\n p.soil.top_flux .= model.F_sfc\n p.soil.bottom_flux .= FT(0)\n end\n return update_boundary_fluxes!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The default tendency function in ClimaLand for any AbstractModel carries out the following:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"function make_exp_tendency(model::AbstractModel)\n update_aux! = make_update_aux(model)\n update_boundary_fluxes! = make_update_boundary_fluxes(model)\n compute_exp_tendency! = make_compute_exp_tendency(model)\n function exp_tendency!(dY,Y,p,t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY,Y,p,t)\n end\n return exp_tendency!\nend;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Therefore, each time we need the tendency, we first update auxiliary variables, then update boundary fluxes, and then compute the tendency itself.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Why do we do this? It would be straightforward, and arguably a lot simpler, to update the cache p within compute_exp_tendency!itself. The reason why we introduce these other functions is because we want to be able to combine standalone \"component\" models, like this one, with others, to create land surface models. For example, if we would like to run a land surface model with the soil and the canopy, the canopy auxiliary variables (e.g. interception of water and snow, transmitted radiation) affect the boundary fluxes of the soil. In this case, we must update auxiliary variables for all components, before computing boundary conditions and tendency functions. Please see the (LSM tutorial) for further explanation.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"More complex cases might require the evaluation of external data. For this, use the TimeVaryingInput interface. You can wrap functions, 1D/2D data in TimeVaryingInput to obtain an object that know how to evaluate that data on the model time (e.g., by performing linear interpolation). Then, in your model, you can just call evaluate!(destination, itp, time) to evaluate the itp on the given time and write the result to dest (typically a Field). With this common interface, you do not have to worry about the detail of the underlying data.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-a-simulation","page":"Intro to standalone models","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create a model instance.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"FT = Float32\nvGmodel = ClimaLand.Soil.vanGenuchten{FT}(; α = 2.3f0, n = 2.0f0)\nKsat = FT(4.0e-7)\nν = 0.5f0\nθ_r = 0.0f0\nF_sfc = FT(-3.0e-8)\ndomain = ClimaLand.Domains.Column(; zlim = (-1.0f0, 0.0f0), nelements = 10)\nsoil = RichardsTutorialModel{Float32, typeof(domain)}(\n vGmodel,\n ν,\n θ_r,\n Ksat,\n F_sfc,\n domain,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Create the initial state structure, using the default method. This step creates the vector Y and cache p, but initializes them with zeros.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Note that Y has the structure we planned on in our compute_exp_tendency! function, for x,","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"1-blocked 10-element ClimaCore.Fields.FieldVector{Float32, @NamedTuple{θ::ClimaCore.Fields.Field{ClimaCore.DataLayouts.VF{Float32, 10, Matrix{Float32}}, ClimaCore.Spaces.FiniteDifferenceSpace{ClimaCore.Grids.FiniteDifferenceGrid{ClimaCore.Topologies.IntervalTopology{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.Meshes.IntervalMesh{ClimaCore.Domains.IntervalDomain{ClimaCore.Geometry.ZPoint{Float32}, Tuple{Symbol, Symbol}}, LinRange{ClimaCore.Geometry.ZPoint{Float32}, Int64}}, @NamedTuple{bottom::Int64, top::Int64}}, ClimaCore.Geometry.CartesianGlobalGeometry, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 10, Matrix{Float32}}, ClimaCore.DataLayouts.VF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, 11, Matrix{Float32}}}, ClimaCore.Grids.CellCenter}}}}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The same is true for p:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"p.soil","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"(K = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ψ = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], top_flux = Float32-valued Field:\n Float32[0.0], bottom_flux = Float32-valued Field:\n Float32[0.0], z = Float32-valued Field:\n Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Here we now update Y in place with initial conditions of our choosing.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Y.soil.θ = 0.25f0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set initial cache variable values, and inspect values:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, 0.0);\n@show p.soil.K\n\n@show p.soil.ψ\n\n@show p.soil.top_flux","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Float32-valued Field:\n Float32[-3.0f-8]","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Next up is to create the exp_tendency! function:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"exp_tendency! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/#Running-the-simulation","page":"Intro to standalone models","title":"Running the simulation","text":"","category":"section"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Set the initial and end times, timestep:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"t0 = 0.0;\ntf = 7 * 24 * 3600.0;\ndt = 1800.0;","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Select the timestepping algorithm we want to use from CTS.jl.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"SciMLBase problem statement using CTS.jl internals:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"Solve command:","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = dt);","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"The solution is stored in sol.u[k].soil.θ, where k ranges over the number of timesteps.","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"","category":"page"},{"location":"generated/standalone/Usage/model_tutorial/","page":"Intro to standalone models","title":"Intro to standalone models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand","page":"ClimaLand","title":"ClimaLand","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/ClimaLand/#LSM-Model-Types-and-methods","page":"ClimaLand","title":"LSM Model Types and methods","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.SoilPlantHydrologyModel\nClimaLand.LandSoilBiogeochemistry\nClimaLand.LandHydrology\nClimaLand.make_interactions_update_aux\nClimaLand.initialize_interactions\nClimaLand.land_components\nClimaLand.lsm_aux_vars\nClimaLand.lsm_aux_types\nClimaLand.lsm_aux_domain_names\nClimaLand.domain_name","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.LandSoilBiogeochemistry","page":"ClimaLand","title":"ClimaLand.LandSoilBiogeochemistry","text":"struct LandSoilBiogeochemistry{\n FT,\n SEH <: Soil.EnergyHydrology{FT},\n SB <: Soil.Biogeochemistry.SoilCO2Model{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil energy, hydrology, and biogeochemistry component.\n\nsoil: The soil model\nsoilco2: The biochemistry model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.LandHydrology","page":"ClimaLand","title":"ClimaLand.LandHydrology","text":"struct LandHydrology{\n FT,\n SM <: Soil.AbstractSoilModel{FT},\n SW <: Pond.AbstractSurfaceWaterModel{FT},\n} <: AbstractLandModel{FT}\n\nA concrete type of land model used for simulating systems with a soil and surface water component.\n\nsoil: The soil model\nsurface_water: The surface water model\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.land_components","page":"ClimaLand","title":"ClimaLand.land_components","text":"land_components(land::AbstractLandModel)\n\nReturns the component names of the land model, by calling propertynames(land).\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_vars","page":"ClimaLand","title":"ClimaLand.lsm_aux_vars","text":"lsmauxvars(m::AbstractLandModel)\n\nReturns the additional aux variable symbols for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_vars(m::SoilCanopyModel)\n\nThe names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_types","page":"ClimaLand","title":"ClimaLand.lsm_aux_types","text":"lsmauxtypes(m::AbstractLandModel)\n\nReturns the shared additional aux variable types for the model in the form of a tuple.\n\n\n\n\n\nlsm_aux_types(m::SoilCanopyModel)\n\nThe types of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.lsm_aux_domain_names","page":"ClimaLand","title":"ClimaLand.lsm_aux_domain_names","text":"lsmauxdomain_names(m::AbstractLandModel)\n\nReturns the additional domain symbols in the form of a tuple e.g. :surface or :subsurface.\n\nThis is only required for variables shared between land submodels, and only needed for multi-component models, not standalone components. Component-specific variables should be listed as prognostic or auxiliary variables which do not require this to initialize.\n\n\n\n\n\nlsm_aux_domain_names(m::SoilCanopyModel)\n\nThe domain names of the additional auxiliary variables that are included in the integrated Soil-Canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#Land-Hydrology","page":"ClimaLand","title":"Land Hydrology","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.infiltration_capacity\nClimaLand.infiltration_at_point\nClimaLand.PrognosticRunoff\nClimaLand.RunoffBC","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_capacity","page":"ClimaLand","title":"ClimaLand.infiltration_capacity","text":"function infiltration_capacity(\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n)\n\nFunction which computes the infiltration capacity of the soil based on soil characteristics, moisture levels, and pond height.\n\nDefined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.infiltration_at_point","page":"ClimaLand","title":"ClimaLand.infiltration_at_point","text":"infiltration_at_point(η::FT, i_c::FT, P::FT)\n\nReturns the infiltration given pond height η, infiltration capacity, and precipitation.\n\nThis is defined such that positive means into soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticRunoff","page":"ClimaLand","title":"ClimaLand.PrognosticRunoff","text":"PrognosticRunoff <: Pond.AbstractSurfaceRunoff\n\nConcrete type of Pond.AbstractSurfaceRunoff for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically.\n\nThis is paired with Soil.RunoffBC: both are used at the same time, ensuring the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RunoffBC","page":"ClimaLand","title":"ClimaLand.RunoffBC","text":"RunoffBC <: Soil.AbstractSoilBC\n\nConcrete type of Soil.AbstractSoilBC for use in LSM models, where precipitation is passed in, but infiltration is computed prognostically. This infiltration is then used to set an upper boundary condition for the soil.\n\nThis is paired with Pond.PrognosticRunoff: both are used at the same time, ensuring that the infiltration used for the boundary condition of soil is also used to compute the runoff for the surface water.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#SoilCanopyModel","page":"ClimaLand","title":"SoilCanopyModel","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticSoil\nClimaLand.RootExtraction","category":"page"},{"location":"APIs/ClimaLand/#ClimaLand.PrognosticSoil","page":"ClimaLand","title":"ClimaLand.PrognosticSoil","text":" PrognosticSoil{FT} <: AbstractSoilDriver\n\nConcrete type of AbstractSoilDriver used for dispatch in cases where both a canopy model and soil model are run.\n\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#ClimaLand.RootExtraction","page":"ClimaLand","title":"ClimaLand.RootExtraction","text":"RootExtraction{FT} <: Soil.AbstractSoilSource{FT}\n\nConcrete type of Soil.AbstractSoilSource, used for dispatch in an LSM with both soil and plant hydraulic components.\n\nThis is paired with the source term Canopy.PrognosticSoil:both are used at the same time, ensuring that the water flux into the roots is extracted correctly from the soil.\n\n\n\n\n\n","category":"type"},{"location":"APIs/ClimaLand/#LandSoilBiogeochemistry","page":"ClimaLand","title":"LandSoilBiogeochemistry","text":"","category":"section"},{"location":"APIs/ClimaLand/","page":"ClimaLand","title":"ClimaLand","text":"ClimaLand.PrognosticMet","category":"page"},{"location":"APIs/shared_utilities/#Shared-Utilities","page":"Shared Utilities","title":"Shared Utilities","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"CurrentModule = ClimaLand","category":"page"},{"location":"APIs/shared_utilities/#Domains","page":"Shared Utilities","title":"Domains","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.Domains.AbstractDomain\nClimaLand.Domains.AbstractLSMDomain\nClimaLand.Domains.SphericalShell\nClimaLand.Domains.SphericalSurface\nClimaLand.Domains.HybridBox\nClimaLand.Domains.Column\nClimaLand.Domains.Plane\nClimaLand.Domains.Point\nClimaLand.Domains.coordinates\nClimaLand.Domains.obtain_face_space\nClimaLand.Domains.obtain_surface_space\nClimaLand.Domains.obtain_surface_domain\nClimaLand.Domains.top_center_to_surface\nClimaLand.Domains.top_face_to_surface\nClimaLand.Domains.linear_interpolation_to_surface!\nClimaLand.Domains.get_Δz\n","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.AbstractDomain","page":"Shared Utilities","title":"ClimaLand.Domains.AbstractDomain","text":"AbstractDomain{FT <:AbstractFloat}\n\nAn abstract type for domains.\n\nThe domain structs typically hold information regarding the bounds of the domain, the boundary condition type (periodic or not), and the spatial discretization.\n\nAdditionally, the domain struct holds the relevant spaces for that domain. For example, a 3D domain holds the center space (in terms of finite difference - the space corresponding to the centers of each element), and the top face space where surface fluxes are computed.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalShell","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalShell","text":"struct SphericalShell{FT} <: AbstractDomain{FT}\n radius::FT\n depth::FT\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) x a 1d finite difference space (radial direction), and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nradius: The radius of the shell\ndepth: The radial extent of the shell\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: The number of elements to be used in the non-radial and radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.SphericalSurface","page":"Shared Utilities","title":"ClimaLand.Domains.SphericalSurface","text":"struct SphericalSurface{FT} <: AbstractDomain{FT}\n radius::FT\n nelements::Tuple{Int, Int}\n npolynomial::Int\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (non-radial directions) and the resulting coordinate field.\n\nspace is a NamedTuple holding the surface space (in this case, the entire SphericalSurface space).\n\nFields\n\nradius: The radius of the surface\nnelements: The number of elements to be used in the non-radial directions\nnpolynomial: The polynomial order to be used in the non-radial directions\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface (SphericalSurface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.HybridBox","page":"Shared Utilities","title":"ClimaLand.Domains.HybridBox","text":"struct HybridBox{FT} <: AbstractDomain{FT}\n xlim::Tuple{FT, FT}\n ylim::Tuple{FT, FT}\n zlim::Tuple{FT, FT}\n longlat::Union{Nothing, Tuple{FT, FT}},\n dz_tuple::Union{Tuple{FT, FT}, Nothing}\n nelements::Tuple{Int, Int, Int}\n npolynomial::Int\n periodic::Tuple{Bool, Bool}\nend\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space (horizontal) x a 1d finite difference space (vertical), and the resulting coordinate field. This domain is not periodic along the z-axis. Note that no-flow boundary conditions are supported in the horizontal.\n\nWhen longlat is not nothing, assume that the box describes a region on the globe centered around the long and lat.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nzlim: Domain interval limits along z axis, in meters\nlonglat: When not nothing, a Tuple that contains the center long and lat.\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nnelements: Number of elements to discretize interval, (nx, ny,nz)\nnpolynomial: Polynomial order for the horizontal directions\nperiodic: Flag indicating periodic boundaries in horizontal\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Column","page":"Shared Utilities","title":"ClimaLand.Domains.Column","text":"Column{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a center and face space, etc. for use when a finite difference in 1D is suitable, as for a soil column model.\n\nspace is a NamedTuple holding the surface space (in this case, the top face space) and the center space for the subsurface. These are stored using the keys :surface and :subsurface.\n\nFields\n\nzlim: Domain interval limits, (zmin, zmax), in meters\nnelements: Number of elements used to discretize the interval\ndz_tuple: Tuple for mesh stretching specifying target (dzbottom, dztop) (m). If nothing, no stretching is applied.\nboundary_names: Boundary face identifiers\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface space and subsurface center space\nfields: Fields associated with the coordinates of the domain that are useful to store\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Plane","page":"Shared Utilities","title":"ClimaLand.Domains.Plane","text":"Plane{FT} <: AbstractDomain{FT}\n\nA struct holding the necessary information to construct a domain, a mesh, a 2d spectral element space, and the resulting coordinate field.\n\nWhen longlat is not nothing, the plane is assumed to be centered around these coordinates. In this case, the curvature of the Earth is not accounted for.\n\nlonglat are in degrees, with longitude going from -180 to 180.\n\n:warning: Only independent columns are supported! (No lateral flow).\n\nspace is a NamedTuple holding the surface space (in this case, the entire Plane space).\n\nFields\n\nxlim: Domain interval limits along x axis, in meters or degrees (if latlong != nothing)\nylim: Domain interval limits along y axis, in meters or degrees (if latlong != nothing)\nlonglat: When not nothing, a Tuple that contains the center long and lat (in degrees, with long from -180 to 180).\nnelements: Number of elements to discretize interval, (nx, ny)\nperiodic: Flags for periodic boundaries. Only periodic or no lateral flow is supported.\nnpolynomial: Polynomial order for both x and y\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the surface(Plane) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.Point","page":"Shared Utilities","title":"ClimaLand.Domains.Point","text":"Point{FT} <: AbstractDomain{FT}\n\nA domain for single column surface variables. For models such as ponds, snow, plant hydraulics, etc. Enables consistency in variable initialization across all domains.\n\nspace is a NamedTuple holding the surface space (in this case, the Point space).\n\nFields\n\nz_sfc: Surface elevation relative to a reference (m)\nspace: A NamedTuple of associated ClimaCore spaces: in this case, the Point (surface) space\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.coordinates","page":"Shared Utilities","title":"ClimaLand.Domains.coordinates","text":"coordinates(domain::AbstractDomain)\n\nReturns the coordinate fields for the domain as a NamedTuple.\n\nThe returned coordinates are stored with keys :surface, :subsurface, e.g. as relevant for the domain.\n\n\n\n\n\nDomains.coordinates(model::AbstractLandModel)\n\nReturns a NamedTuple of the unique set of coordinates for the LSM model, where the unique set is taken over the coordinates of all of the subcomponents.\n\nFor example, an LSM with a single layer snow model, multi-layer soil model, and canopy model would have a coordinate set corresponding to the coordinates of the surface (snow), the subsurface coordinates (soil) and the coordinates of the surface (canopy). This would return the coordinates of the surface and subsurface. These are distinct because the subsurface coordinates correspond to the centers of the layers, while the surface corresponds to the top face of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_face_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_face_space","text":"obtain_face_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the face space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the face space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_face_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_space","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_space","text":"obtain_surface_space(cs::ClimaCore.Spaces.AbstractSpace)\n\nReturns the surface space, if applicable, for the center space cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterExtrudedFiniteDifferenceSpace)\n\nReturns the horizontal space for the CenterExtrudedFiniteDifferenceSpace cs.\n\n\n\n\n\nobtain_surface_space(cs::ClimaCore.Spaces.CenterFiniteDifferenceSpace)\n\nReturns the top level of the face space corresponding to the CenterFiniteDifferenceSpace cs.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.obtain_surface_domain","page":"Shared Utilities","title":"ClimaLand.Domains.obtain_surface_domain","text":"obtain_surface_domain(d::AbstractDomain) where {FT}\n\nDefault method throwing an error; any domain with a corresponding domain should define a new method of this function.\n\n\n\n\n\nobtain_surface_domain(c::Column{FT}) where {FT}\n\nReturns the Point domain corresponding to the top face (surface) of the Column domain c.\n\n\n\n\n\nobtain_surface_domain(b::HybridBox{FT}) where {FT}\n\nReturns the Plane domain corresponding to the top face (surface) of the HybridBox domain b.\n\n\n\n\n\nobtain_surface_domain(s::SphericalShell{FT}) where {FT}\n\nReturns the SphericalSurface domain corresponding to the top face (surface) of the SphericalShell domain s.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_center_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_center_to_surface","text":"top_center_to_surface(center_field::ClimaCore.Fields.Field)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which center_field is defined, with values equal to the those at the level of the top center.\n\nFor example, given a center_field defined on 1D center finite difference space, this would return a field defined on the Point space of the surface of the column. The value would be the value of the oroginal center_field at the topmost location. Given a center_field defined on a 3D extruded center finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\ntop_center_to_surface(val)\n\nWhen val is a scalar (e.g. a single float or struct), returns val.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.top_face_to_surface","page":"Shared Utilities","title":"ClimaLand.Domains.top_face_to_surface","text":"top_face_to_surface(face_field::ClimaCore.Fields.Field, surface_space)\n\nCreates and returns a ClimaCore.Fields.Field defined on the space corresponding to the surface of the space on which face_field is defined, with values equal to the those at the level of the top face.\n\nGiven a face_field defined on a 3D extruded face finite difference space, this would return a 2D field corresponding to the surface, with values equal to the topmost level.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.linear_interpolation_to_surface!","page":"Shared Utilities","title":"ClimaLand.Domains.linear_interpolation_to_surface!","text":"linear_interpolation_to_surface!(sfc_field, center_field, z, Δz_top)\n\nLinearly interpolate the center field center_field to the surface defined by the top face coordinate of z with a center to face distance Δz_top in the first layer; updates the sfc_field on the surface (face) space in place.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.Domains.get_Δz","page":"Shared Utilities","title":"ClimaLand.Domains.get_Δz","text":"get_Δz(z::ClimaCore.Fields.Field)\n\nA function to return a tuple containing the distance between the top boundary and its closest center, and the bottom boundary and its closest center, both as Fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Models","page":"Shared Utilities","title":"Models","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.AbstractModel\nClimaLand.AbstractImExModel\nClimaLand.AbstractExpModel\nClimaLand.make_exp_tendency\nClimaLand.make_imp_tendency\nClimaLand.make_compute_exp_tendency\nClimaLand.make_compute_imp_tendency\nClimaLand.make_update_aux\nClimaLand.make_update_boundary_fluxes\nClimaLand.make_set_initial_cache\nClimaLand.make_update_drivers\nClimaLand.prognostic_vars\nClimaLand.prognostic_types\nClimaLand.prognostic_domain_names\nClimaLand.auxiliary_vars\nClimaLand.auxiliary_types\nClimaLand.auxiliary_domain_names\nClimaLand.initialize_prognostic\nClimaLand.initialize_auxiliary\nClimaLand.initialize\nClimaLand.name\nClimaLand.AbstractBC\nClimaLand.AbstractSource\nClimaLand.source!\nClimaLand.AbstractBoundary\nClimaLand.TopBoundary\nClimaLand.BottomBoundary\nClimaLand.boundary_flux\nClimaLand.diffusive_flux\nClimaLand.boundary_vars\nClimaLand.boundary_var_domain_names\nClimaLand.boundary_var_types\nClimaLand.make_jacobian\nClimaLand.make_compute_jacobian\nClimaLand.set_dfluxBCdY!\nClimaLand.get_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractModel","page":"Shared Utilities","title":"ClimaLand.AbstractModel","text":"abstract type AbstractModel{FT <: AbstractFloat}\n\nAn abstract type for all models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractImExModel","page":"Shared Utilities","title":"ClimaLand.AbstractImExModel","text":"AbstractImExModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated implicitly (and which may also have tendency terms that can be treated explicitly). This inherits all the default function definitions from AbstractModel, as well as make_imp_tendency and make_compute_imp_tendency defaults.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractExpModel","page":"Shared Utilities","title":"ClimaLand.AbstractExpModel","text":"AbstractExpModel{FT} <: AbstractModel{FT}\n\nAn abstract type for models which must be treated explicitly. This inherits all the default function definitions from AbstractModel, as well as a make_imp_tendency default.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.make_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_exp_tendency","text":"make_exp_tendency(model::AbstractModel)\n\nReturns an exp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped explicitly.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_imp_tendency","text":"make_imp_tendency(model::AbstractImExModel)\n\nReturns an imp_tendency that updates auxiliary variables and updates the prognostic state of variables that are stepped implicitly.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\nmake_imp_tendency(model::AbstractModel)\n\nReturns an imp_tendency that does nothing. This model type is not stepped explicity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_exp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_exp_tendency","text":"make_explicit_tendency(model::Soil.RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nConstruct the tendency computation function for the explicit terms of the RHS, which are horizontal components and source/sink terms.\n\n\n\n\n\nmake_compute_exp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_exp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for Y.soil.ϑ_l, Y.soil.θ_i, Y.soil.ρe_int, and updates dY.soil in place with those values. All of these quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::BucketModel{FT}) where {FT}\n\nCreates the computeexptendency! function for the bucket model.\n\n\n\n\n\nmake_compute_exp_tendency(model::AbstractModel)\n\nReturn a compute_exp_tendency! function that updates state variables that we will be stepped explicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any explicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_exp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n ClimaLand.make_compute_exp_tendency(component::AbstractCanopyComponent, canopy)\n\nCreates the computeexptendency!(dY,Y,p,t) function for the canopy component.\n\nSince component models are not standalone models, other information may be needed and passed in (via the canopy model itself). The right hand side for the entire canopy model can make use of these functions for the individual components.\n\n\n\n\n\nmake_compute_exp_tendency(canopy::CanopyModel)\n\nCreates and returns the computeexptendency! for the CanopyModel.\n\n\n\n\n\nmake_compute_exp_tendency(model::SoilCO2Model)\n\nAn extension of the function make_compute_exp_tendency, for the soilco2 equation. This function creates and returns a function which computes the entire right hand side of the PDE for C, and updates dY.soil.C in place with that value. These quantities will be stepped explicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_exp_tendency(model::PlantHydraulicsModel, _)\n\nA function which creates the computeexptendency! function for the PlantHydraulicsModel. The computeexptendency! function must comply with a rhs function of SciMLBase.jl.\n\nBelow, fa denotes a flux multiplied by the relevant cross section (per unit area ground, or area index, AI). The tendency for the ith compartment can be written then as: ∂ϑ[i]/∂t = 1/(AI*dz)[fa[i]-fa[i+1]).\n\nNote that if the area_index is zero because no plant is present, AIdz is zero, and the fluxes fa appearing in the numerator are zero because they are scaled by AI.\n\nTo prevent dividing by zero, we change AI/(AI x dz)\" to \"AI/max(AI x dz, eps(FT))\"\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_imp_tendency","page":"Shared Utilities","title":"ClimaLand.make_compute_imp_tendency","text":"make_compute_imp_tendency(model::RichardsModel)\n\nAn extension of the function make_compute_imp_tendency, for the Richardson- Richards equation.\n\nThis function creates and returns a function which computes the entire right hand side of the PDE for ϑ_l, and updates dY.soil.ϑ_l in place with that value.\n\n\n\n\n\nmake_compute_imp_tendency(model::EnergyHydrology)\n\nAn extension of the function make_compute_imp_tendency, for the integrated soil energy and heat equations, including phase change.\n\nThis version of this function computes the right hand side of the PDE for Y.soil.ϑ_l, which is the only quantity we currently step implicitly.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_compute_imp_tendency(model::AbstractModel)\n\nReturn a compute_imp_tendency! function that updates state variables that we will be stepped implicitly. This fallback sets all tendencies of this model to zero, which is appropriate for models that do not have any implicit tendencies to update. Note that we cannot set dY .= 0 here because this would overwrite the tendencies of all models in the case of an integrated LSM.\n\ncompute_imp_tendency! should be compatible with SciMLBase.jl solvers.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_aux","page":"Shared Utilities","title":"ClimaLand.make_update_aux","text":"make_update_aux(model::RichardsModel)\n\nAn extension of the function make_update_aux, for the Richardson- Richards equation.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::EnergyHydrology)\n\nAn extension of the function make_update_aux, for the integrated soil hydrology and energy model.\n\nThis function creates and returns a function which updates the auxiliary variables p.soil.variable in place.\n\nThis has been written so as to work with Differential Equations.jl.\n\n\n\n\n\nmake_update_aux(model::BucketModel{FT}) where {FT}\n\nCreates the update_aux! function for the BucketModel.\n\n\n\n\n\nmake_update_aux(model::AbstractModel)\n\nReturn an update_aux! function that updates auxiliary parameters p.\n\n\n\n\n\n ClimaLand.make_update_aux(canopy::CanopyModel{FT,\n <:AutotrophicRespirationModel,\n <:Union{BeerLambertModel, TwoStreamModel},\n <:FarquharModel,\n <:MedlynConductanceModel,\n <:PlantHydraulicsModel,},\n ) where {FT}\n\nCreates the update_aux! function for the CanopyModel; a specific method for update_aux! for the case where the canopy model components are of the type in the parametric type signature: AutotrophicRespirationModel, AbstractRadiationModel, FarquharModel, MedlynConductanceModel, and PlantHydraulicsModel.\n\nPlease note that the plant hydraulics model has auxiliary variables that are updated in its prognostic compute_exp_tendency! function. While confusing, this is better for performance as it saves looping over the state vector multiple times.\n\nThe other sub-components rely heavily on each other, so the version of the CanopyModel with these subcomponents has a single update_aux! function, given here.\n\n\n\n\n\nmake_update_aux(model::SoilCO2Model)\n\nAn extension of the function make_update_aux, for the soilco2 equation. This function creates and returns a function which updates the auxiliary variables p.soil.variable in place. This has been written so as to work with Differential Equations.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_boundary_fluxes","page":"Shared Utilities","title":"ClimaLand.make_update_boundary_fluxes","text":"make_update_boundary_fluxes(model::AbstractModel)\n\nReturn an update_boundary_fluxes! function that updates the auxiliary parameters in p corresponding to boundary fluxes or interactions between componets..\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::LandHydrology{FT, SM, SW},\n) where {FT, SM <: Soil.RichardsModel{FT}, SW <: Pond.PondModel{FT}}\n\nA method which makes a function; the returned function updates the auxiliary variable p.soil_infiltration, which is needed for both the boundary condition for the soil model and the source term (runoff) for the surface water model.\n\nThis function is called each ode function evaluation.\n\n\n\n\n\nmake_update_boundary_fluxes(\n land::SoilCanopyModel{FT, MM, SM, RM},\n) where {\n FT,\n MM <: Soil.Biogeochemistry.SoilCO2Model{FT},\n SM <: Soil.RichardsModel{FT},\n RM <: Canopy.CanopyModel{FT}\n }\n\nA method which makes a function; the returned function updates the additional auxiliary variables for the integrated model, as well as updates the boundary auxiliary variables for all component models. \n\nThis function is called each ode function evaluation, prior to the tendency function evaluation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_set_initial_cache","page":"Shared Utilities","title":"ClimaLand.make_set_initial_cache","text":"make_set_initial_cache(model::AbstractModel)\n\nReturns the setinitialcache! function, which updates the auxiliary state p in place with the initial values corresponding to Y(t=t0) = Y0.\n\nIn principle, this function is not needed, because in the very first evaluation of either explicit_tendency or implicit_tendency, at t=t0, the auxiliary state is updated using the initial conditions for Y=Y0. However, without setting the initial p state prior to running the simulation, the value of p in the saved output at t=t0 will be unset.\n\nFurthermore, specific methods of this function may be useful for models which store time indepedent spatially varying parameter fields in the auxiliary state. In this case, update_aux! does not need to do anything, but they do need to be set with the initial (constant) values before the simulation can be carried out.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_update_drivers","page":"Shared Utilities","title":"ClimaLand.make_update_drivers","text":"make_update_drivers(::AbstractClimaLandDrivers)\n\nCreates and returns a function which updates the driver variables in the default case of no drivers. More generally, this should return a function which updates the driver fields stored in p.drivers.\n\n\n\n\n\nmake_update_drivers(driver_tuple)\n\nCreates and returns a function which updates the forcing variables (\"drivers\"). If no drivers are being used, driver_tuple is empty, and the update function does nothing.\n\n\n\n\n\nmake_update_drivers(a::PrescribedAtmosphere{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedAtmosphere.\n\n\n\n\n\nmake_update_drivers(a::PrescribedPrecipitation{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedPrecipitation.\n\n\n\n\n\nmake_update_drivers(r::PrescribedRadiativeFluxes{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedRadiativeFluxes.\n\n\n\n\n\nmake_update_drivers(d::PrescribedSoilOrganicCarbon{FT}) where {FT}\n\nCreates and returns a function which updates the driver variables in the case of a PrescribedSoilOrganicCarbon.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_vars","page":"Shared Utilities","title":"ClimaLand.prognostic_vars","text":"prognostic_vars(soil::RichardsModel)\n\nA function which returns the names of the prognostic variables of RichardsModel.\n\n\n\n\n\nprognostic_vars(soil::EnergyHydrology)\n\nA function which returns the names of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_vars(::SnowModel)\n\nReturns the prognostic variable names of the snow model.\n\nFor this model, we track the snow water equivalent S [m] and the energy per unit area U [J/m^2] prognostically.\n\n\n\n\n\nprognostic_vars(m::AbstractModel)\n\nReturns the prognostic variable symbols for the model in the form of a tuple.\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_vars(::AbstractCanopyComponent)\n\nReturns the prognostic vars of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_vars(canopy::CanopyModel)\n\nReturns the prognostic variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nprognostic_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the prognostic variables of the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_types","page":"Shared Utilities","title":"ClimaLand.prognostic_types","text":"prognostic_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the prognostic variables of EnergyHydrology.\n\n\n\n\n\nprognostic_types(::SnowModel{FT})\n\nReturns the prognostic variable types of the snow model; both snow water equivalent and energy per unit area are scalars.\n\n\n\n\n\nprognostic_types(m::AbstractModel{FT}) where {FT}\n\nReturns the prognostic variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nHere, the coordinate points are those returned by coordinates(model).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nClimaLand.prognostic_types(::AbstractCanopyComponent)\n\nReturns the prognostic types of the canopy component passed in as an argument.\n\n\n\n\n\nprognostic_types(canopy::CanopyModel)\n\nReturns the prognostic types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.prognostic_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the prognostic types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.prognostic_domain_names","page":"Shared Utilities","title":"ClimaLand.prognostic_domain_names","text":"prognostic_domain_names(::SnowModel)\n\nReturns the prognostic variable domain names of the snow model; both snow water equivalent and energy per unit area are modeling only as a function of (x,y), and not as a function of depth. Therefore their domain name is \":surface\".\n\n\n\n\n\nprognosticdomainnames(m::AbstractModel)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\nNote that this default suggests that a model has no prognostic variables, which is an invalid model setup. This function is meant to be extended for all models.\n\n\n\n\n\nprognosticdomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the prognostic variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_vars","page":"Shared Utilities","title":"ClimaLand.auxiliary_vars","text":"auxiliary_vars(soil::RichardsModel)\n\nA function which returns the names of the auxiliary variables of RichardsModel.\n\n\n\n\n\nauxiliary_vars(soil::EnergyHydrology)\n\nA function which returns the names of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_vars(::SnowModel)\n\nReturns the auxiliary variable names for the snow model. These include the mass fraction in liquid water (q_l, unitless), the bulk temperature (T, K), the surface temperature (T_sfc, K), the SHF, LHF, and vapor flux (turbulent_fluxes.shf, etc), the net radiation (R_n, J/m^2/s), the energy flux in liquid water runoff (energy_runoff, J/m^2/s), the water volume in runoff (water_runoff, m/s), and the total energy and water fluxes applied to the snowpack.\n\nSince the snow can melt completely in one timestep, we clip the water and energy fluxes such that SWE cannot become negative and U cannot become unphysical. The clipped values are what are actually applied as boundary fluxes, and are stored in applied_ fluxes.\n\n\n\n\n\nauxiliary_vars(m::AbstractModel)\n\nReturns the auxiliary variable symbols for the model in the form of a tuple.\n\n\n\n\n\nClimaLand.auxiliary_vars(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_vars(canopy::CanopyModel)\n\nReturns the auxiliary variables for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nauxiliary_vars(model::PlantHydraulicsModel)\n\nA function which returns the names of the auxiliary variables of the PlantHydraulicsModel, the transpiration stress factor β (unitless), the water potential ψ (m), the volume fluxcross section fa (1/s), and the volume fluxroot cross section in the roots fa_roots (1/s), where the cross section can be represented by an area index.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_types","page":"Shared Utilities","title":"ClimaLand.auxiliary_types","text":"auxiliary_types(soil::RichardsModel)\n\nA function which returns the names of the auxiliary types of RichardsModel.\n\n\n\n\n\nauxiliary_types(soil::EnergyHydrology{FT}) where {FT}\n\nA function which returns the types of the auxiliary variables of EnergyHydrology.\n\n\n\n\n\nauxiliary_types(m::AbstractModel{FT}) where {FT}\n\nReturns the auxiliary variable types for the model in the form of a tuple.\n\nTypes provided must have ClimaCore.RecursiveApply.rzero(T::DataType) defined. Common examples include\n\nFloat64, Float32 for scalar variables (a scalar value at each\n\ncoordinate point)\n\nSVector{k,Float64} for a mutable but statically sized array of\n\nlength k at each coordinate point.\n\nNote that Arrays, MVectors are not isbits and cannot be used.\n\nHere, the coordinate points are those returned by coordinates(model).\n\n\n\n\n\nClimaLand.auxiliary_types(::AbstractCanopyComponent)\n\nReturns the auxiliary types of the canopy component passed in as an argument.\n\n\n\n\n\nauxiliary_types(canopy::CanopyModel)\n\nReturns the auxiliary types for the canopy model by looping over each sub-component name in canopy_components.\n\nThis relies on the propertynames of CanopyModel being the same as those returned by canopy_components.\n\n\n\n\n\nClimaLand.auxiliary_types(model::PlantHydraulicsModel{FT}) where {FT}\n\nDefines the auxiliary types for the PlantHydraulicsModel.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.auxiliary_domain_names","page":"Shared Utilities","title":"ClimaLand.auxiliary_domain_names","text":"auxiliary_domain_names(soil::RichardsModel)\n\nA function which returns the names of the auxiliary domain names of RichardsModel.\n\n\n\n\n\nauxiliarydomainnames(m::AbstractModel)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\nExamples: (:surface, :surface, :subsurface).\n\n\n\n\n\nauxiliarydomainnames(m::AbstractCanopyComponent)\n\nReturns the domain names for the auxiliary variables in the form of a tuple.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_prognostic","page":"Shared Utilities","title":"ClimaLand.initialize_prognostic","text":"initialize_prognostic(model::AbstractModel, state::NamedTuple)\n\nReturns a FieldVector of prognostic variables for model with the required structure, with values equal to similar(state). This assumes that all prognostic variables are defined over the entire domain, and that all prognostic variables have the same dimension and type.\n\nIf a model has no prognostic variables, the returned FieldVector contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different prognostic variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_prognostic(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the prognostic variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_prognostic(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the prognostic state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input state is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models prognostic state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize_auxiliary","page":"Shared Utilities","title":"ClimaLand.initialize_auxiliary","text":"initialize_auxiliary(model::AbstractModel, state::NamedTuple)\n\nReturns a NamedTuple of auxiliary variables for model with the required structure, with values equal to similar(state). This assumes that all auxiliary variables are defined over the entire domain, and that all auxiliary variables have the same dimension and type. The auxiliary variables NamedTuple can also hold preallocated objects which are not Fields.\n\nIf a model has no auxiliary variables, the returned NamedTuple contains only an empty array.\n\nThe input state is an array-like object, usually a ClimaCore Field or a Vector{FT}.\n\nAdjustments to this - for example because different auxiliary variables have different dimensions - require defining a new method.\n\n\n\n\n\ninitialize_auxiliary(\n component::AbstractCanopyComponent,\n state,\n)\n\nCreates and returns a ClimaCore.Fields.FieldVector with the auxiliary variables of the canopy component component, stored using the name of the component.\n\nThe input state is usually a ClimaCore Field object.\n\n\n\n\n\ninitialize_auxiliary(\n model::CanopyModel{FT},\n coords,\n) where {FT}\n\nCreates the auxiliary state vector of the CanopyModel and returns it as a ClimaCore.Fields.FieldVector.\n\nThe input coords is usually a ClimaCore Field object.\n\nThis function loops over the components of the CanopyModel and appends each component models auxiliary state vector into a single state vector, structured by component name.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.initialize","page":"Shared Utilities","title":"ClimaLand.initialize","text":"initialize(model::AbstractModel)\n\nCreates the prognostic and auxiliary states structures, but with unset values; constructs and returns the coordinates for the model domain. We may need to consider this default more as we add diverse components and Simulations.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.name","page":"Shared Utilities","title":"ClimaLand.name","text":"name(model::AbstractModel)\n\nReturns a symbol of the model component name, e.g. :soil or :vegetation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBC","page":"Shared Utilities","title":"ClimaLand.AbstractBC","text":"AbstractBC\n\nAn abstract type for types of boundary conditions, which will include prescribed functions of space and time as Dirichlet conditions or Neumann conditions, in addition to other convenient conditions.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractSource","page":"Shared Utilities","title":"ClimaLand.AbstractSource","text":"AbstractSource{FT <: AbstractFloat}\n\nAn abstract type for types of source terms.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.source!","page":"Shared Utilities","title":"ClimaLand.source!","text":" source!(dY::ClimaCore.Fields.FieldVector,\n src::PhaseChange{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nComputes the source terms for phase change.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::SoilSublimation{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model\n )\n\nUpdates dY.soil.θ_i in place with a term due to sublimation; this only affects the surface layer of soil.\n\n\n\n\n\n source!(dY::ClimaCore.Fields.FieldVector,\n src::AbstractSource,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n )::ClimaCore.Fields.Field\n\nA stub function, which is extended by ClimaLand.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::RootExtraction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple\n model::EnergyHydrology)\n\nAn extension of the ClimaLand.source! function, which computes source terms for the soil model; this method returns the water and energy loss/gain due to root extraction.\n\n\n\n\n\nClimaLand.source!(dY::ClimaCore.Fields.FieldVector,\n src::MicrobeProduction,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n params)\n\nA method which extends the ClimaLand source! function for the case of microbe production of CO2 in soil.\n\n\n\n\n\nClimaLand.source!(\n dY::ClimaCore.Fields.FieldVector,\n src::TOPMODELSubsurfaceRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n model::AbstractSoilModel{FT},\n) where {FT}\n\nAdjusts dY.soil.ϑ_l in place to account for the loss of water due to subsurface runoff.\n\nThe sink term is given by - Rss/h∇ H(twc - ν), where H is the Heaviside function, h∇ is the water table thickness (defined to be where twc>ν), where twc is the total water content, and Rss is the runoff as a flux(m/s).\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractBoundary","page":"Shared Utilities","title":"ClimaLand.AbstractBoundary","text":"AbstractBoundary\n\nAn abstract type to indicate which boundary we are doing calculations for. Currently, we support the top boundary (TopBoundary) and bottom boundary (BottomBoundary).\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.TopBoundary","page":"Shared Utilities","title":"ClimaLand.TopBoundary","text":"TopBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the top boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.BottomBoundary","page":"Shared Utilities","title":"ClimaLand.BottomBoundary","text":"BottomBoundary{} <: AbstractBoundary{}\n\nA simple object which should be passed into a function to indicate that we are considering the bottom boundary.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_flux","page":"Shared Utilities","title":"ClimaLand.boundary_flux","text":"boundary_flux(bc::WaterFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(bc::RichardsAtmosDrivenFluxBC,\n boundary::ClimaLand.AbstractBoundary,\n model::RichardsModel{FT},\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field where {FT}\n\nA method of boundary fluxes which returns the desired water volume flux for the RichardsModel, at the top of the domain, in the case of a prescribed precipitation flux.\n\nIf model.runoff is not of type NoRunoff, surface runoff is accounted for when computing the infiltration.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the top of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(rre_bc::MoistureStateBC,\n ::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on θ_l at the bottom of the domain into a flux of liquid water.\n\n\n\n\n\nboundary_flux(bc::FreeDrainage,\n boundary::ClimaLand.BottomBoundary,\n model::AbstractSoilModel,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which enforces free drainage at the bottom of the domain.\n\n\n\n\n\nboundary_flux(bc::HeatFluxBC, _...)::ClimaCore.Fields.Field\n\nA method of boundary fluxes which returns the desired flux.\n\nWe add a field of zeros in order to convert the bc (float) into a field.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the top of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(heat_bc::TemperatureStateBC,\n ::ClimaLand.BottomBoundary,\n model::EnergyHydrology,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n )::ClimaCore.Fields.Field\n\nA method of boundary fluxes which converts a state boundary condition on temperature at the bottom of the domain into a flux of energy.\n\n\n\n\n\nboundary_flux(bc::AbstractBC, bound_type::AbstractBoundary, Δz, _...)::ClimaCore.Fields.Field\n\nA function which returns the correct boundary flux given any boundary condition (BC). \n\n\n\n\n\nfunction ClimaLand.boundary_flux(\n bc::RunoffBC,\n ::TopBoundary,\n model::Soil.RichardsModel,\n Δz::FT,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n params,\n)::ClimaCore.Fields.Field\n\nExtension of the ClimaLand.boundary_flux function, which returns the water volume boundary flux for the soil. At the top boundary, return the soil infiltration (computed each step and stored in p.soil_infiltration).\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2FluxBC,\n boundary::ClimaLand.AbstractBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux (kg CO2 /m^2/s) in the case of a prescribed flux BC at either the top or bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::SoilCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at top of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\n bc::SoilCO2StateBC,\n boundary::ClimaLand.BottomBoundary,\n Δz::ClimaCore.Fields.Field,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case of a prescribed state BC at bottom of the domain.\n\n\n\n\n\nClimaLand.boundary_flux(\nbc::AtmosCO2StateBC,\nboundary::ClimaLand.TopBoundary,\nΔz::ClimaCore.Fields.Field,\nY::ClimaCore.Fields.FieldVector,\np::NamedTuple,\nt,\n)::ClimaCore.Fields.Field\n\nA method of ClimaLand.boundary_flux which returns the soilco2 flux in the case when the atmospheric CO2 is ued at top of the domain.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.diffusive_flux","page":"Shared Utilities","title":"ClimaLand.diffusive_flux","text":"diffusive_flux(K, x_2, x_1, Δz)\n\nCalculates the diffusive flux of a quantity x (water content, temp, etc). Here, x2 = x(z + Δz) and x1 = x(z), so x_2 is at a larger z by convention.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_vars","page":"Shared Utilities","title":"ClimaLand.boundary_vars","text":"boundary_vars(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for RichardsAtmosDrivenFluxBC with runoff.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for AtmosDrivenFluxBC. This adds the surface conditions (SHF, LHF, evaporation, and resistance) and the net radiation to the auxiliary variables.\n\nThese variables are updated in place in soil_boundary_fluxes!.\n\n\n\n\n\nboundary_vars(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_vars method for MoistureStateBC at the top boundary.\n\nThese variables are updated in place in boundary_flux.\n\n\n\n\n\nboundary_vars(::AbstractBC , ::ClimaLand.TopBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the top boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :top_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\nboundary_vars(::AbstractBC, ::ClimaLand.BottomBoundary)\n\nThe list of symbols for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage for the bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nFor the Soil and SoilCO2 models - which solve PDEs - the tendency functions and updateboundaryfluxes functions are coded to access the field :bottom_bc to be present in the model cache, which is why this is the default. If this is not your (PDE) model's desired behavior, you can extend this function with a new method.\n\nUse this function in the exact same way you would use auxiliary_vars.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_domain_names","page":"Shared Utilities","title":"ClimaLand.boundary_var_domain_names","text":"boundary_var_domain_names(::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <:Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_domain_names(::AtmosDrivenFluxBC{<:AbstractAtmosphericDrivers,\n <:AbstractRadiativeDrivers,\n <:AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for AtmosDrivenFluxBC. This specifies the part of the domain on which the additional variables should be defined.\n\n\n\n\n\nboundary_var_domain_names(::MoistureStateBC, ::ClimaLand.TopBoundary)\n\nAn extension of the boundary_var_domain_names method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_domain_names(::AbstractBC, ::ClimaLand.AbstractBoundary)\n\nThe list of domain names for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding storage on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_domain_names. \n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.boundary_var_types","page":"Shared Utilities","title":"ClimaLand.boundary_var_types","text":"boundary_var_types(::RichardsModel{FT},\n ::RichardsAtmosDrivenFluxBC{<:PrescribedPrecipitation,\n <: Runoff.AbstractRunoffModel,\n },\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for RichardsAtmosDrivenFluxBC with runoff.\n\n\n\n\n\nboundary_var_types(::Soil.EnergyHydrology{FT}, ::AbstractEnergyHydrologyBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of domain names for additional variables added to the EnergyHydrology model auxiliary state, which defaults to adding storage for the boundary flux field.\n\nBecause we supply boundary conditions for water and heat, we found it convenient to have these stored as a NamedTuple under the names top_bc and bottom_bc.\n\n\n\n\n\nboundary_var_types(\n ::EnergyHydrology{FT},\n ::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere{FT},\n <:AbstractRadiativeDrivers{FT},\n <:AbstractRunoffModel,\n }, ::ClimaLand.TopBoundary,\n) where {FT}\n\nAn extension of the boundary_var_types method for AtmosDrivenFluxBC. This specifies the type of the additional variables.\n\n\n\n\n\nboundary_var_types(::RichardsModel{FT},\n ::MoistureStateBC,\n ::ClimaLand.TopBoundary,\n ) where {FT}\n\nAn extension of the boundary_var_types method for MoistureStateBC at the top boundary.\n\n\n\n\n\nboundary_var_types(model::AbstractModel{FT}, ::AbstractBC, ::ClimaLand.AbstractBoundary) where {FT}\n\nThe list of types for additional variables to add to the model auxiliary state, for models solving PDEs, which defaults to adding a scalar variable on the surface domain for the top or bottom boundary flux fields, but which can be extended depending on the type of boundary condition used.\n\nUse in conjunction with boundary_vars, in the same way you would use auxiliary_var_types. The use of a scalar is appropriate for models with a single PDE; models with multiple PDEs will need to supply multiple scalar fields.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_jacobian","page":"Shared Utilities","title":"ClimaLand.make_jacobian","text":"make_jacobian(model::AbstractModel)\n\nCreates and returns a function which updates the auxiliary variables p in place and then updates the entries of the Jacobian matrix W for the model in place.\n\nThe default is that no updates are required, no implicit tendency is present, and hence the timestepping is entirely explicit.\n\nNote that the returned function jacobian! should be used as Wfact! in ClimaTimeSteppers.jl and SciMLBase.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.make_compute_jacobian","page":"Shared Utilities","title":"ClimaLand.make_compute_jacobian","text":"ClimaLand.make_compute_jacobian(model::RichardsModel{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for RichardsModel. This updates the contribution for the soil liquid water content.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nClimaLand.make_compute_jacobian(model::EnergyHydrology{FT}) where {FT}\n\nCreates and returns the compute_jacobian! function for the EnergyHydrology model. This updates the contribution for the soil liquid water content only.\n\nUsing this Jacobian with a backwards Euler timestepper is equivalent to using the modified Picard scheme of Celia et al. (1990).\n\n\n\n\n\nmake_compute_jacobian(model::AbstractModel)\n\nCreates and returns a function which computes the entries of the Jacobian matrix W in place.\n\nIf the implicit tendency function is given by T!(dY, Y, p, t) = make_implicit_tendency(model), the Jacobian should be given by W_{i,j}! = ∂T!_i/∂Y_j, where Y_j is the j-th state variable and T!_i is the implicit tendency of the i-th state variable.\n\nThe default is that no updates are required, but this function must be extended for models that use implicit timestepping.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_dfluxBCdY!","page":"Shared Utilities","title":"ClimaLand.set_dfluxBCdY!","text":"ClimaLand.set_dfluxBCdY!(\n model::RichardsModel,\n ::MoistureStateBC,\n boundary::ClimaLand.TopBoundary,\n Δz,\n Y,\n p,\n t,\n\n)\n\nComputes the derivative of the flux in the top layer (due to the boundary condition), with respect to the state variable in the top layer. This value is then updated in-place in the cache.\n\nFor Richards equation (a diffusion equation with a single state variable), this is given by ∂F_bc/∂Y_N= -K_N (∂ψ_bc/∂ϑ_N) / Δz, where N indicates the top layer cell index and ψ_bc is the pressure head at the boundary condition.\n\n\n\n\n\nset_dfluxBCdY!(::AbstractModel,\n ::AbstractBC,\n ::AbstractBoundary,\n _...)::Union{ClimaCore.Fields.FieldVector, Nothing}\n\nA function stub which returns the derivative of the implicit tendency term of the model arising from the boundary condition, with respect to the state Y.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.get_drivers","page":"Shared Utilities","title":"ClimaLand.get_drivers","text":"ClimaLand.get_drivers(model::RichardsModel)\n\nReturns the driver variable symbols for the RichardsModel; these depend on the boundary condition type and currently only are required for the RichardsAtmosDrivenFluxBC, which is driven by a prescribed time and space varying precipitation.\n\n\n\n\n\nClimaLand.get_drivers(model::SnowModel)\n\nReturns the driver variable symbols for the SnowModel.\n\n\n\n\n\nget_drivers(model::AbstractModel)\n\nReturns the driver objects for the model - atmospheric and radiative forcing, etc - as a tuple (atmos, radiation, ...). If no drivers are needed by a model, an empty tuple should be returned\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#Drivers","page":"Shared Utilities","title":"Drivers","text":"","category":"section"},{"location":"APIs/shared_utilities/","page":"Shared Utilities","title":"Shared Utilities","text":"ClimaLand.PrescribedAtmosphere\nClimaLand.PrescribedPrecipitation\nClimaLand.PrescribedRadiativeFluxes\nClimaLand.PrescribedSoilOrganicCarbon\nClimaLand.CoupledAtmosphere\nClimaLand.CoupledRadiativeFluxes\nClimaLand.AbstractAtmosphericDrivers\nClimaLand.AbstractRadiativeDrivers\nClimaLand.turbulent_fluxes\nClimaLand.turbulent_fluxes_at_a_point\nClimaLand.radiative_fluxes_at_a_point\nClimaLand.set_atmos_ts!\nClimaLand.surface_air_density\nClimaLand.surface_temperature\nClimaLand.surface_resistance\nClimaLand.surface_specific_humidity\nClimaLand.make_update_drivers","category":"page"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedAtmosphere","page":"Shared Utilities","title":"ClimaLand.PrescribedAtmosphere","text":"PrescribedAtmosphere{FT, CA, DT} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed atmospheric drivers and other information needed for computing turbulent surface fluxes when driving land models in standalone mode.\n\nThe default CO2 concentration is a constant as a function of time, equal to 4.2e-4 mol/mol.\n\nSince not all models require co2 concentration, the default for that is nothing.\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\nsnow_precip: Snow precipitation (m/s) function of time: positive by definition\nT: Prescribed atmospheric temperature (function of time) at the reference height (K)\nu: Prescribed wind speed (function of time) at the reference height (m/s)\nq: Prescribed specific humidity (function of time) at the reference height (_)\nP: Prescribed air pressure (function of time) at the reference height (Pa)\nc_co2: CO2 concentration in atmosphere (mol/mol)\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nh: Reference height (m), relative to surface elevation\ngustiness: Minimum wind speed (gustiness; m/s)\nthermo_params: Thermodynamic parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedPrecipitation","page":"Shared Utilities","title":"ClimaLand.PrescribedPrecipitation","text":"PrescribedPrecipitation{FT, LP} <: AbstractAtmosphericDrivers{FT}\n\nContainer for holding prescribed precipitation driver for models which only require precipitation (RichardsModel).\n\nliquid_precip: Precipitation (m/s) function of time: positive by definition\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.PrescribedRadiativeFluxes","text":"PrescribedRadiativeFluxes{FT, SW, LW, DT, T} <: AbstractRadiativeDrivers{FT}\n\nContainer for the prescribed radiation functions needed to drive land models in standalone mode.\n\nSW_d: Downward shortwave radiation function of time (W/m^2): positive indicates towards surface\nLW_d: Downward longwave radiation function of time (W/m^2): positive indicates towards surface\nref_time: Reference time - the datetime corresponding to t=0 for the simulation\nθs: Sun zenith angle, in radians\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.PrescribedSoilOrganicCarbon","page":"Shared Utilities","title":"ClimaLand.PrescribedSoilOrganicCarbon","text":" PrescribedSoilOrganicCarbon{FT}\n\nA type for prescribing soil organic carbon.\n\nsoc: Soil organic carbon, function of time and space: kg C/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledAtmosphere","page":"Shared Utilities","title":"ClimaLand.CoupledAtmosphere","text":"CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.CoupledRadiativeFluxes","page":"Shared Utilities","title":"ClimaLand.CoupledRadiativeFluxes","text":"CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT}\n\nTo be used when coupling to an atmosphere model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractAtmosphericDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractAtmosphericDrivers","text":" AbstractAtmosphericDrivers{FT}\n\nAn abstract type of atmospheric drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.AbstractRadiativeDrivers","page":"Shared Utilities","title":"ClimaLand.AbstractRadiativeDrivers","text":" AbstractRadiativeDrivers{FT}\n\nAn abstract type of radiative drivers of land models.\n\n\n\n\n\n","category":"type"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes","text":"turbulent_fluxes(atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t\n )\n\nComputes the turbulent surface flux terms at the ground for a standalone simulation, including turbulent energy fluxes as well as the water vapor flux (in units of m^3/m^2/s of water). Positive fluxes indicate flow from the ground to the atmosphere.\n\nIt solves for these given atmospheric conditions, stored in atmos, model parameters, and the surface conditions.\n\n\n\n\n\nturbulent_fluxes(atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t)\n\nComputes the turbulent surface fluxes terms at the ground for a coupled simulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.turbulent_fluxes_at_a_point","page":"Shared Utilities","title":"ClimaLand.turbulent_fluxes_at_a_point","text":"turbulent_fluxes_at_a_point(T_sfc::FT,\n q_sfc::FT,\n ρ_sfc::FT,\n β_sfc::FT,\n h_sfc::FT,\n r_sfc::FT,\n d_sfc::FT,\n ts_in,\n u::FT,\n h::FT,\n gustiness::FT,\n z_0m::FT,\n z_0b::FT,\n earth_param_set::EP,\n ) where {FT <: AbstractFloat, P}\n\nComputes turbulent surface fluxes at a point on a surface given (1) the surface temperature (Tsfc), specific humidity (qsfc), and air density (ρsfc), (2) Other surface properties, such as the factor βsfc which scales the evaporation from the potential rate (used in bucket models), and the surface resistance rsfc (used in more complex land models), and the topographical height of the surface (hsfc) (3) the roughness lengths z_0m, z_0b, and the Earth parameter set for the model earth_params. (4) the prescribed atmospheric state, ts_in, u, h the height at which these measurements are made, and the gustiness parameter (m/s). (5) the displacement height for the model d_sfc\n\nThis returns an energy flux and a liquid water volume flux, stored in a tuple with self explanatory keys.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.set_atmos_ts!","page":"Shared Utilities","title":"ClimaLand.set_atmos_ts!","text":"set_atmos_ts!(ts_in, atmos::PrescribedAtmosphere{FT}, p)\n\nFill the pre-allocated ts_in Field with a thermodynamic state computed from the atmosphere.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_air_density","page":"Shared Utilities","title":"ClimaLand.surface_air_density","text":"surface_air_density(\n atmos::PrescribedAtmosphere,\n model::AbstractModel,\n Y,\n p,\n t,\n T_sfc,\n )\n\nA helper function which returns the surface air density; this assumes that the model has a property called parameters containing earth_param_set.\n\nWe additionally include the atmos type as an argument because the surface air density computation will change between a coupled simulation and a prescibed atmos simulation.\n\nExtending this function for your model is only necessary if you need to compute the air density in a different way.\n\n\n\n\n\nClimaLand.surface_air_density(\n atmos::CoupledAtmosphere,\n model::AbstractModel,\n Y,\n p,\n _...,\n )\n\nReturns the air density at the surface in the case of a coupled simulation.\n\nThis requires the field ρ_sfc to be present in the cache p under the name of the model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_temperature","page":"Shared Utilities","title":"ClimaLand.surface_temperature","text":"ClimaLand.surface_temperature(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface temperature field of the EnergyHydrology soil model.\n\nThe assumption is that the soil surface temperature is the same as the temperature at the center of the first soil layer.\n\n\n\n\n\nClimaLand.surface_temperature(model::SnowModel, Y, p)\n\na helper function which returns the surface temperature for the snow model, which is stored in the aux state.\n\n\n\n\n\nClimaLand.surface_temperature(model::BucketModel, Y, p)\n\na helper function which returns the surface temperature for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_temperature(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface temperature for a given model, needed because different models compute and store surface temperature in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_temperature(model::CanopyModel, Y, p, t)\n\nA helper function which returns the temperature for the canopy model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_resistance","page":"Shared Utilities","title":"ClimaLand.surface_resistance","text":"ClimaLand.surface_resistance(\n model::EnergyHydrology{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the surface resistance field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_resistance(model::AbstractModel, Y, p, t)\n\nA helper function which returns the surface resistance for a given model, needed because different models compute and store surface resistance in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\nThe default is 0, which is no additional resistance aside from the usual aerodynamic resistance from MOST.\n\n\n\n\n\nClimaLand.surface_resistance(\n model::CanopyModel{FT},\n Y,\n p,\n t,\n) where {FT}\n\nReturns the stomatal resistance field of the CanopyModel canopy.\n\n\n\n\n\n","category":"function"},{"location":"APIs/shared_utilities/#ClimaLand.surface_specific_humidity","page":"Shared Utilities","title":"ClimaLand.surface_specific_humidity","text":"ClimaLand.surface_specific_humidity(\n model::EnergyHydrology{FT},\n Y,\n p,\n T_sfc,\n ρ_sfc\n) where {FT}\n\nReturns the surface specific humidity field of the EnergyHydrology soil model.\n\nThis models the specific humidity over the soil liquid water as the saturated value multiplied by the factor exp(ψ_sfc g M_w/(RT_sfc)) in accordance with the Clausius-Clapeyron equation, where ψ_sfc is the matric potential at the surface, T_sfc the surface temperature, g the gravitational acceleration on the surface of the Earth, M_w the molar mass of water, and R the universal gas constant.\n\nOver the soil ice, the specific humidity is the saturated value.\n\nThe total surface specific humidity of the soil is approximated by q = qoverice * f + qoverwater * (1-f), where f is given by the function ice_fraction.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\nComputes and returns the specific humidity over snow as a weighted fraction of the saturated specific humidity over liquid and frozen water.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::BucketModel, Y, p)\n\na helper function which returns the surface specific humidity for the bucket model, which is stored in the aux state.\n\n\n\n\n\nsurface_specific_humidity(model::AbstractModel, Y, p, T_sfc, ρ_sfc)\n\nA helper function which returns the surface specific humidity for a given model, needed because different models compute and store q_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\nClimaLand.surface_specific_humidity(model::CanopyModel, Y, p)\n\nA helper function which returns the surface specific humidity for the canopy model, which is stored in the aux state.\n\n\n\n\n\n","category":"function"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Farquhar-Model","page":"Farquhar model","title":"Farquhar Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO_2 into the leaf, the assimilation of CO_2 during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO_2 concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Rubisco-limited-rate","page":"Farquhar model","title":"Rubisco limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_1(T c_a VPD) =\nbegincases\n V_cmax(T) frac(c_i(T c_a VPD) - Gamma^*(T))(c_i(T c_a VPD) + K_c(T)*(1+o_iK_o(T))) textfor C3\n V_cmax(T) textfor C4\nendcases\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The dependence on the atmospheric CO_2 concentration c_a (mol/mol) and vapor pressure deficit VPD arise in the expression for c_i,","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n c_i(T c_a VPD) = max(c_a(1-1m(VPD)) Gamma^*(T))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where and m is the Medlyn factor (see Stomatal Conductance).","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We also have","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":" Gamma^*(T) = Gamma^*_25expleft(Delta H_Gamma^*fracT - T_oT_o R Tright)","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where Delta H_Gamma^* is the activation energy per mol for Gamma^*.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/#Light-limited-rate","page":"Farquhar model","title":"Light limited rate","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\na_2 =\nbegincases\n J(T PAR) (c_i - Gamma^*)4(c_i + 2 Gamma^*) textfor C3\n J(T PAR) textfor C4\nendcases \nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where J is the rate of electron transport, which has units of mol photon per m^2 per s. It depends on PAR via APAR, as described below, and on T via the dependence on J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"J is given by the root of the equation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n theta_j J^2 - (I + J_max) J + I J_max = 0 nonumber \n I = fracphi2 (APAR) nonumber \n J_max(T) = V_cmax(T)times e expleft(Delta H_J_maxfracT - T_oT_o R Tright)nonumber \nJ(T PAR) = frac(I + J_max - sqrt(I + J_max)^2 - 4theta_j I times J_max2theta_j\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where phi = 06 and theta_j = 09 are the quantum yield of photosystem II and a curvature function (Bonan's book), and Delta H_J_max is the energy of activation of J_max.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The total net carbon assimilation (A_n, mol CO_2 m^-2 s^-1) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nA_n(T PAR VPD c_a) = textmax(0 textmin(a_1 beta a_2) - R_d)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where beta is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R_d is the leaf dark respiration calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n R_d25(psi_l) = f V_cmax25beta(psi_l) nonumber \n R_d (T psi_l) = R_d25(psi_l)expleft(Delta H_R_dfracT - T_oT_o R Tright)\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where f = 0015 is a constant, Delta H_R_d is the energy of activation for R_d, and finally Vcmax is calculated as ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginequation\nV_cmax(T) = V_cmax25 expleft(Delta H_VcmaxfracT - T_oT_o R Tright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"with V_cmax25 is a parameter (Vcmax at the reference temperature 25 C), and Delta H_Vcmax = 65330 Jmol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"The moisture stress factor is related to the leaf water potential psi_l as","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\n beta = frac1+ exp(s_c psi_c)1+ exp(s_c(psi_c - psi_l))\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"where s_c = 4MPa^-1, psi_c = -2MPa, and psi_l is the leaf water potential computed by the plant hydraulics model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"beginalign\nGPP(T PAR c_a VPD theta_s) = A_n (1 - exp(-K LAI Omega))K\nendalign","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"This is not currently needed by other components, but is used for offline validation of the model.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"We need to supply the following parameters and “drivers\"","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"K_c25 and K_o25, V_cmax 25, Gamma^*_25phi, theta_j, o_i, s_c, psi_c\npsi_l, to compute beta\nTemperature T, PAR, c_a, VPD, theta_s.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Output Symbol Unit Range\nTotal net carbon assimilation A_n μmol CO_2 m^-2 s^-1 0–25","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nTemperature T °C 0–50","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Parameters Symbol Unit Range\nMoisture stress β - 0-1\nLeaf Area Index LAI m² m⁻² 1–10\nCO_2 concentration c_a ppm 300e–500\nVapor pressure deficit VPD kPa 1-10","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/farquhar_model/","page":"Farquhar model","title":"Farquhar model","text":"Constants Symbol Unit Value\nZenith angle θ_s rad 0.6\nLeaf angle distribution l_d - 0.5\nCanopy reflectance ρ_leaf - 0.1\nClumping index Ω - 0.69\nCO_2 compensation at 25°C Γ^*_25 mol/mol 4.275e-5\nEnergy of activation for Γ^* ΔH_Γ^* J/mol 37830\nStandard temperature T_o K 298.15\nUniversal gas constant R J/mol 8.314\nThe maximum rate of carboxylation of Rubisco V_cmax25 mol CO_2 m^-2 s^-1 5e-5\nEnergy of activation for J_max ΔH_J_max J/mol 43540\nCurvature parameter, a fitting constant to compute J θ_j - 0.9\nThe quantum yied of photosystem II phi - 0.6\nEnergy of activation for V_cmax ΔH_V_cmax J/mol 58520\nSlope parameter for stomatal conductance models g_1 - 141\nMichaelis Menten constant for CO_2 and at 25\u000e°C K_c25 mol/mol 4.049e-4\nEnergy of activation for CO_2 ΔH_K_c J/mol 79430\nMichaelis Menten constant for O_2 at 25 °C\u000e K_o25 mmol/mol 0.2874\nEnergy of activation for O_2 ΔH_K_o J/mol 36380\nIntercellular O_2 concentration o_i mol/mol 0.209\nConstant factor appearing the dark respiration term\u000e f - 0.015\nEnergy of activation for R_d ΔH_R_d J/mol 43390","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Soil-Biogeochemistry","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"CurrentModule = ClimaLand.Soil.Biogeochemistry","category":"page"},{"location":"APIs/SoilBiogeochemistry/#Model-Structure","page":"Soil Biogeochemistry","title":"Model Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2Model","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2Model","text":"SoilCO2Model\n\nA model for simulating the production and transport of CO₂ in the soil with dynamic source and diffusion terms.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type NamedTuple\nsources: A tuple of sources, each of type AbstractSource\ndrivers: Drivers\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Parameter-Structure","page":"Soil Biogeochemistry","title":"Parameter Structure","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2ModelParameters","text":"SoilCO2ModelParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SoilCO2Model.\n\nAll of these parameters are currently treated as global constants.\n\nD_ref: Diffusion coefficient for CO₂ in air at standard temperature and pressure (m² s⁻¹)\nD_liq: Diffusivity of soil C substrate in liquid (unitless)\nα_sx: Pre-exponential factor (kg C m-3 s-1)\nEa_sx: Activation energy (J mol-1)\nkM_sx: Michaelis constant (kg C m-3)\nkM_o2: Michaelis constant for O2 (m3 m-3)\nO2_a: Volumetric fraction of O₂ in the soil air, dimensionless\nD_oa: Diffusion coefficient of oxygen in air, dimensionless\np_sx: Fraction of soil carbon that is considered soluble, dimensionless\nearth_param_set: Physical constants used Clima-wide\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Model-specific-Types","page":"Soil Biogeochemistry","title":"Model-specific Types","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction\nClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC\nClimaLand.Soil.Biogeochemistry.SoilCO2StateBC\nClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC\nClimaLand.Soil.Biogeochemistry.AbstractSoilDriver\nClimaLand.Soil.Biogeochemistry.SoilDrivers\nClimaLand.Soil.Biogeochemistry.PrescribedMet","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.MicrobeProduction","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.MicrobeProduction","text":"MicrobeProduction{FT} <: AbstractCarbonSource{FT}\n\nStruct for the microbe production of CO2, appearing as a source term in the differential equation.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2FluxBC","text":"SoilCO2FluxBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 flux boundary condition, which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilCO2StateBC","text":"SoilCO2StateBC <: ClimaLand.AbstractBC\n\nA container holding the CO2 state boundary condition (kg CO2 m−3), which is a function f(p,t), where p is the auxiliary state vector.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AtmosCO2StateBC","text":"AtmosCO2StateBC <: ClimaLand.AbstractBC\n\nSet the CO2 concentration to the atmospheric one.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.AbstractSoilDriver","text":"AbstractSoilDriver\n\nAn abstract type for drivers of soil CO2 production and diffusion. These are soil temperature, soil moisture, root carbon, soil organic matter and microbe carbon, and atmospheric pressure. Soil temperature and moisture, as well as soc, vary in space (horizontally and vertically) and time. Atmospheric pressure vary in time (defined at the surface only, not with depth).\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.SoilDrivers","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.SoilDrivers","text":"SoilDrivers\n\nA container which passes in the soil drivers to the biogeochemistry model. These drivers are either of type Prescribed (for standalone mode) or Prognostic (for running with a prognostic model for soil temp and moisture).\n\nmet: Soil temperature and moisture drivers - Prescribed or Prognostic\nsoc: Soil SOM driver - Prescribed only\natmos: Prescribed atmospheric variables\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.PrescribedMet","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.PrescribedMet","text":"PrescribedMet <: AbstractSoilDriver\n\nA container which holds the prescribed functions for soil temperature and moisture.\n\nThis is meant for use when running the biogeochemistry model in standalone mode, without a prognostic soil model.\n\ntemperature: The temperature of the soil, of the form f(z::FT,t) where FT <: AbstractFloat\nvolumetric_liquid_fraction: Soil moisture, of the form f(z::FT,t) FT <: AbstractFloat\nν: Soil porosity (m³ m⁻³)\nθ_a100: Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)\nb: Absolute value of the slope of the line relating log(ψ) versus log(S) (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/SoilBiogeochemistry/#Functions-of-State","page":"Soil Biogeochemistry","title":"Functions of State","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content\nClimaLand.Soil.Biogeochemistry.co2_diffusivity\nClimaLand.Soil.Biogeochemistry.microbe_source","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.volumetric_air_content","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.volumetric_air_content","text":"volumetric_air_content(θ_w::FT,\n ν::FT,\n ) where {FT}\n\nComputes the volumetric air content (θ_a) in the soil, which is related to the total soil porosity (ν) and volumetric soil water content (θ_w = θ_l+θ_i).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.co2_diffusivity","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.co2_diffusivity","text":"co2_diffusivity(\n T_soil::FT,\n θ_w::FT,\n P_sfc::FT,\n θ_a100::FT,\n b::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT},\n ) where {FT}\n\nComputes the diffusivity of CO₂ within the soil (D).\n\nFirst, D0 is computed using the temperature within the soil (T_soil in K) and pressure at the surface of the soil (P_sfc in Pa), using reference values of T_ref and P_ref (273 K and 101325 Pa). Here, θ_a is the volumetric air content and θ_a100 is the volumetric air content at a soil water potential of 100cm, and b is the pore size distribution of the soil.\n\nThis parameterization is from Ryan et al., GMD 11, 1909-1928, 2018, https://doi.org/10.5194/gmd-11-1909-2018.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.microbe_source","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.microbe_source","text":"microbe_source(T_soil::FT,\n θ_l::FT,\n Csom::FT,\n ν::FT,\n params::SoilCO2ModelParameters{FT}\n ) where {FT}\n\nComputes the CO₂ production in the soil by microbes, in depth and time (kg C / m^3/s), using the Dual Arrhenius Michaelis Menten model (Davidson et al., 2012).\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#Extendible-Functions","page":"Soil Biogeochemistry","title":"Extendible Functions","text":"","category":"section"},{"location":"APIs/SoilBiogeochemistry/","page":"Soil Biogeochemistry","title":"Soil Biogeochemistry","text":"ClimaLand.Soil.Biogeochemistry.soil_moisture\nClimaLand.Soil.Biogeochemistry.soil_temperature\nClimaLand.Soil.Biogeochemistry.soil_SOM_C","category":"page"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_moisture","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_moisture","text":"soil_moisture(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the volumetric liquid fraction, computed by the soil model from the prognostic liquid and ice fractions.\n\n\n\n\n\nsoil_moisture(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil moisture at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"APIs/SoilBiogeochemistry/#ClimaLand.Soil.Biogeochemistry.soil_temperature","page":"Soil Biogeochemistry","title":"ClimaLand.Soil.Biogeochemistry.soil_temperature","text":"soil_temperature(driver::PrognosticSoil, p, Y, t, z)\n\nReturns the prognostic soil temperature.\n\n\n\n\n\nsoil_temperature(driver::PrescribedMet, p, Y, t, z)\n\nReturns the soil temperature at location (z) and time (t) for the prescribed soil case.\n\n\n\n\n\n","category":"function"},{"location":"diagnostics/users_diagnostics/#Using-ClimaLand-Diagnostics-when-running-a-simulation","page":"For users","title":"Using ClimaLand Diagnostics when running a simulation","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When running a ClimaLand simulations, you have multiple options on how to write the outputs of that simulation. You may want all variables, or just a selected few. You may want instantaneous values, at the highest temporal and spatial resolution, or you may want to get averages at hourly or monthly time scale, and integrate in space (for example soil moisture from 0 to 1 meter depth). You may want to get more specific reductions, such as 10 days maximums, or compute a new variables that is a function of others. You may want to get your outputs in memory in a Julia Dict, or write them in a NetCDF file.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"This is where ClimaLand Diagnostics comes in for users.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"In this documentation page, we first explain how to use default diagnostics and what are the defaults, and then explain how to define your own diagnostics for more advanced users.","category":"page"},{"location":"diagnostics/users_diagnostics/#Default-Diagnostics","page":"For users","title":"Default Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you have defined your model and are ready to run a simulation, and after adding ClimaDiagnostics (using ClimaDiagnostics), you can add default diagnostics to it by doing the following steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#define-an-output-folder","page":"For users","title":"define an output folder","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"output_dir = ClimaUtilities.OutputPathGenerator.generate_output_path(\"base_output_dir/\")","category":"page"},{"location":"diagnostics/users_diagnostics/#define-a-space","page":"For users","title":"define a space","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in time and space. These may be defined in your model, but usually land model space is a sphere with no vertical dimension. You may have variables varying with soil depth, and so you will need:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"space = bucket_domain.space.subsurface","category":"page"},{"location":"diagnostics/users_diagnostics/#define-your-writter","page":"For users","title":"define your writter","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics will be written in a Julia Dict or a netcdf file, for example. This is up to you. For a netcdf file, you define your writter like this:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"nc_writer = ClimaDiagnostics.Writers.NetCDFWriter(space, output_dir)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"providing the space and output_dir defined in steps 1. and 2.","category":"page"},{"location":"diagnostics/users_diagnostics/#make-your-diagnostics-on-your-model,-using-your-writter,-and-define-a-callback","page":"For users","title":"make your diagnostics on your model, using your writter, and define a callback","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now that you defined your model and your writter, you can create a callback function to be called when solving your model. For example:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"t0 = 0 # the starting time of your simulation\n\nreference_date = DateTime(2024) # reference_date is the DateTime of your starting time\n\ndiags = ClimaLand.default_diagnostics(model, t0, reference_date; output_writer = nc_writer)\n\ndiagnostic_handler =\n ClimaDiagnostics.DiagnosticsHandler(diags, Y, p, t0; dt = Δt)\n\ndiag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Your diagnostics have now been written in netcdf files in your output folder.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Note that by default, default_diagnostics assign two optional kwargs: output_vars = :long and average_period = :daily. output_vars = :long will write all available diagnostics, whereas output_vars = :short will only write essentials diagnostics. average_period defines the period over which diagnostics are averaged, it can be set to :hourly, :daily and :monthly.","category":"page"},{"location":"diagnostics/users_diagnostics/#Custom-Diagnostics","page":"For users","title":"Custom Diagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"When defining a custom diagnostic, follow these steps:","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-compute-your-diagnostic-variable-from-your-model-state-and-cache.","page":"For users","title":"Define how to compute your diagnostic variable from your model state and cache.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"For example, let's say you want the bowen ratio (ratio between sensible heat and latent heat) in the Bucket model.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"function compute_bowen_ratio!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf)\n else\n out .= p.bucket.turbulent_fluxes.shf / p.bucket.turbulent_fluxes.lhf\n end\nend","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Or, for convenience, you can use the @diagnostic_compute macro which generates the same function. However, it is better to use that macro only if you are getting a defined variable, such as latent heat flux. (without an operation like the bowen ratio above). For example,","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"@diagnostic_compute \"latent_heat_flux\" BucketModel p.bucket.turbulent_fluxes.lhf","category":"page"},{"location":"diagnostics/users_diagnostics/#Add-that-diagnostic(s)-variable-to-your-list-of-variables","page":"For users","title":"Add that diagnostic(s) variable to your list of variables","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":" add_diagnostic_variable!(\n short_name = \"bor\",\n long_name = \"Bowen ratio\",\n standard_name = \"bowen_ratio\",\n units = \"\",\n comments = \"Ratio of sensible to latent heat flux.\",\n compute! = (out, Y, p, t) -> compute_bowen_ratio!(out, Y, p, t, land_model),\n)\n\nadd_diagnostic_variable!(\n short_name = \"lhf\",\n long_name = \"Latent Heat Flux\",\n standard_name = \"latent_heat_flux\",\n units = \"W m^-2\",\n comments = \"Exchange of energy at the land-atmosphere interface due to water evaporation or sublimation.\",\n compute! = (out, Y, p, t) ->\n compute_latent_heat_flux!(out, Y, p, t, land_model),\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-how-to-schedule-your-variables.-For-example,-you-want-the-seasonal-maximum-of-your-variables,-where-season-is-defined-as-90-days.","page":"For users","title":"Define how to schedule your variables. For example, you want the seasonal maximum of your variables, where season is defined as 90 days.","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"seasonal_maxs(short_names...; output_writer, t_start) = common_diagnostics(\n 90 * 24 * 60 * 60 * one(t_start),\n max,\n output_writer,\n t_start,\n short_names...,\n)","category":"page"},{"location":"diagnostics/users_diagnostics/#Define-a-function-to-return-your-ScheduledDiagnostics","page":"For users","title":"Define a function to return your ScheduledDiagnostics","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Now, you can call your schedule with your variables.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"my_custom_diagnostics = [\"lhf\", \"bor\"]\n\ndiags = seasonal_maxs(my_custom_diagnostics...; output_writer, t_start)","category":"page"},{"location":"diagnostics/users_diagnostics/#Analyze-your-simulation-output","page":"For users","title":"Analyze your simulation output","text":"","category":"section"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Once you've run your simulation and created an output folder (e.g., output_dir) with diagnostics, you can use ClimaAnalysis to access and analyze your data. For in depth documentation about ClimaAnalysis, see its documentation.","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"Here is an example of how to plot a variable:","category":"page"},{"location":"diagnostics/users_diagnostics/","page":"For users","title":"For users","text":"import ClimaAnalysis\n\nimport ClimaAnalysis.Visualize as viz\n\nimport CairoMakie # the plotting package used by ClimaAnalysis\n\nsimdir = ClimaAnalysis.SimDir(output_dir) # where output_dir is where you saved your diagnostics.\n\nvar = get(simdir; \"lhf\") # assuming lhf, latent_heat_flux used as an example above, is one of your diagnostics variables.\n\nfig = CairoMakie.Figure() # creates an empty figure object\n\nviz.plot!(fig, var) # creates an axis inside fig, and plot your var in it.\n\nCairoMakie.save(fig) # saves the figure in current working directory","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/#Optimality-Model","page":"Optimality model","title":"Optimality Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation (V_textcmax). ","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates V_textcmax and J_textmax as a function of environmental variables as follows:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n V_textcmax^* = varphi I left(fracmm_cright)left(fracoverlineomega^*8thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega^* = 1 + overlineomega - sqrt(1 + overlineomega)^2 - 4thetaoverlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n overlineomega = -(1 - 2theta) + sqrt(1 - theta)left(frac1frac4cmleft(1 - thetafrac4cmright) - 4thetaright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n c = fracm8thetaleft(1 - fracvarphi I + J_textmax - 2thetavarphi Isqrt(varphi I + J_textmax)^2 - 4thetavarphi I J_textmaxright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"and","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n J_textmax = varphi I overlineomega\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m = fracC_i - Gamma^*C_i + 2Gamma^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n C_i = Gamma^* + (C_a - Gamma^*)fracxixi + sqrtD_g\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n xi = sqrtbeta fracK + Gamma^*16eta^*\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n K = K_cleft(1 + fracO_iK_oright)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n m_c = fracC_i - Gamma^*C_i + K\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Gamma^*","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"is the CO_2 compensation point in the absence of mitochondrial respiration","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"beginequation\n Gamma^* = Gamma^*_0 f(T Delta H_a) pp_0\nendequation","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"where Gamma^*_0 = 4332 Pa, p is the atmospheric pressure, p_0 = 101325 Pa, and Delta H_a = 37830 J/mol.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"varphi is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.\ntheta is the curvature of the light response curve (dimensionless). Estimated at 0.85.\nbeta is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"For Smith et al. (2019) Vcmax model:","category":"page"},{"location":"standalone/pages/vegetation/photosynthesis/optimality_model/","page":"Optimality model","title":"Optimality model","text":"Altitude\nD_g is the vapor pressure deficit (VPD) at altitude\nC_a is the CO_2 partial pressure\nI is the incident photosynthetically active photon flux (PAR)\nT is the temperature","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Introduction-to-the-Canopy-Model","page":"Standalone Canopy","title":"Introduction to the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This tutorial shows how to instantiate and run a simulation of the canopy biophysics model in ClimaLand. A CanopyModel including all component models is initialized, then an example simulation is run. The initial conditions, atmospheric and radiative flux conditions, and canopy properties are set up to match those observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and canopy parameters.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The canopy biophysics model in ClimaLand combines a photosynthesis model with a canopy radiative transfer scheme, plant hydraulics model, and stomatal conductance model, placing them under either prescribed or simulated (as in a full Earth System Model) atmospheric and radiative flux conditions.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand supports either Beer-Lambert law or a Two-Stream model for radiative transfer. For this tutorial, we will use the Beer-Lambert law, in which the intensity of light absorbed is a negative exponential function of depth in the canopy and an exinction coefficient determined by optical depth.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The model of photosynthesis in Clima Land is the Farquar Model in which GPP is calculated based on C3 and C4 photosynthesis, which determines potential leaf-level photosynthesis.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"The plant hydraulics model in ClimaLand solves for the water content within bulk root-stem-canopy system using Richards equation discretized into an arbitrary number of layers. The water content is related to the water potential using a retention curve relationship, and the water potential is used to simulate the effect moisture stress has on transpiration and GPP.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Preliminary-Setup","page":"Standalone Canopy","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load External Packages:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing StaticArrays\nusing ClimaLand\nusing ClimaLand.Domains: Point\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Setup-the-Canopy-Model","page":"Standalone Canopy","title":"Setup the Canopy Model","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We want to simulate a vegetative canopy in standalone mode, without coupling the canopy to atmospheric or soil physics models, so we choose a CanopyModel. From the linked documentation, we can see that we need to provide shared parameters, a domain, a radiative transfer model, photosynthesis model, plant hydraulics model, stomatal conductance model, and atmospheric and radiative flux conditions which may be either prescribed or simulated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First, define the parameters of the model domain. These values are needed by some of the component models. Here we are performing a 1-dimensional simulation in a Point domain and will use single stem and leaf compartments, but for 2D simulations, the parameters of the domain would change.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4) # kg/m^2\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Point(; z_sfc = FT(0.0))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ClimaLand.Domains.Point{Float32}(0.0f0, (surface = ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, Vector{Float32}}\n Float32[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),))","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"time_offset = 7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"7","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Site latitude and longitude","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"-92.2f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Height of the sensor at the site","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"atmos_h = FT(32)","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"32.0f0","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Populate the SharedCanopyParameters struct, which holds the parameters shared between all different components of the canopy model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"For this canopy, we are running in standalone mode, which means we need to use a prescribed soil driver, defined as follows:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_soil0 = FT(0.0)\n\nsoil_driver = PrescribedSoil(\n FT;\n root_depths = SVector{10, FT}(-(10:-1:1.0) ./ 10.0 * 2.0 .+ 0.2 / 2.0),\n ψ = t -> ψ_soil0,\n α_PAR = FT(0.2),\n α_NIR = FT(0.4),\n T = t -> 298.0,\n ϵ = FT(0.99),\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, setup the canopy model by component. Provide arguments to each component, beginning with radiative transfer:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"rt_params = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = FT(0.1),\n α_NIR_leaf = FT(0.45),\n τ_PAR_leaf = FT(0.05),\n τ_NIR_leaf = FT(0.25),\n Ω = FT(0.69),\n λ_γ_PAR = FT(5e-7),\n λ_γ_NIR = FT(1.65e-6),\n)\n\nrt_model = TwoStreamModel{FT}(rt_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for conductance model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"cond_params = MedlynConductanceParameters(FT; g1 = FT(141.0))\n\nstomatal_model = MedlynConductanceModel{FT}(cond_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for photosynthesis model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"photo_params = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5))\n\nphotosynthesis_model = FarquharModel{FT}(photo_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for autotrophic respiration model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"AR_params = AutotrophicRespirationParameters(FT)\nAR_model = AutotrophicRespirationModel{FT}(AR_params);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Arguments for plant hydraulics model are more complicated.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Begin by providing general plant parameters. For the area indices of the canopy, we choose a PrescribedSiteAreaIndex, which supports LAI as a function of time, with RAI and SAI as constant.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"LAI = 4.2\nLAIfunction = (t) -> LAI\nSAI = FT(0.00242)\nf_root_to_shoot = FT(3.5)\nRAI = FT((SAI + LAI) * f_root_to_shoot)\nai_parameterization =\n PrescribedSiteAreaIndex{FT}(TimeVaryingInput(LAIfunction), SAI, RAI)\nrooting_depth = FT(1.0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the root distribution function p(z):","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"function root_distribution(z::T; rooting_depth = rooting_depth) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth))\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the component conductivity and retention models of the hydraulics model. In ClimaLand, a Weibull parameterization is used for the conductivity as a function of potential, and a linear retention curve is used.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"K_sat_plant = FT(1.8e-8)\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Use these values to populate the parameters of the PlantHydraulics model:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ν = FT(0.7)\nS_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = ν,\n S_s = S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Define the remaining variables required for the plant hydraulics model.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"plant_hydraulics = PlantHydraulics.PlantHydraulicsModel{FT}(;\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_surfaces = compartment_surfaces,\n compartment_midpoints = compartment_midpoints,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, instantiate the canopy model, using the atmospheric and radiative drivers included from the external file, as well as the soil driver we instantiated above. This contains every piece of information needed to generate the set of ODEs modeling the canopy biophysics, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"canopy = ClimaLand.Canopy.CanopyModel{FT}(;\n parameters = shared_params,\n domain = land_domain,\n autotrophic_respiration = AR_model,\n radiative_transfer = rt_model,\n photosynthesis = photosynthesis_model,\n conductance = stomatal_model,\n hydraulics = plant_hydraulics,\n soil_driver = soil_driver,\n atmos = atmos,\n radiation = radiation,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the state vectors and obtain the model coordinates, then get the explicit time stepping tendency that updates auxiliary and prognostic variables that are stepped explicitly.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Y, p, coords = ClimaLand.initialize(canopy)\nexp_tendency! = make_exp_tendency(canopy);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Provide initial conditions for the canopy hydraulics model","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"ψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n ν,\n S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .= augmented_liquid_fraction.(ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a time range to perform time stepping over, and a dt. Also create the saveat Array to contain the data from the model at each time step. As usual, the timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"t0 = 0.0\nN_days = 364\ntf = t0 + 3600 * 24 * N_days\ndt = 225.0;","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Initialize the cache variables for the canopy using the initial conditions and initial time.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"set_initial_cache! = make_set_initial_cache(canopy)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Allocate the struct which stores the saved auxiliary state and create the callback which saves it at each element in saveat.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"n = 16\nsaveat = Array(t0:(n * dt):tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Create the callback function which updates the forcing variables, or drivers.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"updateat = Array(t0:1800:tf)\nmodel_drivers = ClimaLand.get_drivers(canopy)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Select a timestepping algorithm and setup the ODE problem.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"timestepper = CTS.RK4();\node_algo = CTS.ExplicitAlgorithm(timestepper)\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Now, we can solve the problem and store the model data in the saveat array, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/#Create-some-plots","page":"Standalone Canopy","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"We can now plot the data produced in the simulation. For example, GPP:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Transpiration plot:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Show the two plots together:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"Save the output:","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"savefig(\"ozark_standalone_canopy_test.png\");","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"(Image: )","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"","category":"page"},{"location":"generated/standalone/Canopy/canopy_tutorial/","page":"Standalone Canopy","title":"Standalone Canopy","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Introduction-to-the-Land-Bucket-Model","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The land bucket model implemented in ClimaLand is based off of the models of Manabe (1969)[1], Milly and Shmakin (2002)[2], and the SLIM model (Laguë, Bonan, Swann 2019)[3], with small changes, as noted.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This tutorial explains in brief the core equations and the necessary parameters of the bucket model, and shows how to set up a simulation in standalone mode. More detail for coupled runs can be found in the ClimaCoupler.jl documentation and in the coupled simulation tutorial.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"At each coordinate point on the surface, we solve ordinary differential equations for the subsurface water storage of land (W, m), the snow water equivalent multiplied by the snow cover fraction (σS, m), and the surface water content of land (Ws, m). We additionally solve a partial differential equation for the land temperature as a function of depth (T, K). The snow cover fraction is given by a heaviside function in the current code.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"In what follows, surface fluxes over soil generally indicate fluxes over non-snow-covered regions. The exception is the albedo of vegetated and non-vegetated surfaces, for which we use the symbol α_sfc.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"All equation variables are defined immediately below. We have:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wdt = -I","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd Wsdt = -(P_liq + σM + (1-σ) E_soil - I)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"fracd σSdt = -(P_snow + σE_snow - σM)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ρc fracpartial Tpartial t = κ_soil fracpartial Tpartial z","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_bot = 00 = -κ_soil fracpartial Tpartial z_z = z_bot","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(1-σ) (R_n+ SHF + LHF)_soil + σG_undersnow = -κ_soil fracpartial Tpartial z_z = z_sfc","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"G_undersnow = (R_n+ SHF + LHF)_snow - F_intosnow","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"F_intosnow = -ρ_l L_f0 (P_snow + E_snow-M)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"R_n = -(1-α)*SW -LW + σ_SB T_sfc^4","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where the water fluxes are : I the infiltration as defined in [1], P_liq (m/s) the water volume flux of precipitation, P_snow (m/s) the water volume flux in the form of snow, (1-σ)E_soil (m/s) the water volume flux in evaporation, σE_snow the water volume flux in sublimation from snow, and σM (m/s) the water volume flux in melting of snow. The melt rate is defined via the net surface flux when surface temperatures are above freezing. All fluxes are defined to be positive if towards the atmosphere (cooling land or decreasing water mass in land) and negative if towards land (warming land or increasing water mass). Hence the melting flux is negative since it warms land, and precipitation fluxes are negative since they increase water mass on land.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"For heat fluxes, we have R_n the net radiation, SHF the sensible heat flux, LHF the latent heat flux, G_undersnow the heat flux into snow-covered soil, and F_intosnow the heat flux into the snowpack itself. Note that the water balance equation for snow is equivalent to the heat balance equation, since we neglect the sensible heat contribution and only track the latent heat contribution. We neglect the energy in liquid precipitation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Finally, we have α_bareground_func(lat, lon) the (snow-free) surface albedo, ρc the volumetric heat capacity of the land, σ_SB the Stefan-Boltzmann constant, and κ_soil the thermal conductivity. The albedo is a linear interpolation between the albedo of surface and snow, as decribed in [3]. The surface temperature is taken to be equal to the temperature T at the first grid point, assumed to be the same for soil and snow. At present the snow cover fraction is a heaviside function, and only one set of surface fluxes is computed per grid point.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Turbulent surface fluxes of sensible heat, latent heat, and water vapor (SHF, LHF, E) are computed using Monin-Obukhov theory; SW↓ and LW↓ are the downward fluxes in short and long wavelength bands. We use the same roughness lengths for snow and soil. Note that with the exception of precipitation and downwelling radiation, all fluxes are defined such that positive is towards the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As the temperature at the surface of the soil and snow is the same, only the evaporation changes between the two surface coverage types. We have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_soil = β(W W_f) E(q_sat(T_sfc ρ_sfc liquid)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"where β is the factor used in [1] which accounts for the fact that soil does not evaporate at the potential rate when it is not saturated. This makes use of the field capacity parameter W_f. We also have","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"E_snow = E(q_sat(T_sfc ρ_sfc ice)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#Simulating-a-standalone-bucket-model","page":"Introduction to the Land Bucket Model","title":"Simulating a standalone bucket model","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"First, we need to import necessary packages. We use SciMLBase.jl and ClimaTimeSteppers.jl for the timestepping.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We use ClimaCore for setting up the domain/coordinate points. While this infrastructure isn't really necessary for standalone simulations, adhering to it makes setting up coupled simulations very easy. It also is nice to rely on ClimaCore utilities because they have been designed in advance for running distributed simulations.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaCore","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use ClimaParams, which strives to ensure a common set of parameters across all Clima models, and to make parameter estimation more seamless.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaParams as CP","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also use Insolation to calculate solar zenith angle and solar insolation.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Lastly, let's bring in the bucket model types (from ClimaLand) that we will need access to.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using ClimaLand.Bucket:\n BucketModel, BucketModelParameters, PrescribedBaregroundAlbedo\nusing ClimaLand.Domains: coordinates, Column\nusing ClimaLand:\n initialize,\n make_update_aux,\n make_exp_tendency,\n make_set_initial_cache,\n PrescribedAtmosphere,\n PrescribedRadiativeFluxes\nusing ClimaUtilities.TimeVaryingInputs: TimeVaryingInput","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also want to plot the solution","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Plots","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"And we need to use the DateTime type to store reference times","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"using Dates\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"As mentioned we use ClimaParams for earth parameters that are required across models (e.g. the density of water and ice, the latent heat of fusion at a reference temperature, etc). The land model requires additional parameters as described in the text above. These two sets are combined in the object BucketModelParameters as follows:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"import ClimaLand\nimport ClimaLand.Parameters as LP\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Set up the model domain. At every surface coordinate point, we'll solve an ODE for W and Ws, and for every subsurface point, we solve for T. In coupled simulations run at the same resolution as the atmosphere, the bucket horizontal resolution would match the horizontal resolution at the lowest level of the atmosphere model. In general, however, the two resolutions do not need to match. Here we just set up something simple - a Column.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"soil_depth = FT(3.5);\nbucket_domain = Column(; zlim = (-soil_depth, FT(0.0)), nelements = 10);\nsurface_space = bucket_domain.space.surface","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaCore.Spaces.PointSpace{ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}, ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}}(ClimaComms.SingletonCommsContext{ClimaComms.CPUSingleThreaded}(ClimaComms.CPUSingleThreaded()), ClimaCore.DataLayouts.DataF{ClimaCore.Geometry.LocalGeometry{(3,), ClimaCore.Geometry.ZPoint{Float32}, Float32, StaticArraysCore.SMatrix{1, 1, Float32, 1}}, SubArray{Float32, 1, Matrix{Float32}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}\n Float32[0.0, 0.35, 0.175, 2.85714, 0.35, 2.85714, 8.16327, 0.1225])","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Define our PrescribedBaregroundAlbedo model using a constant bareground surface and snow albedo: The bareground albedo is a function of coordinates, which would be (x,y) on a plane, and (lat,lon) on a sphere. It is also an option to supply a netcdf file with the bareground albedo.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"α_bareground_func = (coordinate_point) -> 0.2;\nα_snow = FT(0.8);\nalbedo =\n PrescribedBaregroundAlbedo{FT}(α_snow, α_bareground_func, surface_space);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The critical snow level setting the scale for when we interpolate between snow and surface albedo","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"σS_c = FT(0.2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The field capacity of the soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W_f = FT(0.15);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Roughness lengths (meters)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"z_0m = FT(1e-2);\nz_0b = FT(1e-3);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Thermal parameters of soil","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"κ_soil = FT(0.7);\nρc_soil = FT(2e6);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Snow melt timescale","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"τc = FT(3600);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Simulation start time, end time, and timestep","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"t0 = 0.0;\ntf = 7 * 86400;\nΔt = 3600.0;\n\nbucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The PrescribedAtmosphere and PrescribedRadiation need to take in a reference time, the date of the start of the simulation. In this tutorial we will consider this January 1, 2005.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ref_time = DateTime(2005);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"To drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes (SW↓, LW↓, W/m^2), for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Here we define the model drivers Prescribed atmospheric variables","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Precipitation:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"precip = (t) -> 0;\nsnow_precip = (t) -> -5e-7 * (t > 3 * 86400) * (t < 4 * 86400);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Diurnal temperature variations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"T_atmos = (t) -> 275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Constant otherwise:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"u_atmos = (t) -> 3.0;\nq_atmos = (t) -> 0.005;\nh_atmos = FT(2);\nP_atmos = (t) -> 101325;","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need to warp all these objects in TimeVaryingInputs (this is because in general PrescribedAtmosphere could take numerical data)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"bucket_atmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(snow_precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Prescribed radiation – a prescribed downwelling SW diurnal cycle, with a peak at local noon, and a prescribed downwelling LW radiative flux, assuming the air temperature is on average 275 degrees K with a diurnal amplitude of 5 degrees K:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"SW_d = (t) -> @. max(1361 * sin(2π * t / 86400 - π / 2));\nLW_d = (t) -> 5.67e-8 * (275.0 + 5.0 * sin(2.0 * π * t / 86400 - π / 2))^4;\nbucket_rad = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then, we create the model object, which contains the drivers, parameters, domain, and is associated with the correct differential equations for the bucket model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"model = BucketModel(\n parameters = bucket_parameters,\n domain = bucket_domain,\n atmosphere = bucket_atmos,\n radiation = bucket_rad,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Note the holder structs for the radiation and atmosphere functions: they are named Prescribed. In coupled simulations, we would use a different type and rely on multiple dispatch to obtain the atmospheric and radiative quantitites from the coupler.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Like all ClimaLand models, we set up the state vector using initialize:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y, p, coords = initialize(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We can inspect the prognostic and auxiliary variables of the model:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.prognostic_vars(model)\nY.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:W, :T, :Ws, :σS)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The auxiliary variables in this case are the surface temperature, the turbulent fluxes, the net radiation, and the surface specific humidity.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaLand.auxiliary_vars(model)\np.bucket |> propertynames","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(:q_sfc, :turbulent_fluxes, :R_n, :T_sfc, :α_sfc, :ρ_sfc, :snow_cover_fraction, :F_sfc, :partitioned_fluxes, :G, :snow_melt, :infiltration)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Next is to set initial conditions.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Y.bucket.T .= FT(270);\nY.bucket.W .= FT(0.05);\nY.bucket.Ws .= FT(0.0);\nY.bucket.σS .= FT(0.08);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We also set the initial values of the cache here:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"set_initial_cache! = make_set_initial_cache(model);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then to create the entire right hand side (tendency) function for the system of ordinary differential equations:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"exp_tendency! = make_exp_tendency(model);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Now we choose our timestepping algorithm.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"timestepper = CTS.RK4()\node_algo = CTS.ExplicitAlgorithm(timestepper)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.RK4, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}, Nothing}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.RK4(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}, ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}, ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}}(ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0]), ClimaTimeSteppers.SparseCoeffs{(4, 4), (true, false, true, true, true, true, false, true, true, true, true, false, true, true, true, true), StaticArraysCore.SMatrix{4, 4, Float64, 16}}([0.0 0.0 0.0 0.0; 0.5 0.0 0.0 0.0; 0.0 0.5 0.0 0.0; 0.0 0.0 1.0 0.0]), ClimaTimeSteppers.SparseCoeffs{(4,), (false, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.16666666666666666, 0.3333333333333333, 0.3333333333333333, 0.16666666666666666]), ClimaTimeSteppers.SparseCoeffs{(4,), (true, false, false, false), StaticArraysCore.SVector{4, Float64}}([0.0, 0.5, 0.5, 1.0])), nothing)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Then we can set up the simulation and solve it:","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(T_exp! = exp_tendency!, dss! = ClimaLand.dss!),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"We need a callback to get and store the auxiliary fields, as they are not stored by default. We also need a callback to update the drivers (atmos and radiation)","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"saveat = collect(t0:Δt:tf);\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\nsaving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);\nupdateat = copy(saveat)\nmodel_drivers = ClimaLand.get_drivers(model)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\n\nsol = SciMLBase.solve(prob, ode_algo; dt = Δt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"Extracting the solution from what is returned by the ODE.jl commands is a bit clunky right now, but we are working on hiding some of this. parent extracts the underlying data from the ClimaCore.Fields.Field object and we loop over the solution sol because of how the data is stored within solutions returned by ODE.jl - indexed by timestep.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"W = [parent(sol.u[k].bucket.W)[1] for k in 1:length(sol.t)];\nWs = [parent(sol.u[k].bucket.Ws)[1] for k in 1:length(sol.t)];\nσS = [parent(sol.u[k].bucket.σS)[1] for k in 1:length(sol.t)];\nT_sfc =\n [parent(saved_values.saveval[k].bucket.T_sfc)[1] for k in 1:length(sol.t)];\nevaporation = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux)[1]\n for k in 1:length(sol.t)\n];\nR_n = [parent(saved_values.saveval[k].bucket.R_n)[1] for k in 1:length(sol.t)];","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"The turbulent energy flux is the sum of latent and sensible heat fluxes.","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"LHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.lhf)[1] for\n k in 1:length(sol.t)\n];\nSHF = [\n parent(saved_values.saveval[k].bucket.turbulent_fluxes.shf)[1] for\n k in 1:length(sol.t)\n];\nturbulent_energy_flux = SHF .+ LHF\n\nplot(\n sol.t ./ 86400,\n W,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"W (m)\",\n title = \"Land water storage (m)\",\n)\nsavefig(\"w.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/w.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n σS,\n label = \"\",\n xlabel = \"time (days)\",\n ylabel = \"σS (m)\",\n title = \"Area weighted SWE (m) \",\n)\nsavefig(\"swe.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/swe.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n snow_precip.(sol.t),\n label = \"Net precipitation\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (m/s)\",\n title = \"Surface water fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, evaporation, label = \"Sublimation/Evaporation\")\nsavefig(\"water_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/water_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n T_sfc,\n title = \"Surface Temperatures\",\n label = \"Ground temperature\",\n xlabel = \"time (days)\",\n ylabel = \"T_sfc (K)\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, T_atmos.(sol.t), label = \"Atmospheric Temperature\")\nsavefig(\"t.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/t.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"plot(\n sol.t ./ 86400,\n R_n,\n label = \"Net radiative flux\",\n xlabel = \"time (days)\",\n ylabel = \"Flux (W/m^2)\",\n title = \"Surface energy fluxes\",\n legend = :bottomright,\n)\nplot!(sol.t ./ 86400, turbulent_energy_flux, label = \"Turbulent fluxes\")\nplot!(sol.t ./ 86400, R_n .+ turbulent_energy_flux, label = \"Net flux\")\nsavefig(\"energy_f.png\")","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Bucket/energy_f.png\"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"(Image: )","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/#References","page":"Introduction to the Land Bucket Model","title":"References","text":"","category":"section"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"[1] Manabe, S. (1969) CLIMATE AND THE OCEAN CIRCULATION I: The Atmospheric Circulation and the Hydrology of the Earth's Surface. Monthly Weather Review, Volume 97: Issue 11, p 739-774. [2] Milly, P. C. D. and Shmakin, A.B. (2002) Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model Journal of Hydrometeorology, Volume 3: Issue 3, p 283-299. [3] Laguë, M., Bonan, G., and Swann, A. (2019) Seperating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land-Atmosphere System Volume 32: Issue 18, p 5725-5744","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"","category":"page"},{"location":"generated/standalone/Bucket/bucket_tutorial/","page":"Introduction to the Land Bucket Model","title":"Introduction to the Land Bucket Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/#The-Two-Stream-Scheme","page":"Two-Stream model","title":"The Two-Stream Scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"-overlinemu(dI^uparrow)dL + 1 - (1 - beta)omegaI^uparrow - omega beta I^downarrow = omega overlinemu K beta_0 exp(-KL)\n-overlinemu(dI^downarrow)dL + 1 - (1 - beta)omegaI^downarrow - omega beta I^uparrow = omega overlinemu K (1-beta_0) exp(-KL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"int_0^1mu^primeG(mu^prime)dmu^prime","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = frach_1exp(-KL)sigma + h_2exp(-hL) + h_3exp(hL)\nI^downarrow = frach_4exp(-KL)sigma + h_5exp(-hL) + h_6exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"I^uparrow = h_7exp(-hL) + h_8exp(hL)\nI^downarrow = h_9exp(-hL) + h_10exp(hL)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Output Symmbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500\nAbsorbed Near-Infrared Radiation ANIR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Parameters Symbol Unit Range\nCanopy PAR Reflectance alpha_PAR_leaf - 0.0–1.0\nCanopy NIR Reflectance alpha_NIR_leaf - 0.0–1.0\nCanopy PAR Transmittance tau_PAR_leaf - 0.0–1.0\nCanopy NIR Transmittance tau_NIR_leaf - 0.0–1.0\nCanopy Emissivity ϵ_canopy - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/twostream_model/","page":"Two-Stream model","title":"Two-Stream model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5\nTypical wavelength per photon PAR lambda_gamma_PAR m 5e-7\nTypical wavelength per photon NIR lambda_gamma_NIR m 1.65e-6","category":"page"},{"location":"APIs/Bucket/#Bucket","page":"Bucket Model","title":"Bucket","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"CurrentModule = ClimaLand.Bucket","category":"page"},{"location":"APIs/Bucket/#Types","page":"Bucket Model","title":"Types","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.BucketModelParameters\nClimaLand.Bucket.PrescribedBaregroundAlbedo\nClimaLand.Bucket.PrescribedSurfaceAlbedo\nClimaLand.Bucket.BucketModel","category":"page"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModelParameters","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModelParameters","text":"struct BucketModelParameters{\n FT <: AbstractFloat,\n PSE,\n}\n\nContainer for holding the parameters of the bucket model.\n\nκ_soil: Conductivity of the soil (W/K/m); constant\nρc_soil: Volumetric heat capacity of the soil (J/m^3/K); constant\nalbedo: Albedo Model\nσS_c: Critical σSWE amount (m) where surface transitions from to snow-covered\nf_snow: Fraction of critical amount of snow at which sublimation β begins to decay to zero (unitless)\nW_f: Capacity of the land bucket (m)\nf_bucket: Fraction of bucket capacity at which evaporation β begins to decay to zero (unitless)\np: Exponent used in β decay (unitless)\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nτc: τc timescale on which snow melts\nearth_param_set: Earth Parameter set; physical constants, etc\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedBaregroundAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedBaregroundAlbedo","text":"PrescribedBaregroundAlbedo{FT, F <: ClimaCore.Fields.Field} <: AbstractBucketAlbedoModel\n\nAn albedo model where the static snow-free bareground albedo is prescribed as a function of space or using data from a file, and the land surface albedo is computed each timestep as a linear combination of the snow albedo and the bareground albedo, following the SLIM model (Lague et al 2019).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.PrescribedSurfaceAlbedo","page":"Bucket Model","title":"ClimaLand.Bucket.PrescribedSurfaceAlbedo","text":"PrescribedSurfaceAlbedo{FT, TV <: AbstractTimeVaryingInput}\n <: AbstractBucketAlbedoModel\n\nAn albedo model where the albedo of different surface types is specified. Albedo is specified via a NetCDF file which is a function of time and covers all surface types (soil, vegetation, snow, etc). This albedo type changes over time according to the input file.\n\nNote that this option should only be used with global simulations, i.e. with a ClimaLand.LSMSphericalShellDomain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#ClimaLand.Bucket.BucketModel","page":"Bucket Model","title":"ClimaLand.Bucket.BucketModel","text":"struct BucketModel{\n FT,\n PS <: BucketModelParameters{FT},\n ATM <: AbstractAtmosphericDrivers{FT},\n RAD <: AbstractRadiativeDrivers{FT},\n D,\n } <: AbstractBucketModel{FT}\n\nConcrete type for the BucketModel, which store the model domain and parameters, as well as the necessary atmosphere and radiation fields for driving the model.\n\nparameters: Parameters required by the bucket model\natmos: The atmospheric drivers: Prescribed or Coupled\nradiation: The radiation drivers: Prescribed or Coupled\ndomain: The domain of the model\n\n\n\n\n\n","category":"type"},{"location":"APIs/Bucket/#Misc-Functions","page":"Bucket Model","title":"Misc Functions","text":"","category":"section"},{"location":"APIs/Bucket/","page":"Bucket Model","title":"Bucket Model","text":"ClimaLand.Bucket.surface_albedo\nClimaLand.Bucket.beta_factor","category":"page"},{"location":"APIs/Bucket/#ClimaLand.surface_albedo","page":"Bucket Model","title":"ClimaLand.surface_albedo","text":"ClimaLand.surface_albedo(\n model::EnergyHydrology{FT},\n Y,\n p,\n) where {FT}\n\nReturns the surface albedo field of the EnergyHydrology soil model.\n\n\n\n\n\nsurface_albedo(model::SnowModel, Y, p)\n\nA helper function which computes and returns the snow albedo.\n\n\n\n\n\nsurface_albedo(model::BucketModel, Y, p)\n\nReturns the bulk surface albedo, which gets updated in update_aux via next_albedo.\n\n\n\n\n\nsurface_albedo(model::AbstractModel, Y, p)\n\nA helper function which returns the surface albedo for a given model, needed because different models compute and store α_sfc in different ways and places.\n\nExtending this function for your model is only necessary if you need to compute surface fluxes and radiative fluxes at the surface using the functions in this file.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Bucket/#ClimaLand.Bucket.beta_factor","page":"Bucket Model","title":"ClimaLand.Bucket.beta_factor","text":"beta_factor(W::FT, σS::FT, fW_f::FT, fσS_c::FT, p::FT) where {FT}\n\nComputes the beta factor which scales the evaporation/sublimation from the potential rate. The beta factor is given by:\n\nβ = (x/xc)^p x < xc 1 otherwise\n\nwhere x = W and xc = fbucket * Wf for the bucket, and x = σS and xc = fsnow *σSc for snow.\n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Coupled-heat-and-water-equations-tending-towards-equilibrium","page":"Energy and Hydrology","title":"Coupled heat and water equations tending towards equilibrium","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The Richards equation tutorial demonstrates how to solve for water flow in soil, without considering heat transfer, phase changes, or the effect of temperature and the effect of ice on the hydraulic properties of the soil.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we show how to solve the interacting heat and water equations, in sand, but without phase changes. This allows us to capture behavior that is not present in Richards equation alone.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"θ_i is the volumetric ice fraction, and","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We will solve this equation in an effectively 1-d domain with z -10, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- κ T(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-κ T(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"T(t = 0 z) = T_min + (T_max-T_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"-K h(t z = -1) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ϑ(t = 0 z) = ϑ_min + (ϑ_max-ϑ_min) e^Cz","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"where C T_min T_max ϑ_min and ϑ_max are constants.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"If we evolve this system for times long compared to the dynamical timescales of the system, we expect it to reach an equilibrium where the LHS of these equations tends to zero. Assuming zero fluxes at the boundaries, the resulting equilibrium state should satisfy hz = 0 and Tz = 0. Physically, this means that the water settles into a vertical profile in which the resulting pressure balances gravity and that the temperature is constant across the domain.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We verify that the system is approaching this equilibrium, and we also sketch out an analytic calculation for the final temperature in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Import-necessary-modules","page":"Energy and Hydrology","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"External (non - CliMA) modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"import SciMLBase\nusing Statistics\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"FT = Float32\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Create-the-model","page":"Energy and Hydrology","title":"Create the model","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν = FT(0.395)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.395f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Soil solids are the components of soil besides water, ice, gases, and air. We specify the soil component fractions, relative to all soil solids. These do not sum to unity; the remainder is νssminerals (=0.08, in this case).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"ν_ss_quartz = FT(0.92)\nν_ss_om = FT(0.0)\nν_ss_gravel = FT(0.0)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"0.0f0","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Other parameters include the hydraulic conductivity at saturation, the specific storage, and the van Genuchten parameters for sand. We recommend Chapter 8 of Bonan (2019) for finding parameters for other soil types.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Ksat = FT(4.42 / 3600 / 100) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.89)\nvg_α = FT(7.5) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nθ_r = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat = Ksat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We also need to pick a domain on which to solve the equations:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"zmax = FT(0)\nzmin = FT(-1.0)\nnelems = 50\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary value problem in this case requires a boundary condition at the top and the bottom of the domain for each equation being solved. We support conditions on the state (ϑ_l or T), or on the fluxes (-K∇h or -κ∇T). In the case of fluxes, we return the magnitude of the flux, assumed to point along ẑ. And, in each case, the boundary conditions are supplied in the form of a function of auxiliary variables p and time t. Here we choose flux boundary conditions. The flux boundary condition requires a function of the cache and simulation time which returns the boundary flux.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Water boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_water_flux = WaterFluxBC((p, t) -> 0.0)\nbottom_water_flux = WaterFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"The boundary conditions for the heat equation:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"surface_heat_flux = HeatFluxBC((p, t) -> 0.0)\nbottom_heat_flux = HeatFluxBC((p, t) -> 0.0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We wrap up all of those in a WaterHeatBC struct:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"boundary_fluxes = (;\n top = WaterHeatBC(; water = surface_water_flux, heat = surface_heat_flux),\n bottom = WaterHeatBC(; water = bottom_water_flux, heat = bottom_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We aren't using any sources or sinks in the equations here, but this is where freeze/thaw terms, runoff, root extraction, etc. would go.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Lastly, we can create the EnergyHydrology model. As always, the model encodes and stores all of the information (parameters, continous equations, prognostic variables, etc) which are needed to turn the PDE system into a set of ODEs, properly spatially discretized for the domain of interest.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Set-up-the-simulation","page":"Energy and Hydrology","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"function init_soil!(Y, z, params)\n ν = params.ν\n θ_r = params.θ_r\n FT = eltype(Y.soil.ϑ_l)\n zmax = FT(0)\n zmin = FT(-1)\n\n theta_max = FT(ν * 0.5)\n theta_min = FT(ν * 0.4)\n T_max = FT(289.0)\n T_min = FT(288.0)\n\n c = FT(20.0)\n @. Y.soil.ϑ_l =\n theta_min +\n (theta_max - theta_min) * exp(-(z - zmax) / (zmin - zmax) * c)\n Y.soil.θ_i .= FT(0.0)\n\n T = @.(T_min + (T_max - T_min) * exp(-(z - zmax) / (zmin - zmax) * c))\n\n θ_l = Soil.volumetric_liquid_fraction.(Y.soil.ϑ_l, ν, θ_r)\n ρc_s =\n Soil.volumetric_heat_capacity.(\n θ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 72);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We use ClimaTimesteppers.jl for carrying out the time integration.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Choose a timestepper and set up the ODE problem:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"dt = Float64(1000.0);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"By default, it only returns Y and t at each time we request output (saveat, below). We use a callback in order to also get the auxiliary vector p back:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"saveat = collect(t0:FT(30000):tf)\nsaved_values = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n);\ncb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat, callback = cb);","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Extract output","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"z = parent(coords.subsurface.z)\nt = parent(sol.t)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nT = [parent(saved_values.saveval[k].soil.T) for k in 1:length(t)];","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Let's look at the initial and final times:","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(ϑ_l[1], z, xlabel = \"ϑ_l\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(ϑ_l[4], z, label = \"t = 1.5d\")\nplot!(ϑ_l[end], z, label = \"t = 3d\")\nsavefig(\"eq_moisture_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"plot(T[1], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 0d\")\nplot!(T[4], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 1.5d\")\nplot!(T[end], z, xlabel = \"T (K)\", ylabel = \"z (m)\", label = \"t = 3d\")\nsavefig(\"eq_temperature_plot.png\");","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#Analytic-Expectations","page":"Energy and Hydrology","title":"Analytic Expectations","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"We can determine a priori what we expect the final temperature to be in equilibrium.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Regardless of the final water profile in equilibrium, we know that the final temperature T_f will be a constant across the domain. All water that began with a temperature above this point will cool to T_f, and water that began with a temperature below this point will warm to T_f. The initial function T(z) is equal to T_f at a value of z = z̃. This is the location in space which divides these two groups (water that warms over time and water that cools over time) spatially. We can solve for z̃(T_f) using T_f = T(z̃).","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Next, we can determine the change in energy required to cool the water above z̃ to T_f: it is the integral from z̃ to the surface at z = 0 of c θ(z) T(z), where c is the volumetric heat capacity - a constant here - and θ(z) is the initial water profile. Compute the energy required to warm the water below z̃ to T_f in a similar way, set equal, and solve for T_f. This results in T_f = 288.056, which is very close to the mean T we observe after 3 days, of 288.054.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"One could also solve the equation for ϑ_l specified by h z = 0 to determine the functional form of the equilibrium profile of the liquid water.","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/#References","page":"Energy and Hydrology","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"Bonan, G. Climate change and terrestrial ecosystem modeling. Cambridge University Press, 2019.\nBalland and Arp, J. Environ. Eng. Sci. 4: 549–558 (2005)","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"","category":"page"},{"location":"generated/standalone/Soil/soil_energy_hydrology/","page":"Energy and Hydrology","title":"Energy and Hydrology","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This shows how to run single column soil model, in standalone mode with spatially varying properties. We are mimicking the experiment carried out in Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118, which measured the infiltration of layered soil in Fort McMurray, Alberta, Canada. We thank Mingbin Huang and S. Lee Barbour for correspondence and support, including sharing of data, with us. Note that all data used in this tutorial is available in their publication.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"using Plots\nimport ClimaUtilities.SpaceVaryingInputs: SpaceVaryingInput\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing ClimaCore\nimport ClimaParams as CP\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nFT = Float64;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define simulation times","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"t0 = Float64(0)\ntf = Float64(60 * 60)\ndt = Float64(30);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Define the domain","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"zmax = FT(0)\nzmin = FT(-1.1)\nnelems = 75\nΔ = FT((zmax - zmin) / nelems / 2)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Download the parameter data. This has been obtained from Table 1b of Infiltration and drainage processes in multi-layered coarse soils Mingbin Huang et. al. Can. J. Soil Sci. (2011) 91: 169\u0001183 doi:10.4141/CJSS09118","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"data_file = ClimaLand.Artifacts.huang_et_al2011_soil_van_genuchten_data();\nparameter_data = readdlm(data_file, ',');","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Our model treats z as increasing in the upwards direction. Values below the surface are negative. Because of this, we convert the (positive-valued) depth of the data into a monotonically increasing z coordinate value. using a negative sign and the reverse function.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"depth = reverse(-parameter_data[1, :] .* 0.01) # convert to m\nksat = reverse(parameter_data[6, :] .* 1 / 100.0 / 60.0) # convert cm/min to m/s\nvgα = reverse(parameter_data[4, :] .* 100 * 2) # they report αᵈ; αʷ = 2αᵈ. This experiment is for infiltration (wetting).\nvgn = reverse(parameter_data[5, :])\nresidual_frac = reverse(parameter_data[2, :])\nporosity = reverse(parameter_data[3, :]);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Create fields corresponding to the parameter","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"ν = SpaceVaryingInput(depth, porosity, soil_domain.space.subsurface)\nK_sat = SpaceVaryingInput(depth, ksat, soil_domain.space.subsurface)\nθ_r = SpaceVaryingInput(depth, residual_frac, soil_domain.space.subsurface);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The specific storativity is not something we have data on, so we approximate it as being constant in depth, and create the parameter field directly:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"S_s = ClimaCore.Fields.zeros(soil_domain.space.subsurface) .+ 1e-3;","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The retention model is a vanGenuchten model with α and n as a function of depth, read from the data:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"hcm = SpaceVaryingInput(\n depth,\n (; α = vgα, n = vgn),\n soil_domain.space.subsurface,\n vanGenuchten{FT},\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"The parameter struct:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"params = ClimaLand.Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"From here on out, everything should look familiar if you've already gone through the other soil tutorials. Set Boundary conditions: At the top, we use the observed value of Ksat at the top of the domain. Setting the flux to be -Ksat is approximating the top as saturated.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"function top_flux_function(p, t)\n return -0.0001033\nend\ntop_bc = ClimaLand.Soil.WaterFluxBC(top_flux_function)\nbottom_bc = ClimaLand.Soil.FreeDrainage()\nboundary_fluxes = (; top = top_bc, bottom = bottom_bc)\nsoil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial the state vectors, and set initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Y.soil.ϑ_l .= 0.0353; # read from Figure 4 of Huang et al.","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"We also set the initial conditions of the auxiliary state here:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"stepper = CTS.ARS111()\n@assert FT in (Float32, Float64)\nerr = (FT == Float64) ? 1e-8 : 1e-4\nconvergence_cond = CTS.MaximumError(err)\nconv_checker = CTS.ConvergenceChecker(norm_condition = convergence_cond)\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 10,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n convergence_checker = conv_checker,\n ),\n)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = make_jacobian(soil)\n\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = [0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 60.0] .* 60 # chosen to compare with data in plots in paper\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = saveat);\n\nz = parent(ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z)\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(sol.t)]\nplot(ϑ_l[1], z, label = \"initial\", color = \"grey\", aspect_ratio = 0.8)\nplot!(ϑ_l[2], z, label = \"8min\", color = \"orange\")\nplot!(ϑ_l[3], z, label = \"16min\", color = \"red\")\nplot!(ϑ_l[4], z, label = \"24min\", color = \"teal\")\nplot!(ϑ_l[5], z, label = \"32min\", color = \"blue\")\nplot!(ϑ_l[6], z, label = \"40min\", color = \"purple\")\nplot!(ϑ_l[7], z, label = \"60min\", color = \"green\")\nscatter!(porosity, depth, label = \"Porosity\")\nplot!(legend = :bottomright)\n\nplot!(xlim = [0, 0.7])\n\nplot!(\n ylim = [-1.1, 0],\n yticks = [-1.1, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1],\n)\n\nplot!(ylabel = \"Depth (m)\")\n\nplot!(xlabel = \"Volumeteric Water Content\")\n\nsavefig(\"./sv62_alpha_2_inf_updated_data_climaland.png\")","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"\"/home/runner/work/ClimaLand.jl/ClimaLand.jl/docs/src/generated/standalone/Soil/sv62_alpha_2_inf_updated_data_climaland.png\"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"","category":"page"},{"location":"generated/standalone/Soil/layered_soil/","page":"Layered Soil","title":"Layered Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy","page":"Canopy Models","title":"Canopy","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Structs","page":"Canopy Models","title":"Canopy Model Structs","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.CanopyModel\nClimaLand.Canopy.SharedCanopyParameters","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.CanopyModel","page":"Canopy Models","title":"ClimaLand.Canopy.CanopyModel","text":" CanopyModel{FT, AR, RM, PM, SM, PHM, EM, SM, A, R, S, PS, D} <: AbstractExpModel{FT}\n\nThe model struct for the canopy, which contains\n\nthe canopy model domain (a point for site-level simulations, or\n\nan extended surface (plane/spherical surface) for regional or global simulations.\n\nsubcomponent model type for radiative transfer. This is of type\n\nAbstractRadiationModel.\n\nsubcomponent model type for photosynthesis. This is of type\n\nAbstractPhotosynthesisModel, and currently only the FarquharModel is supported.\n\nsubcomponent model type for stomatal conductance. This is of type\n\nAbstractStomatalConductanceModel and currently only the MedlynModel is supported\n\nsubcomponent model type for plant hydraulics. This is of type\n\nAbstractPlantHydraulicsModel and currently only a version which prognostically solves Richards equation in the plant is available.\n\nsubcomponent model type for canopy energy. This is of type\n\nAbstractCanopyEnergyModel and currently we support a version where the canopy temperature is prescribed, and one where it is solved for prognostically.\n\nsubcomponent model type for canopy SIF. prognostically.\ncanopy model parameters, which include parameters that are shared\n\nbetween canopy model components or those needed to compute boundary fluxes.\n\nThe atmospheric conditions, which are either prescribed\n\n(of type PrescribedAtmosphere) or computed via a coupled simulation (of type CoupledAtmosphere).\n\nThe radiative flux conditions, which are either prescribed\n\n(of type PrescribedRadiativeFluxes) or computed via a coupled simulation (of type CoupledRadiativeFluxes).\n\nThe soil conditions, which are either prescribed (of type PrecribedSoil, for\n\nrunning the canopy model in standalone mode), or prognostic (of type PrognosticSoil, for running integrated soil+canopy models)\n\nNote that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems. Eventually, when plant biomass becomes a prognostic variable (by integrating with a carbon model), some parameters specified here will be treated differently.\n\nautotrophic_respiration: Autotrophic respiration model, a canopy component model\nradiative_transfer: Radiative transfer model, a canopy component model\nphotosynthesis: Photosynthesis model, a canopy component model\nconductance: Stomatal conductance model, a canopy component model\nhydraulics: Plant hydraulics model, a canopy component model\nenergy: Energy balance model, a canopy component model\nsif: SIF model, a canopy component model\natmos: Atmospheric forcing: prescribed or coupled\nradiation: Radiative forcing: prescribed or coupled\nsoil_driver: Soil pressure: prescribed or prognostic\nparameters: Shared canopy parameters between component models\ndomain: Canopy model domain\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.SharedCanopyParameters","page":"Canopy Models","title":"ClimaLand.Canopy.SharedCanopyParameters","text":"SharedCanopyParameters{FT <: AbstractFloat, PSE}\n\nA place to store shared parameters that are required by multiple canopy components.\n\nz_0m: Roughness length for momentum (m)\nz_0b: Roughness length for scalars (m)\nearth_param_set: Earth param set\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Fluxes","page":"Canopy Models","title":"Canopy Model Fluxes","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.DiagnosticTranspiration","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","page":"Canopy Models","title":"ClimaLand.Canopy.PlantHydraulics.DiagnosticTranspiration","text":"DiagnosticTranspiration{FT} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch in the case where transpiration is computed diagnostically, as a function of prognostic variables and parameters, and stored in p during the update_aux! step.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#Canopy-Model-Soil-Drivers","page":"Canopy Models","title":"Canopy Model Soil Drivers","text":"","category":"section"},{"location":"APIs/canopy/Canopy/","page":"Canopy Models","title":"Canopy Models","text":"ClimaLand.Canopy.AbstractSoilDriver\nClimaLand.Canopy.PrescribedSoil","category":"page"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.AbstractSoilDriver","page":"Canopy Models","title":"ClimaLand.Canopy.AbstractSoilDriver","text":"An abstract type of soil drivers of the canopy model.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Canopy/#ClimaLand.Canopy.PrescribedSoil","page":"Canopy Models","title":"ClimaLand.Canopy.PrescribedSoil","text":" PrescribedSoil <: AbstractSoilDriver\n\nA container for holding prescribed soil parameters needed by the canopy model when running the canopy in standalone mode, including the soil pressure, surface temperature, and albedo.\n\nroot_depths: The depth of the root tips, in meters\nψ: Prescribed soil potential (m) in the root zone a function of time\nT: Prescribed soil surface temperature (K) as a function of time\nα_PAR: Soil albedo for PAR\nα_NIR: Soil albedo for NIR\nϵ: Soil emissivity\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-model","page":"Boundary conditions","title":"Boundary conditions for the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"In general, you must supply two boundary conditions for each PDE being solved. These are passed to the model as a NamedTuple of the form (; top = top_bc, bottom = bottom_bc), where both top_bc and bottom_bc are of type ClimaLand.AbstractBC.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"Flux boundary conditions are always passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-Richards-equation","page":"Boundary conditions","title":"Boundary conditions for Richards equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"FreeDrainage <: AbstractWaterBC: this only can be used at the bottom of the domain.\nWaterFluxBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: WaterFluxBC((p,t) -> 0.0).\nMoistureStateBC <: AbstractWaterBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of ϑ_l at the boundary . e.g: MoistureStateBC((p,t) -> 0.2).\nRichardsAtmosDrivenFluxBC <: AbstractWaterBC: this requires a single argument of abstract type AbstractTimeVaryingInput. Under the hood, this specifies the precipitation as a function of space and time (using data read in from a file, or an analytic function) and applies this a flux BC, optionally accounting for surface/subsurface runoff.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-equation","page":"Boundary conditions","title":"Boundary conditions for the soil heat equation","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"HeatFluxBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the flux value. e.g: HeatFluxBC((p,t) -> 0.0).\nTemperatureStateBC <: AbstractHeatBC: this accepts a prescribed analytic function of the cache p and simulation time t which returns the value of T at the boundary . e.g: TemperatureStateBC((p,t) -> 273.15).","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/#Boundary-conditions-for-the-soil-heat-water-equations-(EnergyHydrology-model)","page":"Boundary conditions","title":"Boundary conditions for the soil heat + water equations (EnergyHydrology model)","text":"","category":"section"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"The full soil model requires boundary conditions for both Richards equation and the soil heat equation.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"WaterHeatBC <: AbstractEnergyHydrologyBC: In many cases, the two boundary conditions can be treated independently. The WaterHeatBC requires a boundary condition of abstract type AbstractWaterBC and one of type AbstractHeatBC, for example, top = WaterHeatBC(; water = MoistureBC(ϑ_l(p,t)), heat = TemperatureBC(T(p,t))).\nAtmosDrivenFluxBC <: AbstractEnergyHydrologyBC: This is an example of a set of boundary conditions for the full soil model which cannot be decomposed into two independent boundary conditions for water and heat. In this case, we compute the turbulent surface fluxes with the atmosphere, obtaining a sensible heat, latent heat, and water vapor flux. We also take into account the net radiation at the surface and any precipitation or runoff. This is the BC type used in most land simulations.","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"","category":"page"},{"location":"generated/standalone/Soil/boundary_conditions/","page":"Boundary conditions","title":"Boundary conditions","text":"This page was generated using Literate.jl.","category":"page"},{"location":"folderstructure/#ClimaLand-folder-structure","page":"Repository structure","title":"ClimaLand folder structure","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand home directory has 5 main folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"docs: contains files to generate the documentation website.\nexperiments: contains simple runs of ClimaLand models. \nparameters: contains a file to retrieve constants such as avogadro's number, the speed of light, etc. \nsrc: contains the code of ClimaLand models. \ntest: contains unit tests, which are meant to ensure small pieces of ClimaLand source code work as intended before merging pull requests.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"and 3 GitHub actions folders. GitHub actions are .yml files, which are bash scripts that runs on a remote computer on each git push. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".buildkite: contains a script building outputs such as figures from experiments and tests folders. These runs are carried out as part of CI and must run without error in order to merge a PR. \n.dev: contains useful tools for developers, such as a format checker for Julia (which is run as part of CI and must pass before a PR can be merged into main). \n.github: contains various scripts, for example, this documentation website is built each time a change is push to ClimaLand. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"as well as 5 files:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":".gitignore: commonly used git file, contains files, files type, and folders that should be ignored by git. \nLICENSE: License file of ClimaLand, you can read it to learn about legal practice regarding use of ClimaLand open source code. \nProject.toml: The Julia programming language requires a Project.toml file to create an environment, which specify dependencies of a project as well as its version, name, authors and a unique identifier number (uuid). Every Julia registered package has a Project.toml file. \nREADME.md: This markdown file contains the info that you can read on ClimaLand GitHub web page","category":"page"},{"location":"folderstructure/#/docs-folder","page":"Repository structure","title":"/docs folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Julia packages are recommended to have a \\docs folder that builds a standardised documentation following the official documentation generator for Julia: Documenter.jl.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The folder /docs contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"a /src folder: It is recommended to put your markdown pages inside this folder. Each markdown file (.md extension text file) is a page accessible through the menu of the documentation. For example, docs/src/Contributing.md contains the text you can read on the documentation \"contribution guide\" menu. The path to this .md file and the name of the menu is set in the docs/make.jl file. \na make.jl file: This Julia file contains your documentation website structure. Running this file will build your website pages, you can run it locally, but it is commonly built remotely via .github/workflows/docs.yml to generate the github static page hosted on the gh-pages branch. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"Note: the documentation can have submenu. For example, APIs have submenu ClimaLand which has many submenu... This structure is built in our current framework via a file docs/list_of_apis.jl in that example, which is then included in docs/make.jl. ","category":"page"},{"location":"folderstructure/#/experiments-folder","page":"Repository structure","title":"/experiments folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The experiments folder contains scripts to run ClimaLand models. It contains a folder for integrated models and a folder for standalone models. It is meant to provide users with simple examples of ClimaLand runs. The files contains meteorological inputs (such as precipitation), values for every parameters, and the domains and timestepper are specified. ","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"For example, /experiments/LSM/ozark/ contains:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ozark_domain.jl: Describes the soil domain (depth, number of layer), and the canopy (number and height of stems and leaves).\nozarkmetdrivers_FLUXNET.jl: This files load meteorological input data from the ozark FLUXNET file, and does additional things such as spline interpolation of these drivers.\nozark_parameters.jl: In this file, parameters values are defined. \nozark_simulation.jl: In this file, initial and final time are set, as well as time resolution and time stepper algorithm. \nozark.jl: running this script will include all the above scripts, and run ClimaLand for the single-site ozark. It will produce output in a text file as well as some figures comparing data and simulation.","category":"page"},{"location":"folderstructure/#/src-folder","page":"Repository structure","title":"/src folder","text":"","category":"section"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"The /src folder contains the source code of ClimaLand models. It contains 3 folders:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"sharedutilities: This is a core folder that defines functions and data structures used across all modules and models types of ClimaLand. For example, `sharedutilities/models.jldefines and export the functionmakeupdateauxwhich will be used to create a function which updates the auxiliary parameters, stored in the vectorp,sharedutilities/boundaryconditions.jl` defines functions for setting boundary condition for PDE domains, etc.\nstandalone: This folder contains standalone models, which are submodels that can be run independently of each other. This is an important aspect of ClimaLand code design: to maximize modularity, sub-models can be run alone, and many different methods of the same sub-model can be defined via Julia multiple dispatch. The standalone folder is independent from the integrated folder. \nintegrated: This folder contains integrated models. It assembles standalone models together, as one would assemble pieces of a puzzle. Thanks to the modularity of ClimaLand design, many configuration of LSM can be assembled in integrated models. The same functions (update_aux!, exp_tendency!, etc.) can be used for standalone and integrated models, and an can be stepped in the same way.","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"As well as one file:","category":"page"},{"location":"folderstructure/","page":"Repository structure","title":"Repository structure","text":"ClimaLand.jl: This file is the main Julia module of ClimaLand.jl repository. It contains all functions defined in /src in a nested way, for example ClimaLand.X, ClimaLand.Soil.X, 'ClimaLand.Canopy.X, etc. When a Julia user install and uses ClimaLand via]add ClimaLand, using ClimaLand`, they are loading those functions, and are ready to use ClimaLand codebase. ","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Stomatal-Conductance","page":"Canopy Stomatal Conductance","title":"Stomatal Conductance","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/StomatalConductance/#Parameters","page":"Canopy Stomatal Conductance","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.MedlynConductanceParameters","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.MedlynConductanceParameters","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.MedlynConductanceParameters","text":"MedlynConductanceParameters{FT <: AbstractFloat}\n\nThe required parameters for the Medlyn stomatal conductance model.\n\nDrel: Relative diffusivity of water vapor (unitless)\ng0: Minimum stomatal conductance mol/m^2/s\ng1: Slope parameter, inversely proportional to the square root of marginal water use efficiency (Pa^{1/2})\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/StomatalConductance/#Methods","page":"Canopy Stomatal Conductance","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/StomatalConductance/","page":"Canopy Stomatal Conductance","title":"Canopy Stomatal Conductance","text":"ClimaLand.Canopy.medlyn_term\nClimaLand.Canopy.medlyn_conductance\nClimaLand.Canopy.upscale_leaf_conductance\nClimaLand.Canopy.penman_monteith","category":"page"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_term","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_term","text":"medlyn_term(g1::FT, T_air::FT, P_air::FT, q_air::FT, thermo_params) where {FT}\n\nComputes the Medlyn term, equal to 1+g1/sqrt(VPD), by first computing the VPD, where VPD is the vapor pressure deficit in the atmosphere (Pa), and g_1 is a constant with units of sqrt(Pa).\n\nthermo_params is the Thermodynamics.jl parameter set.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.medlyn_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.medlyn_conductance","text":"medlyn_conductance(g0::FT,\n Drel::FT,\n medlyn_term::FT,\n An::FT,\n ca::FT) where {FT}\n\nComputes the stomatal conductance according to Medlyn, as a function of the minimum stomatal conductance (g0), the relative diffusivity of water vapor with respect to CO2 (Drel), the Medlyn term (unitless), the biochemical demand for CO2 (An), and the atmospheric concentration of CO2 (ca).\n\nThis returns the conductance in units of mol/m^2/s. It must be converted to m/s using the molar density of water prior to use in SurfaceFluxes.jl.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.upscale_leaf_conductance","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.upscale_leaf_conductance","text":"upscale_leaf_conductance(gs::FT, LAI::FT, T::FT, R::FT, P::FT) where {FT}\n\nThis currently takes a leaf conductance (moles per leaf area per second) and (1) converts it to m/s, (2) upscales to the entire canopy, by assuming the leaves in the canopy are in parallel and hence multiplying by LAI.\n\nTODO: Check what CLM does, and check if we can use the same function for GPP from An, and make more general.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/StomatalConductance/#ClimaLand.Canopy.penman_monteith","page":"Canopy Stomatal Conductance","title":"ClimaLand.Canopy.penman_monteith","text":"penman_monteith(\n Δ::FT, # Rate of change of saturation vapor pressure with air temperature. (Pa K−1) \n Rn::FT, # Net irradiance (W m−2)\n G::FT, # Ground heat flux (W m−2)\n ρa::FT, # Dry air density (kg m−3)\n cp::FT, # Specific heat capacity of air (J kg−1 K−1) \n VPD::FT, # vapor pressure deficit (Pa)\n ga::FT, # atmospheric conductance (m s−1)\n γ::FT, # Psychrometric constant (γ ≈ 66 Pa K−1)\n gs::FT, # surface or stomatal conductance (m s−1)\n Lv::FT, # Volumetric latent heat of vaporization (J m-3)\n ) where {FT}\n\nComputes the evapotranspiration in m/s using the Penman-Monteith equation. \n\n\n\n\n\n","category":"function"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-Tutorial","page":"Intro to ClimaLand Domains","title":"Domain Tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/#Goals-of-the-tutorial","page":"Intro to ClimaLand Domains","title":"Goals of the tutorial","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The goal of this is to outline what is currently implemented in ClimaLand and to serve as a software design document for future development involving the underlying domains.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Background","page":"Intro to ClimaLand Domains","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In both the atmosphere and the ocean, all variables are defined at all locations in the region of interest, or domain. For example, the air density, temperature, pressure, and wind speed are defined everywhere in the domain. After choosing a resolution and discretizing space, the numerical problem is to advance a system of differential equations, where at each coordinate point a value of ρ, T, P, and u⃗ are solved for at each step. The choice of domain is a question \"only\" of geometry: you may be interested in a large eddy simulation (using a box domain), or in a global model (where you would need a spherical shell domain representing the atmosphere or ocean from some depth to z_sfc = 0).","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"For land surface models, each variable is not defined everywhere in space. For example, the soil water content θ is only defined below ground. Snow water equivalent (S) is only defined on the surface itself. Canopy variables are only defined above ground. Once we have discretized the land surface region into a set of points, the numerical problem is to advance a system of ODEs, where at each coordinate point a different subset of (θ, S, ...) are solved for.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"In other words, different variables in land surface models exist in different, overlapping, domains. We need to decide on the geometry of interest (e.g. single column vs a global simulation), but we also need to specify where each variable of the model is defined.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"ClimaLand Domains were designed with this in mind. The domains are defined so that","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the user can easily switch geometries, e.g. single column to global model,\nindividual component models can be run by themselves, using a single domain,\nthe same domains can be used to set up multi-component models (LSMs),\ndifferent variables can exist on different parts of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#What-is-a-ClimaLand-Domain?","page":"Intro to ClimaLand Domains","title":"What is a ClimaLand Domain?","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"A domain represents a region of space. In ClimaLand, domains are simply structs containing parameters that define these regions - for example an x-range and y-range that define a plane. In addition, ClimaLand domains store the ClimaCore function spaces for the physical domain as a NamedTuple. When solving partial differential equations, the spatial discretization is tied to a set of basis functions you wish to use to represent the prognostic variable as a function of space. The nodal points - the locations in space where the variable is solved for - are arranged in space in a manner which depends on these basis functions. Note that these spaces are only mathematically needed when your variables satisfy PDEs[1], but that they still exist when your variables do not, because we are using the same underlying infrastructure in both cases.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Domain-types","page":"Intro to ClimaLand Domains","title":"Domain types","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"All ClimaLand domains are subtypes of abstract type ClimaLand.Domains.AbstractDomain. A variety of concrete domain types are supported:","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"0D: Domains.Point\n1D: Domains.Column\n2D: Domains.Plane, Domains.SphericalSurface\n3D: Domains.HybridBox, Domains.SphericalShell.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"As discussed above, our modeling requires that variables of a model can be defined on different subsets of the domain. Because of that, we define the concept of a surface domain, and a subsurface domain. Not all domains have a surface and subsurface; some only have surface domains, as shown in the Table below.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Domain Surface Domain Subsurface Domain\nColumn Point Column\nHybridBox Plane HybridBox\nSphericalShell SphericalSurface SphericalShell","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"There is a single key method which take a ClimaLand domain as an argument.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"coordinates(domain): under the hood, this function uses","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"the NamedTuple of function spaces (domain.space) to create the coordinate field for the surface and subsurface domains (as applicable), stored in a NamedTuple. Depending on the domain, the returned coordinate field will have elements of different names and types. For example, the SphericalShell domain has subsurface coordinates of latitude, longitude, and depth, while the surface coordinates are latitude and longitude. A Plane domain has coordinates of x and y (surface only), and a Point domain only has a coordinate zsfc (surface only). Column domains have a surface coordinate of zsfc, and subsurface coordinates of z.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"It is important to note that the horizontal domain used for the surface and subsurface domains are identical in all simulations. This ensures that we can use the same indexing of surface and subsurface domains and variables. Otherwise we would need to develop additional infrastructure in order to, for example, select the correct subsurface column corresponding to a particular surface location.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#How-variable-initialization-depends-on-domains","page":"Intro to ClimaLand Domains","title":"How variable initialization depends on domains","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Single component models (soil, snow, vegetation, canopy...) must have an associated domain in order to solve the their equations. Which domain is appropriate depends on the model equations and on the configuration of interest (single column or global, etc.). For example, the soil model is a vertically resolved model, so only domains with a vertical extent (Column, HybridBox, or SphericalShell) make sense to use. A single layer snow model does not require vertical resolution - and so the domains that make sense to use are a Point, Plane, or SphericalSurface.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"When a developer first defines a model, they need to specify the symbols used for the prognostic variables, via prognostic_vars, and the types of those variables, via prognostic_types.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"They additionally need to define which subset of the domain the variables are defined on, using prognostic_domain_names.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"The initialize function (which calls both initialize_prognostic and initialize_auxiliary) creates the prognostic state vector Y (a ClimaCore.Fields.FieldVector). Each field (ClimaCore.Fields.Field) stored within the field vector corresponds to a prognostic variable (identified with the symbol specified). If the prognostic type for that variable is a float, the field will be a field of float values (a scalar field)[4].","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"How do domains tie into this? The field of a prognostic variable corresponds in a 1-1 fashion with the coordinate field of the subset of the domain associated with that variable via prognostic_domain_name. For example, the bucket model has a vertically resolved temperature T, but the bucket water content W is not vertically resolved. If your domain is a Column, the subsurface coordinates may be [-4.5,-3.5,-2.5,-1.5, -0.5], and the surface coordinate would be [-0.0]. Your prognostic variable field for T will be [T[-4.5], T[-3.5]; T[-2.5], T[-1.5], T[-0.5]], and for W it will be [W[0.0],]. Your variable always has the same spatial resolution as the associated subset of the domain.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This functionality is not required for every standalone component model. For example, a single layer snow model will only have variables on the surface of the domain (which in this case, would be the entire Point, Plane, or SphericalShell domain). The user still must define the prognosticdomainnames method. This functionality is required for most multi-component models.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/#Future-work","page":"Intro to ClimaLand Domains","title":"Future work","text":"","category":"section"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"Almost all interactions between variables in land surface models are within column - that is, there is only vertical transport and exchanges. The exception to this is the horizontal flow of water on the surface and within the soil. The tendency (produced by make_exp_tendency and make_imp_tendency) functions (the ODE functions) can be split into \"vertical\" and \"horizontal\" pieces.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"We envision each step of the land surface model simulation to be solved in two steps: (1) the vertical tendency evaluations are carried out (and can be parallelized), and (2) the horizontal tendency functions are then evaluated (possibly less frequently?) and require communication between columns. In this case, tendency functions will need to be aware of the domain. In general, tendencies reflecting horizontal flow will be treated explicitly and include in the explicit tendency function. Tendencies reflecting vertical flow may be treated explicitly or implicitly depending on the use case. To solve the problem, we then use IMEX (mixed explicit/implicit) methods.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[1]: finite differencing is used in the vertical, and spectral elements are used in the horizontal.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[2]: a suprasurface region may also be necessary - for example if the canopy airspace model involves PDEs.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"[3]: We also will support having an array-like type of variable.","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"","category":"page"},{"location":"generated/standalone/Usage/domain_tutorial/","page":"Intro to ClimaLand Domains","title":"Intro to ClimaLand Domains","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Coupling-the-CliMA-Canopy-and-Soil-Hydraulics-Models","page":"Coupled Canopy and Soil","title":"Coupling the CliMA Canopy and Soil Hydraulics Models","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In the previous tutorial, we demonstrated how to run the canopy model in standalone mode using prescribed values for the inputs of soil hydraulics into the canopy hydraulics model. However, ClimaLand has the built-in capacity to couple the canopy model with a soil physics model and timestep the two simulations together to model a canopy-soil system. This tutorial demonstrates how to setup and run a coupled simulation, again using initial conditions, atmospheric and radiative flux conditions, and canopy properties observed at the US-MOz flux tower, a flux tower located within an oak-hickory forest in Ozark, Missouri, USA. See Wang et al. 2021 for details on the site and parameters.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"In ClimaLand, the coupling of the canopy and soil models is done by pairing the inputs and outputs which between the two models so that they match. For example, the root extraction of the canopy hydraulics model, which acts as a boundary flux for the plant system, is paired with a source term for root extraction in the soil model, so that the flux of water from the soil into the roots is equal and factored into both models. This pairing is done automatically in the constructor for a SoilCanopyModel so that a user needs only specify the necessary arguments for each of the component models, and the two models will automatically be paired into a coupled simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Preliminary-Setup","page":"Coupled Canopy and Soil","title":"Preliminary Setup","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load External Packages:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"import SciMLBase\nusing Plots\nusing Statistics\nusing Dates\nusing Insolation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Load CliMA Packages and ClimaLand Modules:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\nusing ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Soil.Biogeochemistry\nusing ClimaLand.Canopy\nusing ClimaLand.Canopy.PlantHydraulics\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the floating point precision desired (64 or 32 bit), and get the parameter set holding constants used across CliMA Models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Setup the domain for the model:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"nelements = 10\nzmin = FT(-2)\nzmax = FT(0)\nf_root_to_shoot = FT(3.5)\nSAI = FT(0.00242)\nmaxLAI = FT(4.2)\nplant_ν = FT(2.46e-4)\nn_stem = Int64(1)\nn_leaf = Int64(1)\nh_stem = FT(9)\nh_leaf = FT(9.5)\ncompartment_midpoints = [h_stem / 2, h_stem + h_leaf / 2]\ncompartment_surfaces = [zmax, h_stem, h_stem + h_leaf]\nland_domain = Column(; zlim = (zmin, zmax), nelements = nelements);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We will be using prescribed atmospheric and radiative drivers from the US-MOz tower, which we read in here. We are using prescribed atmospheric and radiative flux conditions, but it is also possible to couple the simulation with atmospheric and radiative flux models. We also","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"read in the observed LAI and let that vary in time in a prescribed manner.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Use the data tools for reading FLUXNET data sets","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"include(\n joinpath(pkgdir(ClimaLand), \"experiments/integrated/fluxnet/data_tools.jl\"),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"First provide some information about the site Timezone (offset from UTC in hrs)","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"time_offset = 7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"7","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Site latitude and longitude","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"lat = FT(38.7441) # degree\nlong = FT(-92.2000) # degree","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"-92.2f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Height of the sensor at the site","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"atmos_h = FT(32) # m","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"32.0f0","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Provide the site site ID and the path to the data file:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"site_ID = \"US-MOz\"\ndata_link = \"https://caltech.box.com/shared/static/7r0ci9pacsnwyo0o9c25mhhcjhsu6d72.csv\"\n\ninclude(\n joinpath(\n pkgdir(ClimaLand),\n \"experiments/integrated/fluxnet/met_drivers_FLUXNET.jl\",\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: Data for TA_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for VPD_F 0.719% poor quality. Returning with no replacement.\n[ Info: Warning: Data for PA_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for P_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for WS_F 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for LW_IN_F 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for SW_IN_F 0.0114% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for G_F_MDS 0.00571% poor quality. Filled with mean value using QC flag\n[ Info: Information: Data for GPP_DT_VUT_REF is complete and no QC flag present\n[ Info: Information: Data for LE_CORR is complete and no QC flag present\n[ Info: Information: Data for H_CORR is complete and no QC flag present\n[ Info: Warning: Data for LW_OUT 0.00571% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SW_OUT 0.131% has value of -9999. Filled with mean value\n[ Info: Warning: Data for SWC_F_MDS_1 0.0571% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for TS_F_MDS_1 0.0% poor quality. Filled with mean value using QC flag\n[ Info: Warning: Data for CO2_F_MDS 0.0457% poor quality. Filled with mean value using QC flag\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Setup-the-Coupled-Canopy-and-Soil-Physics-Model","page":"Coupled Canopy and Soil","title":"Setup the Coupled Canopy and Soil Physics Model","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We want to simulate the canopy-soil system together, so the model type SoilCanopyModel is chosen. From the linked documentation, we see that we need to provide the soil model type and arguments as well as the canopy model component types, component arguments, and the canopy model arguments, so we first need to initialize all of these.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For our soil model, we will choose the EnergyHydrology and set up all the necessary arguments. See the tutorial on the model for a more detailed explanation of the soil model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Define the parameters for the soil model and provide them to the model parameters struct:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil_ν = FT(0.5) # m3/m3\nsoil_K_sat = FT(4e-7) # m/s\nsoil_S_s = FT(1e-3) # 1/m\nsoil_vg_n = FT(2.05) # unitless\nsoil_vg_α = FT(0.04) # inverse meters\nθ_r = FT(0.067); # m3/m3","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Soil heat transfer parameters","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ν_ss_quartz = FT(0.1)\nν_ss_om = FT(0.1)\nν_ss_gravel = FT(0.0);\nz_0m_soil = FT(0.1)\nz_0b_soil = FT(0.1)\nsoil_ϵ = FT(0.98)\nsoil_α_PAR = FT(0.2)\nsoil_α_NIR = FT(0.4)\n\nsoil_domain = land_domain\nsoil_ps = Soil.EnergyHydrologyParameters(\n FT;\n ν = soil_ν,\n ν_ss_om = ν_ss_om,\n ν_ss_quartz = ν_ss_quartz,\n ν_ss_gravel = ν_ss_gravel,\n hydrology_cm = vanGenuchten{FT}(; α = soil_vg_α, n = soil_vg_n),\n K_sat = soil_K_sat,\n S_s = soil_S_s,\n θ_r = θ_r,\n earth_param_set = earth_param_set,\n z_0m = z_0m_soil,\n z_0b = z_0b_soil,\n emissivity = soil_ϵ,\n PAR_albedo = soil_α_PAR,\n NIR_albedo = soil_α_NIR,\n);\n\nsoil_args = (domain = soil_domain, parameters = soil_ps)\nsoil_model_type = Soil.EnergyHydrology{FT}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ClimaLand.Soil.EnergyHydrology{Float32}","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"For the heterotrophic respiration model, see the documentation to understand the parameterisation. The domain is defined similarly to the soil domain described above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soilco2_type = Soil.Biogeochemistry.SoilCO2Model{FT}\n\nsoilco2_ps = SoilCO2ModelParameters(FT);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"soil microbes args","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Csom = ClimaLand.PrescribedSoilOrganicCarbon{FT}(TimeVaryingInput((t) -> 5))\n\nsoilco2_top_bc = Soil.Biogeochemistry.AtmosCO2StateBC()\nsoilco2_bot_bc = Soil.Biogeochemistry.SoilCO2StateBC((p, t) -> 0.0);\nsoilco2_sources = (MicrobeProduction{FT}(),);\n\nsoilco2_boundary_conditions = (; top = soilco2_top_bc, bottom = soilco2_bot_bc);\n\nsoilco2_args = (;\n boundary_conditions = soilco2_boundary_conditions,\n sources = soilco2_sources,\n domain = soil_domain,\n parameters = soilco2_ps,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Next we need to set up the CanopyModel. For more details on the specifics of this model see the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Begin by declaring the component types of the canopy model. Unlike in the previous tutorial, collect the arguments to each component into tuples and do not instantiate the component models yet. The constructor for the SoilPlantHydrologyModel will use these arguments and internally instatiate the component CanopyModel and RichardsModel instances. This is done so that the constructor may enforce consistency constraints between the two models, and this must be done internally from the constructor.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_types = (;\n autotrophic_respiration = Canopy.AutotrophicRespirationModel{FT},\n radiative_transfer = Canopy.TwoStreamModel{FT},\n photosynthesis = Canopy.FarquharModel{FT},\n conductance = Canopy.MedlynConductanceModel{FT},\n hydraulics = Canopy.PlantHydraulicsModel{FT},\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Then provide arguments to the canopy radiative transfer, stomatal conductance, and photosynthesis models as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"autotrophic_respiration_args =\n (; parameters = AutotrophicRespirationParameters(FT))\n\nradiative_transfer_args = (;\n parameters = TwoStreamParameters(\n FT;\n G_Function = ConstantGFunction(FT(0.5)),\n α_PAR_leaf = 0.1,\n α_NIR_leaf = 0.45,\n τ_PAR_leaf = 0.05,\n τ_NIR_leaf = 0.25,\n Ω = 0.69,\n )\n)\n\nconductance_args = (; parameters = MedlynConductanceParameters(FT; g1 = 141))\n\nphotosynthesis_args =\n (; parameters = FarquharParameters(FT, Canopy.C3(); Vcmax25 = FT(5e-5)));\n\nK_sat_plant = FT(1.8e-8)\nRAI = (SAI + maxLAI) * f_root_to_shoot;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Note: LAIfunction was determined from data in the script we included above.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"ai_parameterization = PrescribedSiteAreaIndex{FT}(LAIfunction, SAI, RAI)\nfunction root_distribution(z::T; rooting_depth = FT(1.0)) where {T}\n return T(1.0 / rooting_depth) * exp(z / T(rooting_depth)) # 1/m\nend\n\nψ63 = FT(-4 / 0.0098)\nWeibull_param = FT(4)\na = FT(0.05 * 0.0098)\n\nconductivity_model =\n PlantHydraulics.Weibull{FT}(K_sat_plant, ψ63, Weibull_param)\n\nretention_model = PlantHydraulics.LinearRetentionCurve{FT}(a)\n\nplant_ν = FT(0.7)\nplant_S_s = FT(1e-2 * 0.0098)\n\nplant_hydraulics_ps = PlantHydraulics.PlantHydraulicsParameters(;\n ai_parameterization = ai_parameterization,\n ν = plant_ν,\n S_s = plant_S_s,\n root_distribution = root_distribution,\n conductivity_model = conductivity_model,\n retention_model = retention_model,\n)\n\nplant_hydraulics_args = (\n parameters = plant_hydraulics_ps,\n n_stem = n_stem,\n n_leaf = n_leaf,\n compartment_midpoints = compartment_midpoints,\n compartment_surfaces = compartment_surfaces,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now collect all of the canopy component argument tuples into one arguments tuple for the canopy component models.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"canopy_component_args = (;\n autotrophic_respiration = autotrophic_respiration_args,\n radiative_transfer = radiative_transfer_args,\n photosynthesis = photosynthesis_args,\n conductance = conductance_args,\n hydraulics = plant_hydraulics_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We also need to provide the shared parameter struct to the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"z0_m = FT(2)\nz0_b = FT(0.2)\n\nshared_params = SharedCanopyParameters{FT, typeof(earth_param_set)}(\n z0_m,\n z0_b,\n earth_param_set,\n)\ncanopy_domain = obtain_surface_domain(land_domain)\ncanopy_model_args = (; parameters = shared_params, domain = canopy_domain);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We may now instantiate the integrated plant and soil model. In this example, we will compute transpiration diagnostically, and work with prescribed atmospheric and radiative flux conditions from the observations at the Ozark site as was done in the previous tutorial.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"land_input = (atmos = atmos, radiation = radiation, soil_organic_carbon = Csom)\n\nland = SoilCanopyModel{FT}(;\n soilco2_type = soilco2_type,\n soilco2_args = soilco2_args,\n land_args = land_input,\n soil_model_type = soil_model_type,\n soil_args = soil_args,\n canopy_component_types = canopy_component_types,\n canopy_component_args = canopy_component_args,\n canopy_model_args = canopy_model_args,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n[ Info: Using the PrescribedAtmosphere air temperature as the canopy temperature\n","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now we can initialize the state vectors and model coordinates, and initialize the explicit/implicit tendencies as usual. The Richard's equation time stepping is done implicitly, while the canopy model may be explicitly stepped, so we use an IMEX (implicit-explicit) scheme for the combined model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y, p, coords = initialize(land);\nexp_tendency! = make_exp_tendency(land);\nimp_tendency! = make_imp_tendency(land);\njacobian! = make_jacobian(land);\njac_kwargs =\n (; jac_prototype = ClimaLand.ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"We need to provide initial conditions for the soil and canopy hydraulics models:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Y.soil.ϑ_l = FT(0.4)\nY.soil.θ_i = FT(0.0)\nT_0 = FT(288.7)\nρc_s =\n volumetric_heat_capacity.(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n land.soil.parameters.ρc_ds,\n earth_param_set,\n )\nY.soil.ρe_int =\n volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)\n\nY.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air\n\nψ_stem_0 = FT(-1e5 / 9800)\nψ_leaf_0 = FT(-2e5 / 9800)\n\nS_l_ini =\n inverse_water_retention_curve.(\n retention_model,\n [ψ_stem_0, ψ_leaf_0],\n plant_ν,\n plant_S_s,\n )\n\nfor i in 1:2\n Y.canopy.hydraulics.ϑ_l.:($i) .=\n augmented_liquid_fraction.(plant_ν, S_l_ini[i])\nend;","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Select the timestepper and solvers needed for the specific problem. Specify the time range and dt value over which to perform the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"t0 = Float64(150 * 3600 * 24)# start mid year\nN_days = 100\ntf = t0 + Float64(3600 * 24 * N_days)\ndt = Float64(30)\nn = 120\nsaveat = Array(t0:(n * dt):tf)\n\ntimestepper = CTS.ARS343()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now set the initial values for the cache variables for the combined soil and plant model.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"set_initial_cache! = make_set_initial_cache(land)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Set the callbacks, which govern how often we save output, and how often we update the forcing data (\"drivers\")","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"sv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nmodel_drivers = ClimaLand.get_drivers(land)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\nupdateat = Array(t0:1800:tf)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Carry out the simulation","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(\n prob,\n ode_algo;\n dt = dt,\n callback = cb,\n adaptive = false,\n saveat = saveat,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/#Plotting","page":"Coupled Canopy and Soil","title":"Plotting","text":"","category":"section"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now that we have both a soil and canopy model incorporated together, we will show how to plot some model data demonstrating the time series produced from each of these models. As before, we may plot the GPP of the system as well as transpiration showing fluxes in the canopy.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"daily = sol.t ./ 3600 ./ 24\nmodel_GPP = [\n parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for\n k in 1:length(sv.saveval)\n]\n\nplt1 = Plots.plot(size = (600, 700));\nPlots.plot!(\n plt1,\n daily,\n model_GPP .* 1e6,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"GPP [μmol/mol]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Transpiration plot:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"T = [\n parent(sv.saveval[k].canopy.conductance.transpiration)[1] for\n k in 1:length(sv.saveval)\n]\nT = T .* (1e3 * 24 * 3600)\n\nplt2 = Plots.plot(size = (500, 700));\nPlots.plot!(\n plt2,\n daily,\n T,\n label = \"Model\",\n xlim = [minimum(daily), maximum(daily)],\n xlabel = \"days\",\n ylabel = \"Vapor Flux [mm/day]\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Show the two plots together:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Plots.plot(plt1, plt2, layout = (2, 1));","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_canopy_flux_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Now, we will plot the augmented volumetric liquid water fraction at different depths in the soil over the course of the simulation.","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"plt1 = Plots.plot(size = (500, 700));\nϑ_l_10 = [parent(sol.u[k].soil.ϑ_l)[end] for k in 1:1:length(sol.t)]\nplt1 = Plots.plot(\n daily,\n ϑ_l_10,\n label = \"10 cm\",\n xlabel = \"Days\",\n ylabel = \"SWC [m/m]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n color = \"blue\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 1] for k in 1:1:length(sol.t)],\n label = \"20cm\",\n color = \"red\",\n);\n\nplot!(\n plt1,\n daily,\n [parent(sol.u[k].soil.ϑ_l)[end - 2] for k in 1:1:length(sol.t)],\n label = \"30cm\",\n color = \"purple\",\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"Save the output:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_test.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And now to demonstrate the coupling of the soil and canopy models we will plot the water fluxes from the soil up into the plant hydraulic system:","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"root_stem_flux = [\n sum(sv.saveval[k].root_extraction) .* (1e3 * 3600 * 24) for\n k in 1:length(sol.t)\n]\nplt1 = Plots.plot(\n daily,\n root_stem_flux,\n label = \"soil-root-stem water flux\",\n ylabel = \"Water flux[mm/day]\",\n xlim = [minimum(daily), maximum(daily)],\n size = (500, 700),\n margins = 10Plots.mm,\n);","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"And save the output","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"savefig(\"ozark_soil_plant_flux.png\");","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"(Image: )","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"","category":"page"},{"location":"generated/integrated/soil_canopy_tutorial/","page":"Coupled Canopy and Soil","title":"Coupled Canopy and Soil","text":"This page was generated using Literate.jl.","category":"page"},{"location":"diagnostics/developers_diagnostics/#ClimaLand-Diagnostics:-why-and-how","page":"For developers","title":"ClimaLand Diagnostics: why and how","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"ClimaLand simulations generates variables in the integrator state (Y) and cache (p) at each time step. A user will need to use these variables in some form, i.e., access them from a file that contains variables at a given temporal and spatial resolution. The user will also want to retrieve metadata about those variables, such as name and units. This is where ClimaLand diagnostics comes in, it writes simulations variables (in a file, such as NetCDF or HDF5, or in Julia Dict), at a specified spatio-temporal reduction (e.g., hourly averages, monthly max, instantaneous, integrated through soil depth...), along with metadata (e.g., soil temperature short name is t_soil, expressed in \"K\" units). We want to provide users with default options, but also the possibility to define their own variables and reductions.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Internally, this is done by using the ClimaDiagnostics.jl package, that provides the functionality to produce a ClimaLand.Diagnostics module in the src/Diagnostics.jl folder. In this folder,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Diagnostics.jl defines the module,\ndiagnostic.jl defines ALL_DIAGNOSTICS, a Dict containing all diagnostics variables defined in define_diagnostics.jl, it also defines the function","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable! which defines a method to add diagnostic variables to ALL_DIAGNOSTICS, finally it contains a function get_diagnostic_variable which returns a DiagnosticVariable from its short_name, if it exists.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"define_diagnostics.jl, mentioned above, contains a function define_diagnostics!(land_model) which contains all default diagnostic variables by calling.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!, and dispatch off the type of land_model to define how to compute a diagnostic (for example, surface temperature is computed in p.bucket.T_sfc in the bucket model).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"compute methods are defined in a separate file, for example, bucket_compute_methods.jl.\nstandard_diagnostic_frequencies.jl defines standard functions to schedule diagnostics, for example, hourly average or monthly max, these functions are called on a list of diagnostic variables. As developers, we can add more standard functions that users may want to have access to easily in this file.\ndefault_diagnostics.jl defines default diagnostics functions to use on a model simulation. For example, default_diagnostics(land_model::BucketModel, t_start; output_writer).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a ScheduledDiagnostics that computes hourly averages for all Bucket variables, along with their metadata, ready to be written on a NetCDF file when running a Bucket simulation.","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The following section give more details on these functions, along with examples. As developers, we want to extand these functionality as ClimaLand progresses.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Compute-methods","page":"For developers","title":"Compute methods","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Each model defines all its compute methods in a file (bucket_compute_methods.jl for the bucket model, for example). The structure of a diagnostic variable compute method is, for example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@with_error function compute_albedo!(out, Y, p, t, land_model::BucketModel)\n if isnothing(out)\n return copy(p.bucket.α_sfc)\n else\n out .= p.bucket.α_sfc\n end\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"It defines how to access your diagnostic (here, p.bucket.α_sfc) with the land_model BucketModel. Note that you can also use the @diagnostic_compute macro to do the same thing:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"@diagnostic_compute \"albedo\" BucketModel p.bucket.α\\_sfc","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"The @with_error macro define helper functions returning error messages if a user tries to compute a diagnostic variable that doesn't exist in their model type.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Define-diagnostics","page":"For developers","title":"Define diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"Once the compute functions have been defined, they are added to define_diagnostics!(land_model), which adds diagnostics variables to ALL_DIAGNOSTICS dict, defined in diagnostic.jl. In these functions, you also define a short_name, long_name, standard_name, units and comment. For example:","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"add_diagnostic_variable!(\n short_name = \"alpha\",\n long_name = \"Albedo\",\n standard_name = \"albedo\",\n units = \"\",\n compute! = (out, Y, p, t) -> compute_albedo!(out, Y, p, t, land_model),\n )","category":"page"},{"location":"diagnostics/developers_diagnostics/#Default-diagnostics","page":"For developers","title":"Default diagnostics","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For each model, we define a function default_diagnostics which will define what diagnostic variables to compute by default for a specific model, and on what schedule (for example, hourly average). For example,","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"function default_diagnostics(land_model::BucketModel, t_start; output_writer)\n\n define_diagnostics!(land_model)\n\n bucket_diagnostics = [\n \"alpha\",\n \"rn\",\n \"tsfc\",\n \"qsfc\",\n \"lhf\",\n \"rae\",\n \"shf\",\n \"vflux\",\n \"rhosfc\",\n \"t\",\n \"w\",\n \"ws\",\n \"sigmas\",\n ]\n\n default_outputs =\n hourly_averages(bucket_diagnostics...; output_writer, t_start)\n return [default_outputs...]\nend","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"is the default for the BucketModel, it will return hourly averages for the variables listed in bucket_diagnostics (which are all variables in the BucketModel).","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"For the SoilCanopyModel and the SoilModel, we added two keyword arguments: output_vars (can be :long or :short) and average_period (can be :hourly, :daily, or :monthly). If output_vars = :long (the default), then soilcanopy_diagnostics is an Array of all short_name, if output_vars = :short, then soilcanopy_diagnostics = [\"gpp\", \"ct\", \"lai\", \"swc\", \"si\"]. If average_period = :hourly, default_outputs calls hourly_averages, et cetera.","category":"page"},{"location":"diagnostics/developers_diagnostics/#Standard-diagnostic-frequencies","page":"For developers","title":"Standard diagnostic frequencies","text":"","category":"section"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"We defined some functions of diagnostic schedule that may often be used in standard_diagnostic_frequencies.jl, for example","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"hourly_averages(short_names...; output_writer, t_start) = common_diagnostics(\n 60 * 60 * one(t_start),\n (+),\n output_writer,\n t_start,\n short_names...;\n pre_output_hook! = average_pre_output_hook!,\n)","category":"page"},{"location":"diagnostics/developers_diagnostics/","page":"For developers","title":"For developers","text":"will return a list of ScheduledDiagnostics that compute the hourly average for the given variables listed in short_names. We also, so far, provide functions for mins, maxs and averages aggregated monthly, over ten days, daily, and hourly. As a developer, you may want to add more standard diagnostics here.","category":"page"},{"location":"","page":"Home","title":"Home","text":"using Pkg\nio = IOBuffer()\nv = Pkg.installed()[\"ClimaLand\"]\nprint(io, \"\"\"\n # ClimaLand.jl Documentation (v$(v))\n\n \"\"\")\nimport Markdown\nMarkdown.parse(String(take!(io)))","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand is the Land model of the Climate Modeling Alliance (CliMA) Earth System Model, which also contains other components (atmosphere, ocean, sea-ice).","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand can be run coupled (or \"online\") with these other components via ClimaCoupler, or it can be run as a standalone, via prescribed meteorological data (\"offline\").","category":"page"},{"location":"","page":"Home","title":"Home","text":"ClimaLand library, described in this documentation, is written in the Julia programming language. It aims to be fast and have a clear syntax. ClimaLand can run on CPU or GPU, it has a modular design, and is flexible in many ways. This documentation will expand on each of these elements.","category":"page"},{"location":"#Important-Links","page":"Home","title":"Important Links","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CliMA Homepage\nCliMA GitHub Organisation\nClimaCoupler\nClimaAnalysis\nJulia Homepage","category":"page"},{"location":"#Documentation-for-Users-and-Developers","page":"Home","title":"Documentation for Users and Developers","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"ClimaLand has documentation for both users and developers. The documentation for users is aimed at scientists who wants to run simulations using ClimaLand, whereas the documentation for developers is aimed at contributors of the ClimaLand code library. As such, users can skip reading the docs for developers, and vice-versa.","category":"page"},{"location":"#Physical-units","page":"Home","title":"Physical units","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Note that CliMA, in all its repositories, uses Standard Units, reminded below","category":"page"},{"location":"","page":"Home","title":"Home","text":"Quantity Unit Name SI Symbol SI Unit Equivalent\nLength Meter m 1 m\nMass Kilogram kg 1 kg\nTime Second s 1 s\nTemperature Kelvin K 1 K\nAmount of Substance Mole mol 1 mol\nEnergy Joule J 1 J = 1 N·m\nPower Watt W 1 W = 1 J/s\nPressure Pascal Pa 1 Pa = 1 N/m²\nFrequency Hertz Hz 1 Hz = 1 s⁻¹","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Eventually this will be a bare soil site experiment, showing how to set up the soil model in a column with prescribed forcing and comparing to data.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 270.0^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set up atmospheric conditions that result in the potential evaporation rate obsereved in the experiment. Some of these conditions are reported in the paper.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"T_air = FT(270.0)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the boundary conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nzero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nboundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the parameters n and alpha estimated by matching vG curve.","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"K_sat = FT(225.1 / 3600 / 24 / 1000)\nvg_n = FT(10.0)\nvg_α = FT(6.0)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.43)\nθ_r = FT(0.045)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(1.0)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Domain - single column","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"zmax = FT(0)\nzmin = FT(-0.35)\nnelems = 12\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z;","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Soil model, and create the prognostic vector Y and cache p:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sources = (PhaseChange{FT}(),);\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n)\n\nY, p, cds = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Set initial conditions","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"function hydrostatic_equilibrium(z, z_interface, params)\n (; ν, S_s, hydrology_cm) = params\n (; α, n, m) = hydrology_cm\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= hydrostatic_equilibrium.(z, FT(-0.1), params)\n Y.soil.θ_i .= 0\n T = FT(275.0)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n FT(0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(FT(0), ρc_s, T, params.earth_param_set)\nend\ninit_soil!(Y, z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"t0 = Float64(0)\ntf = Float64(24 * 3600 * 4)\ndt = Float64(5)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"5.0","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"We also set the initial conditions of the cache here:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"set_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Timestepping functions:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil)\njacobian! = ClimaLand.make_jacobian(soil)\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!)\n\ntimestepper = CTS.ARS111()\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n)","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"ClimaTimeSteppers.IMEXAlgorithm{ClimaTimeSteppers.Unconstrained, ClimaTimeSteppers.ARS111, ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}, ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}}(ClimaTimeSteppers.Unconstrained(), ClimaTimeSteppers.ARS111(), ClimaTimeSteppers.IMEXTableau{ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}, ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}}(ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, false, true, true), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 1 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (false, true), StaticArraysCore.SVector{2, Int64}}([1, 0]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2, 2), (true, true, true, false), StaticArraysCore.SMatrix{2, 2, Int64, 4}}([0 0; 0 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1]), ClimaTimeSteppers.SparseCoeffs{(2,), (true, false), StaticArraysCore.SVector{2, Int64}}([0, 1])), ClimaTimeSteppers.NewtonsMethod{ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}, Nothing, Nothing, ClimaTimeSteppers.Silent}(1, ClimaTimeSteppers.UpdateEvery{ClimaTimeSteppers.NewNewtonIteration}(), nothing, nothing, ClimaTimeSteppers.Silent()))","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Define the problem and callbacks:","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n)\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Solve","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"Extract the evaporation at each saved step","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"evap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n]\nsub = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n sv.saveval[k].soil.ice_frac,\n )[1] for k in 1:length(sol.t)\n]\n\nsavepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (400, 400))\nax = Axis(\n fig[1, 1],\n xlabel = \"Day\",\n ylabel = \"Rate (mm/d)\",\n title = \"Vapor Fluxes\",\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n sub .* (1000 * 3600 * 24),\n label = \"Sublimation\",\n color = :blue,\n)\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"Evaporation\",\n color = :black,\n)\nCairoMakie.axislegend(ax)\n\nsave(\"water_fluxes.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Temperature\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(260, 280)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 3, 4]\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax1,\n parent(sv.saveval[Int(days[i] * 24 + 1)].soil.T)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Ice\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax2,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.θ_i)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Liquid Water\", xlabel = \"\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.5)\nfor i in 1:length(days)\n CairoMakie.lines!(\n ax3,\n parent(sol.u[Int(days[i] * 24 + 1)].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"profiles.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"","category":"page"},{"location":"generated/standalone/Soil/sublimation/","page":"Bare soil site","title":"Bare soil site","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Soil/#Soil-Models","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"CurrentModule = ClimaLand.Soil","category":"page"},{"location":"APIs/Soil/#Soil-Models-2","page":"Soil Energy and Hydrology","title":"Soil Models","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilModel\nClimaLand.Soil.RichardsModel\nClimaLand.Soil.EnergyHydrology","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilModel","text":"AbstractSoilModel{FT} <: ClimaLand.AbstractImExModel{FT}\n\nThe abstract type for all soil models.\n\nCurrently, we only have plans to support a RichardsModel, simulating the flow of liquid water through soil via the Richardson-Richards equation, and a fully integrated soil heat and water model, with phase change.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsModel","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsModel","text":"RichardsModel\n\nA model for simulating the flow of water in a porous medium by solving the Richardson-Richards Equation.\n\nA variety of boundary condition types are supported, including FluxBC, RichardsAtmosDrivenFluxBC, MoistureStateBC, and FreeDrainage (only for the bottom of the domain).\n\nIf you wish to simulate soil hydrology under the context of a prescribed precipitation volume flux (m/s) as a function of time, the RichardsAtmosDrivenFluxBC type should be chosen. Please see the documentation for more details.\n\nparameters: the parameter set\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrology","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrology","text":"EnergyHydrology <: AbstractSoilModel\n\nA model for simulating the flow of water and heat in a porous medium by solving the Richardson-Richards equation and the heat equation, including terms for phase change.\n\nA variety of boundary condition types are supported, including FluxBC, MoistureStateBC/TemperatureStateBC, FreeDrainage (only for the bottom of the domain), and an AtmosDrivenFluxBC (under which radiative fluxes and turbulent surface fluxes are computed and used as boundary conditions). Please see the documentation for this boundary condition type for more details.\n\nparameters: The parameter sets\ndomain: the soil domain, using ClimaCore.Domains\nboundary_conditions: the boundary conditions for RRE and heat, of type AbstractSoilBoundaryConditions\nsources: A tuple of sources, each of type AbstractSoilSource\nlateral_flow: A boolean flag which, when false, turns off the horizontal flow of water and heat\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Parameter-Structs","page":"Soil Energy and Hydrology","title":"Soil Parameter Structs","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.RichardsParameters\nClimaLand.Soil.EnergyHydrologyParameters","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsParameters","text":"RichardsParameters{F <: Union{<: AbstractFloat, ClimaCore.Fields.Field}, C <: AbstractSoilHydrologyClosure}\n\nA struct for storing parameters of the RichardsModel.\n\nν: The porosity of the soil (m^3/m^3)\nhydrology_cm: The hydrology closure model: vanGenuchten or BrooksCorey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.EnergyHydrologyParameters","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.EnergyHydrologyParameters","text":"EnergyHydrologyParameters{\n FT <: AbstractFloat,\n F <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n SF <: Union{<:AbstractFloat, ClimaCore.Fields.Field},\n C,\n PSE,\n }\n\nA parameter structure for the integrated soil water and energy equation system.\n\nNote that we require two different parameter types F and SF; these are for parameters that are defined on the surface only and those defined in the interior of the soil domain:\n\nSurface parameters: albedo in each wavelength band (SF)\nScalar parameters: emissivity, α, β, γ, γT_ref, Ω,\n\nroughness lengths z0, dds ) (FT)\n\nParameters defined in the interior: all else (F)\n\nκ_dry: The dry soil thermal conductivity, W/m/K\nκ_sat_frozen: The saturated thermal conductivity of frozen soil, W/m/K\nκ_sat_unfrozen: The saturated thermal conductivity of unfrozen soil, W/m/K\nρc_ds: The volumetric heat capacity of dry soil, J/m^3/K (per volume dry soil, not per volume soil solids)\nν: The porosity of the soil (m^3/m^3)\nν_ss_om: The volumetric fraction of the soil solids in organic matter (m^3/m^3)\nν_ss_quartz: The volumetric fraction of the soil solids in quartz (m^3/m^3)\nν_ss_gravel: The volumetric fraction of the soil solids in gravel (m^3/m^3)\nα: The parameter α used in computing Kersten number, unitless\nβ: The parameter β used in computing Kersten number, unitless\nhydrology_cm: The soil hydrology closure model: van Genuchten or Brooks and Corey\nK_sat: The saturated hydraulic conductivity (m/s)\nS_s: The specific storativity (1/m)\nθ_r: The residual water fraction (m^3/m^3\nΩ: Ice impedance factor for the hydraulic conductivity\nγ: Coefficient of viscosity factor for the hydraulic conductivity\nγT_ref: Reference temperature for the viscosity factor\nPAR_albedo: Soil PAR Albedo\nNIR_albedo: Soil NIR Albedo\nemissivity: Soil Emissivity\nz_0m: Roughness length for momentum\nz_0b: Roughness length for scalars\nd_ds: Maximum dry soil layer thickness under evaporation (m)\nearth_param_set: Physical constants and clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Hydrology-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Hydrology Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_liquid_fraction\nClimaLand.Soil.pressure_head\nClimaLand.Soil.hydraulic_conductivity\nClimaLand.Soil.impedance_factor\nClimaLand.Soil.viscosity_factor\nClimaLand.Soil.effective_saturation\nClimaLand.Soil.matric_potential\nClimaLand.Soil.dψdϑ\nClimaLand.Soil.inverse_matric_potential\nClimaLand.Soil.AbstractSoilHydrologyClosure\nClimaLand.Soil.vanGenuchten\nClimaLand.Soil.BrooksCorey","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_liquid_fraction","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_liquid_fraction","text":"volumetric_liquid_fraction(ϑ_l::FT, ν_eff::FT, θ_r::FT) where {FT}\n\nA pointwise function returning the volumetric liquid fraction given the augmented liquid fraction and the effective porosity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.pressure_head","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.pressure_head","text":"pressure_head(\n cm::vanGenuchten{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the van Genuchten matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\npressure_head(\n cm::BrooksCorey{FT},\n θ_r::FT,\n ϑ_l::FT,\n ν_eff::FT,\n S_s::FT,\n) where {FT}\n\nA point-wise function returning the pressure head in variably saturated soil, using the Brooks and Corey matric potential if the soil is not saturated, and an approximation of the positive pressure in the soil if the soil is saturated.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.hydraulic_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.hydraulic_conductivity","text":" hydraulic_conductivity(cm::vanGenuchten{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the van Genuchten formulation.\n\n\n\n\n\n hydraulic_conductivity(cm::BrooksCorey{FT}, K_sat::FT, S::FT) where {FT}\n\nA point-wise function returning the hydraulic conductivity, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.impedance_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.impedance_factor","text":"impedance_factor(\n f_i::FT,\n Ω::FT\n) where {FT}\n\nReturns the multiplicative factor reducing conductivity when a fraction of ice f_i is present.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.viscosity_factor","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.viscosity_factor","text":"viscosity_factor(\n T::FT,\n γ::FT,\n γT_ref::FT,\n) where {FT}\n\nReturns the multiplicative factor which accounts for the temperature dependence of the conductivity.\n\nOnly for use with the EnergyHydrology model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.effective_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.effective_saturation","text":"effective_saturation(porosity::FT, ϑ_l::FT, θr::FT) where {FT}\n\nA point-wise function computing the effective saturation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.matric_potential","text":" matric_potential(cm::vanGenuchten{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n matric_potential(cm::BrooksCorey{FT}, S::FT) where {FT}\n\nA point-wise function returning the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.dψdϑ","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dψdϑ","text":"dψdϑ(cm::vanGenuchten{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the van Genuchten formulation.\n\n\n\n\n\ndψdϑ(cm::BrooksCorey{FT}, ϑ, ν, θr, Ss)\n\nComputes and returns the derivative of the pressure head with respect to ϑ for the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.inverse_matric_potential","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.inverse_matric_potential","text":" inverse_matric_potential(cm::vanGenuchten{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the van Genuchten formulation.\n\n\n\n\n\n inverse_matric_potential(cm::BrooksCorey{FT}, ψ::FT) where {FT}\n\nA point-wise function returning the effective saturation, given the matric potential, using the Brooks and Corey formulation.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilHydrologyClosure","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilHydrologyClosure","text":"AbstractSoilHydrologyClosure{FT <: AbstractFloat}\n\nThe abstract type of soil hydrology closure, of which vanGenuchten{FT} and BrooksCorey{FT} are the two supported concrete types.\n\nTo add a new parameterization, methods are required for:\n\nmatric_potential,\ninversematricpotential,\npressure_head,\ndψdϑ,\nhydraulic_conductivity.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.vanGenuchten","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.vanGenuchten","text":"vanGenuchten{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe van Genuchten soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the van Genuchten parameterization (van Genuchten 1980; see also Table 8.2 of G. Bonan 2019).\n\nα: The inverse of the air entry potential (1/m)\nn: The van Genuchten pore-size distribution index (unitless)\nm: The van Genuchten parameter m = 1 - 1/n (unitless)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.BrooksCorey","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.BrooksCorey","text":"BrooksCorey{FT} <: AbstractSoilHydrologyClosure{FT}\n\nThe Brooks and Corey soil hydrology closure, chosen when the hydraulic conductivity and matric potential are modeled using the Brooks and Corey parameterization (Brooks and Corey, 1964, 1966; see also Table 8.2 of G. Bonan 2019).\n\nc: The pore-size distribution index (unitless)\nψb: The air entry matric potential, when S=1 (m)\nS_c: A derived parameter: the critical saturation at which capillary flow no longer replenishes the surface\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Heat-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Heat Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.volumetric_heat_capacity\nClimaLand.Soil.κ_solid\nClimaLand.Soil.κ_sat_frozen\nClimaLand.Soil.κ_sat_unfrozen\nClimaLand.Soil.κ_sat\nClimaLand.Soil.κ_dry\nClimaLand.Soil.kersten_number\nClimaLand.Soil.relative_saturation\nClimaLand.Soil.volumetric_internal_energy\nClimaLand.Soil.volumetric_internal_energy_liq\nClimaLand.Soil.temperature_from_ρe_int\nClimaLand.Soil.thermal_conductivity\nClimaLand.Soil.phase_change_source\nClimaLand.Soil.thermal_time","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_heat_capacity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_heat_capacity","text":"volumetric_heat_capacity(\n θ_l::FT,\n θ_i::FT,\n ρc_ds::FT,\n earth_param_set::EP,\n) where {FT,EP}\n\nCompute the expression for volumetric heat capacity.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_solid","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_solid","text":"κ_solid(ν_ss_om::FT,\n ν_ss_quartz::FT,\n κ_om::FT,\n κ_quartz::FT,\n κ_minerals::FT) where {FT}\n\nComputes the thermal conductivity of the solid material in soil. The _ss_ subscript denotes that the volumetric fractions of the soil components are referred to the soil solid components, not including the pore space.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_frozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_frozen","text":"function κ_sat_frozen(\n κ_solid::FT,\n ν::FT,\n κ_ice::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated frozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat_unfrozen","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat_unfrozen","text":"function κ_sat_unfrozen(\n κ_solid::FT,\n ν::FT,\n κ_l::FT\n) where {FT}\n\nComputes the thermal conductivity for saturated unfrozen soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_sat","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_sat","text":"κ_sat(\n θ_l::FT,\n θ_i::FT,\n κ_sat_unfrozen::FT,\n κ_sat_frozen::FT\n) where {FT}\n\nCompute the expression for saturated thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.κ_dry","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.κ_dry","text":"function κ_dry(ρp::FT,\n ν::FT,\n κ_solid::FT,\n κ_air::FT;\n a = FT(0.053)) where {FT}\n\nComputes the thermal conductivity of dry soil according to the model of Balland and Arp.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.kersten_number","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.kersten_number","text":"kersten_number(\n θ_i::FT,\n S_r::FT,\n α::FT,\n β::FT,\n ν_ss_om::FT,\n ν_ss_quartz::FT,\n ν_ss_gravel::FT,\n ) where {FT}\n\nCompute the expression for the Kersten number, using the Balland and Arp model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.relative_saturation","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.relative_saturation","text":"relative_saturation(\n θ_l::FT,\n θ_i::FT,\n ν::FT\n) where {FT}\n\nCompute the expression for relative saturation. This is referred to as θ_sat in Balland and Arp's paper.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy","text":"volumetric_internal_energy(θ_i::FT, ρc_s::FT, T::FT,\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the soil, given the volumetric ice content, volumetric heat capacity, and temperature.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.volumetric_internal_energy_liq","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.volumetric_internal_energy_liq","text":"volumetric_internal_energy_liq(T::FT, earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the volumetric internal energy of the liquid water in the soil, given the temperature T.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.temperature_from_ρe_int","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.temperature_from_ρe_int","text":"temperature_from_ρe_int(ρe_int::FT, θ_i::FT, ρc_s::FT\n earth_param_set::EP) where {FT, EP}\n\nA pointwise function for computing the temperature from the volumetric internal energy, volumetric ice content, and volumetric heat capacity of the soil.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_conductivity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_conductivity","text":"thermal_conductivity(\n κ_dry::FT,\n K_e::FT,\n κ_sat::FT\n) where {FT}\n\nCompute the expression for thermal conductivity of soil matrix.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.phase_change_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.phase_change_source","text":"phase_change_source(\n θ_l::FT,\n θ_i::FT,\n T::FT,\n τ::FT,\n ν::FT,\n θ_r::FT,\n hydrology_cm::C,\n earth_param_set::EP,\n) where {FT, EP, C}\n\nReturns the source term (1/s) used for converting liquid water and ice into each other during phase changes. Note that there are unitless prefactors multiplying this term in the equations.\n\nNote that these equations match what is in Dall'Amico (for θstar, ψ(T), ψw0). We should double check them in the case where we have ϑl > θl, but they should be very close to the form we want regardless.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.thermal_time","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.thermal_time","text":"thermal_time(ρc::FT, Δz::FT, κ::FT) where {FT}\n\nReturns the thermal timescale for temperature differences across a typical thickness Δz to equilibrate.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Surface-Parameterizations","page":"Soil Energy and Hydrology","title":"Soil Surface Parameterizations","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.soil.soil_resistance\nClimaLand.Soil.dry_soil_layer_thickness\nClimaLand.Soil.soil_tortuosity","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.dry_soil_layer_thickness","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.dry_soil_layer_thickness","text":"dry_soil_layer_thickness(S_w::FT, S_c::FT, d_ds::FT)::FT where {FT}\n\nReturns the maximum dry soil layer thickness that can develop under vapor flux; this is used when computing the soil resistance to vapor flux according to Swenson et al (2012)/Sakaguchi and Zeng (2009).\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_tortuosity","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_tortuosity","text":"soil_tortuosity(θ_l::FT, θ_i::FT, ν::FT) where {FT}\n\nComputes the tortuosity of water vapor in a porous medium, as a function of porosity ν and the volumetric liquid water and ice contents, θ_l and θ_i.\n\nSee Equation (1) of : Shokri, N., P. Lehmann, and D. Or (2008), Effects of hydrophobic layers on evaporation from porous media, Geophys. Res. Lett., 35, L19407, doi:10.1029/ 2008GL035230.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Runoff-Types-and-Methods","page":"Soil Energy and Hydrology","title":"Soil Runoff Types and Methods","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.NoRunoff\nClimaLand.Soil.SurfaceRunoff\nClimaLand.Soil.TOPMODELRunoff\nClimaLand.Soil.TOPMODELSubsurfaceRunoff\nClimaLand.Soil.subsurface_runoff_source\nClimaLand.Soil.update_runoff!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.NoRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.NoRunoff","text":"NoRunoff <: AbstractRunoffModel\n\nA concrete type of soil runoff model; the default choice, which does not include any runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.SurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.SurfaceRunoff","text":"SurfaceRunoff <: AbstractRunoffModel\n\nA simple model for runoff appropriate for single column runs.\n\nOnly surface runoff is computed, using a combination of Dunne and Hortonian runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELRunoff","text":"TOPMODELRunoff{FT <: AbstractFloat, F <: ClimaCore.Fields.Field} <: AbstractRunoffModel\n\nThe TOPMODEL surface runoff parameterization, which is affects the surface boundary condition of the soil model.\n\nThe runoff flux is given by Equation 8 of with fsat given by Equation (11), of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\nf_max: The maximum saturated fraction of a grid cell, computed from the topographic index CDF per grid cell.\nsubsurface_source: The subsurface source term corresponding to this implementation of TOPMODEL.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.TOPMODELSubsurfaceRunoff","text":"TOPMODELSubsurfaceRunoff{FT} <: AbstractSoilSource{FT}\n\nThe TOPMODEL subsurface runoff parameterization, which is implemented as a sink term in the soil equations.\n\nThe runoff flux is given by Equation 12 of Niu et al. (2005), \"A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models\".\n\nR_sb: The subsurface runoff flux (m/s) when the depth to the water table = 1/f_over; calibrated\nf_over: A calibrated parameter defining how subsurface runoff decays with depth to water table (1/m ; calibrated)\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.subsurface_runoff_source","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.subsurface_runoff_source","text":"subsurface_runoff_source(runoff::AbstractRunoffModel)\n\nA helper function which returns the subsurface source of the runoff model runoff.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#ClimaLand.Soil.Runoff.update_runoff!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.Runoff.update_runoff!","text":"update_runoff!(p, runoff::NoRunoff, _...)\n\nUpdates the runoff variables in the cache p.soil in place in the case of NoRunoff: sets infiltration = precipitation.\n\n\n\n\n\nupdate_runoff!(\n p,\n runoff::SurfaceRunoff,\n Y,\n t,\n model::AbstractSoilModel,\n\n)\n\nThe update_runoff! function for the SurfaceRunoff model.\n\nUpdates the runoff model variables in place in p.soil for the SurfaceRunoff parameterization: p.soil.Rs p.soil.issaturated p.soil.infiltration\n\n\n\n\n\nupdate_runoff!(p, runoff::TOPMODELRunoff, Y,t, model::AbstractSoilModel)\n\nUpdates the runoff model variables in place in p.soil for the TOPMODELRunoff parameterization: p.soil.Rs p.soil.Rss p.soil.h∇ p.soil.infiltration\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-BC-Methods-and-Types","page":"Soil Energy and Hydrology","title":"Soil BC Methods and Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.MoistureStateBC\nClimaLand.Soil.HeatFluxBC\nClimaLand.Soil.WaterFluxBC\nClimaLand.Soil.TemperatureStateBC\nClimaLand.Soil.FreeDrainage\nClimaLand.Soil.RichardsAtmosDrivenFluxBC\nClimaLand.Soil.AtmosDrivenFluxBC\nClimaLand.Soil.WaterHeatBC\nClimaLand.Soil.soil_boundary_fluxes!","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.MoistureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.MoistureStateBC","text":"MoistureStateBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition ϑ_l = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.HeatFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.HeatFluxBC","text":"HeatFluxBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterFluxBC","text":"WaterFluxBC <: AbstractWaterBC\n\nA simple concrete type of boundary condition, which enforces a normal flux boundary condition f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.TemperatureStateBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.TemperatureStateBC","text":"TemperatureStateBC <: AbstractHeatBC\n\nA simple concrete type of boundary condition, which enforces a state boundary condition T = f(p,t) at either the top or bottom of the domain.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.FreeDrainage","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.FreeDrainage","text":"FreeDrainage <: AbstractWaterBC\n\nA concrete type of soil boundary condition, for use at the BottomBoundary only, where the flux is set to be F = -K∇h = -K.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.RichardsAtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.RichardsAtmosDrivenFluxBC","text":"RichardsAtmosDrivenFluxBC{F <: PrescribedPrecipitation, R <: AbstractRunoffModel} <: AbstractWaterBC\n\nA concrete type of boundary condition intended only for use with the RichardsModel, which uses a prescribed precipitation rate (m/s) to compute the infiltration into the soil.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. In order to run the simulation without runoff, choose runoff = NoRunoff() - this is also the default.\n\nIf you wish to simulate precipitation and runoff in the full EnergyHydrology model, you must use the AtmosDrivenFluxBC type.\n\nprecip: The prescribed liquid water precipitation rate f(t) (m/s); Negative by convention.\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.AtmosDrivenFluxBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AtmosDrivenFluxBC","text":"AtmosDrivenFluxBC{\n A <: AbstractAtmosphericDrivers,\n B <: AbstractRadiativeDrivers,\n R <: AbstractRunoffModel\n} <: AbstractEnergyHydrologyBC\n\nA concrete type of soil boundary condition for use at the top of the domain. This holds the conditions for the atmosphere AbstractAtmosphericDrivers, for the radiation state AbstractRadiativeDrivers. This is only supported for the EnergyHydrology model.\n\nThis choice indicates the Monin-Obukhov Surface Theory will be used to compute the sensible and latent heat fluxes, as well as evaporation, and that the net radiation and precipitation will also be computed. The net energy and water fluxes are used as boundary conditions.\n\nA runoff model is used to simulate surface and subsurface runoff and this is accounted for when setting boundary conditions. The default is to have no runoff accounted for.\n\natmos: The atmospheric conditions driving the model\nradiation: The radiative fluxes driving the model\nrunoff: The runoff model. The default is no runoff.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.WaterHeatBC","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.WaterHeatBC","text":"WaterHeatBC{W <: AbstractWaterBC, H <: AbstractHeatBC} <:\n AbstractEnergyHydrologyBC\n\nA general struct used to store the boundary conditions for Richards and the soil heat equations separately; useful when the boundary conditions for each component are independent of each other.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.soil_boundary_fluxes!","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.soil_boundary_fluxes!","text":"soil_boundary_fluxes!(bc::WaterHeatBC, boundary::TopBoundary, model, Δz, Y, p, t)\n\nupdates the boundary fluxes for ϑl and ρeint.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{\n <:PrescribedAtmosphere,\n <:PrescribedRadiativeFluxes,\n },\n boundary::ClimaLand.TopBoundary,\n model::EnergyHydrology,\n Δz,\n Y,\n p,\n t,\n)\n\nReturns the net volumetric water flux (m/s) and net energy flux (W/m^2) for the soil EnergyHydrology model at the top of the soil domain.\n\nIf you wish to compute surface fluxes taking into account the presence of a canopy, snow, etc, as in a land surface model, this is not the correct method to be using.\n\nThis function calls the turbulent_fluxes and net_radiation functions, which use the soil surface conditions as well as the atmos and radiation conditions in order to compute the surface fluxes using Monin Obukhov Surface Theory.\n\n\n\n\n\nsoil_boundary_fluxes!(\n bc::AtmosDrivenFluxBC{<:PrescribedAtmosphere, <:CanopyRadiativeFluxes},\n boundary::ClimaLand.TopBoundary,\n soil::EnergyHydrology{FT},\n Δz,\n Y,\n p,\n t,\n) where {FT}\n\nA method of ClimaLand.Soil.soil_boundary_fluxes! which is used for integrated land surface models; this computes and returns the net energy and water flux at the surface of the soil for use as boundary conditions.\n\n\n\n\n\n","category":"function"},{"location":"APIs/Soil/#Soil-Source-Types","page":"Soil Energy and Hydrology","title":"Soil Source Types","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.AbstractSoilSource\nClimaLand.Soil.PhaseChange\nClimaLand.Soil.RootExtraction","category":"page"},{"location":"APIs/Soil/#ClimaLand.Soil.AbstractSoilSource","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.AbstractSoilSource","text":"AbstractSoilSource{FT} <: ClimaLand.AbstractSource{FT}\n\nAn abstract type for types of source terms for the soil equations.\n\nIn standalone mode, the only supported source type is freezing and thawing. ClimaLand.jl creates additional sources to include as necessary e.g. root extraction (not available in stand alone mode).\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#ClimaLand.Soil.PhaseChange","page":"Soil Energy and Hydrology","title":"ClimaLand.Soil.PhaseChange","text":"PhaseChange{FT} <: AbstractSoilSource{FT}\n\nPhaseChange source type.\n\n\n\n\n\n","category":"type"},{"location":"APIs/Soil/#Soil-Jacobian-Structures","page":"Soil Energy and Hydrology","title":"Soil Jacobian Structures","text":"","category":"section"},{"location":"APIs/Soil/","page":"Soil Energy and Hydrology","title":"Soil Energy and Hydrology","text":"ClimaLand.Soil.ImplicitEquationJacobian","category":"page"},{"location":"APIs/Soil/#ClimaLand.ImplicitEquationJacobian","page":"Soil Energy and Hydrology","title":"ClimaLand.ImplicitEquationJacobian","text":"ImplicitEquationJacobian{M, S}\n\nA struct containing the necessary information for constructing a block Jacobian matrix used for implicit timestepping.\n\nmatrix is a block matrix containing one block on the diagonal for each variable in the model. solver is a diagonal solver because our matrix is block diagonal.\n\nNote that the diagonal, upper diagonal, and lower diagonal entry values are stored in this struct and updated in place.\n\n\n\n\n\n","category":"type"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Radiative-transfer-scheme","page":"Beer model","title":"Radiative transfer scheme","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Beer's-law","page":"Beer model","title":"Beer's law","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"APAR(PAR theta_s) = (PAR)(1 - rho_leaf)(1 - e^(-K(theta_s) LAI Omega))","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, ρ_leaf is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, θ_s is the zenith angle, and Ω is the clumping index following Braghiere (2021). K, Ω and ρ_leaf are all unitless. LAI is in m² m⁻². In order to compute K, we need θ_s in radians and the leaf angle distribution l_d (unitless). K is then defined as","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"K = l_dmax(cos(theta_s) epsilon)","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"so that at night, when 3π/2 > θ_s > π/2, K is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Output Symbol Unit Range\nAbsorbed Photosynthetically Active Radiation APAR μmol m⁻² s⁻¹ 0-1500","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Drivers Symbol Unit Range\nPhotosynthetically Active Radiation PAR μmol m⁻² s⁻¹ 0–1500\nLeaf Area Index LAI m² m⁻² 0–10","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Parameters Symbol Unit Range\nCanopy reflectance ρ_leaf - 0.0–1.0\nExtinction coefficient K - 0.0–1.0\nClumping index Ω - 0.0–1.0\nZenith angle θ_s rad 0–π","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"Constants Symbol Unit Value\nLeaf angle distribution l_d - 0.5","category":"page"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/#Interactive-APAR(PAR,-LAI,-ρ_{leaf},-K,-Ω)","page":"Beer model","title":"Interactive APAR(PAR, LAI, ρ_leaf, K, Ω)","text":"","category":"section"},{"location":"standalone/pages/vegetation/radiative_transfer/beer_model/","page":"Beer model","title":"Beer model","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Modeling-a-freezing-front-in-unsaturated-soil","page":"Phase Changes","title":"Modeling a freezing front in unsaturated soil","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Before reading this tutorial, we recommend that you look over the coupled energy and water tutorial. That tutorial showed how to solve the heat equation for soil volumetric internal energy ρe_int, simultaneously with Richards equation for volumetric liquid water fraction ϑ_l, assuming zero volumetric ice fraction θ_i for all time, everywhere in the domain. In this example, we add in a source term to the right hand side for both θ_i and ϑ_l which models freezing and thawing and conserves water mass during the process. The equations are","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ρe_int t = κ(θ_l θ_i ν ) T + ρe_int_liq K (Tθ_l θ_i ν ) nabla h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac ϑ_l t = K (Tθ_l θ_i ν ) h( ϑ_l z ν ) -fracF_Tρ_l","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"frac θ_i t = fracF_Tρ_i","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Here","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int is the volumetric internal energy of the soil (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T is the temperature of the soil (K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"κ is the thermal conductivity (W/m/K),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρe_int_liq is the volumetric internal energy of liquid water (J/m^3),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_i is the volumetric ice fraction,","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν denotes parameters relating to soil type, such as porosity, and","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T is the freeze-thaw term.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, we will show how to implement adding in this source term. After the results are obtained, we will explain how our model parameterizes this effect and compare the results with some analytic expections.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We solve these equations in an effectively 1-d domain with z -020, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z = 0) = 28 Wm^2K (T - 26715K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- κ T(t z= -02) = -3 Wm^2K (T - 27985K) ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"T(t = 0 z) = 27985 K","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"-K h(t z = -02) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ϑ_l(t = 0 z) = 033.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"The problem setup and soil properties are chosen to match the lab experiment of Mizoguchi (1990), as detailed in Hansson (2004) and Dall'Amico (2011). Like Hansson et al., we allow for a small amount of energy leakage at the bottom of the domain to account for imperfect insulation.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Import-necessary-modules","page":"Phase Changes","title":"Import necessary modules","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"import SciMLBase\nimport ClimaTimeSteppers as CTS\nusing DelimitedFiles\nusing CairoMakie\n\nusing ClimaCore\nimport ClimaParams as CP\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Preliminary set-up","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose a floating point precision, and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"FT = Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Float32","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the values of other parameters required by the model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ν = FT(0.535)\nK_sat = FT(3.2e-6) # m/s\nS_s = FT(1e-3) #inverse meters\nvg_n = FT(1.48)\nvg_α = FT(1.11) # inverse meters\nhydrology_cm = vanGenuchten{FT}(; α = vg_α, n = vg_n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"You could also try the Brooks and Corey model:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"#ψb = FT(-0.6)\n#c = FT(0.43)\n#hcm = BrooksCorey(;ψb = ψb, c = c);\nθ_r = FT(0.05)\nν_ss_om = FT(0.3)\nν_ss_quartz = FT(0.7)\nν_ss_gravel = FT(0.0)\nparams = Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm,\n K_sat,\n S_s,\n θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Choose the domain and discretization:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zmax = FT(0)\nzmin = FT(-0.2)\nnelems = 20\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Set the boundary conditions:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"zero_water_flux_bc = WaterFluxBC((p, t) -> 0.0)\nfunction top_heat_flux(p, t)\n FT = eltype(p.soil.T)\n p_len = ClimaCore.Spaces.nlevels(axes(p.soil.T))\n T_c = ClimaCore.Fields.level(p.soil.T, p_len)\n return @. FT(28 * (T_c - 267.15))\nend\nfunction bottom_heat_flux(p, t)\n FT = eltype(p.soil.T)\n T_c = ClimaCore.Fields.level(p.soil.T, 1)\n return @. FT(-3 * (T_c - 279.85))\nend\ntop_heat_flux_bc = HeatFluxBC(top_heat_flux)\nbottom_heat_flux_bc = HeatFluxBC(bottom_heat_flux)\nboundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux_bc, heat = top_heat_flux_bc),\n bottom = WaterHeatBC(;\n water = zero_water_flux_bc,\n heat = bottom_heat_flux_bc,\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the source term instance. Our phase change model requires knowledge of the vertical spacing, so we pass that information in via an attribute of the PhaseChange structure. Sources are added as elements of a list of sources. Here we just add freezing and thawing.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sources = (PhaseChange{FT}(),);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can package this up in the EnergyHydrology model struct:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"soil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Running-a-simulation","page":"Phase Changes","title":"Running a simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Once we have the model, we can initialize the state vectors and obtain the coordinates","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Y, p, coords = initialize(soil);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"After which, we can specify the initial condition function, and initialze the variables:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"function init_soil!(Ysoil, z, params)\n ν = params.ν\n FT = eltype(Ysoil.soil.ϑ_l)\n Ysoil.soil.ϑ_l .= FT(0.33)\n Ysoil.soil.θ_i .= FT(0.0)\n T = FT(279.85)\n ρc_s = Soil.volumetric_heat_capacity(\n FT(0.33),\n FT(0.0),\n params.ρc_ds,\n params.earth_param_set,\n )\n Ysoil.soil.ρe_int .=\n Soil.volumetric_internal_energy.(\n FT(0.0),\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, coords.subsurface.z, soil.parameters);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 50);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Create the tendency function, and choose a timestep, and timestepper:","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ndt = Float64(100)\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 3,\n update_j = CTS.UpdateEvery(CTS.NewTimeStep),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Now we can solve the problem.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"sol = SciMLBase.solve(prob, ode_algo; dt = dt, saveat = 0:3600:tf);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Comparison-to-data","page":"Phase Changes","title":"Comparison to data","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This data was obtained by us from the figures of Hansson et al. (2004), but was originally obtained by Mizoguchi (1990). No error bars were reported, and we haven't quantified the error in our estimation of the data from images.","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"dataset_path = ClimaLand.Artifacts.mizoguchi1990_soil_freezing_data();\nds = readdlm(dataset_path, ',')\nhours = ds[:, 1][2:end]\nvwc = ds[:, 2][2:end] ./ 100.0\ndepth = ds[:, 3][2:end]\nmask_12h = hours .== 12\nmask_24h = hours .== 24\nmask_50h = hours .== 50;\n\nfig = Figure(size = (900, 300))\nax1 = Axis(\n fig[1, 1],\n title = \"12 hours\",\n xlabel = L\"θ_l + θ_i\",\n ylabel = \"Soil depth (m)\",\n)\nlimits!(ax1, 0.2, 0.5, -0.2, 0.0)\nax2 = Axis(\n fig[1, 2],\n title = \"24 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax2, 0.2, 0.5, -0.2, 0.0)\nax3 = Axis(\n fig[1, 3],\n title = \"50 hours\",\n xlabel = L\"θ_l + θ_i\",\n yticksvisible = false,\n yticklabelsvisible = false,\n)\nlimits!(ax3, 0.2, 0.5, -0.2, 0.0)\n\n\nz = parent(coords.subsurface.z)[:];\n\nscatter!(ax1, vwc[mask_12h], -depth[mask_12h], label = \"\", color = \"purple\")\nlines!(\n ax1,\n parent(sol.u[13].soil.ϑ_l .+ sol.u[13].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\n\nscatter!(ax2, vwc[mask_24h], -depth[mask_24h], label = \"\", color = \"purple\")\nlines!(\n ax2,\n parent(sol.u[25].soil.ϑ_l .+ sol.u[25].soil.θ_i)[:],\n z,\n label = \"\",\n color = :green,\n)\n\nscatter!(ax3, vwc[mask_50h], -depth[mask_50h], label = \"Data\", color = \"purple\")\nlines!(\n ax3,\n parent(sol.u[51].soil.ϑ_l .+ sol.u[51].soil.θ_i)[:],\n z,\n label = \"Simulation\",\n color = :green,\n)\naxislegend(ax3, position = :rb)\n\nsave(\"mizoguchi_data_comparison.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#Discussion-and-Model-Explanation","page":"Phase Changes","title":"Discussion and Model Explanation","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"To begin, let's observe that the freeze thaw source term alone conserves water mass, as it satisfies","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"ρ_l partial_tϑ_l + ρ_i partial_tθ_i = -F_T + F_T = 0","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Next, we describe how we define F_T. The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two phases can co-exist. It assumes that the phases are at equal temperature and pressures. For water in soil, however, the liquid water experiences pressure ρ_l g ψ, where ψ is the matric potential. A more general form of the CC equation allows for different pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which we do not model. After that assumption is made, we obtain that, below freezing (T T_f)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"fracdp_lρ_l = L_f fracdTT","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"or","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l = p_l0 + L_f ρ_l fracT-T_fT_f mathcalH(T_f-T)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where we have assumed that assumed T is near the freezing point, and then performed a Taylor explansion of the logarithm, and we are ignoring the freezing point depression, which is small (less than one degree) for non-clay soils. What we have sketched is further explained in Dall'Amico et al. (2011) and Kurylyk and Watanabe (2013).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"What this implies is that above the freezing point, the pressure is equal to p_l0, which is independent of temperature. Once the temperature drops below the freezing point, the pressure drops. Since prior to freezing, the pressure p_l0 is equal to ρ_l g ψ(θ_l), water undergoing freezing alone (without flowing) should satisfy (Dall'Amico et al. (2011)):","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"p_l0 = ρ_l g ψ(θ_l+ρ_iθ_iρ_l)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"where ψ is the matric potential function of van Genuchten. At each step, we know both the water and ice contents, as well as the temperature, and can then solve for","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"θ_l^* = (ν-θ_r) ψ^-1(p_l(ρ_l g)) + θ_r","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"For freezing, the freeze thaw function F_T is equal to","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"F_T = frac1τ ρ_l (θ_l-θ_l^*) mathcalH(T_f-T) mathcalH(θ_l-θ_l^*)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which brings the θ_l to a value which satisfies p_l = ρ_l g ψ(θ_l). This is why, in our simulation, we see the liquid water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction of 0.019, or 0. This behavior is observed, for example, in the experiments of Watanabe et al. (2011).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Although this approach may indicate that we should replace the pressure head appearing in the diffusive water flux term in Richards equation (Dall'Amico et al. (2011)), we do not do so at present. As such, we may not be modeling the flow of water around the freezing front properly. However, we still observe cryosuction, which is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid water content drops, setting up a larger gradient in matric potential across the freezing front, which generates upward flow against gravity. This is evident because the total water content at the top is larger at the end of the simulation than it was at t=0 (when it was 0.33).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This model differs from others (e.g. Painter and Karra (2014), Hansson et al. (2004), Dall'Amico et al. (2011)) in that it requires us to set a timescale for the phase change, τ. We currently use the thermal time","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"τ_LTE= c Δz²κ","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"which seems to work adequately for modeling freezing front propagation and cryosuction, via comparisons with Mizoguchi (1990).","category":"page"},{"location":"generated/standalone/Soil/freezing_front/#References","page":"Phase Changes","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"Mizoguchi, M., 1990. Water, heat and salt transport in freezing soil. Ph.D. thesis. (In Japanese.) University of Tokyo.\nHansson et al., Vadose Zone Journal 3:693–704 (2004).\nM. Dall’Amico et al., The Cryosphere, 5, 469–484 (2011).\nKurylyk and Watanabe, Advances in Water Resources, Volume 60, (2013)\nWatanabe et al. 2011, Annals of Glaciology , Volume 52 , Issue 58\nPainter and Karra, Vadose Zone Journal (2014) 13 (4)","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"","category":"page"},{"location":"generated/standalone/Soil/freezing_front/","page":"Phase Changes","title":"Phase Changes","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/#Scraping-SNOTEL-Data","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"section"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This tutorial shows you how to make use of the code developed for scraping SNOTEL site data in order to generate datasets for use in training artificial intelligence models for seasonal snow forecasting. The code below contains a basic version of the code used to produce training_data.csv, which is used in the base tutorial for snow forecasting, as well as the paper. However, exploration of the optional arguments or requesting of alternative SNOTEL data codes offers additional utility in creating alternative data sets for further investigation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We begin by importing all required packages:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first extract a DataFrame matching station ID to various station metadata, in order to automate some of the scraping process and pass some station metadata that is used for analysis in the paper. This resulting DataFrame can also be used to see other available SNOTEL station IDs for scraping, in order to create custom datasets.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"metadata = DataTools.snotel_metadata();\nmetacols = [\"id\", \"name\", \"state\", \"elev\", \"lat\", \"lon\"]\nDataFrames.rename!(metadata, Symbol.(metacols));","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"At the most user-friendly level, the function scrape_site_paper() provides a wrapper to scrape SNOTEL data in the exact same manner as the paper (it may take a minute or two per site). This function handles all special cases and data processing, allowing the user to only pass a SNOTEL ID number and associated state code to retrieve the same data as that used in the paper. However, this will likely not work or yield unexpected results for sites not used in the paper. Here is an example for how to use the metadata to streamline the process:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"example_ID = 1030\nexample_state = metadata[findfirst(==(example_ID), metadata[!, :id]), :state]\nexample_data = DataTools.scrape_site_paper(example_ID, example_state);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"And that's it! This can be iterated within a loop to gather the data for all sites. However, while straightforward, this wrapper obfuscates many of the underlying steps, or some of the opportunities for using different arguments to generate custom datasets. As such, we can reimplement much of the same code in more detail below to enable more advanced usage.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We first define constants that will be used in the cleaning of the SNOTEL data, such as conversion constants from imperial to metric units, and the sensor limits defined in the SNOTEL Engineering Handbook. Some SNOTEL sensors measure in imperial units, and some measure in metric units, and the data portal will round converted values if a sensor stream is requested in units other than its original measurement. Therefore, we will scrape data in the originally measured units to limit systemic errors.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"const inch2meter = 0.0254\nconst kmphr2mps = 5.0 / 18.0\n\nfilter_val = Dict{Symbol, Tuple{Real, Real}}(\n :SWE => (0.0, 250.0),\n :z => (0.0, 420.0),\n :precip => (0.0, 250.0),\n :rel_hum_avg => (10.0, 100.0),\n :sol_rad_avg => (0.0, 1500.0),\n :wind_speed_avg => (0.0, 216.0),\n :air_temp_avg => (-40.0, 60.0),\n)\n\nscales = Dict{Symbol, Real}(\n :SWE => inch2meter,\n :z => inch2meter,\n :precip => inch2meter,\n :rel_hum_avg => 0.01,\n :wind_speed_avg => kmphr2mps,\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We next proceed to outline which stations will be scraped by defining a dictionary of station IDs, paired with the date range to be scraped if a custom range is desired. \"start\" refers to 1850-01-01 or the first available date, while \"end\" refers to the earlier option bewteen 2024-02-01 or the last available date. Most of these stations are commented out for the sake of speed and readability in generating the tutorial, or due to special handling required, but can be uncommented to yield the full dataset (if special cases are handled) found in training_data.csv used in the base tutorial. Stations were selected based upon their availability of the features utilized in creating the model used in the paper:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"* Indicates alternative handling of the rectify_daily_hourly() function.\n^ Indicates usage of RHUM flag instead of RHUMV flag for relative humidity.\nA Indicates an Alaskan site, which is in the testing data, not the training data, and uses a lower temperature bound of -50 instead of -40 in filter_val.\nT Requires a site that already has had the temperature bias correction at the portal level as of May 2024.\nX Indicates a SNOTEL portal error when trying to scrape into 2024, as of May 2024.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"good_stations = Dict{Int, Tuple{String, String}}(\n #306 => (\"start\", \"end\"), #*\n 316 => (\"start\", \"end\"),\n 344 => (\"start\", \"end\"),\n #=367 => (\"start\", \"end\"),\n 395 => (\"start\", \"end\"),\n 457 => (\"start\", \"end\"),\n 482 => (\"start\", \"end\"),\n 491 => (\"start\", \"end\"),\n 515 => (\"start\", \"2023-06-02\"), #X\n 532 => (\"start\", \"end\"),\n 551 => (\"start\", \"end\"),\n 571 => (\"start\", \"end\"),\n 599 => (\"start\", \"end\"),\n 608 => (\"start\", \"end\"),\n 613 => (\"start\", \"end\"),\n 641 => (\"start\", \"end\"), #A^\n 665 => (\"start\", \"end\"),\n 708 => (\"start\", \"end\"),\n 715 => (\"start\", \"end\"),\n 734 => (\"start\", \"end\"),\n 737 => (\"start\", \"end\"),\n 744 => (\"start\", \"end\"),\n 832 => (\"start\", \"end\"),\n 845 => (\"start\", \"end\"),\n 854 => (\"start\", \"end\"),\n 857 => (\"start\", \"end\"),\n 921 => (\"start\", \"end\"),\n 922 => (\"start\", \"end\"),\n 927 => (\"start\", \"end\"),\n 942 => (\"start\", \"end\"),\n 963 => (\"start\", \"end\"), #A^\n 969 => (\"start\", \"end\"),\n 974 => (\"start\", \"end\"),\n 978 => (\"start\", \"end\"), #*\n 1030 => (\"start\", \"end\"),\n 1035 => (\"start\", \"end\"), #A^\n 1053 => (\"start\", \"end\"),\n 1070 => (\"start\", \"end\"), #A^T\n 1083 => (\"start\", \"end\"),\n 1091 => (\"start\", \"end\"), #A^T\n 1092 => (\"start\", \"end\"), #A^T\n 1105 => (\"start\", \"end\"),\n 1122 => (\"start\", \"end\"), #*\n 1123 => (\"start\", \"end\"),\n 1159 => (\"start\", \"end\"),\n 1168 => (\"start\", \"end\"),\n 1170 => (\"start\", \"end\"),\n 1254 => (\"start\", \"end\"),\n 1286 => (\"start\", \"end\"),\n 2080 => (\"start\", \"end\"), #A^\n 2170 => (\"start\", \"end\"), #^\n =#\n);","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"We then loop through each site to scrape and follow an automated data pipeline, consisting of:","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Extracting the daily and hourly timeseries from the site\nApplying the sensor bounds over each data timeseries (i.e. remove sensor error)\nConverting the hourly dataset into a daily dataset\nCoalescing the converted-hourly and daily data into one dataset\nScaling all data to the appropriate metric units\nRestricting data to complete cases\nMaking the differential variables ( fracdzdt, etc.)\nResetting negative precipitation cases (i.e. where the water year resets), and using daily precipitation rates dprecipdt instead of accumulated precipitation precip\nAttaching appropriate metadata","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"A few steps are commented out, which indicate steps implemented in scrape_site_paper() like quality-control measures, which could be substituted with other user-defined steps.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"allsites = Any[];\nfor site in sort(collect(keys(good_stations)))\n state = metadata[metadata[!, :id] .== site, :state][1]\n start_date = good_stations[site][1]\n end_date = good_stations[site][2]\n\n hourly = DataTools.apply_bounds(\n DataTools.sitedata_hourly(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n hourly[!, :id] .= site\n #hourly = DataTools.bcqc_hourly(hourly)\n hourly_d = DataTools.hourly2daily(hourly)\n #DataFrames.allowmissing!(hourly_d)\n #sflags = DataTools.qc_filter(hourly_d, :sol_rad_avg, t1 = 2)\n #hourly_d[sflags, :sol_rad_avg] .= missing\n\n daily = DataTools.apply_bounds(\n DataTools.sitedata_daily(\n site,\n state,\n start = start_date,\n finish = end_date,\n ),\n filter_val,\n )\n daily[!, :id] .= site\n gap_daily = DataTools.rectify_daily_hourly(daily, hourly_d)\n #gap_daily = DataTools.bcqc_daily(gap_daily, site, state)\n #gap_daily = DataTools.d_impute(gap_daily)\n daily_scaled = DataTools.scale_cols(gap_daily, scales)\n daily_clean = daily_scaled[completecases(daily_scaled), :]\n daily_clean = DataTools.makediffs(daily_clean, Day(1))\n good_vals = daily_clean[!, :dprecipdt] .>= 0.0\n daily_clean[(!).(good_vals), :dprecipdt] .= 0.0\n daily_clean = daily_clean[!, Not(:precip)]\n #show(describe(daily_clean), allrows = true, allcols = true)\n #print(\"\\nSIZE: \", nrow(daily_clean), \"\\n\")\n\n daily_clean[!, :id] .= site\n daily_clean[!, :elev] .= metadata[metadata[!, :id] .== site, :elev][1]\n daily_clean[!, :lat] .= metadata[metadata[!, :id] .== site, :lat][1]\n daily_clean[!, :lon] .= metadata[metadata[!, :id] .== site, :lon][1]\n\n push!(allsites, daily_clean)\nend;","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"With the sites complete, we condense all sites into a single DataFrame,","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"totaldata = deepcopy(allsites[1])\nfor site in allsites[2:end]\n append!(totaldata, site)\nend","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"and a final CSV.write(\"data.csv\", totaldata) call will save the file.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"Many of the functions above contain default or optional arguments which can be explored to obtain a richer set of functionality, or implement some of the special cases mentioned above. Such options can be explored in the code documentation.","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"","category":"page"},{"location":"generated/standalone/Snow/data_tutorial/","page":"Scraping SNOTEL Data","title":"Scraping SNOTEL Data","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Setting-up-a-Coupled-Simulation","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"For more information about the bucket model, please see the bucket model tutorial.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This tutorial shows how to set up a simulation for a coupled simulation. More detail for coupled runs can be found in the ClimaCoupler.jl documentation. In preparation for understanding this tutorial, we recommend also reading the intro to multi-component models tutorial as well as being familiar with multiple dispatch programming in Julia.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Background","page":"Setting up a Coupled Simulation","title":"Background","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Recall that in order to drive the system in standalone mode, the user must provide prescribed functions of time for the water volume flux in precipitation, for the net downward shortwave and longwave radiative energy fluxes, for the atmospheric temperature T_a, wind speed u_a (m/s), specific humidity q_a, and air density ρ_a (kg/m^3) at a reference height h_a (m).","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Turbulent surface fluxes are computed by the bucket model at each step of the simulation, using the land surface properties as well as the prescribed atmospheric properties, according to Monin-Obukhov theory. These fluxes, as well as the net radiation, are stored in the auxiliary state of the bucket model: p.bucket.turbulent_fluxes.lhf, p.bucket.turbulent_fluxes.shf, p.bucket.turbulent_fluxes.vapor_flux, p.bucket.R_n, where they are accessible when boundary conditions are required in the ODE functions (right hand side) of the prognostic equations. Similarily, the precipitation rates are provided from prescribed conditions and stored in p.drivers.P_liq, p.drivers.P_snow.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In a coupled simulation, this changes. The coupler computes turbulent surface fluxes based on information (prognostic state, parameters) passed to it by both the atmosphere and land models. Net radiation is computed within the atmosphere model, using the prognostic land surface temperature and the land surface albedo, and passed back to the land model via the coupler. These details are important, but from the point of view of the land model, we only need to know that the coupler accesses land model variables to compute fluxes, and that the coupler passes these fluxes back to the land model.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In our current setup, \"passed back to the land model via the coupler\" means that the coupler accesses the auxiliary state of the land model and modifies it, at each step in the simulation, so that it holds the current net radiation, precipitation, and turbulent surface fluxes (p.bucket.turbulent_fluxes, p.bucket.R_n, p.drivers.P_liq, p.drivers.P_snow). These quantities are then still available in the ODE functions of the prognostic equations for the bucket model, as in the standalone case.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In order for the land model to be able to run both in standalone mode, and a coupled mode, within a single interface, we make use of multiple dispatch.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Turbulent-Surface-Fluxes-and-Radiation","page":"Setting up a Coupled Simulation","title":"Turbulent Surface Fluxes and Radiation","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Let's review how turbulent surface fluxes and radiation are computed by the land model. The user first creates the prescribed atmosphere and prescribed radiation drivers. In pseudo code, this might look something like:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"prescribed_atmos = PrescribedAtmosphere{FT}(*driver data passed in here*) prescribed_radiation = PrescribedRadiativeFluxes{FT}(*driver data passed in here*)","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These are stored in the BucketModel object, along with BucketParameters. In order to compute turbulent surface fluxes, we call turbulent_fluxes, with arguments including prescribed_atmos. Since this argument is of the type PrescribedAtmosphere, the method of turbulent_fluxes which is executed is one which computes the turbulent surface fluxes using MOST. We have a similar function for net_radiation and which computes the net radiation based on the prescribed downwelling radiative fluxes, stored in an argument prescribed_radiation, which is of type PrescribedRadiation.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we want different behavior. We have defined new coupled types to use instead of the \"prescribed\" types:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"struct CoupledAtmosphere{FT} <: AbstractAtmosphericDrivers{FT} end struct CoupledRadiativeFluxes{FT} <: AbstractRadiativeDrivers{FT} end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Then, we have defined a new method for turbulent_fluxes and net_radiation which dispatch for these types, and simply return the fluxes that the coupler has updated p.bucket.turbulent_fluxes and p.bucket.R_n with. In pseudo code: function ClimaLand.turbulentfluxes( atmos::CoupledAtmosphere, model::BucketModel, p) return ( lhf = p.bucket.turbulentfluxes.lhf, shf = p.bucket.turbulentfluxes.shf, vaporflux = p.bucket.turbulentfluxes.vaporflux, ) end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"similarily:","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"function ClimaLand.netradiation( radiation::CoupledRadiativeFluxes{FT}, model::BucketModel{FT}, p) return p.bucket.Rn end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"These methods simply returns the values stored in the auxiliary state p. Importantly, these functions are called by the bucket model each time step after the coupler has already computed these values (or extracted them from another model) and modifed p!","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/#Surface-air-density","page":"Setting up a Coupled Simulation","title":"Surface air density","text":"","category":"section"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Within the right hand side/ODE function calls for the bucket model, we need both the surface air density (for computing specific humidity at the surface). In standalone runs, we call the function surface_air_density, When the atmos type is PrescribedAtmosphere, this function uses the atmospheric state and surface temperature to estimate the surface air density assuming an ideal gas and hydrostatic balance and by extrapolating from the air density at the lowest level of the atmosphere.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"In the coupled case, we need to extend these functions with a CoupledAtmosphere method: function ClimaLand.surfaceairdensity( atmos::CoupledAtmosphere, model::BucketModel, p) return p.bucket.ρ_sfc end","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"Again, this functions is called in the ODE function of the bucket model after the coupler has updated the values of p with the correct values at that timestep.","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"","category":"page"},{"location":"generated/standalone/Bucket/coupled_bucket/","page":"Setting up a Coupled Simulation","title":"Setting up a Coupled Simulation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Regridder/#Bucket","page":"Parameter Dataset Tools","title":"Bucket","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"CurrentModule = ClimaLand.Regridder","category":"page"},{"location":"APIs/Regridder/#Functions","page":"Parameter Dataset Tools","title":"Functions","text":"","category":"section"},{"location":"APIs/Regridder/","page":"Parameter Dataset Tools","title":"Parameter Dataset Tools","text":"ClimaLand.Regridder.hdwrite_regridfile_rll_to_cgll\nClimaLand.Regridder.swap_space","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Hydrostatic-Equilibrium-test-for-Richards-Equation","page":"Richards Equation","title":"Hydrostatic Equilibrium test for Richards Equation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This tutorial shows how to use ClimaLand code to solve Richards equation in a column of soil. We choose boundary conditions of zero flux at the top and bottom of the column, and then run the simulation long enough to see that the system is approaching hydrostatic equilibrium, where the gradient of the pressure head is equal and opposite the gradient of the gravitational head.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"The equations are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"frac ϑ_l t = K (ϑ_l ν ) h( ϑ_l z ν )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t is the time (s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"z is the location in the vertical (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K is the hydraulic conductivity (m/s),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"h is the hydraulic head (m),","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ_l is the augmented volumetric liquid water fraction,","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ν denotes parameters relating to soil type, such as porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We will solve this equation in a 1-d domain with z -50, and with the following boundary and initial conditions:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"- K h(t z = 0) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"-K h(t z = -5) = 0 ẑ","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"ϑ(t = 0 z) = ν-0001","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"θ_i(t = 0 z) = 00","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"where nu is the porosity.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"When solving Richards equation (not a fully integrated energy and hydrology model), the hydraulic conductivity is only a function of liquid moisture content.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, our formulation of this equation allows for a continuous solution in both saturated and unsaturated areas, following Woodward and Dawson (2000).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Preliminary-setup","page":"Richards Equation","title":"Preliminary setup","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load external packages","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"import SciMLBase\nusing Plots","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Load CliMA packages and ClimaLand modules","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"using ClimaCore\nimport ClimaParams as CP\nimport ClimaTimeSteppers as CTS\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\n\nimport ClimaLand\nimport ClimaLand.Parameters as LP","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Define the float type desired (Float64 or Float32), and get the parameter set, which holds constants used across CliMA models:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"const FT = Float32;\nearth_param_set = LP.LandParameters(FT);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-soil-model","page":"Richards Equation","title":"Set up the soil model","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We want to solve Richards equation alone, without simultaneously solving the heat equation. Because of that, we choose a RichardsModel. Taking a look at the documentation (linked), we see that we need to supply parameters, a domain, boundary conditions, and sources.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"First, we define the parameters: porosity \\nu, Ksat, the van Genuchten parameters `vgα,vgm,vgn,θ_r`, and the specific storage value for the soil. Note that all values must be given in mks units.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"K_sat = FT(0.0443 / (3600 * 100))\nS_s = FT(1e-3)\nν = FT(0.495)\nvg_α = FT(2.6)\nvg_n = FT(2)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0)\nparams = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we define the domain. Here, we are considering a 1D domain, discretized using finite difference, with coordinates z:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"zmax = FT(0)\nzmin = FT(-5)\nnelems = 10\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We also need to specify the boundary conditions. The user must specify two conditions, at the top and at the bottom of the domain. We currently support two broad types of boundary conditions: boundary conditions on the state ϑl = ϑl_BC (MoistureStateBC) or on the flux (WaterFluxBC, FreeDrainage, or RichardsAtmosDrivenFluxBC). Flux boundary conditions are passed as the (scalar) z-component of the flux f, i.e. F⃗ = f ẑ. The flux BC RichardsAtmosDrivenFluxBC is for driving Richards equation with a spatially and temporally varying map of precipitation. FreeDrainage is an option only at the bottom of the domain. Here, we set zero flux boundary conditons. WaterFluxBCs require a function of the cache p and the simulation time t:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"surface_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nbottom_flux = Soil.WaterFluxBC((p, t) -> 0.0)\nboundary_conditions = (; top = surface_flux, bottom = bottom_flux);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Lastly, in this case we don't have any sources, so we pass an empty tuple:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"sources = ();","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now we can make the model itself. This contains every piece of information needed to turn the continuous form of Richards equation into a set of ODEs, ready to be passed off to a timestepper.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"soil = Soil.RichardsModel{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = boundary_conditions,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we create the explicit and implicit tendencies, which update prognostic variable components that are stepped explicitly and implicitly, respectively. We also create the function which is used to update our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"exp_tendency! = make_exp_tendency(soil);\nimp_tendency! = ClimaLand.make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Set-up-the-simulation","page":"Richards Equation","title":"Set up the simulation","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We can now initialize the prognostic and auxiliary variable vectors, and take a peek at what those variables are:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y, p, coords = initialize(soil);\nY.soil |> propertynames\n\np.soil |> propertynames\n\ncoords |> propertynames","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(:surface, :subsurface)","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Note that the variables are nested into Y and p in a hierarchical way. Since we have the vectors (composed of ClimaCore Fields handy, we can now set them to the desired initial conditions.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Y.soil.ϑ_l .= FT(0.494);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We choose the initial and final simulation times:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t0 = Float64(0)\ntf = Float64(60 * 60 * 24 * 36);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We set the cache values corresponding to the initial conditions of the state Y:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"set_initial_cache! = make_set_initial_cache(soil);\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Next, we turn to timestepping. As usual, your timestep depends on the problem you are solving, the accuracy of the solution required, and the timestepping algorithm you are using.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"dt = Float64(1e3);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Now, we choose the timestepping algorithm we want to use. We'll use the ARS111 algorithm with 1 Newton iteration per timestep; you can also specify a convergence criterion and a maximum number of Newton iterations.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"stepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n stepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Here we set up the information used for our Jacobian.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"jac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"And then we can solve the system of equations, using SciMLBase.jl and ClimaTimeSteppers.jl.","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, adaptive = false);","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#Create-some-plots","page":"Richards Equation","title":"Create some plots","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"We'll plot the moisture content vs depth in the soil, as well as the expected profile of ϑ_l in hydrostatic equilibrium. For ϑ_l values above porosity, the soil is saturated, and the pressure head changes from being equal to the matric potential to the pressure generated by compression of water and the soil matrix. The profile can be solved for analytically by (1) solving for the form that ϑ_l(z) must take in both the saturated and unsaturated zones to satisfy the steady-state requirement with zero flux boundary conditions, (2) requiring that at the interface between saturated and unsaturated zones, the water content equals porosity, and (3) solving for the location of the interface by requiring that the integrated water content at the end matches that at the beginning (yielding an interface location of z≈-0.56m).","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"t = sol.t ./ (60 * 60 * 24);\nϑ_l = [parent(sol.u[k].soil.ϑ_l) for k in 1:length(t)]\nz = parent(coords.subsurface.z)\nplot(\n ϑ_l[1],\n z,\n label = string(\"t = \", string(t[1]), \"days\"),\n xlim = [0.47, 0.501],\n ylabel = \"z\",\n xlabel = \"ϑ_l\",\n legend = :bottomleft,\n title = \"Equilibrium test\",\n);\nplot!(ϑ_l[end], z, label = string(\"t = \", string(t[end]), \"days\"));\nfunction hydrostatic_equilibrium(z, z_interface)\n ν = 0.495\n S_s = 1e-3\n α = 2.6\n n = 2.0\n m = 0.5\n if z < z_interface\n return -S_s * (z - z_interface) + ν\n else\n return ν * (1 + (α * (z - z_interface))^n)^(-m)\n end\nend\nplot!(hydrostatic_equilibrium.(z, -0.56), z, label = \"equilibrium solution\");\n\nplot!(1e-3 .+ ϑ_l[1], z, label = \"porosity\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Save the output:","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"savefig(\"equilibrium_test_ϑ_l.png\");","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/richards_equation/#References","page":"Richards Equation","title":"References","text":"","category":"section"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"Woodward and Dawson, (2000) SIAM J. Numer. Anal., 37, 701–724","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"","category":"page"},{"location":"generated/standalone/Soil/richards_equation/","page":"Richards Equation","title":"Richards Equation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#PlantHydraulics","page":"Plant Hydraulics","title":"PlantHydraulics","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"CurrentModule = ClimaLand.PlantHydraulics","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#Models","page":"Plant Hydraulics","title":"Models","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsModel","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsModel","text":"PlantHydraulicsModel{FT, PS, T, AA} <: AbstractPlantHydraulicsModel{FT}\n\nDefines, and constructs instances of, the PlantHydraulicsModel type, which is used for simulation flux of water to/from soil, along roots of different depths, along a stem, to a leaf, and ultimately being lost from the system by transpiration. Note that the canopy height is specified as part of the PlantHydraulicsModel, along with the area indices of the leaves, roots, and stems.\n\nThis model can also be combined with the soil model using ClimaLand, in which case the prognostic soil water content is used to determine root extraction, and the transpiration is also computed diagnostically. In global run with patches of bare soil, you can \"turn off\" the canopy model (to get zero root extraction, zero absorption and emission, zero transpiration and sensible heat flux from the canopy), by setting:\n\nn_leaf = 1\nn_stem = 0\nLAI = SAI = RAI = 0.\n\nA plant model can have leaves but no stem, but not vice versa. If n_stem = 0, SAI must be zero.\n\nFinally, the model can be used in Canopy standalone mode by prescribing the soil matric potential at the root tips or flux in the roots. There is also the option (intendend only for debugging) to use a prescribed transpiration rate.\n\nn_stem: The number of stem compartments for the plant; can be zero\nn_leaf: The number of leaf compartments for the plant; must be >=1\ncompartment_midpoints: The height of the center of each leaf compartment/stem compartment, in meters\ncompartment_surfaces: The height of the compartments' top faces, in meters. The canopy height is the last element of the vector.\ncompartment_labels: The label (:stem or :leaf) of each compartment\nparameters: Parameters required by the Plant Hydraulics model\ntranspiration: The transpiration model, of type AbstractTranspiration\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Diagnostic-Variables","page":"Plant Hydraulics","title":"Plant Hydraulics Diagnostic Variables","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.effective_saturation\nClimaLand.PlantHydraulics.augmented_liquid_fraction\nClimaLand.PlantHydraulics.water_retention_curve\nClimaLand.PlantHydraulics.inverse_water_retention_curve\nClimaLand.PlantHydraulics.root_water_flux_per_ground_area!\nClimaLand.PlantHydraulics.flux\nClimaLand.PlantHydraulics.hydraulic_conductivity","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.effective_saturation","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.effective_saturation","text":"effective_saturation(\n ν::FT,\n ϑ_l::FT) where {FT}\n\nComputes the effective saturation given the augmented liquid fraction.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.augmented_liquid_fraction","text":"augmented_liquid_fraction(\n ν::FT,\n S_l::FT) where {FT}\n\nComputes the augmented liquid fraction from porosity and effective saturation.\n\nAugmented liquid fraction allows for oversaturation: an expansion of the volume of space available for storage in a plant compartment.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.water_retention_curve","text":"water_retention_curve(\n S_l::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the potential ψ given the effective saturation S at a point, according to a linear model for the retention curve with parameters specified by retention_params.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.inverse_water_retention_curve","text":"inverse_water_retention_curve(\n ψ::FT,\n b::FT,\n ν::FT,\n S_s::FT) where {FT}\n\nReturns the effective saturation given the potential at a point, according to the linear retention curve model.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.root_water_flux_per_ground_area!","text":"PlantHydraulics.root_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrognosticSoil,\n model::Canopy.PlantHydraulics.PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nAn extension of the PlantHydraulics.root_water_flux_per_ground_area! function, which returns the net flux of water between the roots and the soil, per unit ground area, when both soil and plant hydraulics are modeled prognostically. This is for use in an LSM.\n\nIt is computed by summing the flux of water per ground area between roots and soil at each soil layer.\n\n\n\n\n\nroot_water_flux_per_ground_area!(\n fa::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::PlantHydraulicsModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which computes the water flux between the soil and the stem, via the roots, and multiplied by the RAI, in the case of a model running without an integrated soil model.\n\nThe returned flux is per unit ground area. This assumes that the stem compartment is the first element of Y.canopy.hydraulics.ϑ_l.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.flux","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.flux","text":"flux(\n z1,\n z2,\n ψ1,\n ψ2,\n K1,\n K2,\n) where {FT}\n\nComputes the water flux given the absolute potential (pressure/(ρg)) at the center of the two compartments z1 and z2, and the conductivity along the flow path between these two points.\n\nWe currently assuming an arithmetic mean for mean Ksat between the two points (Bonan, 2019; Zhu, 2008) to take into account the change in Ksat halfway between z1 and z2; this is incorrect for compartments of differing sizes.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.hydraulic_conductivity","text":"hydraulic_conductivity(conductivity_params::Weibull{FT}, ψ::FT) where {FT}\n\nComputes the hydraulic conductivity at a point, using the Weibull formulation, given the potential ψ.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Parameters","page":"Plant Hydraulics","title":"Plant Hydraulics Parameters","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PlantHydraulicsParameters\nClimaLand.PlantHydraulics.Weibull\nClimaLand.PlantHydraulics.LinearRetentionCurve","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PlantHydraulicsParameters","text":"PlantHydraulicsParameters\n\nA struct for holding parameters of the PlantHydraulics Model.\n\nai_parameterization: The area index model for LAI, SAI, RAI\nν: porosity (m3/m3)\nS_s: storativity (m3/m3)\nconductivity_model: Conductivity model and parameters\nretention_model: Water retention model and parameters\nroot_distribution: Root distribution function P(z)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.Weibull","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.Weibull","text":"Weibull{FT} <: AbstractConductivityModel{FT}\n\nA concrete type specifying that a Weibull conductivity model is to be used; the struct contains the require parameters for this model.\n\nFields\n\nK_sat: Maximum Water conductivity in the above-ground plant compartments (m/s) at saturation\nψ63: The absolute water potential in xylem (or xylem water potential) at which ∼63% of maximum xylem conductance is lost (Liu, 2020).\nc: Weibull parameter c, which controls shape the shape of the conductance curve (Sperry, 2016).\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.LinearRetentionCurve","text":"LinearRetentionCurve{FT} <: AbstractRetentionModel{FT}\n\nA concrete type specifying that a linear water retention model is to be used; the struct contains the require parameters for this model.\n\nWhen ψ = 0, the effective saturation is one, so the intercept is not a free parameter, and only the slope must be specified.\n\nFields\n\na: Bulk modulus of elasticity and slope of potential to volume curve. See also Corcuera, 2002, and Christoffersen, 2016.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/PlantHydraulics/#Plant-Hydraulics-Methods-and-Types","page":"Plant Hydraulics","title":"Plant Hydraulics Methods and Types","text":"","category":"section"},{"location":"APIs/canopy/PlantHydraulics/","page":"Plant Hydraulics","title":"Plant Hydraulics","text":"ClimaLand.PlantHydraulics.PrescribedSoilPressure\nClimaLand.PlantHydraulics.PrescribedTranspiration\nClimaLand.PlantHydraulics.AbstractRootExtraction","category":"page"},{"location":"APIs/canopy/PlantHydraulics/#ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","page":"Plant Hydraulics","title":"ClimaLand.Canopy.PlantHydraulics.PrescribedTranspiration","text":"PrescribedTranspiration{FT, F <: Function} <: AbstractTranspiration{FT}\n\nA concrete type used for dispatch when computing the transpiration from the leaves, in the case where transpiration is prescribed.\n\n\n\n\n\n","category":"type"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The AbstractModel tutorial describes how a user can run simulations of a physical system governed by differential equations. In this framework, the user must define a model type for their problem, which contains all of the information required to set up the system of equations. By extending the methods for make_compute_exp_tendency(model), prognostic_variables(model), etc, the information stored in the model is used to make the system of equations. Given initial conditions, these equations can then be stepped forward in time using the time-stepper of your choice. Note that a model requiring implicit timestepping would instead use an AbstractImExModel framework.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The benefit of this framework is that it can be used for both individual components of an LSM (soil, snow, rivers, canopy biophysics, carbon...) as well as the LSM itself. Here we explain how a simple two component model can be set up using this software interface.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We'll first demonstrate how to set up two components in standalone mode, before spending time explaining the LSM setup. In our example, we have a component which accounts for soil hydrology via the Richardson-Richards (RR) equation. Our second component is a surface water model without lateral flow (standing water, as in a pond). For more details on these models, and how they were set up, please feel free to look at the source code here and here. This tutorial focuses on using the AbstractModels framework to set up the equations, rather than on running simulations.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's load the required modules:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"using ClimaLand\nusing ClimaLand.Domains: Column, obtain_surface_domain\nusing ClimaLand.Soil\nusing ClimaLand.Pond\n\nFT = Float32;","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-I-Soil-Hydrology","page":"Intro to multi-component models","title":"The individual component models I - Soil Hydrology","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The RR equation for the volumetric water content of soil is given by","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S(xyz t)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In order to solve this, one must specify:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"boundary conditions,\nrelevant parameters (closure models for K and ψ),\na domain and a spatial discretization scheme,\nadditional source terms S, if applicable,\na time-stepping algorithm,\ninitial conditions.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We make the distinction between the spatially discretized equations (for which you need parameters, boundary conditions, source terms, and domain/ discretization scheme information in order to write down and evaluate), and the simulation you want to run (for which you need the equations, initial conditions, a time span, and a time-stepping scheme in order to specify completely).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, we'll focus on what you need to write the equations. This information is stored in the model structure itself, so that we can call make_exp_tendency(model) and get back a function which computes the time derivative of the prognostic variables, which the ODE timestepper needs to advance the state forward in time.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"For the RR equation, we can create this as follows. First, we specify parameters:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ν = FT(0.495);\nK_sat = FT(0.0443 / 3600 / 100); # m/s\nS_s = FT(1e-3); #inverse meters\nvg_n = FT(2.0);\nvg_α = FT(2.6); # inverse meters\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n);\nθ_r = FT(0);\nsoil_ps = Soil.RichardsParameters(;\n ν = ν,\n hydrology_cm = hcm,\n K_sat = K_sat,\n S_s = S_s,\n θ_r = θ_r,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next, let's define the spatial domain and discretization:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"zmax = FT(0);\nzmin = FT(-1);\nnelems = 20;\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And boundary conditions and source terms (none currently):","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"top_flux_bc = WaterFluxBC((p, t) -> 0.0);\nbot_flux_bc = WaterFluxBC((p, t) -> 0.0);\nsources = ()\nboundary_fluxes = (; top = top_flux_bc, bottom = bot_flux_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(top = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#1#2\"}(Main.var\"##494\".var\"#1#2\"()), bottom = ClimaLand.Soil.WaterFluxBC{Main.var\"##494\".var\"#3#4\"}(Main.var\"##494\".var\"#3#4\"()))","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"With this information, we can make our model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil = Soil.RichardsModel{FT}(;\n parameters = soil_ps,\n domain = soil_domain,\n boundary_conditions = boundary_fluxes,\n sources = sources,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We also can create the soil prognostic and auxiliary ClimaCore.Field.FieldVectors using the default method for initialize,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_soil, p_soil, coords_soil = initialize(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and we can set up the tendency function using the default as well,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_ode! = make_exp_tendency(soil);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which computes, for the column domain,","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-frac z (-Kfrac(ψ+z) z)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"for each value of ϑ on the mesh of our soil_domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note that the soil model does includes hydraulic K, pressure head ψ, and the boundary fluxes at the top and bottom of the domain in the auxiliary vector. These are updated first in each call to soil_ode!, as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function soil_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # updates p.soil.K, p.soil.ψ in place\n update_boundary_fluxes!(p,Y,t) # updates p.soil.top_bc, p.soil.bottom_bc in place\n compute_exp_tendency!(dY, Y, p, t) # computes the divergence of the Darcy flux, updates dY in place.\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"It is crucial the the cache p is correctly updated before the tendency is computed. The default method for make_exp_tendency creates the update_aux! and update_boundary_fluxes! functions, given the model, and evaluates them before computing the tendency, so we do not need to define that for the soil model.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Note also that we have defined methods make_compute_exp_tendency, make_update_aux, and make_update_boundary_fluxes, which only take the model as argument, and which return the functions compute_exp_tendency!, update_aux!, and update_boundary_fluxes!. Please see the API documentation or source code for more information.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Lastly, the coordinates returned by initialize contain the z-coordinates of the centers of the finite difference layers used for spatial discretization of the PDE.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#The-individual-component-models-II-Surface-Water","page":"Intro to multi-component models","title":"The individual component models II - Surface Water","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond model has a single variable, the pond height η, which satisfies the ODE:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"frac η t = -(P - I) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"where P is the precipitation, I the infiltration into the soil, and R is the runoff. Note that P, I < 0 indicates flow in the -ẑ direction.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To write down the pond equations, we need to specify","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"P\nI","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"which are akin to boundary fluxes. In standalone mode, one would need to pass in prescribed functions of time and store them inside our pond model, since again, the pond model structure must contain everything needed to make the tendency function:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"precipitation(t) = t < 20 ? -1e-5 : 0.0 # m/s\n\ninfiltration(t) = -(1e-6) #m/s\npond_model =\n Pond.PondModel{FT}(; runoff = PrescribedRunoff(precipitation, infiltration));","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, PrescribedRunoff is the structure holding the prescribed driving functions for P and I.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Again we can initialize the state vector and auxiliary vectors:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y_pond, p_pond, coords_pond = initialize(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We can make the tendency function in the same way, for stepping the state forward in time:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"pond_ode! = make_exp_tendency(pond_model);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The pond_ode! function works in the same way as for the soil model:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function pond_ode!(dY, Y, p, t)\n update_aux!(p,Y,t) # falls back to default; does nothing\n update_boundary_fluxes!(p,Y,t) # p.surface_water.runoff in place\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#An-LSM-with-pond-and-soil:","page":"Intro to multi-component models","title":"An LSM with pond and soil:","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The LSM model must contain everything needed to write down the joint system of equations","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial etapartial t = -(P(t) - I(ϑ η P)) = R","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"fracpartial ϑpartial t = - (-K(ψ+z)) + S","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmax z = I(ϑ η P)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"-K (ψ+z)_z = zmin z = 00","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"These two components interact via the infiltration term I. Infiltration is a boundary condition for the soil, and affects the source term for the surface water equation. Infiltration depends on precipitation, the soil moisture state, and the pond height.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"As in the standalone cases, defining our model requires specifying","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"parameters,\ndomains, discretizations\nprecipitation,\nboundary conditions,\nsources in the soil equation, if any.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"First, let's make our single column domain.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"lsm_domain = Column(; zlim = (zmin, zmax), nelements = nelems);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Let's now collect the needed arguments for the soil model. The pond model only has one argument, the runoff model, but that will be set internally. Similarily, the boundary conditions of the soil model will be set internally to be consisent with the equations of the pond-soil model - see below for detail.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"soil_args = (parameters = soil_ps, domain = lsm_domain, sources = ());\nsurface_water_args = NamedTuple();","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Atmospheric drivers don't \"belong\" to either component alone:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_args = (precip = precipitation,);\nland = LandHydrology{FT}(;\n land_args = land_args,\n soil_model_type = Soil.RichardsModel{FT},\n soil_args = soil_args,\n surface_water_model_type = Pond.PondModel{FT},\n surface_water_args = surface_water_args,\n);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, LandHydrology is a type of AbstractModel which has a surface water model (Pond or otherwise) and a soil model (RR, or perhaps otherwise).","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Now, note that we did not specify the infiltration function, like we did in standalone pond mode, nor did we specify boundary conditions for the soil model, nor did we specify the pond model domain. Yet, before we stressed that the model needs to have everything required to write down and evaluate the time derivative of the ODEs. So, how does this work?","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Here, the LSM model constructor is given the information needed to make both the soil model and the pond model. Then, it is like running the pond and soil model in standalone mode, in series, except we have defined methods internally for computing the boundary condition and pond source term correctly, based on I, instead of using prescribed values passed in. The LSM constructor creates the correct boundary_fluxes object for the soil model, and the correct infiltration object for the pond model under the hood.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"To advance the state of the joint system (ϑ, η) from time t to time t+Δt, we must compute the infiltration at t. This value is stored in p.soil_infiltration. In pseudo code, we have:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_aux(land)\n soil_update_aux! = make_update_aux(land.soil)\n surface_update_aux! = make_update_aux(land.surface_water)\n function update_aux!(p,Y,t)\n surface_update_aux!(p,Y,t) # does nothing to `p`\n soil_update_aux!(p,Y,t) # updates p.soil.K and p.soil.ψ\n end\n return update_aux!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_update_boundary_fluxes(land)\n update_soil_bf! = make_update_boundary_fluxes(land.soil)\n update_pond_bf! = make_update_boundary_fluxes(land.surface_water)\n function update_boundary_fluxes!(p,Y,t)\n p.soil_infiltration = compute_infiltration(Y,p, t)\n update_soil_bf!(p,Y,t) # updates p.soil.top_bc using p.soil_infiltration\n update_pond_bf!(p,Y,t) # updates p.surface_water.runoff using p.soil_infiltration\n end\n return update_boundary_fluxes!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and similarily for the compute_exp_tendency! functions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function make_compute_exp_tendency(land)\n soil_compute_exp_tendency! = make_update_aux(land.soil)\n surface_compute_exp_tendency! = make_update_aux(land.surface_water)\n function compute_exp_tendency!(dY,Y,p,t)\n surface_compute_exp_tendency!(dY,Y,p, t), # computes dY.surface.η\n soil_compute_exp_tendency!(dY,Y,p,t) # computes dY.soil.ϑ\n end\n return compute_exp_tendency!\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"The exp_tendency! for the land model is then again just","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"function exp_tendency!(dY, Y, p, t)\n update_aux!(p,Y,t)\n update_boundary_fluxes!(p,Y,t)\n compute_exp_tendency!(dY, Y, p, t)\nend","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"In the above, we showed explicitly what occurs by hardcoding the compute_exp_tendency!, update_aux! with names for soil and surface_water. In reality, this is done by looping over the components of the land model, meaning that we can use the same code internally for land models with different components.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"A similar composition occurs for initializing the state itself: Calling initialize(land) does four things:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"initialize(land.soil)\ninitialize(land.surface_water)\ninitializes additional auxiliary variables, like p.soil_infiltration\nappend these into Y, p, and coords:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Y, p, coords = initialize(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"We have volumetric liquid water fraction:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:ϑ_l,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and surface height of the pond:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(Y.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:η,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"as well as auxiliary variables for the soil:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.soil)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:K, :ψ, :top_bc, :bottom_bc)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the runoff for surface water:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p.surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:runoff,)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the additional variable required in the LSM is stored here as well:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"propertynames(p)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"(:soil_infiltration, :soil, :surface_water)","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and finally, coordinates - useful for visualization of solutions:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.subsurface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"and the coordinates of the surface variables:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"coords.surface","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"ClimaCore.Geometry.ZPoint{Float32}-valued Field:\n z: Float32[0.0]","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"And we can make the tendency function as before:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"land_ode! = make_exp_tendency(land);","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Next up would be to set initial conditions, choose a timestepping scheme, and run your simulation.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/#Advantages-and-disadvantages","page":"Intro to multi-component models","title":"Advantages and disadvantages","text":"","category":"section"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Some advantages to our interface design are as follows:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"a developer only needs to learn a few concepts (compute_exp_tendency!, prognostic vs. aux variables, update_aux!/update_boundary_fluxes!, initialize, domains) to make a model which can be run in standalone or work with other components.\nlikewise, a user only needs to learn one interface to run all models, regardless of if they are standalone components or LSMs with multiple components.\nthe exp_tendency!is completely seperate from the timestepping scheme used, so any scheme can be used (with the exception of mixed implicit/explicit schemes, which we can't handle yet).\nalthough we wrote it here in a hardwired fashion for surface water and soil, the update_aux!, compute_exp_tendency! methods for LSM models generalize to any number and mix of components. One just needs to write a new model type (e.g. BiophysicsModel <: AbstractModel for a vegetation and carbon component model) and the appropriate make_update_boundary_var methods for that model.\nthe order in which the components are treated in the tendency or in update aux does not matter. What matters is that (1) auxiliary/cache variables are updated prior to calling update_boundary_fluxes!, and that (2) update_boundary_fluxes! is called prior to evaluating the tendency.\nthe code is also modular in terms of swapping out a simple component model for a more complex version.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Possible disadvantages to our interface design:","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"Even in standalone model, variables are accessed in a nested way: Y.soil, p.soil, etc, which is excessive.\nTo accomodate the fact that some components involve PDEs, a developer for purely ODE based component does need to at least handle ClimaCore.Field.FieldVectors.\nstandalone models need to play by the rules of AbstractModels, and LSMs need to play by the rules of ClimaLand.jl.\nwe need to define multiple update cache functions in order to handle dependencies between cache variables of one component model and boundary fluxes of another.","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"","category":"page"},{"location":"generated/standalone/Usage/LSM_single_column_tutorial/","page":"Intro to multi-component models","title":"Intro to multi-component models","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This sets up the simulation that mimicks the lab experiment presented in Gardener 1970b and modeled also by Lehmann and Or, 2024.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"For further details on how to setup a simulation, please see our other Soil tutorials. This one is very terse and does not provide complete explanations","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"The same experiment is carried out 3 times","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"No evaporation (zero flux boundary conditions)\nWith evaporation but no drainage (Ksat = 0)\nWith evaporation and drainage","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"using CairoMakie\nimport SciMLBase\nimport ClimaTimeSteppers as CTS\nusing Thermodynamics\n\nusing ClimaCore\nimport ClimaParams as CP\nusing SurfaceFluxes\nusing StaticArrays\nusing Dates\nusing DelimitedFiles: readdlm\n\nusing ClimaLand\nusing ClimaLand.Domains: Column\nusing ClimaLand.Soil\nimport ClimaLand\nimport ClimaLand.Parameters as LP\nimport SurfaceFluxes.Parameters as SFP\n\nFT = Float64;\nearth_param_set = LP.LandParameters(FT)\nthermo_params = LP.thermodynamic_parameters(earth_param_set);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Parameters","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"K_sat = FT(0.01 / 3600 / 24)\nvg_n = FT(1.45)\nvg_α = FT(1.5)\nhcm = vanGenuchten{FT}(; α = vg_α, n = vg_n)\nν = FT(0.4)\nθ_r = FT(0.04)\nS_s = FT(1e-3)\nν_ss_om = FT(0.0)\nν_ss_quartz = FT(0.3)\nν_ss_gravel = FT(0.0)\nemissivity = FT(1.0)\nPAR_albedo = FT(0.2)\nNIR_albedo = FT(0.4)\nz_0m = FT(1e-3)\nz_0b = FT(1e-4)\nd_ds = FT(0.01)# 10mm\nparams = ClimaLand.Soil.EnergyHydrologyParameters(\n FT;\n ν,\n ν_ss_om,\n ν_ss_quartz,\n ν_ss_gravel,\n hydrology_cm = hcm,\n K_sat,\n S_s,\n θ_r,\n PAR_albedo,\n NIR_albedo,\n emissivity,\n z_0m,\n z_0b,\n earth_param_set,\n d_ds,\n);\n\nref_time = DateTime(2005)\nSW_d = (t) -> 0\nLW_d = (t) -> 294.15^4 * 5.67e-8\nradiation = PrescribedRadiativeFluxes(\n FT,\n TimeVaryingInput(SW_d),\n TimeVaryingInput(LW_d),\n ref_time,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedRadiativeFluxes{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}, Dates.DateTime, Nothing}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#1#2\"}(Main.var\"##317\".var\"#1#2\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#3#4\"}(Main.var\"##317\".var\"#3#4\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), nothing)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Atmos","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"T_air = FT(301.15)\nrh = FT(0.38)\nesat = Thermodynamics.saturation_vapor_pressure(\n thermo_params,\n T_air,\n Thermodynamics.Liquid(),\n)\ne = rh * esat\nq = FT(0.622 * e / (101325 - 0.378 * e))\nprecip = (t) -> 0.0\nT_atmos = (t) -> T_air\nu_atmos = (t) -> 1.0\nq_atmos = (t) -> q\nh_atmos = FT(0.1)\nP_atmos = (t) -> 101325\ngustiness = FT(1e-2)\natmos = PrescribedAtmosphere(\n TimeVaryingInput(precip),\n TimeVaryingInput(precip),\n TimeVaryingInput(T_atmos),\n TimeVaryingInput(u_atmos),\n TimeVaryingInput(q_atmos),\n TimeVaryingInput(P_atmos),\n ref_time,\n h_atmos,\n earth_param_set;\n gustiness = gustiness,\n)","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"PrescribedAtmosphere{Float64, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}, ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}, Dates.DateTime, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}}(ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#5#6\"}(Main.var\"##317\".var\"#5#6\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#7#8\"}(Main.var\"##317\".var\"#7#8\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#9#10\"}(Main.var\"##317\".var\"#9#10\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#11#12\"}(Main.var\"##317\".var\"#11#12\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{Main.var\"##317\".var\"#13#14\"}(Main.var\"##317\".var\"#13#14\"()), ClimaUtilitiesClimaCoreNCDatasetsExt.TimeVaryingInputsExt.AnalyticTimeVaryingInput{ClimaLand.var\"#21#24\"}(ClimaLand.var\"#21#24\"()), Dates.DateTime(\"2005-01-01T00:00:00\"), 0.1, 0.01, Thermodynamics.Parameters.ThermodynamicsParameters{Float64}(273.16, 101325.0, 100000.0, 1859.0, 4181.0, 2100.0, 2.5008e6, 2.8344e6, 611.657, 273.16, 273.15, 1.0, 1000.0, 150.0, 298.15, 6864.8, 10513.6, 0.28571428571, 8.3144598, 0.02897, 0.01801528, 290.0, 220.0, 9.81, 233.0, 1.0))","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Simulation setup - no evaporation Boundary conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"zero_water_flux = WaterFluxBC((p, t) -> 0)\nzero_heat_flux = HeatFluxBC((p, t) -> 0)\nno_flux_boundary_fluxes = (;\n top = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n);\n\nt0 = Float64(0)\ntf = Float64(24 * 3600 * 15)\ndt = Float64(900.0)\nΔz = 0.01\nzmax = FT(0)\nzmin = FT(-1.6)\nnelems = Int((zmax - zmin) / Δz)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_flux_boundary_fluxes,\n sources = (),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Y, p, cds = initialize(soil)\nfunction estimated_ic(z)\n 0.34 / (1 + exp(-(z + 0.165) / 0.005)) + 0.05\nend\nfunction init_soil!(Y, z, params)\n FT = eltype(Y.soil.ϑ_l)\n Y.soil.ϑ_l .= estimated_ic.(z)\n Y.soil.θ_i .= 0\n T = FT(294.15)\n ρc_s = @. Soil.volumetric_heat_capacity(\n Y.soil.ϑ_l,\n Y.soil.θ_i,\n params.ρc_ds,\n params.earth_param_set,\n )\n Y.soil.ρe_int =\n Soil.volumetric_internal_energy.(\n Y.soil.θ_i,\n ρc_s,\n T,\n params.earth_param_set,\n )\nend\n\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Timestepping:","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"exp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\n\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Problem definition and callbacks","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"prob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\ncb = SciMLBase.CallbackSet(saving_cb);\n\nsol_no_evap =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Repeat with evaporation and drainage This requires different initial conditions","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"top_bc = ClimaLand.Soil.AtmosDrivenFluxBC(atmos, radiation)\nevap_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = zero_water_flux, heat = zero_heat_flux),\n)\n\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = evap_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nsoil_exp_tendency! = make_exp_tendency(soil)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol = SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];\n\n# Repeat with no drainage (Ksat = 0, different BC), and with evaporation, in shorter domain","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"[ Info: Warning: No runoff model was provided; zero runoff generated.\n","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This requires different boundary conditions yet again: Wet boundary at bottom, zero heat flux at bottom, the previously defined atmos driven evaporation at the top.","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"bottom_water_bc = MoistureStateBC((p, t) -> 0.35)\nno_drainage_boundary_fluxes = (;\n top = top_bc,\n bottom = WaterHeatBC(; water = bottom_water_bc, heat = zero_heat_flux),\n)\nzmax = FT(0)\nzmin = FT(-0.16)\nnelems = Int((zmax - zmin) / Δz)\ndt = Float64(10.0)\nsoil_domain = Column(; zlim = (zmin, zmax), nelements = nelems)\nz_no_evap = ClimaCore.Fields.coordinate_field(soil_domain.space.subsurface).z\nsoil = Soil.EnergyHydrology{FT}(;\n parameters = params,\n domain = soil_domain,\n boundary_conditions = no_drainage_boundary_fluxes,\n sources = (),\n)\nY, p, cds = initialize(soil)\ninit_soil!(Y, z_no_evap, soil.parameters)\nset_initial_cache! = make_set_initial_cache(soil)\nset_initial_cache!(p, Y, t0)\nexp_tendency! = make_exp_tendency(soil)\nimp_tendency! = make_imp_tendency(soil);\njacobian! = ClimaLand.make_jacobian(soil);\ntimestepper = CTS.ARS111();\node_algo = CTS.IMEXAlgorithm(\n timestepper,\n CTS.NewtonsMethod(\n max_iters = 1,\n update_j = CTS.UpdateEvery(CTS.NewNewtonIteration),\n ),\n);\njac_kwargs = (; jac_prototype = ImplicitEquationJacobian(Y), Wfact = jacobian!);\nprob = SciMLBase.ODEProblem(\n CTS.ClimaODEFunction(\n T_exp! = exp_tendency!,\n T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),\n dss! = ClimaLand.dss!,\n ),\n Y,\n (t0, tf),\n p,\n);\nsaveat = Array(t0:3600.0:tf)\nsv = (;\n t = Array{Float64}(undef, length(saveat)),\n saveval = Array{NamedTuple}(undef, length(saveat)),\n)\nsaving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)\nupdateat = deepcopy(saveat)\nmodel_drivers = ClimaLand.get_drivers(soil)\nupdatefunc = ClimaLand.make_update_drivers(model_drivers)\ndriver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)\ncb = SciMLBase.CallbackSet(driver_cb, saving_cb)\nsol_no_drainage =\n SciMLBase.solve(prob, ode_algo; dt = dt, callback = cb, saveat = saveat)\nevap_no_drainage = [\n parent(\n sv.saveval[k].soil.turbulent_fluxes.vapor_flux .*\n (1 .- sv.saveval[k].soil.ice_frac),\n )[1] for k in 1:length(sol.t)\n];","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"Figures","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"savepath = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Soil/\")\n\nfig = Figure(size = (800, 400))\nax = Axis(fig[1, 1], xlabel = \"Day\", ylabel = \"Evaporation rate (mm/d)\")\nCairoMakie.lines!(\n ax,\n sol.t ./ 3600 ./ 24,\n evap .* (1000 * 3600 * 24),\n label = \"With drainage\",\n color = :red,\n)\nCairoMakie.lines!(\n ax,\n sol_no_drainage.t ./ 3600 ./ 24,\n evap_no_drainage .* (1000 * 3600 * 24),\n label = \"No drainage\",\n color = :blue,\n)\n\nCairoMakie.axislegend(ax)\nax2 = Axis(fig[1, 2], xlabel = \"Day\", ylabel = \"Cumulative evaporation (mm)\")\nCairoMakie.lines!(\n ax2,\n sol.t ./ 3600 ./ 24,\n cumsum(evap) .* (1000 * 3600),\n color = :red,\n)\nCairoMakie.lines!(\n ax2,\n sol_no_drainage.t ./ 3600 ./ 24,\n cumsum(evap_no_drainage) .* (1000 * 3600),\n color = :blue,\n)\nsave(\"evaporation_lehmann2024_figS6.png\", fig);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"fig2 = Figure(size = (800, 1200))\nax1 = Axis(fig2[1, 1], title = \"Drainage only\")\nCairoMakie.ylims!(-0.35, 0)\nCairoMakie.xlims!(0.0, 0.4)\nlinestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]\ndays = [0, 1, 2, 10]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax1,\n parent(sol_no_evap.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax2 = Axis(fig2[2, 1], title = \"Evap+Drainage\", ylabel = \"Depth(cm)\")\n\nCairoMakie.ylims!(-0.3, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 1, 2, 5, 13]\nfor i in 1:1:5\n CairoMakie.lines!(\n ax2,\n parent(sol.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n parent(z)[:],\n label = \"$(days[i]) days\",\n color = :black,\n linestyle = linestyles[i],\n )\nend\nax3 = Axis(fig2[3, 1], title = \"Evap only\", xlabel = \"Volumetric Water Content\")\nCairoMakie.ylims!(-0.15, 0)\nCairoMakie.xlims!(0.0, 0.4)\ndays = [0, 2, 9, 14]\nfor i in 1:1:4\n CairoMakie.lines!(\n ax3,\n parent(sol_no_drainage.u[days[i] * 24 + 1].soil.ϑ_l)[:],\n label = \"$(days[i]) days\",\n parent(z_no_evap)[:],\n color = :black,\n linestyle = linestyles[i],\n )\nend\n\nCairoMakie.axislegend(ax3, position = :lt)\nCairoMakie.axislegend(ax2, position = :lt)\nCairoMakie.axislegend(ax1, position = :lt)\nsave(\"evaporation_gardner_fig1.png\", fig2);","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"(Image: )","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"","category":"page"},{"location":"generated/standalone/Soil/evaporation_gilat_loess/","page":"Gilat Loess Evaporation","title":"Gilat Loess Evaporation","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"Snow Model","category":"page"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"CurrentModule = ClimaLand.Snow","category":"page"},{"location":"APIs/Snow/#Snow-Parameters","page":"Snow Model","title":"Snow Parameters","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"ClimaLand.Snow.SnowParameters","category":"page"},{"location":"APIs/Snow/#ClimaLand.Snow.SnowParameters","page":"Snow Model","title":"ClimaLand.Snow.SnowParameters","text":"SnowParameters{FT <: AbstractFloat, PSE}\n\nA struct for storing parameters of the SnowModel.\n\nNote that in our current implementation of runoff, a physical timescale is required and computed using Ksat and the depth of the snow. For shallow snowpacks, this will fall below the timestep of the model. For that reason, we pass the timestep of the model as a parameter, and take the larger of the timestep and the physical timescale as the value used in the model. Future implementations will revisit this.\n\nρ_snow: Density of snow (kg/m^3)\nz_0m: Roughness length over snow for momentum (m)\nz_0b: Roughness length over snow for scalars (m)\nα_snow: Albedo of snow (unitless)\nϵ_snow: Emissivity of snow (unitless)\nθ_r: Volumetric holding capacity of water in snow (unitless)\nKsat: Hydraulic conductivity of wet snow (m/s)\nκ_ice: Thermal conductivity of ice (W/m/K)\nΔt: Timestep of the model (s)\nρcD_g: Areal specific heat of ground interacting with snow (J/m^2/K)\nearth_param_set: Clima-wide parameters\n\n\n\n\n\n","category":"type"},{"location":"APIs/Snow/#Snow-Functions-of-State","page":"Snow Model","title":"Snow Functions of State","text":"","category":"section"},{"location":"APIs/Snow/","page":"Snow Model","title":"Snow Model","text":"```@docs ClimaLand.Snow.specificheatcapacity ClimaLand.Snow.snowsurfacetemperature ClimaLand.Snow.snowdepth ClimaLand.Snow.snowthermalconductivity ClimaLand.Snow.snowbulktemperature ClimaLand.Snow.snowliquidmassfraction ClimaLand.Snow.maximumliquidmassfraction ClimaLand.Snow.runofftimescale ClimaLand.Snow.computewaterrunoff\t ClimaLand.Snow.energyfromqlandswe ClimaLand.Snow.energyfromTand_swe","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Stomatal-conductance","page":"Medlyn model","title":"Stomatal conductance","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"This section describes multiple models of stomatal conductance implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/#Medlyn-Model","page":"Medlyn model","title":"Medlyn Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\nT = -rho_a g_mathrmeff leftq_a- q_v(T_mathrmleaf rho_mathrmsfc) right\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where T is the transpiration (mass flux of water vapor), q_a is the specific humidity at the lowest level of the atmosphere, q_v(T_mathrmT_leaf rho_mathrmsfc) is the saturated specific humidity over liquid water, given the temperature of the leave T_leaf and air density at the surface rho_sfc. We will approximate T_leaf = T_a and rho_mathrmsfc = rho_a.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"We also need the effective conductivity, given by","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n g_mathrmeff = frac1g_mathrmae^-1+g_mathrms^-1\nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g_ae is the aerodynamic conductance, computed by the MOST solve, and g_s is the stomatal conductance to water vapor per unit ground area. The units of all conductances are ms.","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginalign\ng_sm(PAR T VPD c_a) = g_0m + D_rel times m fracA_n(PAR T VPD c_a)c_anonumber \ng_s = fracg_smrho_m\nendalign","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where D_rel =16 (unitless) is the relative diffusivity of water vapor with respect to CO_2, rho_m is the molar density of water, and m is the Medlyn factor,","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"beginequation\n m = left( 1 + fracg_1sqrtVPD right) \nendequation","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A_n as the biochemical demand for CO_2 calculated using the photosynthesis model (Farquhar, 1980; Equation \\eqref{eq:an}; units of molar flux). The resulting units are ms. g{0,m}$ is a minimum molar conductivity. (subscript m indicates molar).","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"The model has the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/stomatal_conductance/medlyn_model/","page":"Medlyn model","title":"Medlyn model","text":"Constants Symbol Unit Value\nRelative diffusivity of water vapor D_rel - 1.6\nMinimum stomatal conductance g_0 mol/m^2/s 1e-4\nSlope parameter g_1 sqrtPa 790","category":"page"},{"location":"standalone/README/#DynamicDocs","page":"DynamicDocs","title":"DynamicDocs","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Plant-Hydraulics","page":"Van Genuchten model","title":"Plant Hydraulics","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The plant hydraulics code solves for the volumetric water content in the stem and leaf (theta_stem and theta_leaf). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/#Van-Genuchten-Model","page":"Van Genuchten model","title":"Van Genuchten Model","text":"","category":"section"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The volume flux of water q (m/s) between compartments with centers at two heights, z_1 and z_2, is given by Darcy's law as","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n q = -int_z_1^z_2 k(psi) dh\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where h = psi+z is the head (in meters), and k is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\\footnote{Double check this - the units of k in our code are ms.}","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\nq = -int_h_1^h_2 k(psi) dh approx -frack_1(psi_1) + k_2(psi_2)2 * (psi_2 - psi_1) + (z_2 - z_1)\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"In order to close the set of equations, the user will have to specify k(psi) and a function psi(theta). In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of K_sat","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The change of water volume (m^3) V, in the compartments is then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd V_w stemdt = q_rootssigma_roots - q_stemsigma_stem nonumber \n fracd V_w leafdt = q_stemsigma_stem - tau sigma_leaf\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where tau is a transpiration volume flux per unit emitting area, and sigma is the total emitting/conducting area\\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals N. We can make use of the fact that NsigmaA, where A is the area of the ground those N individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Then we have:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd v_stemdt = q_rootsRAI - q_stemSAI nonumber \n fracd v_leafdt = q_stemSAI - tau LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where v now represents the volume of water in that compartment (of a bulk plant) per unit ground area.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We also need to convert from the variable v to psi, in order to compute root extraction with the soil. To do so, we can convert v to the volumetric water content, and from theta to psi using a van Genuchten relationship. To convert, let the volume of water per area of compartment be V_wstem, and H the typical ``length\" of the compartment. Then","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n theta_stem=fracV_wstemA_ground times fracA_groundA_stem times frac1H_stem = fracv_stemH_stem times SAI \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Substituting in the volumetric water content, we have","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginalign\n fracd theta_stemdt = fracq_rootsRAI - q_stemSAIH_stem SAI nonumber \n fracd theta_leafdt = fracq_stemSAI - tau LAIH_leaf LAI\nendalign","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction P(z), satisfying int P(z) dz = 1. Instead of having a single root at one discrete location, we can distribute the root system over different depths using P(z). The total flux from roots between z and z+dz is given by","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n dq_roots(z) = -P(z) dz int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"so that the net flux for the plant system would sum over this","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n q_roots = -int_z_min^z_sfc fracdq_roots(z)dzdz \nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"where z_min is the minimum soil layer of the simulation. ","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"beginequation\n S(z) = -(RAI) dq_roots(z)dz = (RAI) P(z) int_h_soil(z)^h_stem k(psi) dh\nendequation","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The sign change occurs in the expression for S(z) because a positive value of q_roots indicates flow from the soil to the plant. This is a sink term for the soil.","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"The model needs the following parameters:","category":"page"},{"location":"standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/","page":"Van Genuchten model","title":"Van Genuchten model","text":"Drivers Symbol Unit Range\nA function of simulation time t given the leaf area index LAI m²/m² 0–10\nThe constant stem area index SAI m²/m² 0–10\nThe constant root area index RAI m²/m² 0–10","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Microbial-respiration","page":"DAMM model","title":"Microbial respiration","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand. ","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/#Dual-Arrhenius-Michaelis-Menten","page":"DAMM model","title":"Dual Arrhenius Michaelis-Menten","text":"","category":"section"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration (Rh) as a function of soil temperature (T_s) and soil moisture (theta).","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The rate of respiration, Rh, is expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n Rh = V_textmaxs_xMM_s_xMM_O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where V_textmaxs_x is the maximum potential rate of respiration, MM_s_x represents the availability of substrate, and MM_O_2 is used as the oxygen limitation factor. MM_s_x and MM_O_2 are between 0 (limiting) and 1 (non limiting). V_textmaxs_x, MM_s_x, and MM_O_2 are expressed as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n V_textmaxs_x = alpha_s_x exp(frac-Ea_s_xRT_s)\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textsx = fracs_xkM_s_x+s_x\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n MM_textO_2 = fracO_2kM_O_2+O_2\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where alpha_s_x is the pre-exponential factor, Ea_s_x is the activation energy of the reaction, R is the gas constant, and T_s is soil temperature. s_x is the concentration of all soluble substrate, and O_2 is the oxygen concentration. kM_s_x and kM_O_2 are the Michaelis constant for soil and oxygen, respectively.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, s_x is calculated as:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n s_x = p_s_xtimesC_somtimes D_liqtimestheta^3\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where C_som is the total amount of soil organic carbon, and p_s_x is the fraction of C_som that is soluble. D_liq is the diffusion coefficient of the soluble carbon. theta is soil moisture.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The concentration of O_2 depends on the diffusion of gases within the soil, which is calculated as below:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n O_2 = D_Oatimes O_2a times porosity_air^43\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"where D_Oa is the diffusion coefficient for O_2 in air, O_2a is the volume fraction of O_2 in air, and porosity_air is the air-filled porosity.","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"The air-filled porosity is calculated by subtracting the soil moisture from the total porosity (nu):","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"beginequation\n porosity_air = nu - theta\nendequation","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"To sum up, the model has the following parameters:","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Output Symbol Unit Range\nHeterotrophic respiration Rh mumol m^-2 s^-2 0–25","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Drivers Symbol Unit Range\nSoil temperature T_s C -20–50\nSoil moisture theta m^3 m^-3 0.0–1.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Parameters Symbol Unit Range\nSoil porosity nu m^3 m^-3 0.0–1.0\nPre-exponential factor alpha_s_x kg C m^-3 s^-1 100e3–300e3\nActivation energy Ea_s_x Jmol^-1 50e3–70e3\nMichaelis constant for soil kM_s_x kg C m^-3 1e-10–0.1\nMichaelis constant for O_2 kM_O_2 m^3 m^-3 1e-10–0.1\nVolumetric fraction of O_2 in the soil air content O_2_a - 0.005–0.5\nFraction of soil carbon that is considered soluble p_s_x - 0.005–0.5\nSoil organic C C_som kg C m^-3 1.0–10.0","category":"page"},{"location":"standalone/pages/soil/biogeochemistry/DAMM_model/","page":"DAMM model","title":"DAMM model","text":"Constants Symbol Unit Value\nAir-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa) O_a100 - 0.1816\nDiffusivity of soil C substrate in liquid D_liq - 3.17\nDiffusion coefficient of oxygen in air D_Oa - 1.67","category":"page"},{"location":"Contributing/#Contributing","page":"Contribution guide","title":"Contributing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Thank you for contributing to ClimaLand! We encourage Pull Requests (PRs). Please do not hesitate to ask questions.","category":"page"},{"location":"Contributing/#Some-useful-tips","page":"Contribution guide","title":"Some useful tips","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"When you start working on a new feature branch, make sure you start from main by running: git checkout main.\nMake sure you add tests for your code in test/ and appropriate documentation in the code and/or in docs/. All exported functions and structs must be documented.\nWhen your PR is ready for review, clean up your commit history by squashing and make sure your code is current with ClimateMachine main by rebasing.","category":"page"},{"location":"Contributing/#Continuous-integration","page":"Contribution guide","title":"Continuous integration","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"After rebasing your branch, you can ask for review. Fill out the template and provide a clear summary of what your PR does. When a PR is created or updated, a set of automated tests are run on the PR in our continuous integration (CI) system.","category":"page"},{"location":"Contributing/#Automated-testing","page":"Contribution guide","title":"Automated testing","text":"","category":"section"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"Currently a number of checks are run per commit for a given PR.","category":"page"},{"location":"Contributing/","page":"Contribution guide","title":"Contribution guide","text":"JuliaFormatter checks if the PR is formatted with .dev/climaformat.jl.\nDocumentation rebuilds the documentation for the PR and checks if the docs are consistent and generate valid output.\nTests runs the file test/runtests.jl, using Pkg.test(). These are a mix of unit tests and fast integration tests.","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Canopy-Energy-Model","page":"Canopy Energy","title":"Canopy Energy Model","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#Methods","page":"Canopy Energy","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.canopy_temperature\nClimaLand.Canopy.root_energy_flux_per_ground_area!","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.canopy_temperature","page":"Canopy Energy","title":"ClimaLand.Canopy.canopy_temperature","text":"canopy_temperature(model::PrescribedCanopyTempModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the PrescribedCanopyTemp model, where the canopy temperature is assumed to be the same as the atmosphere temperature.\n\n\n\n\n\ncanopy_temperature(model::BigLeafEnergyModel, canopy, Y, p, t)\n\nReturns the canopy temperature under the BigLeafEnergyModel model, where the canopy temperature is modeled prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.root_energy_flux_per_ground_area!","page":"Canopy Energy","title":"ClimaLand.Canopy.root_energy_flux_per_ground_area!","text":"root_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrognosticSoil{F},\n model::Canopy.AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT, F}\n\nA method computing the energy flux associated with the root-soil water flux, which returns 0 in cases where we do not need to track this quantity: in this case, when the canopy energy is tracked, but we are using a PrescribedSoil model (non-prognostic soil model).\n\nNote that this energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\nroot_energy_flux_per_ground_area!(\n fa_energy::ClimaCore.Fields.Field,\n s::PrescribedSoil,\n model::AbstractCanopyEnergyModel{FT},\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n) where {FT}\n\nA method which updates the ClimaCore.Fields.Field fa_energy in place with the energy flux associated with the root-soil water flux for the CanopyModel run in standalone mode, with a PrescribedSoil model.This value is ignored and set to zero in this case.\n\nBackground information: This energy flux is not typically included in land surface models. We account for it when the soil model is prognostic because the soil model includes the energy in the soil water in its energy balance; therefore, in order to conserve energy, the canopy model must account for it as well.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/CanopyEnergy/#Types","page":"Canopy Energy","title":"Types","text":"","category":"section"},{"location":"APIs/canopy/CanopyEnergy/","page":"Canopy Energy","title":"Canopy Energy","text":"ClimaLand.Canopy.AbstractCanopyEnergyModel\nClimaLand.Canopy.PrescribedCanopyTempModel\nClimaLand.Canopy.BigLeafEnergyModel","category":"page"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.PrescribedCanopyTempModel","page":"Canopy Energy","title":"ClimaLand.Canopy.PrescribedCanopyTempModel","text":"PrescribedCanopyTempModel{FT} <: AbstractCanopyEnergyModel{FT}\n\nA model for the energy of the canopy which assumes the canopy temperature is the same as the atmosphere temperature prescribed in the PrescribedAtmos struct.\n\nNo equation for the energy of the canopy is solved.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/CanopyEnergy/#ClimaLand.Canopy.BigLeafEnergyModel","page":"Canopy Energy","title":"ClimaLand.Canopy.BigLeafEnergyModel","text":"BigLeafEnergyModel{FT} <: AbstractCanopyEnergyModel{FT}\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#SurfaceWater","page":"Surface Water Models","title":"SurfaceWater","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"CurrentModule = ClimaLand.Pond","category":"page"},{"location":"APIs/SurfaceWater/#Models","page":"Surface Water Models","title":"Models","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PondModel","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PondModel","page":"Surface Water Models","title":"ClimaLand.Pond.PondModel","text":"PondModel{FT, D, R} <: AbstractSurfaceWaterModel{FT}\n\nA stand-in model for models like the snow or river model. In standalone mode, a prescribed soil infiltration rate and precipitation rate control the rate of change of the pond height variable η via an ODE. In integrated LSM mode, the infiltration into the soil will be computed via a different method, and also be applied as a flux boundary condition for the soil model.\n\ndomain: The domain for the pond model\nrunoff: The runoff model for the pond model\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#Methods-and-Types","page":"Surface Water Models","title":"Methods and Types","text":"","category":"section"},{"location":"APIs/SurfaceWater/","page":"Surface Water Models","title":"Surface Water Models","text":"ClimaLand.Pond.PrescribedRunoff\nClimaLand.Pond.surface_runoff","category":"page"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.PrescribedRunoff","page":"Surface Water Models","title":"ClimaLand.Pond.PrescribedRunoff","text":"PrescribedRunoff{F1 <: Function, F2 <: Function} <: AbstractSurfaceRunoff\n\nThe required input for driving the simple pond model: precipitation, as a function of time, soil effective saturation at a depth Δz below the surface, as a function of time, and soil parameters, which affect infiltration.\n\n\n\n\n\n","category":"type"},{"location":"APIs/SurfaceWater/#ClimaLand.Pond.surface_runoff","page":"Surface Water Models","title":"ClimaLand.Pond.surface_runoff","text":"function Pond.surface_runoff(\n runoff::PrognosticRunoff,\n Y::ClimaCore.Fields.FieldVector,\n p::NamedTuple,\n t,\n)\n\nExtension of the Pond.surface_runoff function, which computes the surface runoff, for use in an LSM when the runoff is determined prognostically.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#Autotrophic-Respiration","page":"Canopy Autotrophic Respiration","title":"Autotrophic Respiration","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#Parameters","page":"Canopy Autotrophic Respiration","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.AutotrophicRespirationParameters","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.AutotrophicRespirationParameters","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.AutotrophicRespirationParameters","text":"AutotrophicRespirationParameters{FT<:AbstractFloat}\n\nThe required parameters for the autrophic respiration model, which is based off of the JULES model. Clark, D. B., et al. \"The Joint UK Land Environment Simulator (JULES), model description–Part 2: carbon fluxes and vegetation dynamics.\" Geoscientific Model Development 4.3 (2011): 701-722.\n\nne: Vcmax25 to N factor (mol CO2 m-2 s-1 kg C (kg C)-1)\nηsl: Live stem wood coefficient (kg C m-3)\nσl: Specific leaf density (kg C m-2 [leaf])\nμr: Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\nμs: Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1\nRel: Relative contribution or Rgrowth (-)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/AutotrophicRespiration/#Methods","page":"Canopy Autotrophic Respiration","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/AutotrophicRespiration/","page":"Canopy Autotrophic Respiration","title":"Canopy Autotrophic Respiration","text":"ClimaLand.Canopy.nitrogen_content\nClimaLand.Canopy.plant_respiration_maintenance\nClimaLand.Canopy.plant_respiration_growth","category":"page"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.nitrogen_content","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.nitrogen_content","text":"nitrogen_content(\n ne::FT, # Mean leaf nitrogen concentration (kg N (kg C)-1)\n Vcmax25::FT, #\n LAI::FT, # Leaf area index\n SAI::FT,\n RAI::FT,\n ηsl::FT, # live stem wood coefficient (kg C m-3) \n h::FT, # canopy height (m)\n σl::FT # Specific leaf density (kg C m-2 [leaf])\n μr::FT, # Ratio root nitrogen to top leaf nitrogen (-), typical value 1.0\n μs::FT, # Ratio stem nitrogen to top leaf nitrogen (-), typical value 0.1 \n ) where {FT}\n\nComputes the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_maintenance","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_maintenance","text":"plant_respiration_maintenance(\n Rd::FT, # Dark respiration\n β::FT, # Soil moisture factor\n Nl::FT, # Nitrogen content of leafs\n Nr::FT, # Nitrogen content of roots\n Ns::FT, # Nitrogen content of stems\n ) where {FT}\n\nComputes plant maintenance respiration as a function of dark respiration (Rd), the nitrogen content of leafs (Nl), roots (Nr) and stems (Ns), and the soil moisture factor (β).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/AutotrophicRespiration/#ClimaLand.Canopy.plant_respiration_growth","page":"Canopy Autotrophic Respiration","title":"ClimaLand.Canopy.plant_respiration_growth","text":"plant_respiration_growth(\n Rel::FT, # Factor of relative contribution\n An::FT, # Net photosynthesis\n Rpm::FT # Plant maintenance respiration\n ) where {FT}\n\nComputes plant growth respiration as a function of net photosynthesis (An), plant maintenance respiration (Rpm), and a relative contribution factor, Rel.\n\n\n\n\n\n","category":"function"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../..\"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Using-atmospheric-and-radiative-drivers","page":"Intro to forced site-level runs","title":"Using atmospheric and radiative drivers","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The goal of this is to outline how to set up simulations driven by prescribed forcing data (``drivers\"). These are grouped into radiative forcing and atmospheric forcing. We will first cover the types of forcing we support, followed by how to specify the driver structs given the forcing data and how to update the values used during a simulation.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Types-of-forcing-data","page":"Intro to forced site-level runs","title":"Types of forcing data","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"We currently support site-level simulations and have two site-level driver types, PrescribedAtmosphere and PrescribedRadiativeFluxes.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The atmosphere driver stores the atmospheric state data as a function of time, including the liquid precipitation rate (m/s), the snow precipitation rate converted into an equivalent rate of liquid water (m/s), the atmopheric pressure (Pa), specific humidity, horizontal wind speed (m/s), temperature (K), CO2 concentration (mol/mol), and the height at which these measurements were taken (currently assumed to be the same value for all variables).","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The radiative fluxes driver stores the data required to specify the radiative forcing. We currently support only a single downwelling shortwave and longwave flux (W/m^2). The radiative driver is also where a function which computes the zenith angle for the site is stored.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Both drivers store the reference time for the data/simulation. This is the DateTime object which corresponds to the time at which t=0 in the simulation. Additionally, for site-level runs, both drivers store the forcing data as a spline function fit to the data which takes the time t as an argument, where t is the simulation time measured in seconds since the reference time. The reference time should be in UTC.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Note: for coupled runs, corresponding types CoupledAtmosphere and CoupledRadiativeFluxes exist. However, these are not defined in ClimaLand, but rather inside of the Clima Coupler repository.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Creating-site-level-drivers-for-radiation","page":"Intro to forced site-level runs","title":"Creating site-level drivers for radiation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"First, assume that we have data stored for the longwave and shortwave radiation at a particular site, and that we have read it in to an array, along with the times at which the observations were made and the latitude and longitude of the site.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"using Dates\nusing Insolation # for computing zenith angle given lat, lon, time.\nusing ClimaLand\nimport ClimaLand.Parameters as LP\nimport ClimaParams","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the local_datetime array is read in from the data file.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"local_datetime = DateTime(2013):Dates.Hour(1):DateTime(2013, 1, 7); # one week, hourly data","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Timezone (offset of local time from UTC in hrs)","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"time_offset = 7;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Site latitude and longitude","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"lat = 38.7441; # degree\nlong = -92.2000; # degree","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Compute the reference time in UTC, and convert local datetime vector into a vector of seconds since the reference time","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"ref_time = local_datetime[1] + Dates.Hour(time_offset);\ndata_dt = 3600.0;\nseconds = 0:data_dt:((length(local_datetime) - 1) * data_dt);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Assume the downwelling long and shortwave radiation are read in from the file and are measured at the times in local_datetime. Here, we'll just make them up periodic on daily timescales:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"T = @. 298.15 + 5.0 * sin(2π * (seconds - 3600 * 6) / (3600 * 24));\nLW_d = 5.67 * 10^(-8) .* T .^ 4;\nSW_d = @. max(1400 * sin(2π * (seconds - 3600 * 6) / (3600 * 24)), 0.0);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Next, fit interpolators to the data. These interpolators are what are stored in the driver function. Then we can evaluate the radiative forcing at any simulation time (and not just at times coinciding with measurements). By default, linear interpolation is used.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"LW_d = TimeVaryingInput(seconds, LW_d)\nSW_d = TimeVaryingInput(seconds, SW_d);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Finally, for many models we also need to specify the function for computing the zenith angle as a function of simulation time. To do so, we use the Insolation package as follows:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"earth_param_set = LP.LandParameters(Float64);\ninsol_params = earth_param_set.insol_params # parameters of Earth's orbit required to compute the insolation\nfunction zenith_angle(\n t,\n ref_time;\n latitude = lat,\n longitude = long,\n insol_params = insol_params,\n)\n current_datetime = ref_time + Dates.Second(round(t)) # Time in UTC\n\n d, δ, η_UTC = (Insolation.helper_instantaneous_zenith_angle(\n current_datetime,\n ref_time,\n insol_params,\n ))\n\n\n return Insolation.instantaneous_zenith_angle(\n d,\n δ,\n η_UTC,\n longitude,\n latitude,\n )[1]\nend;","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"Lastly, we store the interpolators for downwelling fluxes and the zenith angle function in the PrescribedRadiativeFluxes struct.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"radiation = ClimaLand.PrescribedRadiativeFluxes(\n Float64,\n SW_d,\n LW_d,\n ref_time;\n θs = zenith_angle,\n);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/#Updating-the-driver-variables-during-the-simulation","page":"Intro to forced site-level runs","title":"Updating the driver variables during the simulation","text":"","category":"section"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"The values for LWd, SWd, and zenith angle θ_s are stored in the simulation/model cache p under the name drivers. When you initialize the variables and cache of a model, the cache p will be returned with memory allocated but all values set to zero:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p = (; drivers = (LW_d = [0.0], SW_d = [0.0], θs = [0.0]));","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"In order to update them, we can make use of default update functions:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"update_radiation! = ClimaLand.make_update_drivers(radiation)\nt0 = seconds[1] # midnight local time\nupdate_radiation!(p, t0);\n@show(p.drivers);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"p.drivers = (LW_d = [418.7382685853159], SW_d = [0.0], θs = [2.814306258618376])\n","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"During a simulation, the drivers are updated in place in p.drivers via a \"callback\", which is a function which is called a specified times or when certain criteria are met during a simulation. In general, then, we don't update drivers every timestep, but less frequently. For example, the simulation timestep may be 10 minutes, but we may only update the drivers every three hours:","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"updateat = collect(seconds[1]:(3600 * 3):seconds[end]);\nupdatefunc = update_radiation!;\ncb = ClimaLand.DriverUpdateCallback(updateat, updatefunc);","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This callback must then be provided to the simulation solve function.","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"","category":"page"},{"location":"generated/shared_utilities/driver_tutorial/","page":"Intro to forced site-level runs","title":"Intro to forced site-level runs","text":"This page was generated using Literate.jl.","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Photosynthesis","page":"Canopy Photosynthesis","title":"Photosynthesis","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"CurrentModule = ClimaLand.Canopy","category":"page"},{"location":"APIs/canopy/Photosynthesis/#Parameters","page":"Canopy Photosynthesis","title":"Parameters","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.SIFParameters\nClimaLand.Canopy.FarquharParameters\nClimaLand.Canopy.OptimalityFarquharParameters","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.SIFParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.SIFParameters","text":"SIFParameters{FT<:AbstractFloat}\n\nThe required parameters for the SIF parameterisation Lee et al, 2015. Global Change Biology 21, 3469-3477, doi:10.1111/gcb.12948.\n\nkf: The rate coefficient for florescence, unitless\nkd_p1: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkd_p2: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nmin_kd: Parameter used to compute the rate coefficient for heat loss in dark-adapted conditions, Tol et al. 2014, unitless\nkn_p1: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkn_p2: Parameter used to compute the rate coefficient for heat loss in light-adapted conditions, Lee et al 2013 (unitless)\nkp: Rate coefficient for photochemical quenching\nkappa_p1: Slope of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\nkappa_p2: Intercept of line relating leaf-level fluorescence to spectrometer-observed fluorescence as a function of Vcmax 25. Lee et al 2015.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.FarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.FarquharParameters","text":"FarquharParameters{FT<:AbstractFloat, MECH <: AbstractPhotosynthesisMechanism}\n\nThe required parameters for the Farquhar photosynthesis model.\n\nVcmax25: Vcmax at 25 °C (mol CO2/m^2/s)\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercelluar O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Sensitivity to low water pressure, in the moisture stress factor, (Pa^{-1}) [Tuzet et al. (2003)]\npc: Reference water pressure for the moisture stress factor (Pa) [Tuzet et al. (2003)]\nmechanism: Photosynthesis mechanism: C3 or C4\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.OptimalityFarquharParameters","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.OptimalityFarquharParameters","text":"OptimalityFarquharParameters{FT<:AbstractFloat}\n\nThe required parameters for the optimality Farquhar photosynthesis model. Currently, only C3 photosynthesis is supported.\n\nmechanism: Photosynthesis mechanism: C3 only\nΓstar25: Γstar at 25 °C (mol/mol)\nKc25: Michaelis-Menten parameter for CO2 at 25 °C (mol/mol)\nKo25: Michaelis-Menten parameter for O2 at 25 °C (mol/mol)\nΔHkc: Energy of activation for CO2 (J/mol)\nΔHko: Energy of activation for oxygen (J/mol)\nΔHVcmax: Energy of activation for Vcmax (J/mol)\nΔHΓstar: Energy of activation for Γstar (J/mol)\nΔHJmax: Energy of activation for Jmax (J/mol)\nΔHRd: Energy of activation for Rd (J/mol)\nTo: Reference temperature equal to 25 degrees Celsius (K)\noi: Intercellular O2 concentration (mol/mol); taken to be constant\nϕ: Quantum yield of photosystem II (Bernacchi, 2003; unitless)\nθj: Curvature parameter, a fitting constant to compute J, unitless\nf: Constant factor appearing the dark respiration term, equal to 0.015.\nsc: Fitting constant to compute the moisture stress factor (Pa^{-1})\npc: Fitting constant to compute the moisture stress factor (Pa)\nc: Constant describing cost of maintaining electron transport (unitless)\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#Methods","page":"Canopy Photosynthesis","title":"Methods","text":"","category":"section"},{"location":"APIs/canopy/Photosynthesis/","page":"Canopy Photosynthesis","title":"Canopy Photosynthesis","text":"ClimaLand.Canopy.arrhenius_function\nClimaLand.Canopy.intercellular_co2\nClimaLand.Canopy.co2_compensation\nClimaLand.Canopy.rubisco_assimilation\nClimaLand.Canopy.light_assimilation\nClimaLand.Canopy.C3\nClimaLand.Canopy.C4\nClimaLand.Canopy.max_electron_transport\nClimaLand.Canopy.electron_transport\nClimaLand.Canopy.net_photosynthesis\nClimaLand.Canopy.optimality_max_photosynthetic_rates\nClimaLand.Canopy.moisture_stress\nClimaLand.Canopy.dark_respiration\nClimaLand.Canopy.compute_GPP\nClimaLand.Canopy.MM_Kc\nClimaLand.Canopy.MM_Ko\nClimaLand.Canopy.compute_Vcmax","category":"page"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.arrhenius_function","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.arrhenius_function","text":"arrhenius_function(T::FT, To::FT, R::FT, ΔH::FT)\n\nComputes the Arrhenius function at temperature T given the reference temperature To=298.15K, the universal gas constant R, and the energy activation ΔH.\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.intercellular_co2","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.intercellular_co2","text":"intercellular_co2(ca::FT, Γstar::FT, medlyn_factor::FT) where{FT}\n\nComputes the intercellular CO2 concentration (mol/mol) given the atmospheric concentration (ca, mol/mol), the CO2 compensation (Γstar, mol/mol), and the Medlyn factor (unitless).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.co2_compensation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.co2_compensation","text":"co2_compensation(Γstar25::FT,\n ΔHΓstar::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the CO2 compensation point (Γstar), in units of mol/mol, as a function of its value at 25 °C (Γstar25), a constant energy of activation (ΔHΓstar), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.rubisco_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.rubisco_assimilation","text":"rubisco_assimilation(::C3,\n Vcmax::FT,\n ci::FT,\n Γstar::FT,\n Kc::FT,\n Ko::FT,\n oi::FT) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C3 plants (Ac), in units of moles CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax), the leaf internal carbon dioxide partial pressure (ci), the CO2 compensation point (Γstar), and Michaelis-Menten parameters for CO2 and O2, respectively, (Kc) and (Ko).\n\nThe empirical parameter oi is equal to 0.209 (mol/mol). See Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nrubisco_assimilation(::C4, Vcmax::FT,_...) where {FT}\n\nComputes the Rubisco limiting rate of photosynthesis for C4 plants (Ac) in units of moles CO2/m^2/s, as equal to the maximum rate of carboxylation of Rubisco (Vcmax).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.light_assimilation","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.light_assimilation","text":"light_assimilation(::C3,\n J::FT,\n ci::FT,\n Γstar::FT) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C3 plants as a function of the rate of electron transport (J), the leaf internal carbon dioxide partial pressure (ci), and the CO2 compensation point (Γstar).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\nlight_assimilation(::C4, J::FT, _...) where {FT}\n\nComputes the electron transport limiting rate (Aj), in units of moles CO2/m^2/s, for C4 plants, as equal to the rate of electron transport (J).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C3","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C3","text":"C3 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.C4","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.C4","text":"C4 <: AbstractPhotosynthesisMechanism\n\nHelper struct for dispatching between C3 and C4 photosynthesis.\n\n\n\n\n\n","category":"type"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.max_electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.max_electron_transport","text":"max_electron_transport(Vcmax::FT) where {FT}\n\nComputes the maximum potential rate of electron transport (Jmax), in units of mol/m^2/s, as a function of Vcmax at 25 °C (Vcmax25), a constant (ΔHJmax), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.electron_transport","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.electron_transport","text":"electron_transport(APAR::FT,\n Jmax::FT,\n θj::FT,\n ϕ::FT) where {FT}\n\nComputes the rate of electron transport (J), in units of mol/m^2/s, as a function of the maximum potential rate of electron transport (Jmax), absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) and the quantum yield of photosystem II (ϕ). \n\nSee Ch 11, G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.net_photosynthesis","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.net_photosynthesis","text":"net_photosynthesis(Ac::FT,\n Aj::FT,\n Rd::FT,\n β::FT) where {FT}\n\nComputes the total net carbon assimilation (An), in units of mol CO2/m^2/s, as a function of the Rubisco limiting factor (Ac), the electron transport limiting rate (Aj), dark respiration (Rd), and the moisture stress factor (β). \n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.optimality_max_photosynthetic_rates","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.optimality_max_photosynthetic_rates","text":"optimalitymaxphotosynthetic_rates(APAR::FT, θj::FT, ϕ::FT, oi::FT, ci::FT, Γstar::FT, Kc::FT, Ko::FT)\n\nComputes the photosynthesis rates Vcmax and Jmax in mol/m^2/s given absorbed photosynthetically active radiation (APAR), an empirical \"curvature parameter\" (θj; Bonan Eqn 11.21) the quantum yield of photosystem II (ϕ), the intercellular o2 content (oi), the intercellular CO2 concentration (ci), Γstar, and Kc and Ko.\n\nSee Smith et al. 2019.\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.moisture_stress","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.moisture_stress","text":"moisture_stress(pl::FT,\n sc::FT,\n pc::FT) where {FT}\n\nComputes the moisture stress factor (β), which is unitless, as a function of a constant (sc, 1/Pa), a reference pressure (pc, Pa), and the leaf water pressure (pl, Pa) . \n\nSee Eqn 12.57 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.dark_respiration","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.dark_respiration","text":"dark_respiration(Vcmax25::FT,\n β::FT,\n f::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes dark respiration (Rd), in units of mol CO2/m^2/s, as a function of the maximum rate of carboxylation of Rubisco (Vcmax25), and the moisture stress factor (β), an empirical factor f is equal to 0.015, a constant (ΔHRd), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_GPP","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_GPP","text":"compute_GPP(An::FT,\n K::FT,\n LAI::FT,\n Ω::FT) where {FT}\n\nComputes the total canopy photosynthesis (GPP) as a function of the total net carbon assimilation (An), the extinction coefficient (K), leaf area index (LAI) and the clumping index (Ω).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Kc","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Kc","text":"MM_Kc(Kc25::FT,\n ΔHkc::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for CO2 (Kc), in units of mol/mol, as a function of its value at 25 °C (Kc25), a constant (ΔHkc), a standard temperature (To), the unversal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.MM_Ko","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.MM_Ko","text":"MM_Ko(Ko25::FT,\n ΔHko::FT,\n T::FT,\n To::FT,\n R::FT) where {FT}\n\nComputes the Michaelis-Menten coefficient for O2 (Ko), in units of mol/mol, as a function of its value at 25 °C (Ko25), a constant (ΔHko), a standard temperature (To), the universal gas constant (R), and the temperature (T).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"APIs/canopy/Photosynthesis/#ClimaLand.Canopy.compute_Vcmax","page":"Canopy Photosynthesis","title":"ClimaLand.Canopy.compute_Vcmax","text":"compute_Vcmax(Vcmax25::FT,\n T::FT,\n To::FT,\n R::FT,\n ep5::FT) where {FT}\n\nComputes the maximum rate of carboxylation of Rubisco (Vcmax), in units of mol/m^2/s, as a function of temperature (T), Vcmax at the reference temperature 25 °C (Vcmax25), the universal gas constant (R), and the reference temperature (To).\n\nSee Table 11.5 of G. Bonan's textbook, Climate Change and Terrestrial Ecosystem Modeling (2019).\n\n\n\n\n\n","category":"function"},{"location":"getting_started/#Getting-Started","page":"Getting Started","title":"Getting Started","text":"","category":"section"},{"location":"getting_started/#For-Users","page":"Getting Started","title":"For Users","text":"","category":"section"},{"location":"getting_started/#Installation","page":"Getting Started","title":"Installation","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, download and install Julia by following the instructions at https://julialang.org/downloads/. Then, you can install the ClimaLand package by doing:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"julia> ] # Enter Package REPL mode\nPkg> add ClimaLand # Install ClimaLand\nPkg> # Go back to Julia REPL mode\nJulia> using ClimaLand","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"A typical land simulation employs several different parameterizations to model the various land-surface processes. Let's start our journet into ClimaLand by looking at one of those.","category":"page"},{"location":"getting_started/#Parameterization","page":"Getting Started","title":"Parameterization","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Let's start with a basic example: compute canopy gross photosynthesis (GPP).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\n@doc ClimaLand.Canopy.compute_GPP","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"As you can see, our parameterization for GPP is located in the Canopy Module, and requires four arguments. For example, with An = 5 µmol m⁻² s⁻¹, K = 0.5, LAI = 3 m² m⁻², Ω = 0.7, you can compute GPP like below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"import ClimaLand.Canopy as canopy\ncanopy.compute_GPP(5.0, 0.5, 3.0, 0.7)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Et voilà!","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Note that our package ParamViz allows interactive visualisation of our parameterizations. See examples in the standalone models pages.","category":"page"},{"location":"getting_started/#ClimaLand-structure","page":"Getting Started","title":"ClimaLand structure","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ClimaLand contains multiple modules. They are listed below:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using MethodAnalysis, ClimaLand\nchild_modules(ClimaLand)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To explore what modules, functions and types are exported in a particular module, you can use About.jl:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using ClimaLand\nusing About\nabout(ClimaLand.Soil.Biogeochemistry)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To see the documentation about a particular module, function or type, you can use ? to go in help mode in the REPL, or @doc as in Parameterization above.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"EditURL = \"https://github.com/CliMA/ClimaLand.jl/../../../../..\"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/#Seasonal-Snow-Timeseries-Generation-with-a-Neural-Network","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"section"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This tutorial explains how to make use of the code developed for forecasting seasonal snow depth evolution, using a neural network with structurally-enforced constraints. The following steps through a basic use-case of the system on an already-cleaned dataset, though exploration of optional keyword arguments in the developed code and additional tools for scraping data (explained in the data tutorial) provide for a richer set of functionality.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The updates of the neural snow model follow the equation","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"fracdzdt = mathcalMleft(z SWE φ R v T_air P_snowright)","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"where","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"t is the time (s),\nz is the snow depth (m),\nmathcalM is the neural network,\nSWE is the Snow Water Equivalent, or the height of water if all the snow melted (m),\nφ is the relative humidity (0-1),\nR is the solar radiation (W/m²).\nv is the wind speed (W/m²).\nT_air is the air temperature (degrees C).\nP_snow is the water equivalent rate of snow precipitation (m/s).","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The model is a 1D model to permit utilization over any desired grid resolution and shape.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We will use the forcings and snow depth data as a validation of the model, so the initial conditions will be the initial value provided in the existing data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We begin by importing the developed code to create and run the neural network, as well as some preliminary packages:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\nusing DataFrames, CSV, HTTP, Dates, Flux, StatsBase, cuDNN","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"┌ Warning: Package cuDNN not found in current path.\n│ - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia.\n│ - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU.\n└ @ FluxCUDAExt ~/.julia/packages/Flux/HBF2N/ext/FluxCUDAExt/FluxCUDAExt.jl:57\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"The code lives in an extenson that we have to manually load. The extension can be loaded only if \"CSV\", \"HTTP\", \"Flux\", \"StatsBase\", \"cuDNN\" and \"ClimaLand\" are loaded.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"DataTools = Base.get_extension(ClimaLand, :NeuralSnowExt).DataTools\nModelTools = Base.get_extension(ClimaLand, :NeuralSnowExt).ModelTools;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"and also, for this tutorial, some purpose-made functions for displaying the output. A similar analysis_tools.jl file exists alongside display_tools.jl for some basic functions for analyzing/scoring the model, if desired.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"using ClimaLand\ncode_dir = joinpath(pkgdir(ClimaLand), \"docs/tutorials/standalone/Snow\")\ninclude(joinpath(code_dir, \"display_tools.jl\"));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Next, we set up values of the network hyperparameters, including the number of epochs to train it, as well as the width parameter n as outlined in the associated parameter, and the two loss function hyperparameters n_1, n_2.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"n = 4\nn1 = 2\nn2 = 4;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next outline which variables in the dataset will be used as predictors, calling them by their column name as a Symbol. The number and choice of these can be changed to reflect any dataset. Another column is specified as the target variable, in this case, the fracdzdt column.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"pred_vars = [\n :z,\n :SWE,\n :rel_hum_avg,\n :sol_rad_avg,\n :wind_speed_avg,\n :air_temp_avg,\n :dprecipdt_snow,\n]\ntarget = :dzdt;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Specifying the indices of the depth and precipitation variables (used in the constraints) and the total number of input features will be necessary when creating the model, so we will specify them here as well.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"nfeatures = length(pred_vars)\nz_idx = 1\np_idx = 7;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We next read in the already-cleaned training dataset, though for custom datasets there is plenty of functionality provided in the DataTools module to scrape SNOTEL data directly. We also set the unit timestep seen in this data (daily, so 1 day) to be used for setting the network's constraints as well as generating timeseries during usage. To see the code that generated this data file, check out the data tutorial. We also specify the maximum gap size in the data (in units of Δt) that the network can traverse before requiring a reset, via hole_thresh.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"training_data_download_link = \"https://caltech.box.com/shared/static/1gfyh71c44ljzb9xbnza3lbzj6p9723x.csv\"\ntesting_data_download_link = \"https://caltech.box.com/shared/static/qb2ze1wcc1a37fgt5k9wsj27gpoh39ax.csv\"\ndata_train = CSV.read(HTTP.get(training_data_download_link).body, DataFrame)\nvaldata = CSV.read(HTTP.get(testing_data_download_link).body, DataFrame)\nΔt = Second(86400)\nhole_thresh = 5;","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With this, we can begin the actual usage pipeline. First, we split the precipitation feature into rain and snow constituents, and apply a set of filters before extracting the necessary features with prep_data (the split already exists in the testing data):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"usedata = DataTools.prep_data(data_train);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"After this, we determine scalings for the input and target data that are conducive to beneficial weight updates. In this case, the target data during training will be scaled in the -1 to 1 range, and the neural network will scale input features according to their standard deviations (no shifting is carried out in this case, so that the physical meaning of \"0\" is preserved). This data is then converted into matrix form for ease of its conversion into a Flux DataLoader object, later, during training.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"out_scale = maximum(abs.(usedata[!, target]))\nin_scales = std.(eachcol(select(usedata, pred_vars)))\nx_train, y_train = DataTools.make_data(usedata, pred_vars, target, out_scale);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"We then create the model itself given the hyperparameters specified above, and indicate which features are to be used to determine the boundary constraints on the network, and return the trainable weights for the overall model.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"model = ModelTools.make_model(nfeatures, n, z_idx, p_idx, in_scale = in_scales)\nps = ModelTools.get_model_ps(model);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"As training updates are better with the scaled data, we have to modify the timescale and output scaling of the model structure prior to training. This step is undone/reset after training is over.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.settimescale!(model, Dates.value(Δt) * out_scale)\nModelTools.setoutscale!(model, 1.0);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"With that, training is as simple as calling the trainmodel! function:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"print(\"\\nTraining model!\\n\")\nModelTools.trainmodel!(model, ps, x_train, y_train, n1, n2, verbose = true);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"\nTraining model!\nEpoch: 10 | training loss: 0.0028616574\nEpoch: 20 | training loss: 0.0029524094\nEpoch: 30 | training loss: 0.0027160626\nEpoch: 40 | training loss: 0.0026204009\nEpoch: 50 | training loss: 0.0026128984\nEpoch: 60 | training loss: 0.0025816862\nEpoch: 70 | training loss: 0.0026889986\nEpoch: 80 | training loss: 0.002563585\nEpoch: 90 | training loss: 0.0026516444\nEpoch: 100 | training loss: 0.0025791847\n","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"To show the model's output on some of our training data in physically meaningful units, we first reset the timesacle and output scaling constants. From there, all we do is pass the dataframe for a given SNOTEL site and the trained model to the make_timeseries function, and we can compare the result to the actual data.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"ModelTools.setoutscale!(model, out_scale)\nModelTools.settimescale!(model, Dates.value(Δt));","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"For instance, let's show the results on SNOTEL site 1286 (Slagamount Lakes site, Montana):","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Note that gaps in the data are shown as shaded regions on the plotted timeseries.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = 1286\nsitedata = usedata[usedata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Slagamount Lakes, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot1.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Or, alternatively, SNOTEL site 1070 (Anchorage Hillside, Alaska) from the testing data:","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"site_id = \"1070\" #string format for the testing ids is due to non-numerical testing site codes.\nsitedata = valdata[valdata[!, :id] .== site_id, :]\ntrue_series = sitedata[!, :z]\npred_series, _, _ =\n ModelTools.make_timeseries(model, sitedata, Δt, hole_thresh = hole_thresh)\nptitle = \"Anchorage Hillside, Snow Depth (m)\"\nsiteplot(\n ptitle,\n sitedata[!, :date],\n [true_series, pred_series],\n [\"Data\", \"Neural Model\"],\n [:black, :red],\n savename = \"base_tutorial_plot2.png\",\n display_plot = false,\n);","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"(Image: )","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"Additional functionality can be explored through the optional arguments to the developed functions, though creating timeseries for any validation dataset can be handled with a similar call to make_timeseries. The timestep Δt (as well as a matching call to the network with settimescale!) can also be changed to different values to evaluate the network's capability on validation data with different temporal resolutions, without the need for retraining.","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"","category":"page"},{"location":"generated/standalone/Snow/base_tutorial/","page":"Seasonal Snow Timeseries Generation with a Neural Network","title":"Seasonal Snow Timeseries Generation with a Neural Network","text":"This page was generated using Literate.jl.","category":"page"}] +} diff --git a/previews/PR754/siteinfo.js b/previews/PR754/siteinfo.js new file mode 100644 index 0000000000..1b1778931c --- /dev/null +++ b/previews/PR754/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR754"; diff --git a/previews/PR754/standalone/Project.toml b/previews/PR754/standalone/Project.toml new file mode 100644 index 0000000000..e0874fbf1e --- /dev/null +++ b/previews/PR754/standalone/Project.toml @@ -0,0 +1,7 @@ +[deps] +Bonito = "824d6782-a2ef-11e9-3a09-e5662e0c26f8" +ClimaLand = "08f4d4ce-cf43-44bb-ad95-9d2d5f413532" +ClimaLandSimulations = "348a0bd3-1299-4261-8002-d2cd97df6055" +ClimaParams = "5c42b081-d73a-476f-9059-fd94b934656c" +ParamViz = "9706fb73-1de9-418d-83da-fef8cd99bb4a" +Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" diff --git a/previews/PR754/standalone/README/index.html b/previews/PR754/standalone/README/index.html new file mode 100644 index 0000000000..da8cd23ad8 --- /dev/null +++ b/previews/PR754/standalone/README/index.html @@ -0,0 +1,2 @@ + +DynamicDocs · ClimaLand.jl
GitHub
diff --git a/previews/PR754/standalone/apps.jl b/previews/PR754/standalone/apps.jl new file mode 100644 index 0000000000..cdb1002497 --- /dev/null +++ b/previews/PR754/standalone/apps.jl @@ -0,0 +1,47 @@ +# Load packages +using ParamViz +using Bonito +using ClimaLand +using ClimaLand.Canopy +using ClimaLand.Soil.Biogeochemistry +import ClimaParams as CP +import ClimaLand.Parameters as LP +using ClimaLandSimulations +FT = Float64 +earth_param_set = LP.LandParameters(FT) +RTparams = BeerLambertParameters(FT) + +# Create server +IPa = "127.0.0.1" +port = 9385 +server = Server( + IPa, + port; + proxy_url = "https://clima.westus3.cloudapp.azure.com/jsserve/", +) + +# Load and create apps +include("apps/leaf_an.jl"); +An_app = An_app_f(); +An_app = An_app_f(); # need to run twice for unicode character... (bug) +include("apps/beer.jl"); +beer_app = Beer_app_f(); +beer_app = Beer_app_f(); +include("apps/hetero_resp.jl"); +Rh_app = Rh_app_f(); +Rh_app = Rh_app_f(); +include("apps/leaf_an_ci.jl"); +An_ci_app = An_ci_app_f(); +An_ci_app = An_ci_app_f(); +# Fluxnet DashBoard +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); +fluxnet_app = ClimaLandSimulations.Dashboards.fluxnet_app(); + +# Route apps +route!(server, "/leaf_An" => An_app) +route!(server, "/beer_APAR" => beer_app) +route!(server, "/leaf_An_ci" => An_ci_app) +route!(server, "/Rh" => Rh_app) +route!(server, "/fluxnet" => fluxnet_app) + +wait() diff --git a/previews/PR754/standalone/apps/beer.jl b/previews/PR754/standalone/apps/beer.jl new file mode 100644 index 0000000000..266f3b0da7 --- /dev/null +++ b/previews/PR754/standalone/apps/beer.jl @@ -0,0 +1,35 @@ +using Unitful: m, s, mol, μmol + +function ParamViz.parameterisation(PAR, LAI, ρ_leaf, K, Ω, a, b) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + return APAR +end + +function Beer_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "LAI (m² m⁻²)"), + (FT.([0, 1500 * 1e-6]), FT.([0, 10])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (m^2 * m^-2, m^2 * m^-2)), + ) + + parameters = Parameters( + ( + "canopy reflectance, ρ_leaf", + "extinction coefficient, K", + "clumping index, Ω", + ), + (FT.([0, 1]), FT.([0, 1]), FT.([0, 1])), + ((m, m), (m, m), (m, m)), # dummy units, no conversion + ) + + constants = Constants(("a", "b"), (FT(1), FT(2))) # dummy constants + inputs = Inputs(drivers, parameters, constants) + output = Output( + "APAR (μmol m⁻² s⁻¹)", + [0, 1500 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + beer_app = webapp(ParamViz.parameterisation, inputs, output) + return beer_app +end diff --git a/previews/PR754/standalone/apps/hetero_resp.jl b/previews/PR754/standalone/apps/hetero_resp.jl new file mode 100644 index 0000000000..0a6ef56fe6 --- /dev/null +++ b/previews/PR754/standalone/apps/hetero_resp.jl @@ -0,0 +1,92 @@ +using Unitful: K, °C, mol, μmol, m, s, kg, J + +function ParamViz.parameterisation( + Tₛ, + θ, # drivers + ν, + α_sx, + Ea_sx, + kM_sx, + kM_O₂, + O₂_a, + p_sx, + Csom, # parameters + θ_a100, + D_liq, + D_oa, +) # constants + + params = SoilCO2ModelParameters(FT; ν) + # θ has to be lower than porosity + if θ > ν + θ = ν + end + + Rh = microbe_source(Tₛ, θ, Csom, params) + return Rh +end + +function Rh_app_f() + drivers = Drivers( + ("Tₛ (°C)", "θ (m³ m⁻³)"), # drivers.names + (FT.([273, 323]), FT.([0.0, 1.0])), # drivers.ranges + ((K, °C), (m^3 * m^-3, m^3 * m^-3)), + ) + + parameters = Parameters( + (# names + "Soil porosity, ν (m³ m⁻³)", + "Pre-exponential factor, α_sx (kg C m⁻³ s⁻¹)", + "Activation energy, Ea_sx (J mol⁻¹)", + "Michaelis constant for soil, kM_sx (kg C m⁻³)", + "Michaelis constant for O₂, kM_O₂ (m³ m⁻³)", + "Volumetric fraction of O₂ in the soil air, O₂_a (dimensionless)", + "Fraction of soil carbon that is considered soluble, p_sx (dimensionless)", + "Soil organic C, Csom (kg C m⁻³)", + ), + (# ranges + FT.([0.0, 1.0]), # porosity + FT.([100e3, 300e3]), # α_sx + FT.([50e3, 70e3]), # Ea_sx + FT.([1e-10, 0.1]), # kM_sx + FT.([1e-10, 0.1]), # kM_o2 + FT.([0.005, 0.5]), # O2_a + FT.([0.005, 0.5]), # p_sx + FT.([1.0, 10.0]), # Csom + ), + ( + (m^3 * m^-3, m^3 * m^-3), # porosity + (kg * m^-3 * s^-1, kg * m^-3 * s^-1), # α_sx + (J * mol^-1, J * mol^-1), # Ea_sx + (kg * m^-3, kg * m^-3), # kM_sx + (m^3 * m^-3, m^3 * m^-3), # kM_O2 + (m, m), # O2_a + (m, m), # p_sx + (kg * m^-3, kg * m^-3), # Csom + ), + ) + + constants = Constants( + (# names + "Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)", + "Diffusivity of soil C substrate in liquid (unitless)", + "Diffusion coefficient of oxygen in air, dimensionless", + ), + (# values + FT(0.1816), # θ_a100 + FT(3.17), # D_liq + FT(1.67), # D_oa + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "Rh (mg C m⁻³ s⁻¹)", # name #NEED TO CONVERT THE UNIT + [0, 20 * 1e-6], # range + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), # unit from, unit to --> actually need to fix this, should be g to mg C m-3 s-1 + ) + + Rh_app = webapp(ParamViz.parameterisation, inputs, output) + return Rh_app +end diff --git a/previews/PR754/standalone/apps/leaf_an.jl b/previews/PR754/standalone/apps/leaf_an.jl new file mode 100644 index 0000000000..5960996a6c --- /dev/null +++ b/previews/PR754/standalone/apps/leaf_an.jl @@ -0,0 +1,137 @@ +using Unitful: K, °C, mol, μmol, m, s, Pa, kPa + +function ParamViz.parameterisation( + PAR, + T, + β, + LAI, + ca, + VPD, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + Vcmax25, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + g1, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + medlynt = 1 + g1 / sqrt(VPD) + ci = intercellular_co2(ca, Γstar, medlynt) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_app_f() + drivers = Drivers( + ("PAR (μmol m⁻² s⁻¹)", "T (°C)"), + (FT.([0, 1500 * 1e-6]), FT.([273, 323])), + ((mol * m^-2 * s^-1, μmol * m^-2 * s^-1), (K, °C)), + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "CO2 concentration, ca (ppm)", + "Vapor pressure deficit, VPD (Pa)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([300 * 1e-6, 500 * 1e-6]), # ca + FT.([500, 10000]), # VPD, Pa + ), + ( + (m, m), # dummy units, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol / mol, μmol / mol), + (Pa, kPa), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "Vcmax25", # the maximum rate of carboxylation of Rubisco + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "g1", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd", + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(5e-5), # Vcmax25 - ozark model + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(141), # g1 - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + + An_app = webapp(ParamViz.parameterisation, inputs, output) + return An_app +end diff --git a/previews/PR754/standalone/apps/leaf_an_ci.jl b/previews/PR754/standalone/apps/leaf_an_ci.jl new file mode 100644 index 0000000000..351338a131 --- /dev/null +++ b/previews/PR754/standalone/apps/leaf_an_ci.jl @@ -0,0 +1,127 @@ +using Unitful: K, °C, mol, μmol, m, s + +function ParamViz.parameterisation( + ci, + T, + β, + LAI, + PAR, + Vcmax25, + θs, + ld, + ρ_leaf, + Ω, + Γstar25, + ΔHΓstar, + To, + R, + ΔHJmax, + θj, + ϕ, + ΔHVcmax, + Kc25, + ΔHkc, + Ko25, + ΔHko, + oi, + f, + ΔHRd, +) + K = extinction_coeff(RTparams.G_Function, θs) + # APAR = plant_absorbed_ppfd(PAR, ρ_leaf, K, LAI, Ω) + APAR = PAR * (1 - ρ_leaf) * (1 - exp(-K * LAI * Ω)) # not sure how to call new plant_absorbed_pfd + Jmax = max_electron_transport(Vcmax25, ΔHJmax, T, To, R) + J = electron_transport(APAR, Jmax, θj, ϕ) + Vcmax = compute_Vcmax(Vcmax25, T, To, R, ΔHVcmax) + Γstar = co2_compensation(Γstar25, ΔHΓstar, T, To, R) + Kc = MM_Kc(Kc25, ΔHkc, T, To, R) + Ko = MM_Ko(Ko25, ΔHko, T, To, R) + Ac = rubisco_assimilation(Canopy.C3(), Vcmax, ci, Γstar, Kc, Ko, oi) + Aj = light_assimilation(Canopy.C3(), J, ci, Γstar) + Rd = dark_respiration(Vcmax25, β, f, ΔHRd, T, To, R) + An = net_photosynthesis(Ac, Aj, Rd, β) + return An +end + +function An_ci_app_f() + drivers = Drivers( + ("ci (ppm)", "T (°C)"), # names + (FT.([0.0001, 0.001]), FT.([273, 323])), # ranges + ((mol / mol, μmol / mol), (K, °C)), # units from, unit to + ) + + parameters = Parameters( + ( + "Moisture stress, β", + "Leaf area index, LAI (m² m⁻²)", + "Photosynthetic radiation, PAR (μmol m⁻² s⁻¹)", + "Vcmax25, (μmol m⁻² s⁻¹)", + ), + ( + FT.([0, 1]), # β + FT.([1, 10]), # LAI, m2 m-2 + FT.([0, 1500 * 1e-6]), # PAR + FT.([1e-5, 1e-4]), # Vcmax25 + ), + ( + (m, m), # dummy unit, no conversion + (m^2 * m^-2, m^2 * m^-2), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ), + ) + + constants = Constants( + ( + "θs", # Sun zenith angle + "ld", # Leaf angle distribution + "ρ_leaf", # PAR canopy reflectance + "Ω", # Clumping index + "Γstar25", # co2 compensation at 25c + "ΔHΓstar", # a constant energy of activation + "To", # a standard temperature + "R", # the universal gas constant + "ΔHJmax", # a constant + "θj", # an empirical "curvature parameter" + "ϕ", # the quantum yield of photosystem II + "ΔHVcmax", # a constant + "Kc25", # a constant + "ΔHkc", # a constant + "Ko25", # a constant + "ΔHko", # a constant + "oi", # an empirical parameter + "f", # an empirical factor + "ΔHRd",# a constant + ), + ( + FT(0.6), # θs - is that a good value? in radian, right? -------- + FT(0.5), # ld - ozark val + FT(0.1), # ρ_leaf - ozark val + FT(0.69), # Ω - ozark val + FT(4.275e-5), # Γstar25 - ozark val + FT(37830), # ΔHΓstar - ozark val + FT(298.15), # To - ozark vak + FT(8.314), # R, J/mol - FT(LSMP.gas_constant(earth_param_set)) + FT(43540), # ΔHJmax - ozark model + FT(0.9), # θj - ozark model + FT(0.6), # ϕ - ozark model + FT(58520), # ΔHVcmax - ozark model + FT(4.049e-4), # Kc25 - ozark model + FT(79430), # ΔHkc - ozark model + FT(0.2874), # Ko25 - ozark model + FT(36380), # ΔHko - ozark model + FT(0.209), # oi - ozark model + FT(0.015), # f - ozark model + FT(43390), # ΔHRd - ozark model + ), + ) + + inputs = Inputs(drivers, parameters, constants) + output = Output( + "An (μmol m⁻² s⁻¹)", + [0, 20 * 1e-6], + (mol * m^-2 * s^-1, μmol * m^-2 * s^-1), + ) + An_ci_app = webapp(ParamViz.parameterisation, inputs, output) + return An_ci_app +end diff --git a/previews/PR754/standalone/pages/snow/snow_model/index.html b/previews/PR754/standalone/pages/snow/snow_model/index.html new file mode 100644 index 0000000000..de60b2c77c --- /dev/null +++ b/previews/PR754/standalone/pages/snow/snow_model/index.html @@ -0,0 +1,2 @@ + +Snow model · ClimaLand.jl
GitHub
diff --git a/previews/PR754/standalone/pages/soil/biogeochemistry/DAMM_model/index.html b/previews/PR754/standalone/pages/soil/biogeochemistry/DAMM_model/index.html new file mode 100644 index 0000000000..af59710d06 --- /dev/null +++ b/previews/PR754/standalone/pages/soil/biogeochemistry/DAMM_model/index.html @@ -0,0 +1,18 @@ + +DAMM model · ClimaLand.jl
GitHub

Microbial respiration

This section describes multiple models of soil organic decomposition by microbes, implemented in ClimaLand.

Dual Arrhenius Michaelis-Menten

The Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model in ClimaLand.jl follows Davidson et al. 2012. DAMM models heterotrophic respiration ($Rh$) as a function of soil temperature ($T_s$) and soil moisture ($\theta$).

The rate of respiration, $Rh$, is expressed as:

\[\begin{equation} + Rh = V_\text{maxs_x}MM_{s_x}MM_{O_2} +\end{equation}\]

where $V_\text{max}{s_x}$ is the maximum potential rate of respiration, $MM_{s_x}$ represents the availability of substrate, and $MM_{O_2}$ is used as the oxygen limitation factor. $MM_{s_x}$ and $MM_{O_2}$ are between 0 (limiting) and 1 (non limiting). $V_\text{max}{s_x}$, $MM_{s_x}$, and $MM_{O_2}$ are expressed as:

\[\begin{equation} + V_\text{max}{s_x} = \alpha_{s_x} \exp(\frac{-Ea_{s_x}}{RT_s}) +\end{equation}\]

\[\begin{equation} + MM_\text{sx} = \frac{[s_x]}{kM_{s_x}+[s_x]} +\end{equation}\]

\[\begin{equation} + MM_\text{O_2} = \frac{[O_2]}{kM_{O_2}+[O_2]} +\end{equation}\]

where $\alpha_{s_x}$ is the pre-exponential factor, $Ea_{s_x}$ is the activation energy of the reaction, $R$ is the gas constant, and $T_s$ is soil temperature. $[s_x]$ is the concentration of all soluble substrate, and $[O_2]$ is the oxygen concentration. $kM_{s_x}$ and $kM_{O_2}$ are the Michaelis constant for soil and oxygen, respectively.

The concentration of soluble carbon substrates is affected by soil water content, and specifically by diffusion of substrates through soil water films. Using these underlying principles, $[s_x]$ is calculated as:

\[\begin{equation} + [s_x] = p_{s_x}\times[C_{som}]\times D_{liq}\times\theta^3 +\end{equation}\]

where $[C_{som}]$ is the total amount of soil organic carbon, and $p_{s_x}$ is the fraction of $[C_{som}]$ that is soluble. $D_{liq}$ is the diffusion coefficient of the soluble carbon. $\theta$ is soil moisture.

The concentration of $O_2$ depends on the diffusion of gases within the soil, which is calculated as below:

\[\begin{equation} + [O_2] = D_{Oa}\times O_{2a} \times porosity_{air}^{4/3} +\end{equation}\]

where $D_{Oa}$ is the diffusion coefficient for $O_2$ in air, $O_{2a}$ is the volume fraction of $O_2$ in air, and $porosity_{air}$ is the air-filled porosity.

The air-filled porosity is calculated by subtracting the soil moisture from the total porosity ($\nu$):

\[\begin{equation} + porosity_{air} = \nu - \theta +\end{equation}\]

To sum up, the model has the following parameters:

OutputSymbolUnitRange
Heterotrophic respirationRh$\mu$mol $m^{-2}$ $s^{-2}$0–25
DriversSymbolUnitRange
Soil temperature$T_s$$°C$-20–50
Soil moisture$\theta$$m^3$ $m^{-3}$0.0–1.0
ParametersSymbolUnitRange
Soil porosity$\nu$$m^3$ $m^{-3}$0.0–1.0
Pre-exponential factor$\alpha_{s_x}$kg C $m^{-3}$ $s^{-1}$100e3–300e3
Activation energy$Ea_{s_x}$J$mol^{-1}$50e3–70e3
Michaelis constant for soil$kM_{s_x}$kg C $m^{-3}$1e-10–0.1
Michaelis constant for $O_2$$kM_{O_2}$$m^3$ $m^{-3}$1e-10–0.1
Volumetric fraction of $O_2$ in the soil air content$O_{2_a}$-0.005–0.5
Fraction of soil carbon that is considered soluble$p_{s_x}$-0.005–0.5
Soil organic C$C_{som}$kg C $m^{-3}$1.0–10.0
ConstantsSymbolUnitValue
Air-filled porosity at soil water potential of -100 cm H₂O (~ 10 Pa)$O_{a100}$-0.1816
Diffusivity of soil C substrate in liquid$D_{liq}$-3.17
Diffusion coefficient of oxygen in air$D_{Oa}$-1.67
diff --git a/previews/PR754/standalone/pages/soil/energy/energy_model/index.html b/previews/PR754/standalone/pages/soil/energy/energy_model/index.html new file mode 100644 index 0000000000..5984449096 --- /dev/null +++ b/previews/PR754/standalone/pages/soil/energy/energy_model/index.html @@ -0,0 +1,2 @@ + +Energy model · ClimaLand.jl
GitHub
diff --git a/previews/PR754/standalone/pages/soil/hydrology/richards_model/index.html b/previews/PR754/standalone/pages/soil/hydrology/richards_model/index.html new file mode 100644 index 0000000000..67c066c7f6 --- /dev/null +++ b/previews/PR754/standalone/pages/soil/hydrology/richards_model/index.html @@ -0,0 +1,2 @@ + +Richards model · ClimaLand.jl
GitHub
diff --git a/previews/PR754/standalone/pages/surface_water/surface_water_model/index.html b/previews/PR754/standalone/pages/surface_water/surface_water_model/index.html new file mode 100644 index 0000000000..7474cb1530 --- /dev/null +++ b/previews/PR754/standalone/pages/surface_water/surface_water_model/index.html @@ -0,0 +1,2 @@ + +Surface water model · ClimaLand.jl
GitHub
diff --git a/previews/PR754/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html b/previews/PR754/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html new file mode 100644 index 0000000000..e4d858b497 --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/photosynthesis/farquhar_model/index.html @@ -0,0 +1,36 @@ + +Farquhar model · ClimaLand.jl
GitHub

Farquhar Model

This section breaks down the Farquhar model that describes the biochemical process of photosynthesis in plants as environmental conditions change.

The biochemical processes within a leaf determine the rate of photosynthesis, particularly the diffusion of CO$_2$ into the leaf, the assimilation of CO$_2$ during photosynthesis, and the transpiration of water vapor. It takes into account factors such as light intensity, temperature, and CO$_2$ concentration to estimate the rate at which plants convert light energy into chemical energy through photosynthesis.

The net assimilation by a leaf (An) is calculated based on the biochemistry of C3 and C4 photosynthesis to determine potential (unstressed by water availability) leaf-level photosynthesis. This is calculated in terms of two potentially-limiting rates:

An vs. air Temperature (T, °C) and Photosynthetically Active Radiation (PAR, μmol m⁻² s⁻¹)

An vs. air Temperature (T, °C) and intra-cellular CO2 (ci, ppm)

Rubisco limited rate

\[\begin{equation} +a_1(T, c_a, VPD) = +\begin{cases} + V_{cmax}(T) \frac{(c_i(T, c_a, VPD) - \Gamma^*(T))}{(c_i(T, c_a, VPD) + K_c(T)*(1+o_i/K_o(T)))} & \text{for C3}\\ + V_{cmax}(T) & \text{for C4} +\end{cases} +\end{equation}\]

The dependence on the atmospheric CO$_2$ concentration $c_a$ (mol/mol) and vapor pressure deficit $VPD$ arise in the expression for $c_i$,

\[\begin{align} + c_i(T, c_a, VPD) = \max{(c_a(1-1/m(VPD)), \Gamma^*(T)}), +\end{align}\]

where and $m$ is the Medlyn factor (see Stomatal Conductance).

We also have

\[ \Gamma^*(T) = \Gamma^*_{25}\exp\left(\Delta H_{\Gamma^*}\frac{T - T_o}{T_o R T}\right),\]

where $\Delta H_{\Gamma^*}$ is the activation energy per mol for $\Gamma^*$.

Light limited rate

\[\begin{equation} +a_2 = +\begin{cases} + J(T, PAR) (c_i - \Gamma^*)/4(c_i + 2 \Gamma^*) & \text{for C3}\\ + J(T, PAR) & \text{for C4} +\end{cases} +\end{equation}\]

where J is the rate of electron transport, which has units of mol photon per m$^2$ per s. It depends on $PAR$ via $APAR$, as described below, and on $T$ via the dependence on $J_{max}$.

J is given by the root of the equation

\[\begin{align} + \theta_j J^2 - (I + J_{max}) J + I J_{max} &= 0 \nonumber \\ + I &= \frac{\phi}{2} (APAR) \nonumber \\ + J_{max}(T) &= V_{cmax}(T)\times e \exp\left(\Delta H_{J_{max}}\frac{T - T_o}{T_o R T}\right),\nonumber \\ +J(T, PAR) &= \frac{(I + J_{max} - \sqrt{(I + J_{max})^2 - 4\theta_j I \times J_{max}}}{2\theta_j}, +\end{align}\]

where $\phi = 0.6$ and $\theta_j = 0.9$ are the quantum yield of photosystem II and a curvature function (Bonan's book), and $\Delta H_{J_{max}}$ is the energy of activation of $J_{max}$.

The total net carbon assimilation (A$_n$, mol CO$_2$ m$^{-2}$ s$^{-1}$) is given by the weighted sum of C3 and C4 net carbon assimilation fractions following:

\[\begin{align} +A_n(T, PAR, VPD, c_a) = \text{max}(0, \text{min}(a_1 \beta, a_2) - R_d) +\end{align}\]

where $\beta$ is the moisture stress factor which is related to the mean soil moisture concentration in the root zone and R$_d$ is the leaf dark respiration calculated as

\[\begin{align} + R_{d,25}(\psi_l) &= f V_{cmax,25}\beta(\psi_l), \nonumber \\ + R_d (T, \psi_l) & = R_{d,25}(\psi_l)\exp\left(\Delta H_{R_{d}}\frac{T - T_o}{T_o R T}\right), +\end{align}\]

where $f = 0.015$ is a constant, $\Delta H_{R_d}$ is the energy of activation for $R_d$, and finally Vcmax is calculated as

\[\begin{equation} +V_{cmax}(T) = V_{cmax,25} \exp\left(\Delta H_{Vcmax}\frac{T - T_o}{T_o R T}\right)\\ +\end{equation}\]

with $V_{cmax,25}$ is a parameter (Vcmax at the reference temperature 25 C), and $\Delta H_{Vcmax} = 65,330 J/mol$.

The moisture stress factor is related to the leaf water potential $\psi_l$ as

\[\begin{align} + \beta = \frac{1+ \exp{(s_c \psi_c)}}{1+ \exp{(s_c(\psi_c - \psi_l))}}, +\end{align}\]

where $s_c = 4$MPa$^{-1}$, $\psi_c = -2$MPa, and $\psi_l$ is the leaf water potential computed by the plant hydraulics model.

GPP is the total canopy photosynthesis calculated as the integral of leaf-level photosynthesis over the entire canopy leaf area index:

\[\begin{align} +GPP(T, PAR, c_a, VPD, \theta_s) = A_n (1 - \exp(-K LAI \Omega))/K. +\end{align}\]

This is not currently needed by other components, but is used for offline validation of the model.

We need to supply the following parameters and “drivers"

  • $K_{c,25}$ and $K_{o,25}$, $V_{cmax, 25}$, $\Gamma^*_{25},\phi$, $\theta_j$, $o_i$, $s_c$, $\psi_c$
  • $\psi_l$, to compute $\beta$
  • Temperature $T$, $PAR$, $c_a$, VPD, $\theta_s$.
OutputSymbolUnitRange
Total net carbon assimilation$A_n$μmol CO$_2$ m$^{-2}$ s$^{-1}$0–25
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Temperature$T$°C0–50
ParametersSymbolUnitRange
Moisture stress$β$-0-1
Leaf Area IndexLAIm² m⁻²1–10
$CO_2$ concentration$c_a$ppm300e–500
Vapor pressure deficitVPDkPa1-10
ConstantsSymbolUnitValue
Zenith angle$θ_s$rad0.6
Leaf angle distribution$l_d$-0.5
Canopy reflectance$ρ_{leaf}$-0.1
Clumping index$Ω$-0.69
$CO_2$ compensation at 25°CΓ$^*_{25}$mol/mol4.275e-5
Energy of activation for $Γ^*$$ΔH_{Γ^*}$J/mol37830
Standard temperature$T_o$K298.15
Universal gas constant$R$J/mol8.314
The maximum rate of carboxylation of Rubisco$V_{cmax25}$mol CO$_2$ m$^{-2}$ s$^{-1}$5e-5
Energy of activation for $J_max$$ΔH_{J_max}$J/mol43540
Curvature parameter, a fitting constant to compute $J$$θ_j$-0.9
The quantum yied of photosystem II$\phi$-0.6
Energy of activation for $V_{cmax}$$ΔH_{V_{cmax}}$J/mol58520
Slope parameter for stomatal conductance models$g_1$-141
Michaelis Menten constant for $CO_2$ and at 25°C$K_{c25}$mol/mol4.049e-4
Energy of activation for $CO_2$$ΔH_{K_c}$J/mol79430
Michaelis Menten constant for $O_2$ at 25 °C$K_{o25}$mmol/mol0.2874
Energy of activation for $O_2$$ΔH_{K_o}$J/mol36380
Intercellular $O_2$ concentration$o_i$mol/mol0.209
Constant factor appearing the dark respiration term$f$-0.015
Energy of activation for $R_d$$ΔH_{R_d}$J/mol43390
diff --git a/previews/PR754/standalone/pages/vegetation/photosynthesis/optimality_model/index.html b/previews/PR754/standalone/pages/vegetation/photosynthesis/optimality_model/index.html new file mode 100644 index 0000000000..dc347201f4 --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/photosynthesis/optimality_model/index.html @@ -0,0 +1,24 @@ + +Optimality model · ClimaLand.jl
GitHub

Optimality Model

Photosynthetic coordination theory, originally proposed by Von Caemmerer & Farquhar (1981), provides an approach to predict dynamic responses of photosynthetic capacity to environmental constraints. It primarily focuses on how leaf nitrogen (N) affects the photosynthetic capacity. Photosynthetic capacity varies both among plant types and over time and space, and a major determinant of photosynthetic capacity is the maximum rate of Rubisco carboxylation ($V_\text{cmax}$).

In this optimality model, Smith et al. (2019) assumes that plants are able to acquire the N necessary to build leaves that can photosynthesize at the fastest possible rate given light availability and biophysical constraints. The Vcmax model estimates $V_\text{cmax}$ and $J_\text{max}$ as a function of environmental variables as follows:

\[\begin{equation} + V_\text{cmax}^* = \varphi I \left(\frac{m}{m_c}\right)\left(\frac{\overline{\omega}^*}{8\theta}\right), +\end{equation}\]

where

\[\begin{equation} + \overline{\omega}^* = 1 + \overline{\omega} - \sqrt{(1 + \overline{\omega})^2 - 4\theta\overline{\omega}}, +\end{equation}\]

and

\[\begin{equation} + \overline{\omega} = -(1 - 2\theta) + \sqrt{(1 - \theta)\left(\frac{1}{\frac{4c}{m}\left(1 - \theta\frac{4c}{m}\right)} - 4\theta\right)}, +\end{equation}\]

and

\[\begin{equation} + c = \frac{m}{8\theta}\left(1 - \frac{\varphi I + J_\text{max} - 2\theta\varphi I}{\sqrt{(\varphi I + J_\text{max})^2 - 4\theta\varphi I J_\text{max}}}\right) +\end{equation}\]

and

\[\begin{equation} + J_\text{max} = \varphi I \overline{\omega} +\end{equation}\]

\[\begin{equation} + m = \frac{C'_i - \Gamma^*}{C'_i + 2\Gamma^*} +\end{equation}\]

\[\begin{equation} + C'_i = \Gamma^* + (C_a - \Gamma^*)\frac{\xi}{\xi + \sqrt{D_g}} +\end{equation}\]

\[\begin{equation} + \xi = \sqrt{\beta \frac{K + \Gamma^*}{1.6\eta^*}} +\end{equation}\]

\[\begin{equation} + K = K_c\left(1 + \frac{O_i}{K_o}\right) +\end{equation}\]

\[\begin{equation} + m_c = \frac{C'_i - \Gamma^*}{C'_i + K} +\end{equation}\]

\[\Gamma^*\]

is the CO$_2$ compensation point in the absence of mitochondrial respiration

\[\begin{equation} + \Gamma^* = \Gamma^*_0 f(T, \Delta H_a) p/p_0 +\end{equation}\]

where $\Gamma^*_0 = 4.332$ Pa, $p$ is the atmospheric pressure, $p_0 = 101325$ Pa, and $\Delta H_a = 37830$ J/mol.

The model has the following parameters:

  • $\varphi$ is the realized quantum yield of photosynthetic electron transport (dimensionless). Estimated at 0.257.
  • $\theta$ is the curvature of the light response curve (dimensionless). Estimated at 0.85.
  • $\beta$ is the ratio of the carbon cost of maintaining photosynthetic proteins to the carbon cost of maintaining a transpiration stream (dimensionless). Estimated at 146.

For Smith et al. (2019) Vcmax model:

  • Altitude
  • $D_g$ is the vapor pressure deficit (VPD) at altitude
  • $C_a$ is the CO$_2$ partial pressure
  • $I$ is the incident photosynthetically active photon flux (PAR)
  • $T$ is the temperature
diff --git a/previews/PR754/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html b/previews/PR754/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html new file mode 100644 index 0000000000..ef266a3852 --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/plant_hydraulics/van_genuchten_model/index.html @@ -0,0 +1,23 @@ + +Van Genuchten model · ClimaLand.jl
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Plant Hydraulics

Additional understanding of stomatal behavior comes from the transport of water through the soil-plant–atmosphere continuum. Plants reduce stomatal conductance as needed to regulate transpiration and prevent desiccation or other hydraulic failure.

Specifically, water loss during day-time transpiration drives plants to draw water from the soil by roots and transport it through the stem to leaves. Transpiration provides the force that pulls water from soil. As transpiration is dominant during the day, water in soil near the roots, water in the stem, and water in foliage create a water potential gradient that allows leaves to draw water from the soil.

The plant hydraulics code solves for the volumetric water content in the stem and leaf ($\theta_{stem}$ and $\theta_{leaf}$). It allows for an arbitrary number of stem/leaf compartments, but for now we will start with a single stem and leaf compartment.

Van Genuchten Model

As explained above, soil moisture is an indispensable variable in studying plant water uptake. Van Genuchten (1980) developed a widely used soil moisture retention curve, which is used below to describe plant hydraulics.

The volume flux of water $q$ (m/s) between compartments with centers at two heights, $z_1$ and $z_2$, is given by Darcy's law as

\[\begin{align} + q = -\int_{z_1}^{z_2} k(\psi) dh +\end{align}\]

where $h = \psi+z$ is the head (in meters), and $k$ is the conductance (units of 1/s). As this is the conductance unit that CLM uses, there should be data bases with this information. We approximate this using finite difference as\footnote{Double check this - the units of $k$ in our code are $m/s$.}

\[\begin{equation} +q = -\int_{h_1}^{h_2} k(\psi) dh \approx -\frac{k_1(\psi_1) + k_2(\psi_2)}{2} * [(\psi_2 - \psi_1) + (z_2 - z_1)]. +\end{equation}\]

In order to close the set of equations, the user will have to specify $k(\psi)$ and a function $\psi(\theta)$. In our current implementation, we use a van Genuchten relationship with the same parameters for all compartments, but differing values of $K_{sat}.$

The change of water volume (m$^3), V$, in the compartments is then

\[\begin{align} + \frac{d V_{w, stem}}{dt} = q_{roots}\sigma_{roots} - q_{stem}\sigma_{stem} \nonumber \\ + \frac{d V_{w, leaf}}{dt} = q_{stem}\sigma_{stem} - \tau \sigma_{leaf}, +\end{align}\]

where $\tau$ is a transpiration volume flux per unit emitting area, and $\sigma$ is the total emitting/conducting area\footnote{Note that these are actually the areas at the faces between compartments. In the code, we take the average of the cross section of the compartments to estimate this.}.

This currently holds for a single plant. To convert to fluxes from an entire surface, we can multiply by the number of individuals $N$. We can make use of the fact that $N\sigma/A$, where $A$ is the area of the ground those $N$ individuals are occupying, is the area index for that plant type. Following CLM, we incorporate a root, stem, and leaf area index (RAI, SAI, LAI) in order to model fluxes across an entire grid cell.

Then we have:

\[\begin{align} + \frac{d v_{stem}}{dt} = q_{roots}RAI - q_{stem}SAI \nonumber \\ + \frac{d v_{leaf}}{dt} = q_{stem}SAI - \tau LAI, +\end{align}\]

where $v$ now represents the volume of water in that compartment (of a bulk plant) per unit ground area.

We also need to convert from the variable $v$ to $\psi$, in order to compute root extraction with the soil. To do so, we can convert $v$ to the volumetric water content, and from $\theta$ to $\psi$ using a van Genuchten relationship. To convert, let the volume of water per area of compartment be $V_{w,stem}$, and $H$ the typical ``length" of the compartment. Then

\[\begin{equation} + \theta_{stem}=\frac{V_{w,stem}}{A_{ground}} \times \frac{A_{ground}}{A_{stem}} \times \frac{1}{H_{stem}} = \frac{v_{stem}}{H_{stem} \times SAI }. +\end{equation}\]

Substituting in the volumetric water content, we have

\[\begin{align} + \frac{d \theta_{stem}}{dt} &= \frac{q_{roots}RAI - q_{stem}SAI}{H_{stem} SAI} \nonumber \\ + \frac{d \theta_{leaf}}{dt} &= \frac{q_{stem}SAI - \tau LAI}{H_{leaf} LAI}, +\end{align}\]

We can also account for the distribution of roots as a function of depth. A quantity that is modeled in plant hydraulic models is the root fraction $P(z)$, satisfying $\int P(z) dz = 1$. Instead of having a single root at one discrete location, we can distribute the root system over different depths using $P(z)$. The total flux from roots between $z$ and $z+dz$ is given by

\[\begin{equation} + dq_{roots}(z) = -P(z) dz \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh, +\end{equation}\]

so that the net flux for the plant system would sum over this

\[\begin{equation} + q_{roots} = -\int_{z_{min}}^{z_{sfc}} \frac{dq_{roots}(z)}{dz}dz , +\end{equation}\]

where $z_{min}$ is the minimum soil layer of the simulation.

The sink term of the soil is in terms of a volumetric fraction change, i.e. we need a volume of water per volume of soil per second. We can obtain this with

\[\begin{equation} + S(z) = -(RAI) dq_{roots}(z)/dz = (RAI) P(z) \int_{h_{soil}(z)}^{h_{stem}} k(\psi) dh. +\end{equation}\]

The sign change occurs in the expression for $S(z)$ because a positive value of $q_{roots}$ indicates flow from the soil to the plant. This is a sink term for the soil.

The model needs the following parameters:

DriversSymbolUnitRange
A function of simulation time t given the leaf area indexLAIm²/m²0–10
The constant stem area indexSAIm²/m²0–10
The constant root area indexRAIm²/m²0–10
diff --git a/previews/PR754/standalone/pages/vegetation/radiative_transfer/beer_model/index.html b/previews/PR754/standalone/pages/vegetation/radiative_transfer/beer_model/index.html new file mode 100644 index 0000000000..fdfb2ab346 --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/radiative_transfer/beer_model/index.html @@ -0,0 +1,4 @@ + +Beer model · ClimaLand.jl
GitHub

Radiative transfer scheme

This section describes multiple models of radiative transfer through the vegetation canopy, implemented in ClimaLand.

Beer's law

Plants utilize Photosynthetically Active Radiation (PAR) for the process of photosynthesis, during which they convert light energy into chemical energy, fueling the synthesis of sugars and other organic compounds. PAR refers to the portion of the electromagnetic spectrum that is essential for photosynthesis in plants. PAR includes wavelengths ranging from approximately 400 to 700 nanometers and corresponds to the visible light spectrum. The unit used to measure PAR is called micromoles per square meter per second (μmol/m²/s), representing the number of photons within the PAR range that strike a square meter of a surface per second.

The portion of PAR that is actually absorbed by the vegetation canopy for photosynthesis is called Absorbed Photosynthetically Active Radiation (APAR). The APAR driving photosynthesis is calculated following the Beer- Lambert law:

\[APAR(PAR, \theta_s) = (PAR)(1 - \rho_{leaf})(1 - e^{(-K(\theta_s) LAI \Omega)})\]

where PAR ≈ SW/2 is the incident moles of photons per meter squared per second in the PAR window, approximated as half of the incident shortwave flux. If PAR is not directly available, $ρ_{leaf}$ is the PAR canopy reflectance, K is the vegetation extinction coefficient following Campbell (1998), LAI is the leaf area index, $θ_s$ is the zenith angle, and $Ω$ is the clumping index following Braghiere (2021). $K$, $Ω$ and $ρ_{leaf}$ are all unitless. LAI is in m² m⁻². In order to compute $K$, we need $θ_s$ in radians and the leaf angle distribution $l_d$ (unitless). K is then defined as

\[K = l_d/\max{(\cos{(\theta_s)}, \epsilon)}\]

so that at night, when 3π/2 > $θ_s$ > π/2, $K$ is large (lots of extinction) and non-negative. The small value ε prevents dividing by zero.

The model has the following parameters:

OutputSymbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy reflectance$ρ_{leaf}$-0.0–1.0
Extinction coefficient$K$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5

Interactive APAR(PAR, LAI, $ρ_{leaf}$, $K$, $Ω$)

diff --git a/previews/PR754/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html b/previews/PR754/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html new file mode 100644 index 0000000000..2f91f47f0d --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/radiative_transfer/twostream_model/index.html @@ -0,0 +1,5 @@ + +Two-Stream model · ClimaLand.jl
GitHub

The Two-Stream Scheme

In order to treat the effects of multiple scattering by cloud particles, aerosols and air molecules, the two-stream approximations are employed in most shortwave radiation (i.e., solar, 300-2500 nm) schemes presently used in LSMs for numerical weather prediction and climate modelling. In two-stream approximations, the radiation field is divided into the direct solar beam, plus the diffuse solar radiation (i.e., radiation scattered at least once), and in two directions, downward and upward fluxes. The angular distribution of scattered radiation is not computed in any further detail, which means they are considered to be isotropic (Raisaenen, 2002).

The two-stream approximation, or scheme has been used to deal with radiative transfer in the atmosphere for many years. The basic procedure in applying it to vegetation is to expand a complex function in the control equations into Legendre functions and then truncate them to the first order closure to get a simple solution (Dai, 2007). After reviewing several variants of the two-stream approximation model in the calculation of atmospheric radiation, Meador (1980) presented a unified form of the variants and introduced a new and improved method.

Dickinson 1983 introduced this new two-stream method to estimate radiative transfer in a vegetated canopy, and Sellers 1985 used the two-stream approximation to calculate values of hemispheric canopy reflectance in the visible or photosynthecially active radiation (PAR) and near-infrared (NIR) wavelength intervals. The two-stream approximation treatment has been widely used in land surface process models until nowadays. The approximation assumes that diffuse radiative fluxes are isotropic in the upward and downward directions. Supposing that the upper and lower leaf optical properties are identical, the two-stream approximation used to model radiative transfer in plant canopies is given in the following form:

\[-\overline{\mu}(dI^{\uparrow})/dL + [1 - (1 - \beta)\omega]I^{\uparrow} - \omega \beta I^{\downarrow} = \omega \overline{\mu} K \beta_0 \exp{(-KL)},\\ +-\overline{\mu}(dI^{\downarrow})/dL + [1 - (1 - \beta)\omega]I^{\downarrow} - \omega \beta I^{\uparrow} = \omega \overline{\mu} K (1-\beta_0) \exp{(-KL)}\]

where I↑ and I↓ are the upward and downward diffuse radiative fluxes normalized by the incident flux respectively, μ is the cosine of the zenith angle of the incident beam, K is the optical depth of direct beam per unit leaf area and is equal to G(μ)/μ, G(μ) is the relative projected area of leaf elements in the direction cos−1μ, μ is the average inverse diffuse optical depth per unit leaf area and is equal to

\[\int_{0}^{1}[\mu^{\prime}/G(\mu^{\prime})]d\mu^{\prime}\]

μ′ is the direction of scattered flux, ω is the scattering coefficient and is equal to ρleaf +τleaf , and L is the cumulative LAI. β and β0 are upscattering parameters for the diffuse and direct beams respectively. (See Sellers 1985 for details)

These equations can be solved as an exact solution with appropriate boundary conditions. For direct incident radiation, the appropriate top boundary condition is I↓ = 0 for L = 0, and the bottom boundary condition is I↑ = ρs[I↓ + exp (−kLT )] for L = LT , where ρs is the soil reflectance and LT is the total LAI. The corresponding solution yielded is then:

\[I^{\uparrow} = \frac{h_1\exp{(-KL)}}{\sigma} + h_2\exp{(-hL)} + h_3\exp{(hL)},\\ +I^{\downarrow} = \frac{h_4\exp{(-KL)}}{\sigma} + h_5\exp{(-hL)} + h_6\exp{(hL)}\]

For diffuse radiation, the appropriate top boundary condition is I↓ = 1 for L = 0, and the bottom boundary condition is I↑ = ρsI↓ for L = LT. Then, the corresponding solution is:

\[I^{\uparrow} = h_7\exp{(-hL)} + h_8\exp{(hL)},\\ +I^{\downarrow} = h_9\exp{(-hL)} + h_{10}\exp{(hL)}\]

where coefficients such as σ and h1 to h10 are given in Sellers 1985. Note that there is an error in the expression for h4 in the appendix of Sellers 1985. The correct expression may be found in Sellers 1996.

The model has the following parameters:

OutputSymmbolUnitRange
Absorbed Photosynthetically Active RadiationAPARμmol m⁻² s⁻¹0-1500
Absorbed Near-Infrared RadiationANIRμmol m⁻² s⁻¹0-1500
DriversSymbolUnitRange
Photosynthetically Active RadiationPARμmol m⁻² s⁻¹0–1500
Leaf Area IndexLAIm² m⁻²0–10
ParametersSymbolUnitRange
Canopy PAR Reflectance$\alpha\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Reflectance$\alpha\_NIR\_{leaf}$-0.0–1.0
Canopy PAR Transmittance$\tau\_PAR\_{leaf}$-0.0–1.0
Canopy NIR Transmittance$\tau\_NIR\_{leaf}$-0.0–1.0
Canopy Emissivity$ϵ\_canopy$-0.0–1.0
Clumping index$Ω$-0.0–1.0
Zenith angle$θ_s$rad0–π
ConstantsSymbolUnitValue
Leaf angle distribution$l_d$-0.5
Typical wavelength per photon PAR$\lambda\_\gamma\_PAR$m5e-7
Typical wavelength per photon NIR$\lambda\_\gamma\_NIR$m1.65e-6
diff --git a/previews/PR754/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html b/previews/PR754/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html new file mode 100644 index 0000000000..2f10633fd5 --- /dev/null +++ b/previews/PR754/standalone/pages/vegetation/stomatal_conductance/medlyn_model/index.html @@ -0,0 +1,11 @@ + +Medlyn model · ClimaLand.jl
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Stomatal conductance

Stomata play an important role in uptaking CO2 for photosynthesis while limiting water loss during transpiration. Consequently, an accurate depiction of stomatal conductance is required to study leaf energy fluxes, transpiration, and photosynthesis.

This section describes multiple models of stomatal conductance implemented in ClimaLand.

Medlyn Model

The Medlyn model is a semiempirical model that relates stomatal conductance and photosynthesis and is derived from water-use efficiency optimization theory.

Transpiration is computed using the stomatal conductance and Monin-Obukhov theory.

\[\begin{equation} +T = -\rho_a g_{\mathrm{eff}} \left[q_{a}- q_v(T_\mathrm{leaf}, \rho_\mathrm{sfc}) \right], +\end{equation}\]

where $T$ is the transpiration (mass flux of water vapor), $q_{a}$ is the specific humidity at the lowest level of the atmosphere, $q_v(T_\mathrm{T_{leaf}}, \rho_\mathrm{sfc})$ is the saturated specific humidity over liquid water, given the temperature of the leave $T_{leaf}$ and air density at the surface $\rho_{sfc}$. We will approximate $T_{leaf} = T_{a}$ and $\rho_{\mathrm{sfc}} = \rho_a$.

We also need the effective conductivity, given by

\[\begin{equation} + g_{\mathrm{eff}} = \frac{1}{g_{\mathrm{ae}}^{-1}+g_{\mathrm{s}}^{-1}}, +\end{equation}\]

where $g_{ae}$ is the aerodynamic conductance, computed by the MOST solve, and $g_s$ is the stomatal conductance to water vapor per unit ground area. The units of all conductances are $m/s$.

The stomatal conductance is calculated using the Medlyn stomatal conductance model (Medlyn, 2011), while omitting cuticular and epidermal losses by assuming zero minimum stomatal conductance:

\[\begin{align} +g_{s,m}(PAR, T, VPD, c_a) &= g_{0,m} + D_{rel} \times m \frac{A_n(PAR, T, VPD, c_a)}{c_a}\nonumber \\ +g_s &= \frac{g_{s,m}}{\rho_m} +\end{align}\]

where $D_{rel} =1.6$ (unitless) is the relative diffusivity of water vapor with respect to CO$_2$, $\rho_m$ is the molar density of water, and $m$ is the Medlyn factor,

\[\begin{equation} + m = \left( 1 + \frac{g_1}{\sqrt{VPD}} \right), +\end{equation}\]

where g1 is the slope parameter, inversely proportional to the square root of marginal water use efficiency (Medlyn, 2011). We also have A$_n$ as the biochemical demand for CO$_2$ calculated using the photosynthesis model (Farquhar, 1980; Equation \eqref{eq:an}; units of molar flux). The resulting units are $m/s$. g{0,m}$ is a minimum molar conductivity. (subscript $m$ indicates molar).

The model has the following parameters:

ConstantsSymbolUnitValue
Relative diffusivity of water vapor$D_{rel}$-1.6
Minimum stomatal conductance$g_0$mol/$m^2$/s1e-4
Slope parameter$g_1$$\sqrt{Pa}$790