climate vignette filled

vignettes/climate.Rmd

@@ -1,6 +1,9 @@
 ---
 title: "Climate data"
 output: html_document
+editor_options: 
+  markdown: 
+    wrap: 72
 ---
 
 ```{r setup, include=FALSE}
@@ -9,40 +12,134 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ## Introduction
 
-Text
+Climate data in the [OPTAIN project](https://www.optain.eu/) for each
+case study site could be obtained from
+[UFZ-Cloud](https://nc.ufz.de/s/KA9Cr2bbtALGMHr/authenticate/showShare).
+It is located under *WPs&Tasks\>WP3\>Task_3\_2\>data* path. Numbers are
+referring to case study number and *gridded.zip* files contains `NetCDF`
+files with climate data for the case study basin. Data are provided for
+precipitation, solar radiation, relative humidity, min, max, mean
+temperature and wind speed variables. Time period covered is from
+1981-01-01 the end of this century with three representative
+concentration pathways (RCPs) available (RCP2.6, RCP4.5, RCP8.5) and six
+regional climate models (RCMs). Detained information about RCMs is
+provided in Table \@ref(tab:rcms).
 
-```{r fig-cloud, echo=FALSE, out.width='100%', fig.align = 'center'}
+| Model number | Driving Model (GCM) | Ensemble |    RCM     |  End date  |
+|:------------:|:-------------------:|:--------:|:----------:|:----------:|
+|      1       |      EC-EARTH       | r12i1p1  | CCLM4-8-17 | 31.12.2100 |
+|      2       |      EC-EARTH       |  r3i1p1  |  HIRHAM5   | 31.12.2100 |
+|      3       |     HadGEM2-ES      |  r1i1p1  |  HIRHAM5   | 30.12.2099 |
+|      4       |     HadGEM2-ES      |  r1i1p1  |  RACMO22E  | 30.12.2099 |
+|      5       |     HadGEM2-ES      |  r1i1p1  |    RCA4    | 30.12.2099 |
+|      6       |     MPI-ESM-LR      |  r2i1p1  |  REMO2009  | 31.12.2100 |
+
+: (#tab:rcms) RCMs models provided in OPTAIN.
+
+```{r fig-cloud, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'}
 knitr::include_graphics('cloud.png')
 ```
 
-Text
+All RCMs data were bias-corrected to the
+[ERA5-Land](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview)
+climate data. Description of data preparation and analysis is provided
+in the project deliverable [D3.1: Climate scenarios for integrated
+modelling](https://www.optain.eu/sites/default/files/delivrables/OPTAIN%20D3.1%20-%20Climate%20scenarios%20for%20integrated%20modelling.pdf).
+
+## NetCDF to SWAT+ input
+
+There is no direct way of using `NetCDF` data in the SWAT model.
+Therefore tools have been prepared for the users and provided with this
+package.
 
-```{r ext_cli, message = FALSE, warning = FALSE}
+### Extraction from NetCDF
+
+First step on a way to prepare SWAT+ climate input data is to download
+*XX-gridded.zip* file for your case study site and to unzip it. Then
+locate *CORDEX-BC* folder. Data extraction from `NetCDF` is done with
+[load_climate_lst](../reference/load_climate_lst.html) function, which
+needs path to your basin shape file and path to *CORDEX-BC* folder.
+
+```{r ext_cli, eval = FALSE, message = FALSE, warning = FALSE}
 library(svatools)
-basin_path <- system.file("extdata", "GIS/basin.shp", package = "svatools")
-climate_lst <- load_climate_lst("../output/climate/CORDEX-BC", basin_path)
+basin_path <- "GIS/basin.shp"
+cordex_path <- "climate/CORDEX-BC"
+climate_lst <- load_climate_lst(cordex_path, basin_path)
 ```
-Text
 
-```{r plot_cli, message = FALSE, warning = FALSE}
+This function creates virtual stations for each `NetCDF` grid cell,
+which basin boundary touches. Virtual stations are defined and center of
+grid cell. Then for each of this station data are extracted and saved in
+list of list format. 
+
+```{r fig-basin, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'}
+knitr::include_graphics('fig_basin.png')
+```
+
+### Evaluation 
+
+Extraction result is arranged in specific format. *RCP -\> RCM* are the top levels of the list format. Then format is same as with other meteorological data used in
+this package.
+
+-   Meteo data list
+    -   stations sf Data frame (ID, Name, Elevation, Source, geometry,
+        Long, Lat)
+    -   data -\> Station ID -\> Variable -\> Data frame (DATE, Variable)
+
+
+Therefore other package function as
+[plot_weather](../reference/plot_weather.html) could be used to examine
+function results or to compare to measurement data for example with
+[plot_weather_compare](../reference/plot_weather_compare.html).
+
+```{r plot_cli, eval = FALSE, message = FALSE, warning = FALSE}
 plot_weather(climate_lst[["rcp85"]][["1"]],"TMP_MAX", "year", "max")
 ```
 
-Text
+```{r fig-temp, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'}
+knitr::include_graphics('fig_temp.png')
+```
+[plot_wgn_comparison](../reference/plot_wgn_comparison.html) function might be useful to get climate set statistical comparison to motoring data. 
+
+```{r plot_wng, eval = FALSE, message = FALSE, warning = FALSE}
+temp_path <- system.file("extdata", "weather_data.xlsx", package = "svatools")
+met_lst <- load_template(temp_path, 4326)
+plot_wgn_comparison(climate_lst[["rcp85"]][["1"]], met_lst, "ID4", "ID2", "Climate set", "Monitoring data", "comparison")
+```
+
+```{r fig-wng, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'}
+knitr::include_graphics('fig_wng.png')
+```
+
+Other functions from [Weather data section](../articles/weather.html) are also applicable. This allows to update SWAT+ *.sqlite* [database](https://biopsichas.github.io/svatools/articles/weather.html#wgn-input-function) with climate data, prepare [weather generator](https://biopsichas.github.io/svatools/articles/weather.html#wgn-input-function) parameters, etc.
+
+### Preparing all climate sets
+
+Nevertheless [prepare_climate](../reference/prepare_climate.html) function could be used to directly prepare/update model input text files needed to run each climate scenario. Nothing else is needed just to overwrite model input files with prepared text files. 
 
-```{r get_cli, message = FALSE, warning = FALSE}
-f_dir <- "../output/overwrite/tmp"
-files_to_copy <- paste(f_dir, c("aquifer.con", "chandeg.con", "hru.con", "reservoir.con", "rout_unit.con", "time.sim"), sep="/")
+Following lines runs [prepare_climate](../reference/prepare_climate.html) function in a loop and prepares all climate data input sets and folder stucture.  
+
+```{r get_cli, eval = FALSE, message = FALSE, warning = FALSE}
+##Path to your SWAT+ model project directory
+project_dir <- "../output/overwrite/tmp"
+##These files will be copied from model directory and updated
+files_to_copy <- paste(project_dir, c("aquifer.con", "chandeg.con", "hru.con", "reservoir.con", "rout_unit.con", "time.sim"), sep="/")
+
+##Main loop to each rcp
 for (rcp in c("rcp26", "rcp45", "rcp85")){
+  ##Each rcm
   for (rcm in as.character(c(1:6))){
+    ##Each period
     for(p in list(c("H", "1988-01-01", "2020-12-31"), c("N", "2032-01-01", "2065-12-31"), c("E", "2067-01-01", "2099-12-31"))){
+      ##Creating new directory
       cli_dir <- paste("../output/cli", rcp, paste0("rcm", rcm), p[1], sep = "/")
       dir.create(cli_dir, recursive = TRUE)
+      ##Coping files to update
       file.copy(files_to_copy, paste(cli_dir, c("aquifer.con", "chandeg.con", "hru.con", "reservoir.con", "rout_unit.con", "time.sim"), sep = "/"), overwrite = TRUE)
+      ##Running function to prepare climate input for scenario
       prepare_climate(climate_lst[[rcp]][[rcm]], cli_dir, p[2], p[3])
       print(paste(rcp, "rcm", rcm, p[1], p[2], "-", p[3], "set prepared."))
     }
   }
 }
-
 ```