Estimating trends in phenology

The goal of this project is to estimate spatial and temporal trends in phenology in the Northeastern US using PRISM temperature data. This analytical pipeline downloads daily data, calculates growing degree days (GDD) for each day, and then finds the day of year (DOY) that certain threshold GDD are reached for this region. Products will include rasters of mean DOY for a variety of GDD thresholds over the normals period (1991-2020), and rasters of estimated rates of change in DOY for these thresholds.

Report of work in progress: https://usa-npn.github.io/cales-thermal-calendars/spatial-trends-report.html

Reproducibility

renv

This project uses renv for package management. When opening this repo as an RStudio Project for the first time, renv should automatically install itself and prompt you to run renv::restore() to install all package dependencies.

targets

This project uses the targets package for workflow management. Run targets::tar_make() from the console to run the workflow and reproduce all results. The graph below shows the workflow:

graph LR
  style Legend fill:#FFFFFF00,stroke:#000000;
  style Graph fill:#FFFFFF00,stroke:#000000;
  subgraph Legend
    direction LR
    xf1522833a4d242c5([""Up to date""]):::uptodate --- xd03d7c7dd2ddda2b([""Stem""]):::none
    xd03d7c7dd2ddda2b([""Stem""]):::none --- x6f7e04ea3427f824[""Pattern""]:::none
  end
  subgraph Graph
    direction LR
    xff529209e87def7b["gdd_doy_1000<br>1000"]:::uptodate --> x46cc5bc3b6c68c1a(["gdd_doy_stack_1000<br>1000"]):::uptodate
    x47d67438bee47c7b(["normals_summary_1000<br>1000"]):::uptodate --> xd1093b48fdaa3b26(["normals_mean_plot_1000<br>1000"]):::uptodate
    x21326ed6e10cd0d0(["gdd_doy_stack_2500<br>2500"]):::uptodate --> xfdc192d56cd8f9ef(["normals_summary_2500<br>2500"]):::uptodate
    x21326ed6e10cd0d0(["gdd_doy_stack_2500<br>2500"]):::uptodate --> xe29a7570fd64b783(["doy_trend_2500<br>2500"]):::uptodate
    x21326ed6e10cd0d0(["gdd_doy_stack_2500<br>2500"]):::uptodate --> x07c639dff4c1c0c9(["doy_plot_2500<br>2500"]):::uptodate
    x6976cced396df4c9(["casc_ne"]):::uptodate --> x28c62ae9542e7849["gdd_doy_2500<br>2500"]:::uptodate
    xf1e1014b1abe0030["prism_tmean"]:::uptodate --> x28c62ae9542e7849["gdd_doy_2500<br>2500"]:::uptodate
    xb106621904434716(["casc_ne_file"]):::uptodate --> x6976cced396df4c9(["casc_ne"]):::uptodate
    x05f2370eca178222(["doy_trend_1000<br>1000"]):::uptodate --> x31a3593b535e23c4(["trend_plot_1000<br>1000"]):::uptodate
    xd87e155a2058b73d(["normals_summary_50<br>50"]):::uptodate --> xd07aca72e6461b3b(["normals_sd_plot_50<br>50"]):::uptodate
    x08bdbd4f78dad638(["doy_trend_50<br>50"]):::uptodate --> x5a26c52d2d28f1dc(["trend_plot_50<br>50"]):::uptodate
    xd87e155a2058b73d(["normals_summary_50<br>50"]):::uptodate --> xb6b5b21811b62e8e(["normals_mean_plot_50<br>50"]):::uptodate
    x0b494d9bc4b357f4(["gdd_doy_stack_50<br>50"]):::uptodate --> x233d6af147d6e9d1(["doy_plot_50<br>50"]):::uptodate
    x0b494d9bc4b357f4(["gdd_doy_stack_50<br>50"]):::uptodate --> xd87e155a2058b73d(["normals_summary_50<br>50"]):::uptodate
    x46cc5bc3b6c68c1a(["gdd_doy_stack_1000<br>1000"]):::uptodate --> x47d67438bee47c7b(["normals_summary_1000<br>1000"]):::uptodate
    x46cc5bc3b6c68c1a(["gdd_doy_stack_1000<br>1000"]):::uptodate --> x05f2370eca178222(["doy_trend_1000<br>1000"]):::uptodate
    x46cc5bc3b6c68c1a(["gdd_doy_stack_1000<br>1000"]):::uptodate --> x2c8ecb326142bdff(["doy_plot_1000<br>1000"]):::uptodate
    xfdc192d56cd8f9ef(["normals_summary_2500<br>2500"]):::uptodate --> x387144ee5a388fce(["normals_sd_plot_2500<br>2500"]):::uptodate
    x28c62ae9542e7849["gdd_doy_2500<br>2500"]:::uptodate --> x21326ed6e10cd0d0(["gdd_doy_stack_2500<br>2500"]):::uptodate
    x786a1a0a06ddc553["gdd_doy_50<br>50"]:::uptodate --> x0b494d9bc4b357f4(["gdd_doy_stack_50<br>50"]):::uptodate
    xfdc192d56cd8f9ef(["normals_summary_2500<br>2500"]):::uptodate --> xa1c9e5079d21856f(["normals_mean_plot_2500<br>2500"]):::uptodate
    xf9ac23fbc741da6f(["years"]):::uptodate --> xf1e1014b1abe0030["prism_tmean"]:::uptodate
    x6976cced396df4c9(["casc_ne"]):::uptodate --> xff529209e87def7b["gdd_doy_1000<br>1000"]:::uptodate
    xf1e1014b1abe0030["prism_tmean"]:::uptodate --> xff529209e87def7b["gdd_doy_1000<br>1000"]:::uptodate
    x47d67438bee47c7b(["normals_summary_1000<br>1000"]):::uptodate --> x042637b768dd479e(["normals_sd_plot_1000<br>1000"]):::uptodate
    x0b494d9bc4b357f4(["gdd_doy_stack_50<br>50"]):::uptodate --> x08bdbd4f78dad638(["doy_trend_50<br>50"]):::uptodate
    xe29a7570fd64b783(["doy_trend_2500<br>2500"]):::uptodate --> x4a7371f634a1c6ee(["trend_plot_2500<br>2500"]):::uptodate
    x6976cced396df4c9(["casc_ne"]):::uptodate --> x786a1a0a06ddc553["gdd_doy_50<br>50"]:::uptodate
    xf1e1014b1abe0030["prism_tmean"]:::uptodate --> x786a1a0a06ddc553["gdd_doy_50<br>50"]:::uptodate
  end
  classDef uptodate stroke:#000000,color:#ffffff,fill:#354823;
  classDef none stroke:#000000,color:#000000,fill:#94a4ac;
  linkStyle 0 stroke-width:0px;
  linkStyle 1 stroke-width:0px;

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Developed in collaboration with the University of Arizona CCT Data Science group.