Woodwell Risk Drought Monitor and Forecast

This site displays near real-time moisture anomalies along with an experimental 6-month forecast. Anomalies are measured as water balance percentiles relative to levels from 1991 to 2020. Values close to 0.5 represent normal conditions. Values below and above that mid-value indicate dryer- and wetter-than-normal conditions, respectively. Moisture anomalies are monitored on a monthly basis, from 2001 to present.

Python environment

To recreate the conda environment we use in this repository, please run:

conda env create -f environment.yml

And to activate the environment:

conda activate shiny

Run the app locally

To start a local server and see the app, please run the following command from within the app/ directory:

shiny run --reload drought.py

Data sources and processing steps

Vector data

National and state outlines were downloaded from Natural Earth. Crop masks were created using a modified version of the SPAM 2020 combined rainfed- and irrigated production data for specific crops.

Raster data

The temperature, precipitation, and potential evapotranspiration data used to create the water balance index come from ERA5 monthly averaged data and were downloaded using the Copernicus Climate Data Store (CDS) Application Program Interface (API), or CDS API.

For back-end data analysis/transformation of NetCDF and TIF files, we used Python and R.