A simple Python package to calculate and visualize some popular drought indices such as the SPI, SPEI and SGI.

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

Drought analysis with Google Earth Engine. (Compare SPEI with NDVI anomalies)

Full-featured Raspberry Pi based irrigation controller and web application for scheduling.

R package for the calculation of Standardized Index values (SPI, SPEI, SSI,...) on a daily basis.

run theory to characterize drought indices

An interface for managing SWATPlus input and output files to aid in implementing, and visualizing the impact of land use changes on catchment hydrology in the SWATPlus model

Forecasting of forest drought impacts in Switzerland from satellite imagery, weather reanalysis and remote sensing data. Pixel-wise forecasts with feature engineering to provide model explainability.

Quantification of global drought recovery probability based on Vine Copula

NextGen In A Box: NextGen Generation Water Modeling Framework for Community Release (Docker version)

REGIONAL DROUGHT AND CROP YIELD INFORMATION SYSTEM (RDCYIS)

Systematic assessment of the impacts of climate change on human pathogenic diseases

Climate data, indices and it's application

Repository for performing climate extraction and climate extreme calculation for ethnographic site of the IBSS project

An R package for creating tables of hydrologic drought statistics by natural resource management area

Package R pour traiter les données ONDE de suivi des étiages en France

Delayed flow separation to estimate multiple delayed streamflow contributions

Analysis scripts of the paper "Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model?", published in HESS

A Matlab function to calculate Palmer Drought Severity Index (PDSI) using monthly temperature and precipitation data. Can determine values for multiple sites simultaneously.

Investigating how carbon emissions, particulate matter, and climate variables/indices impact mortality from chronic respiratory disease. Working with pollutant, climate, mortality, population, and geographic datasets. Modeling with Random Forest regression.