Reproducible Workflows for Global Drought Clustering with Socioeconomic Exposure (v1.0.0)

Overview This dataset contains code and processed data for a reproducible workflow that generates drought indices and links them to socio-economic impacts and exposures. The workflow is based on ERA5 reanalysis data and produces standardized outputs in NetCDF and Excel formats. Contents Scripts (Python): create_spi_spei_index.py — compute SPI and SPEI indices from aggregated ERA5 precipitation and temperature. build_country_masks.py — generate country masks on the drought index grid. preprocess_impact_data.py — clean and standardize disaster impact tables (EM-DAT, GDIS). preprocess_exposure_data.py — align population and GDP rasters to the drought grid. link_Impact_data.py — link drought clusters to impact events. link_exposure_data.py — calculate GDP and population exposure for clusters. read_netcdf.py — read cluster results from NetCDF and output tidy tabular DataFrames. Results Data: A few sample Excel files containing the outputs of link_Impact_data.py and link_exposure_data.py have been provided. Data: Monthly precipitation and mean monthly temperature (ERA5, aggregated from hourly fields). Pre-computed SPI and SPEI indices for multiple timescales. Land–sea mask file matching the precipitation and temperature grid. Data Source Precipitation and temperature were derived from the ERA5 hourly data on single levels (1940–present), distributed by the Copernicus Climate Data Store (CDS). Precipitation was aggregated to monthly totals. Temperature was aggregated to monthly means. These datasets were used as inputs to create_spi_spei_index.py. Acknowledgement We acknowledge the use of Copernicus Climate Change Service information (ERA5). Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Outputs Drought indices (SPI, SPEI at multiple scales, NetCDF) Country masks (NPZ) Impact tables (Excel/CSV) Exposure datasets (GDP & population, NetCDF/Excel) Usage The code is modular: each script can be run independently once inputs are prepared. See included README files for detailed instructions.