Monthly 1-km SPI, SPEI and SRI drought-indicator rasters for Poland (1995-2024)

Monthly 1-km SPI, SPEI and SRI drought-indicator rasters for Poland (1995–2024) This dataset provides monthly gridded drought indicators for the entire territory of Poland at 1-km resolution for the period 1995–2024. Three complementary standardized indices are included: the Standardized Precipitation Index (SPI, precipitation-based), the Standardized Precipitation-Evapotranspiration Index (SPEI, based on the climatic water balance P − PET) and the Standardized Runoff Index (SRI, runoff-based). Each index is provided at five accumulation scales: 1, 3, 6, 12 and 24 months. Methods. Station indices were computed from IMGW-PIB precipitation and river-runoff observations and from AgERA5 potential evapotranspiration (used for SPEI). SPI and SRI were fitted with a gamma distribution and SPEI with a log-logistic distribution. Station values were then interpolated to a 1-km grid (EPSG:2180, PUWG 1992) by ordinary kriging. Elevation-assisted kriging (kriging with external drift) was evaluated by leave-one-out cross-validation but produced no net national improvement and was not adopted for the final product. File structure. Data are provided as CF-compliant NetCDF (CF-1.8), one file per indicator and accumulation scale (e.g. SPI_s06m_PL_1km_1995-2024_EPSG2180.nc). Each file has dimensions (time, y, x) with a regular monthly time axis (360 steps, 1995-01 to 2024-12) and the coordinate reference system stored as a grid_mapping variable. Months in which an index cannot be formed at the edges of the accumulation window are present as fill-valued (missing) layers, so the time axis is continuous; their completeness is documented in raster_index.csv. Contents. netcdf/ — the 15 index rasters; metadata/raster_index.csv — per-layer inventory with min/max/mean, missing-data fraction and a "present" flag; metadata/variables_dictionary.csv — variable definitions; metadata/mckee_classes.csv — the seven-class drought/wetness classification (McKee et al., 1993) with colours; CITATION.cff and checksums.md5. Usage note. In GIS software the temporal dimension is read via the temporal/time controls (e.g. in QGIS: Layer Properties → Temporal → Dynamic Temporal Control, then the Temporal Controller); in Python the files open directly with xarray, with time parsed as dates. Coordinate reference system. EPSG:2180 (PUWG 1992 / Poland CS92). Map extents delineate the study area and do not necessarily depict accepted national boundaries.