Global groundwater dependent wetland dynamics affected by climate variability and groundwater exaction over the last recent decades

Dataset description This dataset provides monthly estimates of groundwater dependent wetland area and presence at the global scale for the period 1979–2019. Wetland extent was derived from PCR-GLOBWB 2 model outputs of saturated area fraction, combined with GLOBGM water table depth simulations at 1 km spatial resolution. Groundwater dependence was determined by identifying wetland areas with shallow water table conditions consistent with ecological reliance on groundwater. Results were aggregated to biome–realm combinations defined by the World Wide Fund for Nature (WWF) terrestrial ecoregion framework, which consists of 14 terrestrial biomes and 8 biogeographic realms, excluding Antarctica. Two complementary data products are provided: Area product (Parquet format): Monthly area of groundwater dependent wetlands aggregated by WWF biome–realm unit, reported in square kilometres. The table includes fields for time (monthly timestamps from 1979–01 to 2019–12), biome and realm identifiers, and wetland area (gde_area_km2). Presence product (NetCDF format): Monthly boolean presence of groundwaterdependent wetlands on a global 30 arc second grid. The variable wetGDE encodes wetland dependence as 1 (presence) and 0 (absence). Dimensions are latitude, longitude, and time (monthly 1979–01 to 2019–12), in WGS84 geographic coordinates. Key features Spatial resolution: 1 km (model input and aggregation), 30 arc second (presence grid) Temporal resolution: Monthly Time period: 1979–2019 Spatial units: WWF biome–realm combinations (excluding Antarctica) Variables: gde_area_km2 (float, km²), aggregated wetland area per biome–realm and month (Parquet) wetGDE (byte, 0/1), presence or absence of groundwater dependent wetlands (NetCDF) Both products are suitable for analyzing the spatial and temporal variability of groundwater dependent wetlands at regional to global scales, and for integration with ecological, hydrological, and climate data.