GloWS: A Global Large-Sample Watershed Synthesis Dataset Empowering Hydrological Simulation and Future Projection

Risk assessment of hydrological extremes as well as process-based simulation and projection under climate change rely on high-quality watershed datasets. However, existing large-sample hydrology datasets are usually constrained by limited spatial coverage and lack future climate change scenarios at a catchment scale. Here, we introduce a Global Large-Sample Watershed Synthesis dataset (GloWS), which covers 23,029 basins from 24 international, national and sub-national sources. GloWS is generated after rigorous station screening, careful visual inspection by Google Map satellite imagery, extensive calculations based on flow records, and systematical data quality control. This dataset includes metadata (e.g., station locations, watershed polygons and areas, streamflow records length and scores), hydrological indices (e.g., streamflow statistics, drought indexes, flood peaks and volumes), 14 meteorological variables (including precipitation, potential evaporation, air temperature, wind speed, and radiation), land-use and land-cover characteristics (e.g., fractions of urban, cropland and forest areas, and leaf area indices of low and high vegetation), water storage terms (e.g., soil water content and snow water equivalence), and other static attributes (e.g., climate classifications and average Gross Domestic Product). To empower future projections and assess climate change impacts, GloWS also includes nine daily catchment-mean bias-corrected variables from 22 Global Climate Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). Additionally, we apply skill and independence weighting to generate multi-model ensemble mean scenarios throughout the 21st century.