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, and systematical data quality control. This dataset includes metadata, hydrological indices, meteorological variables, land-use and land-cover characteristics, water storage terms, and other static attributes. 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), and provides the multi-model ensemble mean scenarios throughout the 21 st century. We envision that GloWS, with its comprehensive attributes and extensive spatiotemporal coverage, could provide support for a broad range of large-sample hydrology applications.