Jiabo Yin

Abstract 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.

Abstract Bankfull discharge, the maximum flow a river can convey before spilling over its banks, is central to modelling flood risk and understanding river-channel evolution. Global flood inundation models assume a 2-year return period for bankfull conditions, but this assumption remains untested globally. Here we use observations and machine learning to estimate bankfull discharge at ~1-km resolution along a recently developed global river network.