Pauline Delorme

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.

Abstract More than one billion people are exposed to flood risk globally, with this number projected to double by 2050. Global flood models underpin risk assessment and adaptation planning, yet typically assume that river bankfull capacity corresponds to a fixed two-year return period, neglecting spatial and temporal variability in channel characteristics.