Global flood projection and socioeconomic implications under a physics-constrained deep learning framework

As the planet warms, the frequency and severity of weather-related hazards such as floods are intensifying, posing substantial threats to communities around the globe. Rising flood peaks and volumes can claim lives, damage infrastructure, and compromise access to essential services. However, the physical mechanisms behind global flood evolution are still uncertain, and their implications for socioeconomic systems remain unclear. In this study, we leverage a supervised machine learning technique to identify the dominant factors influencing daily streamflow. We then propose a physics-constrained cascade model chain which assimilates water and heat transport processes to project bivariate risk (i.e. flood peak and volume together), along with its socioeconomic consequences. To achieve this, we drive a hybrid deep learning-hydrological model with bias-corrected outputs from twenty global climate models (GCMs) under four shared socioeconomic pathways (SSPs). Our results project considerable increases in flood risk under the medium to high-end emission scenario (SSP3-7.0) over most catchments of the globe. The median future joint return period decreases from 50 years to around 27.6 years, with 186 trillion dollars and 4 billion people exposed. Downwelling shortwave radiation is identified as the dominant factor driving changes in daily streamflow, accelerating both terrestrial evapotranspiration and snowmelt. As future scenarios project enhanced radiation levels along with an increase in precipitation extremes, a heightened risk of widespread flooding is foreseen. This study aims to provide valuable insights for policymakers developing strategies to mitigate the risks associated with river flooding under climate change.