Data-driven global projection of future flooding in 18.5 million river reaches

Reliable flood projection is crucial for designing suitable flood protection structures and for enhancing resilience in vulnerable regions. However, projections of future flooding suffer from cascading uncertainties arising from the climate model outputs, emission scenarios, hydrological models, and the shortage of observations in data-sparse regions. To overcome these limitations, we design a new hybrid model, blending machine learning and climate model simulations, for global-scale projection of river flooding. This is achieved by training a random forest model directly on climate simulations from 20 CMIP6 models over the historical period (19852014), with extreme discharges observed at approximately 15,000 hydrologic stations as the target variable. The random forest model also includes static geographic predictors including land cover, climate, geomorphology, soil, human impacts, and hydrologic signatures. We make the explicit assumption that the random forest model can learn systematic biases in the relationship between the climate simulations and flood regimes in different regions of the globe. We then apply the well-calibrated random forest model to a new vector-based, global river network in approximately 18.51 million reaches with drainage areas greater than 100 km2. Global changes in flood hazard are projected for the 21st century (20152100) under SSP2-4.5 and SSP5-8.5. We show that the data-driven method reproduces historical annual maximum discharges better than the physically-based hydrological models driven by bias-corrected climate simulations in the ISIMIP3b experiment. We then use the machine learning model with explainable AI to diagnose spatial biases in the climate simulations and future flood projections in different regions of the globe.