Flood frequency hydrology with a non-stationary, climate-informed weather generator

Flood risk management faces a fundamental challenge in robustly estimating flood quantiles in a changing climate and developing appropriate adaptation measures. Furthermore, sound risk estimates require spatially coherent and temporally consistent scenarios of extreme precipitation and flood events. In this contribution, we address both challenges by deploying a novel non-stationary climate-informed stochastic weather generator conditioned on dynamic and thermodynamic change signals from global climate models. We generate synthetic weather datasets for present and future climate states in Germany, which are subsequently used to estimate flood quantiles through continuous hydrologic simulations. The seasonality of extremes is analyzed and compared between present and future periods. The robustness of the weather generator-based estimates is exemplified for the flood frequency estimation in the Ahr basin hit by an extreme flood in July 2021 and benchmarked against temporal information expansion using historical floods.