Catchment-based post-processing of probabilistic weather forecasts for AI-supported flood prediction in Germany

Small and medium-size catchments respond rapidly to intense rainfall and are therefore especially challenging for flood forecasting, as short warning times coincide with large meteorological and hydrological uncertainties. Within the KI-HopE-De (AI-based flood prediction in small river basins in Germany) project, which aims to develop AI-based flood forecasting for small catchments, we investigate how probabilistic numerical weather prediction can be transformed into hydrologically more useful inputs for machine-learning-based runoff prediction. The project focuses on short-range forecasts up to 48 hours and on catchments smaller than 500 km².

Our contribution addresses the meteorological side of this challenge through catchment-based post-processing of ensemble precipitation forecasts. We use hourly operational ICON-D2 ensemble precipitation forecasts with 3-hourly initializations and lead times up to 48 hours, aggregated from the native model grid to catchments. The resulting catchment-scale ensemble time series are post-processed using statistical and machine-learning-oriented methods designed to improve both calibration and hydrological relevance. In particular, we examine ensemble model output statistics (EMOS) and isotonic distributional regression (IDR) for marginal adjustment, and reconstruct temporal dependence using approaches such as ensemble copula coupling and the Schaake shuffle. This setup is designed to retain the benefits of probabilistic weather forecasts while generating physically and hydrologically more consistent forcing data for downstream flood-prediction models.

The presentation will show how these post-processing strategies perform at two connected levels. First, we will compare post-processed precipitation forecasts against the raw ensemble using probabilistic verification metrics and event-based diagnostics, with particular emphasis on extremes. Second, we will assess whether improved meteorological inputs translate into improved flood forecasts in selected severe-flood cases. In this way, the study highlights not only whether post-processing improves rainfall forecasts, but also whether these improvements are relevant for downstream hydrological AI applications in small, fast-responding catchments.