Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-5067-2026
https://doi.org/10.5194/hess-30-5067-2026
Research article
 | 
12 Aug 2026
Research article |  | 12 Aug 2026

BiasCast: learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions

Oliver Konold, Moritz Feigl, Patrick Podest, Christoph Klingler, and Karsten Schulz

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Latest update: 03 Sep 2026
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Short summary
Flood forecasting systems depend on weather forecasts. However, weather forecasts always have an error when compared with historical observations. This causes flood predictions to become less accurate when switching from historical to forecast data. We tested artificial intelligence (AI) methods across 451 European river basins to address this challenge and found that using appropriate model design can turn this accuracy problem into something the system can learn to fix "on the fly".
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