Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 15
Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-5067-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-5067-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 15
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

Data sets

Experimental Setups and Results for "BiasCast: Learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions" Oliver Konold et al. https://doi.org/10.5281/zenodo.17241922

Extended LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe Oliver Konold et al. https://doi.org/10.5281/zenodo.17119635

Model code and software

conestone/biascast: v1.0 (Version v1.0) Oliver Konold https://doi.org/10.5281/zenodo.17293199

Experiments and Results Code for "BiasCast: Learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions" Oliver Konold https://github.com/conestone/biascast

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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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