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

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

doi:10.5194/hess-30-5067-2026

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

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Cited articles

Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Tan, J., and Beck, H. E.: Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling, Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, 2026.

Acuña Espinoza, E., Loritz, R., Kratzert, F., Klotz, D., Gauch, M., Álvarez Chaves, M., and Ehret, U.: Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events, Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, 2025.

Ahmed, S. F., Alam, Md. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., and Gandomi, A. H.: Deep learning modelling techniques: current progress, applications, advantages, and challenges, Artif. Intell. Rev., 56, 13521–13617, https://doi.org/10.1007/s10462-023-10466-8, 2023.

Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.

Bárdossy, A., Kilsby, C., Birkinshaw, S., Wang, N., and Anwar, F.: Is Precipitation Responsible for the Most Hydrological Model Uncertainty?, Front. Water, 4, 836554, https://doi.org/10.3389/frwa.2022.836554, 2022.