Articles | Volume 30, issue 14
https://doi.org/10.5194/hess-30-4667-2026
https://doi.org/10.5194/hess-30-4667-2026
Research article
 | 
27 Jul 2026
Research article |  | 27 Jul 2026

Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models

Jan P. Bohl, Raul R. Wood, Corinna Frank, Paul C. Astagneau, Jonas Peters, and Manuela I. Brunner

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Cited articles

Acuna Espinoza, E.: Analyzing the generalization capabilities of hybrid hydrological models for extrapolation to extreme events, Zenodo [code], https://doi.org/10.5281/ZENODO.14191623, 2024. a
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. a, b, c
Andréassian, V., Bourgin, F., Oudin, L., Mathevet, T., Perrin, C., Lerat, J., Coron, L., and Berthet, L.: Seeking Genericity in the Selection of Parameter Sets: Impact on Hydrological Model Efficiency, Water Resour. Res., 50, 8356–8366, https://doi.org/10.1002/2013WR014761, 2014. a
Arsenault, R., Martel, J.-L., Brunet, F., Brissette, F., and Mai, J.: Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models, Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, 2023. a
Astagneau, P., Peters, J., Sandra, P., Muñoz-Castro, E., and Brunner, M. I.: RESIdual STability (RESIST) Calibration for Improved Hydrological Model Time Generalizability, Water Resour. Res., 62, https://doi.org/10.1029/2025WR041435, 2026. a
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Short summary
To assess climate impacts on streamflow, we need models that can predict streamflow under future conditions. This study compares three model types: data-driven (LSTM - long short-term memory), conceptual (HBV - Hydrologiska Byråns Vattenbalansavdelning), and hybrid (LSTM-HBV). LSTMs perform best overall, but HBV and hybrid models generalize better to warmer climates. Hybrid models are a promising tool for climate impact assessments, combining LSTMs accuracy with better generalizability of traditional models. In snowy regions, all models struggle to generalize.
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