Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5005-2025
https://doi.org/10.5194/hess-29-5005-2025
Research article
 | 
08 Oct 2025
Research article |  | 08 Oct 2025

How well do process-based and data-driven hydrological models learn from limited discharge data?

Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret

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Cited articles

Abhinav, G. and Rao, S. G.: Uncertainty quantification in watershed hydrology: Which method to use?, Journal of Hydrology, 616, 128749, https://doi.org/10.1016/j.jhydrol.2022.128749, 2023. a
Acuña Espinoza, E., Loritz, R., Álvarez Chaves, M., Bäuerle, N., and Ehret, U.: To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization, Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, 2024. a, b, c
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment part I: model development 1, JAWRA Journal of the American Water Resources Association, 34, 73–89, 1998. a
Ayzel, G. and Heistermann, M.: The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset, Computers & Geosciences, 149, 104708, https://doi.org/10.1016/j.cageo.2021.104708, 2021. a
Azmi, E., Ehret, U., Weijs, S. V., Ruddell, B. L., and Perdigão, R. A. P.: Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance, Hydrol. Earth Syst. Sci., 25, 1103–1115, https://doi.org/10.5194/hess-25-1103-2021, 2021. a
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Short summary
Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
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