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https://doi.org/10.5194/egusphere-2024-2147
https://doi.org/10.5194/egusphere-2024-2147
30 Jul 2024
 | 30 Jul 2024

Analyzing the generalization capabilities of hybrid hydrological models for extrapolation to extreme events

Eduardo Acuna Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret

Abstract. Data-driven techniques have shown the potential to outperform process-based models for rainfall-runoff simulation. Recently, hybrid models, which combine data-driven methods with process-based approaches, have been proposed to leverage the strengths of both methodologies, aiming to enhance simulation accuracy while maintaining certain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events, comparing their performance against Long Short-Term Memory (LSTM) networks and process based models. Our results indicate that hybrid models show similar performance as LSTM networks for most cases. However, hybrid models reported slightly lower errors in the most extreme cases, and were able to produce higher peak discharges.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint

11 Mar 2025
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025,https://doi.org/10.5194/hess-29-1277-2025, 2025
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Data-driven techniques have shown the potential to outperform process-based models in...
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