Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jun 2024
Research article |
| 13 Jun 2024
| 13 Jun 2024
Metamorphic testing of machine learning and conceptual hydrologic models
Peter Reichert
CORRESPONDING AUTHOR
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
retired
Kai Ma
Institute of International Rivers and Eco-Security, Yunnan University, Kunming, China
Yunnan Key Laboratory of International Rivers and Transboundary Eco-security, Yunnan University, Kunming, China
Marvin Höge
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Fabrizio Fenicia
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Marco Baity-Jesi
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Dapeng Feng
Civil and Environmental Engineering, Pennsylvania State University, University Park, State College, PA, USA
Chaopeng Shen
Civil and Environmental Engineering, Pennsylvania State University, University Park, State College, PA, USA
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Cited
13 citations as recorded by crossref.
- Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment Z. Dong et al. https://doi.org/10.1016/j.ejrh.2025.102228
- Scientific software development in the AI era: reproducibility, MLOps, and applications in soft matter physics N. Cheimarios https://doi.org/10.3389/fphy.2025.1711356
- Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. https://doi.org/10.1016/j.advwatres.2025.104955
- Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models S. Poudel et al. https://doi.org/10.1016/j.jhydrol.2025.134427
- Exploring inductive transfer approaches for machine learning based rainfall-runoff modelling A. Kenne et al. https://doi.org/10.1080/15715124.2026.2729475
- On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al. https://doi.org/10.1080/02626667.2025.2452357
- Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al. https://doi.org/10.1007/s11269-025-04231-5
- Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. https://doi.org/10.5194/gmd-17-7181-2024
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Graph Learning for River Water Quality Forecasting: Advances in Hydrological Connectivity Representation, Dynamic Topology, and Physics-Informed Modeling Z. Xu et al. https://doi.org/10.3390/math14183359
- Deep learning error post-processing improves stochastic watershed modeling B. Manoli et al. https://doi.org/10.1016/j.jhydrol.2026.135663
- Enhanced Prediction of Reservoir Inflow Via an SVR–Harris Hawks Optimization Hybrid Model: A Case Study of the Mahabad Dam S. Enayati & S. Abravesh https://doi.org/10.1007/s41101-026-00503-2
- An argument for parsimony in differentiable hydrologic models S. Poudel & S. Steinschneider https://doi.org/10.5194/hess-30-5173-2026
13 citations as recorded by crossref.
- Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment Z. Dong et al. https://doi.org/10.1016/j.ejrh.2025.102228
- Scientific software development in the AI era: reproducibility, MLOps, and applications in soft matter physics N. Cheimarios https://doi.org/10.3389/fphy.2025.1711356
- Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. https://doi.org/10.1016/j.advwatres.2025.104955
- Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models S. Poudel et al. https://doi.org/10.1016/j.jhydrol.2025.134427
- Exploring inductive transfer approaches for machine learning based rainfall-runoff modelling A. Kenne et al. https://doi.org/10.1080/15715124.2026.2729475
- On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al. https://doi.org/10.1080/02626667.2025.2452357
- Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al. https://doi.org/10.1007/s11269-025-04231-5
- Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. https://doi.org/10.5194/gmd-17-7181-2024
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Graph Learning for River Water Quality Forecasting: Advances in Hydrological Connectivity Representation, Dynamic Topology, and Physics-Informed Modeling Z. Xu et al. https://doi.org/10.3390/math14183359
- Deep learning error post-processing improves stochastic watershed modeling B. Manoli et al. https://doi.org/10.1016/j.jhydrol.2026.135663
- Enhanced Prediction of Reservoir Inflow Via an SVR–Harris Hawks Optimization Hybrid Model: A Case Study of the Mahabad Dam S. Enayati & S. Abravesh https://doi.org/10.1007/s41101-026-00503-2
- An argument for parsimony in differentiable hydrologic models S. Poudel & S. Steinschneider https://doi.org/10.5194/hess-30-5173-2026
Saved (final revised paper)
Latest update: 01 Oct 2026
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
We compared the predicted change in catchment outlet discharge to precipitation and temperature...
