Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-4867-2026
© Author(s) 2026. 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-30-4867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2026
Research article |
| 04 Aug 2026
| 04 Aug 2026
Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models
Institute of Hydrology and Meteorology, TUD Dresden University of Technology, Dresden, Germany
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Canada
Wouter J. M. Knoben
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Canada
Niels Schütze
Institute of Hydrology and Meteorology, TUD Dresden University of Technology, Dresden, Germany
Diana Spieler
Institute of Hydrology and Meteorology, TUD Dresden University of Technology, Dresden, Germany
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Canada
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Cyril Thébault, Wouter J. M. Knoben, Nans Addor, Andrew J. Newman, Diana Spieler, Nicolás A. Vásquez, Yalan Song, Gaby J. Gründemann, Shaun Carney, Mukesh Kumar, Katie van Werkhoven, Chaopeng Shen, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 30, 3945–3977, https://doi.org/10.5194/hess-30-3945-2026, https://doi.org/10.5194/hess-30-3945-2026, 2026
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Reliable river flow prediction guide water supply planning and flood protection. We tested whether selecting or combining multiple models improves accuracy compared with a single model. 78 models were used and tested in 559 river basins across the United States. A carefully chosen single model nearly matched more complex multi-model approaches, while combining models gave slightly higher accuracy and lower uncertainty. However, no approach worked best everywhere.
Gaby J. Gründemann, Wouter J. M. Knoben, Yalan Song, Katie van Werkhoven, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 30, 3439–3453, https://doi.org/10.5194/hess-30-3439-2026, https://doi.org/10.5194/hess-30-3439-2026, 2026
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The quality of large-domain hydrologic model simulations is often quantified with so-called accuracy metrics. Here we use simple benchmarks to provide relevant context for these accuracy metrics. Results show that areas where the model cannot beat the benchmarks do not always align with areas where the accuracy metrics are low. This suggests that model improvements are possible in regions that under more typical model evaluation approaches (i.e., without benchmarks) might not be obvious.
Sacha W. Ruzzante, Wouter J. M. Knoben, Thorsten Wagener, Tom Gleeson, and Markus Schnorbus
Hydrol. Earth Syst. Sci., 30, 2337–2355, https://doi.org/10.5194/hess-30-2337-2026, https://doi.org/10.5194/hess-30-2337-2026, 2026
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Common metrics used to evaluate hydrologic models make it relatively easy to achieve high performance scores in highly seasonal catchments. However, we analysed 18 hydrologic models and found that almost all were worse at simulating interannual variability and change in seasonal streamflow regimes. This suggests that climate change impacts on streamflow may not be accurately predicted in highly seasonal tropical, alpine, and polar regions, which are highly vulnerable to climate change.
Nicolás A. Vásquez, Pablo A. Mendoza, Wouter Knoben, Martyn Clark, Tricia Stadnyk, and Naoki Mizukami
EGUsphere, https://doi.org/10.5194/egusphere-2026-1363, https://doi.org/10.5194/egusphere-2026-1363, 2026
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Although distributed hydrological models are often calibrated using only streamflow data, this practice may provide unrealistic representations of the water cycle. We show that, while streamflow annual cycles can be reasonably simulated, the seasonality of other key variables - such as evapotranspiration, soil moisture, and snow cover - may be severely misrepresented. Our results highlight the need to assess seasonal patterns of variables beyond streamflow when calibrating hydrological models.
Wouter J. M. Knoben, Cyril Thébault, Kasra Keshavarz, Laura Torres-Rojas, Nathaniel W. Chaney, Alain Pietroniro, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 29, 5791–5833, https://doi.org/10.5194/hess-29-5791-2025, https://doi.org/10.5194/hess-29-5791-2025, 2025
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Many existing datasets for hydrologic analysis tend to treat catchments as single spatially homogeneous units focusing on daily data and typically do not support more complex models. This paper introduces a dataset that goes beyond this set-up by (1) providing data at a higher spatial and temporal resolution, (2) specifically considering the data requirements of all common hydrologic model types, and (3) using statistical summaries of the data aimed at quantifying spatial and temporal heterogeneity.
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
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
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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.
Aatralarasi Saravanan, Daniel Karthe, Selvaprakash Ramalingam, and Niels Schütze
Hydrol. Earth Syst. Sci., 29, 4847–4870, https://doi.org/10.5194/hess-29-4847-2025, https://doi.org/10.5194/hess-29-4847-2025, 2025
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In water-scarce regions, precipitation is a highly variable and essential resource for crop production. Developing countries like India have an uneven distribution of rain gauges, so reliance on satellite- and reanalysis-based precipitation products is critical for their prudent management. Hence, this study statistically evaluated different precipitation products against station data for water-scarce regions in Tamil Nadu and found that the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 Land (ERA5-Land) performed the best, followed by the Multi-Source Weighted-Ensemble Precipitation (MSWEP).
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 29, 2361–2375, https://doi.org/10.5194/hess-29-2361-2025, https://doi.org/10.5194/hess-29-2361-2025, 2025
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Hydrologic models are needed to provide simulations of water availability, floods, and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models and thus cannot easily be used to select an appropriate model for a specific place.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Diogo Costa, Kyle Klenk, Wouter Knoben, Andrew Ireson, Raymond J. Spiteri, and Martyn Clark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2787, https://doi.org/10.5194/egusphere-2023-2787, 2023
Preprint archived
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This work helps improve water quality simulations in aquatic ecosystems through a new modeling concept, which we termed “OpenWQ”. It allows tailoring biogeochemistry calculations and integration with existing hydrological (water quantity) simulation tools. The integration is demonstrated with two hydrological models. The models were tested for different pollution scenarios. This paper helps improve interoperability, transparency, flexibility, and reproducibility in water quality simulations.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
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This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
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Short summary
Hydrologic models help predict floods and droughts, but how we calibrate them changes what they get right. Across 47 model structures and 10 diverse catchments, we tested how 8 metrics shape the reproduction of 15 streamflow features, from runoff ratios to high and low flows. Metric choice often mattered more than model structure, yet no single metric is best for all flow conditions. We provide guidance on metric strengths and weaknesses to help match the calibration metric to the study purpose.
Hydrologic models help predict floods and droughts, but how we calibrate them changes what they...
