Articles | Volume 30, issue 8
https://doi.org/10.5194/hess-30-2337-2026
© 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-30-2337-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| Highlight paper
| 
23 Apr 2026
Technical note | Highlight paper |
| 23 Apr 2026
| 23 Apr 2026
Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes
Department of Civil Engineering, University of Victoria, Victoria, Canada
Wouter J. M. Knoben
Department of Civil Engineering, University of Calgary, Calgary, Canada
Thorsten Wagener
Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Tom Gleeson
Department of Civil Engineering, University of Victoria, Victoria, Canada
Markus Schnorbus
Pacific Climate Impacts Consortium, University of Victoria, Victoria, Canada
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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
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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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.
Francesca Pianosi, Georgios Sarailidis, Kirsty Styles, Philip Oldham, Stephen Hutchings, Rob Lamb, and Thorsten Wagener
Nat. Hazards Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/nhess-26-1727-2026, https://doi.org/10.5194/nhess-26-1727-2026, 2026
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Mathematical modelling is essential to support flood risk management. As these models simulate complex interactions between climate, the natural and the built environment, they unavoidably embed a range of simplifying assumptions. In this paper, we propose a more rigorous approach to analyse the impact of uncertain assumptions on modelling results. This is important to improve model transparency and set priorities for improving models.
Gaby J. Gründemann, Wouter J. M. Knoben, Yalan Song, Katie van Werkhoven, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-6460, https://doi.org/10.5194/egusphere-2025-6460, 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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-6083, https://doi.org/10.5194/egusphere-2025-6083, 2026
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Reliable river flow prediction guide water supply planning and flood protection. We tested whether selecting or combining many models improves accuracy compared with 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.
Simon P. Heselschwerdt, Thorsten Wagener, Lan Wang-Erlandsson, Anna M. Ukkola, Yannis Markonis, Yuting Yang, and Peter Greve
EGUsphere, https://doi.org/10.5194/egusphere-2025-5896, https://doi.org/10.5194/egusphere-2025-5896, 2025
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Precipitation on land is split into different pathways, contributing to runoff (blue water) or to plant water use (green water). Climate change alters this balance and shapes how future precipitation is divided. We use global climate models to study these changes and their drivers. We find that more extreme five-day precipitation is the main driver and routes more future precipitation into blue water, even where average precipitation decreases, with consequences for water and land management.
Peter Wagener, Wouter J. M. Knoben, Niels Schütze, and Diana Spieler
EGUsphere, https://doi.org/10.5194/egusphere-2025-5413, https://doi.org/10.5194/egusphere-2025-5413, 2025
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Hydrologic models help predict floods and droughts, but how we calibrate them changes what they get right. By testing eight objective functions across many model types and catchments, we found that each highlights different flow behaviours, such as floods, low flows, or water balance. No single approach is best for all flow conditions. Matching the calibration method to the study's purpose, or combining several methods, can make models more applicable to real-world water decisions.
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.
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.
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
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The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023, https://doi.org/10.5194/hess-27-2523-2023, 2023
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This publication provides an introduction to the CREDIBLE Uncertainty Estimation (CURE) toolbox. CURE offers workflows for a variety of uncertainty estimation methods. One of its most important features is the requirement that all of the assumptions on which a workflow analysis depends be defined. This facilitates communication with potential users of an analysis. An audit trail log is produced automatically from a workflow for future reference.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Chinchu Mohan, Tom Gleeson, James S. Famiglietti, Vili Virkki, Matti Kummu, Miina Porkka, Lan Wang-Erlandsson, Xander Huggins, Dieter Gerten, and Sonja C. Jähnig
Hydrol. Earth Syst. Sci., 26, 6247–6262, https://doi.org/10.5194/hess-26-6247-2022, https://doi.org/10.5194/hess-26-6247-2022, 2022
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The relationship between environmental flow violations and freshwater biodiversity at a large scale is not well explored. This study intended to carry out an exploratory evaluation of this relationship at a large scale. While our results suggest that streamflow and EF may not be the only determinants of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods or with other biodiversity data or metrics.
Rosanna A. Lane, Gemma Coxon, Jim Freer, Jan Seibert, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, https://doi.org/10.5194/hess-26-5535-2022, 2022
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This study modelled the impact of climate change on river high flows across Great Britain (GB). Generally, results indicated an increase in the magnitude and frequency of high flows along the west coast of GB by 2050–2075. In contrast, average flows decreased across GB. All flow projections contained large uncertainties; the climate projections were the largest source of uncertainty overall but hydrological modelling uncertainties were considerable in some regions.
Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5431–5447, https://doi.org/10.5194/hess-26-5431-2022, https://doi.org/10.5194/hess-26-5431-2022, 2022
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Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
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.
Vili Virkki, Elina Alanärä, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu
Hydrol. Earth Syst. Sci., 26, 3315–3336, https://doi.org/10.5194/hess-26-3315-2022, https://doi.org/10.5194/hess-26-3315-2022, 2022
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Direct and indirect human actions have altered streamflow across the world since pre-industrial times. Here, we apply a method of environmental flow envelopes (EFEs) that develops the existing global environmental flow assessments by methodological advances and better consideration of uncertainty. By assessing the violations of the EFE, we comprehensively quantify the frequency, severity, and trends of flow alteration during the past decades, illustrating anthropogenic effects on streamflow.
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.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
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How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
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Editorial statement
Are the current hydrologic models able to simulate non-stationary responses to climate change in highly seasonal climates, which include tropical, alpine, and polar regions that are some of the most vulnerable regarding climate change. This paper addresses this research question in a compelling, novel and comprehensive way, with a focus on the suitability of our performance metrics for assessing the reproduction of interannual variability.
Are the current hydrologic models able to simulate non-stationary responses to climate change in...
Short summary
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.
Common metrics used to evaluate hydrologic models make it relatively easy to achieve high...
