Articles | Volume 29, issue 11
https://doi.org/10.5194/hess-29-2361-2025
© Author(s) 2025. 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-29-2361-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
04 Jun 2025
Technical note |
| 04 Jun 2025
| 04 Jun 2025
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Wouter J. M. Knoben
CORRESPONDING AUTHOR
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Ashwin Raman
Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, Alabama, United States of America
Gaby J. Gründemann
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Mukesh Kumar
Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, Alabama, United States of America
Alain Pietroniro
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Chaopeng Shen
Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
Yalan Song
Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
Cyril Thébault
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Katie van Werkhoven
Research Triangle Institute, Research Triangle Park, North Carolina, United States of America
Andrew W. Wood
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, Colorado, United States of America
Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado, United States of America
Martyn P. Clark
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
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Peter Wagener, Wouter J. M. Knoben, Niels Schütze, and Diana Spieler
Hydrol. Earth Syst. Sci., 30, 4867–4888, https://doi.org/10.5194/hess-30-4867-2026, https://doi.org/10.5194/hess-30-4867-2026, 2026
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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.
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.
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.
Lauren H. North, Adrienne M. Marshall, Glenn A. Tootle, Lisa Davis, Andy W. Wood, and Eric J. Anderson
Hydrol. Earth Syst. Sci., 30, 5195–5214, https://doi.org/10.5194/hess-30-5195-2026, https://doi.org/10.5194/hess-30-5195-2026, 2026
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We assessed the U.S. National Hydrologic Model's ability to simulate several components of the water cycle using multiple datasets of environmental variables. We find that the model's accuracy in streamflow simulation is positively and negatively impacted by the additional constraints, and more model parameters are identified as important. Our results inform operational hydrologic modeling by illuminating the complexities of using the continually expanding suite of data products.
Peter Wagener, Wouter J. M. Knoben, Niels Schütze, and Diana Spieler
Hydrol. Earth Syst. Sci., 30, 4867–4888, https://doi.org/10.5194/hess-30-4867-2026, https://doi.org/10.5194/hess-30-4867-2026, 2026
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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.
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
Short summary
Short summary
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.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, JongCheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Jackson Tan, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, https://doi.org/10.5194/hess-30-3399-2026, 2026
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Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Kamlesh Sawadekar, Seth McGinnis, Peijun Li, Kathryn Lawson, and Chaopeng Shen
EGUsphere, https://doi.org/10.48550/arXiv.2604.23045, https://doi.org/10.48550/arXiv.2604.23045, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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δCLIMBA can fix consistent errors with global circulation models so their precipitation is locally relevant. Instead of making corrections
directly, it predicts the coefficients of interpretable formulas to minimize bias. δCLIMBA learns to make interpretable corrections that,
once trained, can be easily applied on places without data. It accurately corrects both average rainfall and extreme storm events and
shows the promise to preserve future trends, and generalizes to new regions.
Abdolmehdi Behroozi, Kathryn Lawson, and Chaopeng Shen
EGUsphere, https://doi.org/10.5194/egusphere-2026-1982, https://doi.org/10.5194/egusphere-2026-1982, 2026
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A hyper-efficient learnable model predicts flooding faster and more accurately than traditional methods. The two-part approach captures broad water movement then refines local flood details. Tested on rivers, dam breaks, and floodplains, it reproduced water depth, flooded areas, and flow timing. Water volume stays consistent and uncertain terrain data is handled well. The model delivers high-resolution predictions with far less computing effort, enabling faster, scalable flood risk assessment.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
Geosci. Model Dev., 19, 3427–3453, https://doi.org/10.5194/gmd-19-3427-2026, https://doi.org/10.5194/gmd-19-3427-2026, 2026
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Soil moisture helps control how plants grow and how land exchanges water and energy with the atmosphere. We evaluated how well 16 Earth System Models represent soil moisture and its links to plant activity using a benchmarking system and observational data. Many models simulate surface soil moisture well but show larger errors deeper in the soil and in colder regions. Models that represent plant–soil water interactions more directly better capture how ecosystems respond to water availability.
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
Short summary
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.
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
Short summary
Short summary
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.
Delanie Williams, Mukesh Kumar, Katie van Werkhoven, Martyn Clark, Christopher Wilson, and Paul Miller
EGUsphere, https://doi.org/10.5194/egusphere-2026-583, https://doi.org/10.5194/egusphere-2026-583, 2026
Preprint archived
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Machine learning (ML) models have potential within water resource management to be used in place of traditional models. However, there is hesitance for many agencies and institutions to pursue ML models, due to existing familiarity, reliability, interpretability, resources, and workflows. Thus, instead of opting for complete retention of traditional models, or complete replacement by ML models, this perspective argues for the selective integration of ML within existing models.
David R. Casson, Guoqiang Tang, Nicolás Vásquez, Andrew W. Wood, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-6066, https://doi.org/10.5194/egusphere-2025-6066, 2026
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This study generates meteorological ensembles tailored for mountain snow estimation, accounting for topography, wind-driven undercatch, and large-scale atmospheric patterns. Driving a physics-based snow model with these ensembles allowed uncertainty in weather inputs to carry through to estimates of snow accumulation and melt. Across three mountain basins, this led to realistic and reliable snow estimates useful for data assimilation, water supply and forecasting applications.
Scott Peckham, Keith Jennings, Wanru Wu, Andy Wood, and Lauren Bolotin
EGUsphere, https://doi.org/10.5194/egusphere-2025-5786, https://doi.org/10.5194/egusphere-2025-5786, 2026
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Several US federal agencies (e.g., USGS, NOAA, USDA, and NSF) collect information for river basins to support their water-related missions. Data is published online in named collection that each have their own attributes and objectives. HARBOR harmonizes and brings together all these datasets, just as many large cargo ships can be moored in one harbor. It also classifies basins based on hydrologic similarity, helping researchers find the best model for predicting their hydrologic response.
Jiangtao Liu, Chaopeng Shen, Fearghal O'Donncha, Yalan Song, Wei Zhi, Hylke E. Beck, Tadd Bindas, Nicholas Kraabel, and Kathryn Lawson
Hydrol. Earth Syst. Sci., 29, 6811–6828, https://doi.org/10.5194/hess-29-6811-2025, https://doi.org/10.5194/hess-29-6811-2025, 2025
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Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.
Peijun Li, Yalan Song, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 29, 6829–6861, https://doi.org/10.5194/hess-29-6829-2025, https://doi.org/10.5194/hess-29-6829-2025, 2025
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This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.
Ethan Ritchie, Andrew W. Wood, Ryan Johnson, Adrienne Marshall, Josh Sturtevant, Dane Liljestrand, and Emily Golitzin
EGUsphere, https://doi.org/10.5194/egusphere-2025-5514, https://doi.org/10.5194/egusphere-2025-5514, 2025
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Snow water equivalent (SWE) is a critical water resource to many regions globally. Estimating SWE remains a challenge in hydrology highlighting the need for consistent evaluation frameworks. This study applied a standard approach for SWE evaluation across a range of datasets in the western US, using the Airborne Snow Observatory (ASO) SWE dataset as the reference observational dataset. We outline and demonstrate an example of a community evaluation protocol using datasets in this study.
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.
Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
Hydrol. Earth Syst. Sci., 29, 5453–5476, https://doi.org/10.5194/hess-29-5453-2025, https://doi.org/10.5194/hess-29-5453-2025, 2025
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 d, 1–6 months) and timescales (daily and monthly) over Western US basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western US.
Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami
Hydrol. Earth Syst. Sci., 29, 4515–4537, https://doi.org/10.5194/hess-29-4515-2025, https://doi.org/10.5194/hess-29-4515-2025, 2025
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We present a new strategy to calibrate large-domain land/hydrology models over diverse regions. Using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models, our approach integrates catchment attributes, parameters, and performance metrics to optimize streamflow simulations. Leveraging advances in machine learning for hydrology, we improve calibration and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Pragnaditya Malakar, Aatish Anshuman, Mukesh Kumar, Georgios Boumis, T. Prabhakar Clement, Arik Tashie, Hitesh Thakur, Nagaraj Bhat, and Lokendra Rathore
Earth Syst. Sci. Data, 17, 1515–1528, https://doi.org/10.5194/essd-17-1515-2025, https://doi.org/10.5194/essd-17-1515-2025, 2025
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Groundwater dynamics depend on groundwater recharge, but daily benchmark data of recharge are scarce. Here we present a daily groundwater recharge per unit specified yield (RpSy) data at 485 US groundwater monitoring wells. RpSy can be used to validate the temporal consistency of recharge products from land surface and hydrologic models and facilitate assessment of recharge-driver functional relationships in them.
Mohammad Sina Jahangir, John Quilty, Chaopeng Shen, Andrea Scott, Scott Steinschneider, and Jan Adamowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-846, https://doi.org/10.5194/egusphere-2025-846, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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This study presents a novel hybrid approach to streamflow prediction, significantly improving the efficiency and accuracy of fine-tuning deep learning models for hydrological prediction. Tested across numerous catchments in the U.S. and Europe, this method accelerates the fine-tuning process and improves prediction accuracy in locations beyond the training data. This innovative approach sets the stage for future hydrological models leveraging transfer learning.
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andy Wood
Hydrol. Earth Syst. Sci., 29, 1117–1133, https://doi.org/10.5194/hess-29-1117-2025, https://doi.org/10.5194/hess-29-1117-2025, 2025
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There is a perceived mismatch between the spatial scales on which global climate models can produce data and those needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We assessed the credibility of a set of water management decision metrics in the Community Earth System Model v2 (CESM2). CESM2 shows potentially greater use of its output in long-range water management decisions.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Shervan Gharari, Paul H. Whitfield, Alain Pietroniro, Jim Freer, Hongli Liu, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 28, 4383–4405, https://doi.org/10.5194/hess-28-4383-2024, https://doi.org/10.5194/hess-28-4383-2024, 2024
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This study provides insight into the practices that are incorporated into discharge estimation across the national Canadian hydrometric network operated by the Water Survey of Canada (WSC). The procedures used to estimate and correct discharge values are not always understood by end-users. Factors such as ice cover and sedimentation limit accurate discharge estimation. Highlighting these challenges sheds light on difficulties in discharge estimation and the associated uncertainty.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
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.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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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.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
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
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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.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
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Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
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 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.
Hydrologic models are needed to provide simulations of water availability, floods, and droughts....
