Articles | Volume 20, issue 10
https://doi.org/10.5194/tc-20-5895-2026
© 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/tc-20-5895-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
15 Oct 2026
Research article | Highlight paper |
| 15 Oct 2026
| 15 Oct 2026
Propagating meteorological uncertainty in physically based mountain snow modeling
David R. Casson
CORRESPONDING AUTHOR
Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
Guoqiang Tang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Nicolás Vásquez
Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
Andrew W. Wood
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, USA
Civil and Environmental Engineering, Colorado School of Mines, Golden, USA
Martyn P. Clark
Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
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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.
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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.
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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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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2026-1363, https://doi.org/10.5194/egusphere-2026-1363, 2026
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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
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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.
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.
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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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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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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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
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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
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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
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Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
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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.
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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.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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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.
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Editorial statement
Quantifying the distribution of mountain snow is critical issue to ensure effective water resource management, skillful hydrological prediction, and hazard mitigation. This manuscript presents a highly impactful framework for quantifying meteorological forcing uncertainty in model simulations of mountain snow using novel machine learning applications and large sample emulators. By strictly adhering to FAIR data principles and utilizing open-source, automated workflows, the study provides a compelling demonstration of best practices in hydrological modeling. This paper will serve as an invaluable guide for early-career hydrologists seeking practical examples of rigorous code development and modeling workflows.
Quantifying the distribution of mountain snow is critical issue to ensure effective water...
Short summary
Improving mountain snow prediction requires better representation of meteorological uncertainty. This study generates meteorological ensembles accounting for topography, gauge undercatch, and large-scale atmospheric patterns. Driving a physics-based snow model calibrated using a Large Sample Emulator with these ensembles propagates forcing uncertainty into snow accumulation and melt, producing probablistic estimates for data assimilation and hydrological forecasting.
Improving mountain snow prediction requires better representation of meteorological uncertainty....
