Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5791-2025
© 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-29-5791-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2025
Research article |
| 28 Oct 2025
| 28 Oct 2025
Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America
Wouter J. M. Knoben
CORRESPONDING AUTHOR
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Cyril Thébault
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Kasra Keshavarz
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Laura Torres-Rojas
Atmospheric & Oceanic Sciences, Princeton University, Princeton, New Jersey, US
Nathaniel W. Chaney
Civil and Environmental Engineering, Duke University, Durham, North Carolina, US
Alain Pietroniro
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
Martyn P. Clark
Schulich School of Engineering, Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
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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.
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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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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.
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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, 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
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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.
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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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HydroBlocks model, was configured over Finland. Time series of snow water equivalent, soil temperature, soil water content and soil ice content were created. Observational data was used to evaluate the model performance. HydroBlocks can generally predict soil conditions in Finland with some uncertainty. The model performance is improved with using local soil data and local soil hydraulic parameters. In the future, the modelling outputs show potential to be used in environmental applications.
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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.
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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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Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
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This study shows the significance of groundwater in resolving the coupled terrestrial water–energy cycle. LM4-SHARC (soil–hillslope aquifer–river continuum) describes the hillslope groundwater using its emergent properties, yielding noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implications of groundwater-mediated hydrologic interactions between hillslopes and streams need further exploration in the Earth system modeling community.
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.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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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.
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
Short summary
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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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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.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
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
Short summary
Short summary
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.
Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros
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Preprint archived
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The study explores a computationally efficient probabilistic precipitation forecast approach to generate multiple flood scenarios. It reveals the limitations in predicting flash floods accurately and the need for advanced ensemble methodologies to combine different sources of precipitation forecasts. It highlights the scale-dependency of flood predictions at higher spatial resolutions, shedding light on the relationship between river hydraulics and flood propagation in the river network.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
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Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
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
Short summary
Short summary
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
Short summary
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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.
Nathaniel W. Chaney, Laura Torres-Rojas, Noemi Vergopolan, and Colby K. Fisher
Geosci. Model Dev., 14, 6813–6832, https://doi.org/10.5194/gmd-14-6813-2021, https://doi.org/10.5194/gmd-14-6813-2021, 2021
Short summary
Short summary
Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.
Cited articles
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrol. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020. a
Ahmed, M. I., Shook, K., Pietroniro, A., Stadnyk, T., Pomeroy, J. W., Pers, C., and Gustafsson, D.: Implementing a parsimonious variable contributing area algorithm for the prairie pothole region in the HYPE modelling framework, Environ. Model. Softw., 167, 105769, https://doi.org/10.1016/j.envsoft.2023.105769, 2023. a, b
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: guidelines for computing crop water requirements, no. 56 in FAO irrigation and drainage paper, Food and Agriculture Organization of the United Nations, Rome, ISBN 978-92-5-104219-9, 1998. a
Almagro, A., Oliveira, P. T. S., Meira Neto, A. A., Roy, T., and Troch, P.: CABra: a novel large-sample dataset for Brazilian catchments, Hydrol. Earth Syst. Sci., 25, 3105–3135, https://doi.org/10.5194/hess-25-3105-2021, 2021. a
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. a
Arsenault, R., Bazile, R., Ouellet Dallaire, C., and Brissette, F.: CANOPEX: A Canadian hydrometeorological watershed database: CANOPEX: A Canadian Hydrometeorological Watershed Database, Hydrol. Process., 30, 2734–2736, https://doi.org/10.1002/hyp.10880, 2016. a
Arsenault, R., Brissette, F., Martel, J.-L., Troin, M., Lévesque, G., Davidson-Chaput, J., Gonzalez, M. C., Ameli, A., and Poulin, A.: A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds, Sci. Data, 7, 243, https://doi.org/10.1038/s41597-020-00583-2, 2020. a
Budhathoki, S., Rokaya, P., and Lindenschmidt, K.-E.: Improved modelling of a Prairie catchment using a progressive two-stage calibration strategy with in situ soil moisture and streamflow data, Hydrol. Res., 51, 505–520, https://doi.org/10.2166/nh.2020.109, 2020. a
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020. a
Clark, M. P. and Shook, K. R.: The Numerical Formulation of Simple Hysteretic Models to Simulate the Large‐Scale Hydrological Impacts of Prairie Depressions, Water Resour. Res., 58, e2022WR032694, https://doi.org/10.1029/2022WR032694, 2022. a, b
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015a. a, b, c
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Gochis, D. J., Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger, G. N., and Marks, D. G.: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies, Water Resour. Res., 51, 2515–2542, https://doi.org/10.1002/2015WR017200, 2015b. a, b, c
Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H., Jackson, B. M., Freer, J. E., Arnold, J. R., Moore, R. D., Istanbulluoglu, E., and Ceola, S.: Improving the theoretical underpinnings of process‐based hydrologic models, Water Resour. Res., 52, 2350–2365, https://doi.org/10.1002/2015WR017910, 2016. a
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K. R., and Papalexiou, S. M.: The Abuse of Popular Performance Metrics in Hydrologic Modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021. a
Clerc-Schwarzenbach, F., Selleri, G., Neri, M., Toth, E., van Meerveld, I., and Seibert, J.: Large-sample hydrology – a few camels or a whole caravan?, Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, 2024. a
Cloke, H. L. and Hannah, D. M.: Large‐scale hydrology: advances in understanding processes, dynamics and models from beyond river basin to global scale, Hydrol. Process., 25, 991–995, https://doi.org/10.1002/hyp.8059, 2011. a
Commission for Environmental Cooperation: North American Atlas – Political Boundaries, statistics Canada, United States Census Bureau, Instituto Nacional de Estadística y Geografía (INEGI), http://www.cec.org/north-american-environmental-atlas/political-boundaries-2021/ (last access: 20 December 2023), 2022. a
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984. a
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020. a
Delaigue, O., Guimarães, G. M., Brigode, P., Génot, B., Perrin, C., Soubeyroux, J.-M., Janet, B., Addor, N., and Andréassian, V.: CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking, Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, 2025. a, b
Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V., Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley, G., Liang, X., Nasonova, O. N., Noilhan, J., Oudin, L., Sorooshian, S., Wagener, T., and Wood, E. F.: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, Journal of Hydrology, 320, 31117, https://doi.org/10.1016/j.jhydrol.2005.07.031, 2006. a
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Blower, J., Caron, J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Horne, R., Whiteaker, T., Blodgett, D., Zender, C., Lee, D., Hassell, D., Snow, A. D., Kölling, T., Allured, D., Jelenak, A., Meier Soerensen, A., Gaultier, L., Herlédan, S., Manzano, F., Bärring, L., Barker, C., and Bartholomew, S.: NetCDF Climate and Forecast (CF) Metadata Conventions v1.11, http://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html, last access: 11 January 2024, 2023. a, b
Eckhardt, K.: How to construct recursive digital filters for baseflow separation, Hydrol. Process., 19, 507–515, https://doi.org/10.1002/hyp.5675, 2005. a
Environment and Climate Change Canada: National Water Data Archive: HYDAT, https://www.canada.ca/en/environment-climate-change/services/water-overview/quantity/monitoring/survey/data-products-services/national-archive-hydat.html (last access: 5 July 2018), 2010. a
Environment and Climate Change Canada: Reference Hydrometric Basin Network, https://www.canada.ca/en/environment-climate-change/services/water-overview/quantity/monitoring/survey/data-products-services/reference-hydrometric-basin-network.html (last access: 26 February 2021), 2020a. a
European Centre for Medium-range Weather Forecasting: ERA5: How to calculate wind speed and wind direction from u and v components of the wind? – Copernicus Knowledge Base – ECMWF Confluence Wiki, https://confluence.ecmwf.int/pages/viewpage.action?pageId=133262398 (last access: 2 January 2024), 2023a. a
European Centre for Medium-range Weather Forecasting: ERA5: data documentation – Copernicus Knowledge Base – ECMWF Confluence Wiki, https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Howtoacknowledge,citeandrefertoERA5 (last access: 3 January 2024), 2023b. a
European Centre for Medium-range Weather Forecasting: ERA5 terminology: analysis and forecast; time and steps; instantaneous and accumulated and mean rates and min/max parameters – Copernicus Knowledge Base – ECMWF Confluence Wiki, https://confluence.ecmwf.int/pages/viewpage.action?pageId=85402030#ERA5terminology:analysisandforecast;timeandsteps;instantaneousandaccumulatedandmeanratesandmin/maxparameters-Instantaneous,accumulated,meanrateandmin/maxparameters (last access: 3 January 2024), 2023c. a
Färber, C., Plessow, H., Mischel, S., Kratzert, F., Addor, N., Shalev, G., and Looser, U.: GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-427, in review, 2024. a
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017. a, b
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021. a
Fowler, K. J. A., Zhang, Z., and Hou, X.: CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia, Earth Syst. Sci. Data, 17, 4079–4095, https://doi.org/10.5194/essd-17-4079-2025, 2025. a
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022. a, b
Gasset, N., Fortin, V., Dimitrijevic, M., Carrera, M., Bilodeau, B., Muncaster, R., Gaborit, É., Roy, G., Pentcheva, N., Bulat, M., Wang, X., Pavlovic, R., Lespinas, F., Khedhaouiria, D., and Mai, J.: A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model, Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, 2021. a, b
Gharari, S., Keshavarz, K., Knoben, W. J., Tang, G., and Clark, M. P.: EASYMORE: A Python package to streamline the remapping of variables for Earth System models, SoftwareX, 24, 101547, https://doi.org/10.1016/j.softx.2023.101547, 2023. a
Gharari, S., Whitfield, P. H., Pietroniro, A., Freer, J., Liu, H., and Clark, M. P.: Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification, Hydrol. Earth Syst. Sci., 28, 4383–4405, https://doi.org/10.5194/hess-28-4383-2024, 2024. a, b
Gleeson, T.: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Borealis [data set], https://doi.org/10.5683/SP2/DLGXYO, 2018. a, b
Gleeson, T., Moosdorf, N., Hartmann, J., and Van Beek, L. P. H.: A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Geophys. Res. Lett., 41, 3891–3898, https://doi.org/10.1002/2014GL059856, 2014. a, b
Government of Canada: Web Service Links Interface – Water Level and Flow – Environment Canada, https://wateroffice.ec.gc.ca/services/links_e.html, last access: 22 December 2023. a
Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, 2018. a
Hansen, M. C., Potapov, P. V., Pickens, A. H., Tyukavina, A., Hernandez-Serna, A., Zalles, V., Turubanova, S., Kommareddy, I., Stehman, S. V., Song, X.-P., and Kommareddy, A.: Global land use extent and dispersion within natural land cover using Landsat data, Environ. Res. Lett., 17, 034050, https://doi.org/10.1088/1748-9326/ac46ec, 2022. a, b
Hao, Z., Jin, J., Xia, R., Tian, S., Yang, W., Liu, Q., Zhu, M., Ma, T., Jing, C., and Zhang, Y.: CCAM: China Catchment Attributes and Meteorology dataset, Earth Syst. Sci. Data, 13, 5591–5616, https://doi.org/10.5194/essd-13-5591-2021, 2021. a
Hayashi, M., Van Der Kamp, G., and Rosenberry, D. O.: Hydrology of Prairie Wetlands: Understanding the Integrated Surface-Water and Groundwater Processes, Wetlands, 36, 237–254, https://doi.org/10.1007/s13157-016-0797-9, 2016. a
Helgason, H. B. and Nijssen, B.: LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland, Earth Syst. Sci. Data, 16, 2741–2771, https://doi.org/10.5194/essd-16-2741-2024, 2024. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., and Fenicia, F.: CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland, Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, 2023. a
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, 2005WR004362, https://doi.org/10.1029/2005WR004362, 2006. a
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021. a, b
Knoben, W. J. M.: Catchment Attributes and MEteorology for Large-sample Studies for SPATially distributed modeling (CAMELS-SPAT) – code (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.16751492, 2025. a
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations, Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019. a
Knoben, W. J. M., Freer, J. E., Peel, M. C., Fowler, K. J. A., and Woods, R. A.: A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments, Water Resour. Res., 56, e2019WR025975, https://doi.org/10.1029/2019WR025975, 2020. a
Knoben, W., Thébault, C., Keshavarz, K., Torres-Rojas, L., Chaney, N., Pietroniro, A., and Clark, M.: Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America, Federated Research Data Repository [data set], https://doi.org/10.20383/103.01306, 2025. a, b, c
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019. a
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan - A global community dataset for large-sample hydrology, Sci. Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w, 2023. a
Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., and Morin, S.: A multiphysical ensemble system of numerical snow modelling, The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017. a
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14415, https://doi.org/10.1029/94JD00483, 1994. a, b
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood, E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019WR025287, 2019. a, b, c
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997. a
Lins, H. F.: USGS Hydro-Climatic Data Network 2009 (HCDN–2009), Tech. Rep. U.S. Geological Survey Fact Sheet 2012–3047, United States Geological Survey, https://pubs.usgs.gov/fs/2012/3047/ (last access: 21 March 2024), 2012. a
Liu, J., Koch, J., Stisen, S., Troldborg, L., Højberg, A. L., Thodsen, H., Hansen, M. F. T., and Schneider, R. J. M.: CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations, Earth Syst. Sci. Data, 17, 1551–1572, https://doi.org/10.5194/essd-17-1551-2025, 2025. a
Loritz, R., Dolich, A., Acuña Espinoza, E., Ebeling, P., Guse, B., Götte, J., Hassler, S. K., Hauffe, C., Heidbüchel, I., Kiesel, J., Mälicke, M., Müller-Thomy, H., Stölzle, M., and Tarasova, L.: CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany, Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, 2024. a
Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, https://doi.org/10.5194/essd-17-461-2025, 2025. a
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview, Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020, 2020. a
Maurer, T. and Zehe, E.: CATFLOW: A Physically Based and Distributed Hydrological Model for Continuous Simulation of Catchment Water- and Solute Dynamics – User Guide and Program Documentation (Version CATSTAT), Tech. rep., INSTITUTE FOR WATER RESOURCES PLANNING, HYDRAULICS AND RURAL ENGINEERING (IWK), University of Karlsruhe (TH), 2007. a
Maxwell, R. M., Kollet, S. J., Condon, L. E., Smith, S. G., Woodward, C. S., Falgout, R. D., Ferguson, I. M., Engdahl, N., Hector, B., Lopez, S. R., Gilbert, J., Bearup, L., Jefferson, J., Collins, C., De Graaf, I., Prubilick, C., Baldwin, C., Bosl, W. J., Hornung, R., and Ashby, S.: PARFLOW User's Manual, Tech. rep., Integrated GroundWater Modeling Center, 2019. a
McMahon, T. A., Peel, M. C., Lowe, L., Srikanthan, R., and McVicar, T. R.: Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis, Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, 2013. a, b
McMillan, H., Coxon, G., Araki, R., Salwey, S., Kelleher, C., Zheng, Y., Knoben, W., Gnann, S., Seibert, J., and Bolotin, L.: When good signatures go bad: Applying hydrologic signatures in large sample studies, Hydrol. Process., 37, e14987, https://doi.org/10.1002/hyp.14987, 2023. a
McMillan, H. K.: A review of hydrologic signatures and their applications, WIREs Water, 8, e1499, https://doi.org/10.1002/wat2.1499, 2021. a
Mekonnen, M. and Brauner, H.: MESH – A Community Hydrology-Land Surface Model: Meteorological Input, https://wiki.usask.ca/display/MESH/Meteorological+Input (last access: 27 January 2022), 2020. a
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016. a, b
Mitchell, K., Ek, M., Wong, V., Lohmann, D., Koren, V., Schaake, J., Duan, Q., Gayno, G., Moore, B., Grunmann, P., Tarpley, D., Ramsay, B., Chen, F., Kim, J., Pan, H.-L., Lin, Y., Marshall, C., Mahrt, L., Meyers, T., and Ruscher, P.: THE COMMUNITY Noah LAND-SURFACE MODEL (LSM) – User's guide Public Release Version 2.7.1, Tech. rep., ftp://ftp.emc.ncep.noaa.gov/mmb/gcp/ldas/noahlsm/ver_2.7.1 (last access: 28 January 2022), 2005. a
Muhammad, A., Evenson, G. R., Stadnyk, T. A., Boluwade, A., Jha, S. K., and Coulibaly, P.: Impact of model structure on the accuracy of hydrological modeling of a Canadian Prairie watershed, J. Hydrol.: Regional Studies, 21, 40–56, https://doi.org/10.1016/j.ejrh.2018.11.005, 2019. a
Myneni, R., Knyazikhin, Y., and Park, T.: MODIS/Terra+Aqua Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD15A2H.061, 2021. a, b
National Research Council Canada: Time zones and daylight saving time, https://nrc.canada.ca/en/certifications-evaluations-standards/canadas-official-time/time-zones-daylight-saving-time (last access: 25 October 2022), 2019. a
National Weather Service: II.3-SAC-SMA: Conceptualization of the Sacramento Soil Moisture Accounting model, in: National Weather Service River Forecast System (NWSRFS) User Manual, 1–13, http://www.nws.noaa.gov/ohd/hrl/nwsrfs/users_manual/htm/xrfsdocpdf.php (last access: 8 April 2020), 2005. a
Newman, A., Sampson, K., Clark, M., Bock, A., Viger, R., and Blodgett, D.: A large-sample watershed-scale hydrometeorological dataset for the contiguous USA, artwork Size: approximately 2.5 GB Medium: text/plain, text/tab-separated-values, png, shp Pages: approximately 2.5 GB, Zenodo [data set], https://doi.org/10.5065/D6MW2F4D, 2014. a, b
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015. a, b, c, d, e, f, g, h, i, j, k
Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and Nearing, G.: Benchmarking of a physically based hydrologic model, J. Hydrometeorol., 18, 2215–2225, 2017. a
Nijssen, B.: SUMMA Input – SUMMA Meteorological Forcing Files, https://summa.readthedocs.io/en/latest/input_output/SUMMA_input/#meteorological-forcing-files (last access: 30 December 2023), 2017. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F., and Loumagne, C.: Which potential evapotranspiration input for a lumped rainfall–runoff model?, J. Hydrol., 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a, b, c, d
Pellerin, J. and Nzokou Tanekou, F.: Reference Hydrometric Basin Network Update, Tech. rep., Environment and Climate Change Canada, Gatineau, QC, https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/RHBN/RHBN_EN.pdf (last access: 18 December 2023), 2020. a
Pelletier, J., Broxton, P., Hazenberg, P., Zeng, X., Troch, P., Niu, G., Williams, Z., Brunke, M., and Gochis, D.: Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers, p. 1032.940581 MB, ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1304, 2016a. a, b, c
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Sy., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016b. a, b, c
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003. a
PIHM team: PennState Integrated Hydrologic Model (PIHM) – Version 2.0 – Input File Formats, Tech. rep., Hydrology Group, Civil & Environmental Engineering, Pennsylvania State University, http://www.pihm.psu.edu/Downloads/Doc/pihm2.0_input_file_format.pdf (last access: 28 January 2022), 2007. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b, c
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L., Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a platform for basing process representation and model structure on physical evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021. a, b
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a, b
Rakovec, O., Kumar, R., Shrestha, P. K., and Samaniego, L.: Global assessment of hydrological components using a seamless multiscale modelling system, EGU General Assembly 2023, Vienna, Austria, 24–28 April 2023, EGU23-11945, https://doi.org/10.5194/egusphere-egu23-11945, 2023. a
Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate elasticity of streamflow in the United States, Water Resour. Res., 37, 1771–1781, https://doi.org/10.1029/2000WR900330, 2001. a
Schulla, J.: Model Description WaSIM (Water balance Simulation Model), Tech. rep., Hydrology Software Consulting J. Schulla, http://www.wasim.ch/en/products/wasim_description.htm (last access: 30 December 2023), 2021. a
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981–present, Sci. Data, 8, 224, https://doi.org/10.1038/s41597-021-01003-9, 2021. a, b, c
SMHI: HYPE file reference [HYPE Model Documentation], http://www.smhi.net/hype/wiki/doku.php?id=start:hype_file_reference#observation_data_files (last access: 27 January 2022), 2022. a
Stein, L., Clark, M. P., Knoben, W. J. M., Pianosi, F., and Woods, R. A.: How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA, Water Resour. Res., 57, e2020WR028300, https://doi.org/10.1029/2020WR028300, 2021. a
Tang, G., Clark, M., and Papalexiou, S.: EM-Earth: The Ensemble Meteorological Dataset for Planet Earth, Federated Research Data Repository [data set], https://doi.org/10.20383/102.0547, 2022a. a, b
Tang, G., Clark, M. P., and Papalexiou, S. M.: EM-Earth: The Ensemble Meteorological Dataset for Planet Earth, B. Am. Meteorol. Soc., 103, E996–E1018, https://doi.org/10.1175/BAMS-D-21-0106.1, 2022b. a, b
Tarasova, L., Gnann, S., Yang, S., Hartmann, A., and Wagener, T.: Catchment characterization: current descriptors, knowledge gaps and future opportunities, Earth Sciences Reviews, 252, 104739, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2024.104739, 2024. a, b, c, d
Teluguntla, P., Thenkabail, P., Oliphant, A., Gumma, M., Aneece, I., Foley, D., and McCormick, R.: Landsat-Derived Global Rainfed and Irrigated-Cropland Product 30 m V001, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/COMMUNITY/LGRIP/LGRIP30.001, 2023. a, b
Teutschbein, C.: CAMELS‐SE: Long‐termhydroclimatic observations (1961–2020) across 50 catchments in Sweden as a resource for modelling, education, and collaboration, Geoscience Data Journal, 11, 655–668, https://doi.org/10.1002/gdj3.239, 2024. a, b
Thenkabail, P. S., Teluguntla, P. G., Xiong, J., Oliphant, A., Congalton, R. G., Ozdogan, M., Gumma, M. K., Tilton, J. C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D.: Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud, USGS Numbered Series, U.S. Geological Survey, series: Professional Paper, https://doi.org/10.3133/pp1868, 2021. a, b
Thornton, M., Shrestha, R., Wei, Y., Thornton, P., and Kao, S.-C.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1, ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/2129, 2022. a
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and Wilson, B. E.: Gridded daily weather data for North America with comprehensive uncertainty quantification, Sci. Data, 8, 190, https://doi.org/10.1038/s41597-021-00973-0, 2021. a, b
Towler, E., Foks, S. S., Dugger, A. L., Dickinson, J. E., Essaid, H. I., Gochis, D., Viger, R. J., and Zhang, Y.: Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States, Hydrol. Earth Syst. Sci., 27, 1809–1825, https://doi.org/10.5194/hess-27-1809-2023, 2023. a
Trotter, L., Knoben, W. J. M., Fowler, K. J. A., Saft, M., and Peel, M. C.: Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability, Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, 2022. a
Water Survey of Canada: Sub-daily data download location, https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/UnitValueData/ (last access: 2 June 2025), 2025. a
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill, J. Hydrometeorol., 17, 651–668, 2016. a
Woods, R. A.: Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks, Adv. Water Resour., 32, 1465–1481, https://doi.org/10.1016/j.advwatres.2009.06.011, 2009. a, b
Xie, J., Liu, X., Wang, K., Yang, T., Liang, K., and Liu, C.: Evaluation of typical methods for baseflow separation in the contiguous United States, J. Hydrol., 583, 124628, https://doi.org/10.1016/j.jhydrol.2020.124628, 2020. a
Yuning Shi: MM-PIHM, v. 0.10.10, Zenodo [code], https://doi.org/10.5281/ZENODO.4533260, 2018. a
Short summary
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.
Many existing datasets for hydrologic analysis tend to treat catchments as single spatially...
