Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1809-2023
© Author(s) 2023. 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-27-1809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 May 2023
Research article |
| 09 May 2023
| 09 May 2023
Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Sydney S. Foks
Water Resources Mission Area, US Geological Survey (USGS), Lakewood, CO, USA
Aubrey L. Dugger
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Jesse E. Dickinson
Arizona Water Science Center, US Geological Survey, Tucson, AZ, USA
Hedeff I. Essaid
Water Resources Mission Area, US Geological Survey, Moffett Field, CA, USA
David Gochis
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Roland J. Viger
Water Resources Mission Area, US Geological Survey (USGS), Lakewood, CO, USA
Yongxin Zhang
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
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26 citations as recorded by crossref.
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- Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. https://doi.org/10.1029/2023JD038534
- Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin A. Hamilton et al. https://doi.org/10.1016/j.envsoft.2024.106185
- Integrated river basin assessment framework combining probabilistic streamflow reconstruction, Bayesian bias correction, and drought storyline analysis T. Amestoy et al. https://doi.org/10.1016/j.envsoft.2025.106756
- Comment on Williams (2025): “Friends don't let friends use NSE or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice” M. Clark et al. https://doi.org/10.1016/j.envsoft.2026.106869
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- Study on the Impact of Climate Change on Water Cycle Processes in Cold Mountainous Areas—A Case Study of Water Towers in Northeastern China Z. Li et al. https://doi.org/10.3390/w17070969
- Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al. https://doi.org/10.3389/frwa.2025.1709138
- How GPM IMERG and GSMaP advance hydrological applications: A global perspective Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2025.133514
- Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes P. Bosompemaa et al. https://doi.org/10.1016/j.envsoft.2024.106234
- Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform D. Sales et al. https://doi.org/10.3390/eng6040065
- Flood frequency sampling error: insights from regional analysis, stochastic storm transposition, and physics-based modeling M. Abbasian et al. https://doi.org/10.1016/j.jhydrol.2025.133802
- Lagged streamflow depletion due to pumping-induced stream drying: Incorporation into analytical streamflow depletion estimation methods S. Zipper et al. https://doi.org/10.1016/j.jhydrol.2026.134909
- Impact of Spatial Resolution on River Flow Simulation Based on the Total Runoff Integrating Pathway (TRIP) Model M. Kim et al. https://doi.org/10.3390/atmos16091083
- A tolerant hydrologic technique for real-time selection of optimum QPFs from NWPMs for flood warning applications M. Salah et al. https://doi.org/10.2166/wst.2024.046
- Technical note: Benchmarking large-domain model performance under sampling uncertainty G. Gründemann et al. https://doi.org/10.5194/hess-30-3439-2026
- A comprehensive assessment and comparison of streamflow simulations performance between the national water model versions 3.0 and 2.1 across the continental United States A. Ojha et al. https://doi.org/10.1016/j.jhydrol.2026.136046
- Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States C. Simeone et al. https://doi.org/10.3390/w16202996
- Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. https://doi.org/10.3389/frwa.2024.1439906
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
- Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning A. Husic et al. https://doi.org/10.5194/hess-29-4457-2025
- 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 W. Knoben et al. https://doi.org/10.5194/hess-29-5791-2025
- BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. https://doi.org/10.1038/s41597-024-03594-5
- Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States A. Putman et al. https://doi.org/10.5194/hess-28-2895-2024
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider https://doi.org/10.5194/hess-28-479-2024
- Over 100 global climate sensitive rivers are experiencing large and severe changes in streamflow volume and timing R. Patil et al. https://doi.org/10.1088/1748-9326/ae1e16
26 citations as recorded by crossref.
- smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al. https://doi.org/10.5194/gmd-18-7003-2025
- Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. https://doi.org/10.1029/2023JD038534
- Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin A. Hamilton et al. https://doi.org/10.1016/j.envsoft.2024.106185
- Integrated river basin assessment framework combining probabilistic streamflow reconstruction, Bayesian bias correction, and drought storyline analysis T. Amestoy et al. https://doi.org/10.1016/j.envsoft.2025.106756
- Comment on Williams (2025): “Friends don't let friends use NSE or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice” M. Clark et al. https://doi.org/10.1016/j.envsoft.2026.106869
- Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al. https://doi.org/10.5194/hess-30-2135-2026
- Study on the Impact of Climate Change on Water Cycle Processes in Cold Mountainous Areas—A Case Study of Water Towers in Northeastern China Z. Li et al. https://doi.org/10.3390/w17070969
- Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al. https://doi.org/10.3389/frwa.2025.1709138
- How GPM IMERG and GSMaP advance hydrological applications: A global perspective Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2025.133514
- Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes P. Bosompemaa et al. https://doi.org/10.1016/j.envsoft.2024.106234
- Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform D. Sales et al. https://doi.org/10.3390/eng6040065
- Flood frequency sampling error: insights from regional analysis, stochastic storm transposition, and physics-based modeling M. Abbasian et al. https://doi.org/10.1016/j.jhydrol.2025.133802
- Lagged streamflow depletion due to pumping-induced stream drying: Incorporation into analytical streamflow depletion estimation methods S. Zipper et al. https://doi.org/10.1016/j.jhydrol.2026.134909
- Impact of Spatial Resolution on River Flow Simulation Based on the Total Runoff Integrating Pathway (TRIP) Model M. Kim et al. https://doi.org/10.3390/atmos16091083
- A tolerant hydrologic technique for real-time selection of optimum QPFs from NWPMs for flood warning applications M. Salah et al. https://doi.org/10.2166/wst.2024.046
- Technical note: Benchmarking large-domain model performance under sampling uncertainty G. Gründemann et al. https://doi.org/10.5194/hess-30-3439-2026
- A comprehensive assessment and comparison of streamflow simulations performance between the national water model versions 3.0 and 2.1 across the continental United States A. Ojha et al. https://doi.org/10.1016/j.jhydrol.2026.136046
- Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States C. Simeone et al. https://doi.org/10.3390/w16202996
- Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. https://doi.org/10.3389/frwa.2024.1439906
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
- Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning A. Husic et al. https://doi.org/10.5194/hess-29-4457-2025
- 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 W. Knoben et al. https://doi.org/10.5194/hess-29-5791-2025
- BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. https://doi.org/10.1038/s41597-024-03594-5
- Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States A. Putman et al. https://doi.org/10.5194/hess-28-2895-2024
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider https://doi.org/10.5194/hess-28-479-2024
- Over 100 global climate sensitive rivers are experiencing large and severe changes in streamflow volume and timing R. Patil et al. https://doi.org/10.1088/1748-9326/ae1e16
Saved (final revised paper)
Latest update: 09 Sep 2026
Short summary
Hydrologic models developed to assess water availability need to be systematically evaluated. This study evaluates the long-term performance of two high-resolution hydrologic models that simulate streamflow across the contiguous United States. Both models show similar performance overall and regionally, with better performance in minimally disturbed basins than in those impacted by human activity. At about 80 % of the sites, both models outperform the seasonal climatological benchmark.
Hydrologic models developed to assess water availability need to be systematically evaluated....
