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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Cited articles
Abramowitz, G., Leuning, R., Clark, M., and Pitman A. J.:
Evaluating the performance of land surface models, J. Climate, 21, 5468–5481, 2008.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.:
The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.:
A ranking of hydrological signatures based on their predictability in space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018.
Archfield, S. A., Clark, M., Arheimer, B., Hay, L. E., Mcmillan, H., Kiang, J. E., Seibert, J., Hakala, K., Bock, A., Wagener, T., Farmer, W.H., Andreassian, V., Attinger, S., Viglione, A., Knight, R., Markstrom, S., and Over, T.: Accelerating advances in continental domain hydrologic modeling, Water Resour. Res., 10078–10091, https://doi.org/10.1002/2015WR017498, 2015.
Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., and Pineda, L.:
Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation, Hydrol. Earth Syst. Sci., 24, 535–559, https://doi.org/10.5194/hess-24-535-2020, 2020.
Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello Jr., J. A., Stevens, L., and Vuichard, N.: The plumbing of land surface models: Benchmarking model performance, J. Hydrometeorol., 16, 1425–1442, https://doi.org/10.1175/JHM-D-14-0158.1, 2015.
Bock, A. R., Hay, L. E., McCabe, G. J., Markstrom, S. L., and Atkinson, R. D.:
Parameter regionalization of a monthly water balance model for the conterminous United States, Hydrol. Earth Syst. Sci., 20, 2861–2876, https://doi.org/10.5194/hess-20-2861-2016, 2016.
Buchanan, B., Auerbach, D. A., Knighton, J., Evensen, D., Fuka, D. R., Easton, Z. Wieczorek, M., Archibald, J. A., McWilliams, B., and Walter, T.:
Estimating dominant runoff modes across the conterminous United States, Hydrol. Process., 32, 3881–3890, https://doi.org/10.1002/hyp.13296, 2018.
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M., Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015.
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.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C. D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation, J. Adv. Model. Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354, 2018.
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, J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031, 2006.
Esri: USA States Generalized Boundaries, Esri [data set], https://esri.maps.arcgis.com/home/item.html?id=8c2d6d7df8fa4142b0a1211c8dd66903 (last access: 4 May 2023), 2022a.
Esri: World Countries (Generalized), Esri [data set], https://hub.arcgis.com/datasets/esri::world-countries-generalized/about (last access: 4 May 2023), 2022b.
Falcone, J. A.: GAGES-II: Geospatial attributes of gages for evaluating streamflow, US Geological Survey, https://doi.org/10.3133/70046617, 2011.
Famiglietti, J. S., Murdoch, L., Lakshmi, V., Arrigo, J., and Hooper, R.:
Establishing a Framework for Community Modeling in Hydrologic Science, 3rd Workshop on Community Hydrologic Modeling Platform (CHyMP), 15–17 March 2011, University of California, Irvine, https://www.hydroshare.org/resource/2b8c9e2ecf014cf284278b1784b16570/data/contents/CUAHSI-TR10_-_Establishing_a_Framework_for_Community_Modeling_in_Hydrologic_Science_-_2011.pdf (last access: 4 May 2023), 2011.
Farrar, M.: Service Change Notice, https://www.weather.gov/media/notification/pdf2/scn20-119nwm_v2_1aad.pdf (last access: 9 November 2022), 2021.
Foks, S. S., Towler, E., Hodson, T. O., Bock, A. R., Dickinson, J. E., Dugger, A. L., Dunne, K. A., Essaid, H. I., Miles, K. A., Over, T. M., Penn, C. A., Russell, A. M., Saxe, S. W., and Simeone, C. E.:
Streamflow benchmark locations for conterminous United States, version 1.0 (cobalt gages), US Geological Survey data release [data set], https://doi.org/10.5066/P972P42Z, 2022.
Frame, J. M., Kratzert, F., Raney II, A., Rahman, M., Salas F. R., and Nearing G. S.: Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics, J. Am. Water Resour. As., 57, 885–905, https://doi.org/10.1111/1752-1688.12964, 2021.
Friedrich, K., Grossman, R. L., Huntington, J., Blanken, P. D., Lenters, J., Holman, K. D., Gochis, D., Livneh, B., Prairie, J., Skeie, E., Healey, N. C., Dahm, K., Pearson, C., Finnessey, T., Hook, S. J., Kowalski, T.: Reservoir evaporation in the Western United States, B. Am. Meteorol. Soc., 99, 167–187, https://doi.org/10.1175/BAMS-D-15-00224.1, 2018.
Gochis, D. J., Barlage, M., Cabell, R., Casali, M., Dugger, A., FitzGerald, K., McAllister, M., McCreight, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Zhang, Y.: The WRF-Hydro modeling system technical description, (Version 5.1.1). NCAR Technical Note, 107 pp., https://ral.ucar.edu/sites/default/files/public/projects/wrf-hydro/technical-description-user-guide/wrf-hydrov5.2technicaldescription.pdf (last access: 4 May 2023), 2020a.
Gochis, D., Barlage, M., Cabell, R., Dugger, A., Fanfarillo, A., FitzGerald, K., McAllister, M., McCreight, J., RafieeiNasab, A., Read, L., Frazier, N., Johnson, D., Mattern, J. D., Karsten, L., Mills, T. J., and Fersch, B.:
WRF-Hydro v5.1.1, Zenodo [data set], https://doi.org/10.5281/zenodo.3625238, 2020b.
Grannemann, N. G.: Great Lakes and Watersheds Shapefiles [Data set], USGS, https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd (last access: 4 May 2023), 2010.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.:
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.:
Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
Hay, L. E. and LaFontaine, J. H.: Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1980–2016, Daymet Version 3 calibration, US Geological Survey data release [data set], https://doi.org/10.5066/P9PGZE0S, 2020.
Hodson, T. O., Over, T. M., and Foks, S. F.: Mean squared error, deconstructed, J. Adv. Model. Earth Sy., 13, e2021MS002681, https://doi.org/10.1029/2021MS002681, 2021.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Knoben, W. J. M., Freer, J. E., Peel, M. C., Fowler, K. J. A., and Woods, R. A.:
A brief analysisof conceptual model structureuncertainty using 36 models and 559 catchments, Water Resourc. Res., 56, e2019WR025975, https://doi.org/10.1029/2019WR025975, 2020.
LaFontaine, J. H., Hart, R. M., Hay, L. E., Farmer, W. H., Bock, A. R., Viger, R. J., Markstrom, S. L., Regan, R. S., and Driscoll, J. M.: Simulation of water availability in the Southeastern United States for historical and potential future climate and land-cover conditions, US Geological Survey Scientific Investigations Report 2019–5039, US Geological Survey, p. 83, https://doi.org/10.3133/sir20195039, 2019.
Lamontagne, J. R., Barber C., and Vogel R. M.:
Improved estimators of model performance efficiency for skewed hydrologic data, Water Resour. Res., 56, e2020WR027101, https://doi.org/10.1029/2020WR027101, 2020.
Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J. A., and Reaney, S. M.:
Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019, 2019.
Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J. B., Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C. D., Koven, C., Lawrence, D., Li, D. J., Mahecha, M., Niu, S. L., Norby, R., Piao, S. L., Qi, X., Peylin, P., Prentice, I. C., Riley, W., Reichstein, M., Schwalm, C., Wang, Y. P., Xia, J. Y., Zaehle, S., and Zhou, X. H.:
A framework for benchmarking land models, Biogeosciences, 9, 3857–3874, https://doi.org/10.5194/bg-9-3857-2012, 2012.
Mai, J., Craig, J. R., Tolson, B. A., and Arsenault, R.:
The sensitivity of simulated streamflow to individual hydrologic processes across North America, Nat. Commun., 13, 455, https://doi.org/10.1038/s41467-022-28010-7, 2022a.
Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W.: The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL), Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, 2022b.
Martinez, G. F., and Gupta, H. V.:
Toward improved identification of hydrological models: A diagnostic evaluation of the “abcd” monthly water balance model for the conterminous United States, Water Resour. Res., 46, W08507, https://doi.org/10.1029/2009WR008294, 2010.
McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., and Rea, A.:
NHDPlus Version 2: user guide, National Operational Hydrologic Remote Sensing Center, Washington, DC, 2012.
McMillan, H.:
Linking hydrologic signatures to hydrologic processes: A review, Hydrol. Process., 34, 1393–1409, https://doi.org/10.1002/hyp.13632, 2019.
Nash, J. E. and Sutcliffe, J. V.:
River flow forecasting through conceptual models. Part I: A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
National Weather Service: Analysis of Record for Calibration: Version 1.1 Sources, Methods, and Verification, https://hydrology.nws.noaa.gov/aorc-historic/Documents/AORC-Version1.1-SourcesMethodsandVerifications.pdf (last access: 17 March 2022), 2021.
Natural Earth Data: Ocean (version 5.1.1), Natural Earth Data [data set], https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-ocean/ (last access: 4 May 2023), 2009.
Nearing, G. S., Ruddell, B. L., Clark, M. P., Nijssen, B., and Peters-Lidard, C.:
Benchmarking and process diagnostics of land models, J. Hydrometeorol., 19, 1835–1852, https://doi.org/10.1175/JHM-D-17-0209.1, 2018.
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.
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, https://doi.org/10.1175/JHM-D-16-0284.1, 2017.
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.
NOAA: National Water Model CONUS Retrospective Dataset, NOAA [data set], https://registry.opendata.aws/nwm-archive (last access: 4 May 2023), 2023.
OWP – Office of Water Prediction: The National Water Model, https://water.noaa.gov/about/nwm, last access: 9 November 2022.
Pappenberger, F., Ramos, M. H., Cloke, H. L., Wetterhall, F., Alfieri, L., Bogner, K., Mueller, A., and Salamon, P.: How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction, J. Hydrol., 522, 697–713, https://doi.org/10.1016/j.jhydrol.2015.01.024, 2015.
R Core Team:
R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org (last access: 4 May 2022), 2021.
Regan, R. S., Markstrom, S. L., Hay, L. E., Viger, R. J., Norton, P. A., Driscoll, J. M., and LaFontaine, J. H.: Description of the National Hydrologic Model for use with the PRMS, USGS Techniques and Methods, 6-B9, USGS, https://doi.org/10.3133/tm6B9, 2018.
Rupp, D. E., Abatzoglou, J. T., Hegewisch, K. C., and Mote, P. W.: Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA, J. Geophys. Res.-Atmos., 118, 10884–10906, https://doi.org/10.1002/jgrd.50843, 2013.
Schaefli, B. and Gupta, H. V.:
Do Nash values have value?, Hydrol. Process., 21, 2075–2080, 2007.
Seibert, J.:
On the need for benchmarks in hydrological modelling, Hydrol. Process., 15, 1063–1064, 2001.
Seibert, J., Vis, M. J. P., Lewis, E., and van Meerveld, H. J.:
Upper and lower benchmarks in hydrological modelling, Hydrol. Process., 32, 1120–1125, https://doi.org/10.1002/hyp.11476, 2018.
Shen, H., Tolson, B. A., and Mai, J.: Time to update the split-sample approach in hydrological model calibration, Water Resour. Res., 58, e2021WR031523, https://doi.org/10.1029/2021WR031523, 2022.
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda, R., Vose, R. S., and Cook, R. B.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1328, 2016.
Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M. M., Sampson, K., Gochis, D., and Maxwell, R.: Continental Hydrologic Intercomparison Project, Phase 1: A large-scale hydrologic model comparison over the continental United States, Water Resour. Res., 57, 1–27, https://doi.org/10.1029/2020wr028931, 2021.
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, W01413, https://doi.org/10.1029/2005WR004723, 2007.
Towler, E., Foks, S. S., Staub, L. E., Dickinson, J. E., Dugger, A. L., Essaid, H. I., Gochis, D., Hodson, T. O., Viger, R. J., and Zhang, Y.:
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations for the conterminous United States (ver 3.0, March 2023), US Geological Survey data release [data set], https://doi.org/10.5066/P9QT1KV7, 2023a.
Towler, E., Foks, S. S., Staub, L. E., Dickinson, J. E., Dugger, A. L., Essaid, H. I., Gochis, D., Hodson, T. O., Viger, R. J., and Zhang, Y.:
Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations for the conterminous United States (ver 3.0, March 2023), US Geological Survey data release [data set], https://doi.org/10.5066/P9DKA9KQ, 2023b.
van den Hurk, B., M. Best, P. Dirmeyer, A. Pitman, J. Polcher, and Santanello, J.:
Acceleration of land surface model development over a decade of GLASS, B. Am. Meteorol. Soc., 92, 1593–1600, 2011.
Viger, R. J. and Bock, A.: GIS features of the geospatial fabric for national hydrologic modeling, US Geological Survey data release [data set], https://doi.org/10.5066/F7542KMD, 2014.
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.: Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res., 117, D03110, https://doi.org/10.1029/2011JD016051, 2012.
Yilmaz, K., Gupta, H., and Wagener, T.: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008.
Zambrano-Bigiarini, M.: Package `hydroGOF', GitHub [code], https://github.com/hzambran/hydroGOF (last access: 12 April 2022), 2020.
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....
