Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-3079-2024
© Author(s) 2024. 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-28-3079-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving runoff simulation in the Western United States with Noah-MP and VIC models
Lu Su
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, CA, USA
Department of Geography, University of California, Los Angeles, CA, USA
Dennis P. Lettenmaier
CORRESPONDING AUTHOR
Department of Geography, University of California, Los Angeles, CA, USA
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, CA, USA
Benjamin Bass
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA
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Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2025-1708, https://doi.org/10.5194/egusphere-2025-1708, 2025
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
Peijun Li, Yalan Song, Ming Pan, Kathryn Lawson, and Chaopeng Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-483, https://doi.org/10.5194/egusphere-2025-483, 2025
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This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194, https://doi.org/10.5194/egusphere-2024-4194, 2025
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Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-340, https://doi.org/10.5194/hess-2024-340, 2024
Revised manuscript accepted for HESS
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We explore tile drainage’ impacts on the integrated hydrology-biogeochemistry-plant system, using ecosys with soil oxygen and microbe dynamics. We found that tile drainage lowers soil water content and improves soil oxygen levels, which helps crops grow better, especially during wet springs, and the developed root system also helps mitigate drought stress on dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Xiaoyu Ma, Dongyue Li, Yiwen Fang, Steven A. Margulis, and Dennis P. Lettenmaier
Hydrol. Earth Syst. Sci., 27, 21–38, https://doi.org/10.5194/hess-27-21-2023, https://doi.org/10.5194/hess-27-21-2023, 2023
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We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks that would allow estimation of SWE over an entire watershed. The retrieval of SWE from satellites has proved elusive, but there are now technological options that do so along essentially one-dimensional tracks. We use machine learning (ML) algorithms as the basis for a track-to-area (TTA) transformation and show that at least one is robust enough to estimate domain-wide SWE with high accuracy.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Peng Ji, Xing Yuan, Feng Ma, and Ming Pan
Hydrol. Earth Syst. Sci., 24, 5439–5451, https://doi.org/10.5194/hess-24-5439-2020, https://doi.org/10.5194/hess-24-5439-2020, 2020
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By performing high-resolution land surface modeling driven by the latest CMIP6 climate models, we find both the dry streamflow extreme over the drought-prone Yellow River headwater and the wet streamflow extreme over the flood-prone Yangtze River headwater will increase under 1.5, 2.0 and 3.0 °C global warming levels and emphasize the importance of considering ecological changes (i.e., vegetation greening and CO2 physiological forcing) in the hydrological projection.
Cited articles
Adam, J. C. and Lettenmaier, D. P.: Adjustment of global gridded precipitation for systematic bias, J. Geophys. Res., 108, 1–14, https://doi.org/10.1029/2002JD002499, 2003.
Adam, J. C., Clark, E. A., Lettenmaier, D. P., and Wood, E. F.: Correction of Global Precipitation Products for Orographic Effects, J. Climate, 19, 15–38, https://doi.org/10.1175/JCLI3604.1, 2006.
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and Lettenmaier, D. P.: Value of Long-Term Streamflow Forecasts to Reservoir Operations for Water Supply in Snow-Dominated River Catchments, Water Resour. Res., 52, 4209–4225, 2016.
Arsenault, R. and Brissette, F. P.: Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches, Water Resour. Res., 50, 6135–6153, https://doi.org/10.1002/2013WR014898, 2014.
Bass, B., Rahimi, S., Goldenson, N., Hall, A., Norris, J., and Lebow, Z. J.: Achieving Realistic Runoff in the Western United States with a Land Surface Model Forced by Dynamically Downscaled Meteorology, J. Hydrometeorol., 24, 269–283, 2023.
Beck, H. E., de Roo, A., and van Dijk, A. I. J. M.: Global maps of streamflow characteristics based on observations from several thousand catchments, J. Hydrometeorol., 16, 1478–1501, https://doi.org/10.1175/JHM-D-14-0155.1, 2015.
Bohn, T. J., Livneh, B., Oyler, J. W., Running, S. W., Nijssen, B., and Lettenmaier, D. P.: Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models, Agr. Forest Meteorol., 176, 38–49, https://doi.org/10.1016/j.agrformet.2013.03.003, 2013.
Burn, D. H. and Boorman, D. B.: Estimation of hydrological parameters at ungauged catchments, J. Hydrol., 143, 429454, https://doi.org/10.1016/0022-1694(93)90203-L, 1993.
Cai, X., Yang, Z.-L., David, C. H., Niu, G.-Y., and Rodell, M.: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin, J. Geophys. Res.-Atmos., 119, 23–38, https://doi.org/10.1002/2013JD020792, 2014.
California Department of Water Resources: California data exchange center: Daily full natural flow for December 2022, California Department of Water Resources, https://cdec.water.ca.gov/reportapp/javareports?name=FNF (last access: 1 October 2021), 2021.
Cao, Q., Mehran, A., Ralph, F. M., and Lettenmaier, D. P.: The role of hydrological initial conditions on atmospheric river floods in the Russian River basin, J. Hydrometeorol., 20, 16671686, https://doi.org/10.1175/JHM-D-19-0030.1, 2019.
Cao, Q., Gershunov, A., Shulgina, T., Ralph, F. M., Sun, N., and Lettenmaier, D. P.: Floods due to atmospheric rivers along the U.S. West Coast: The role of antecedent soil moisture in a warming climate, J. Hydrometeorol., 21, 1827–1845, https://doi.org/10.1175/JHM-D-19-0242.1, 2020.
Chen, F. and Dudhia, J.: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H.L., Koren, V., Duan, Q. Y., Ek, M., and Betts, A.: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res.-Atmos., 101, 7251–7268, 1996.
Demaria, E. M., Nijssen, B., and Wagener, T.: Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model, J. Geophys. Res., 112, D11113, https://doi.org/10.1029/2006JD007534, 2007.
Dembélé, M., Hrachowitz, M., Savenije, H. H., Mariéthoz, G., and Schaefli, B.: Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets, Water Resour. Res., 56, e2019WR026085, https://doi.org/10.1029/2019WR026085, 2020.
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen, S.: Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model, Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.: Biosphere–Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR Community Climate Model, NCAR Tech. Note TN383+STR, NCAR, https://www.osti.gov/biblio/5733868 (last access: 12 July 2023), 1993.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Environmental Protection Agency (EPA) Office of Water: Low Flow Statistics Tools: A How-To Handbook for NPDES Permit Writers, EPA-833-B-18-001, https://www.epa.gov/sites/default/files/2018-11/documents/low_flow_stats_tools_handbook.pdf (last access: 1 July 2024), 2018.
Falcone, J.: GAGES-II: Geospatial attributes of gages for evaluating streamflow, U.S. Geological Survey, https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml (last access: 1 April 2021), 2011.
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020. .
Franchini, M., Galeati, G., and Berra S.: Global optimization techniques for the calibration of conceptual rainfall-runoff models, Hydrolog. Sci. J., 43, 443–458, 1998.
Gao, H., Birkel, C., Hrachowitz, M., Tetzlaff, D., Soulsby, C., and Savenije, H. H. G.: A simple topography-driven and calibration-free runoff generation module, Hydrol. Earth Syst. Sci., 23, 787–809, https://doi.org/10.5194/hess-23-787-2019, 2019.
Gochis, D., Yates, D., Sampson, K., Dugger, A., McCreight, J., Barlage, M., RafieeiNasab, A., Karsten, L., Read, L., Zhang, Y., and McAllister, M.: Overview of National Water Model Calibration: General strategy and optimization, National Center for Atmospheric Research, 30 pp., https://ral.ucar.edu/sites/default/files/public/9_RafieeiNasab_CalibOverview_CUAHSI_Fall019_0.pdf (last access: 1 January 2023), 2019.
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/docs/water/wrf-hydro-v511-technical-description.pdf (last access: 10 July 2024), 2020.
Gong, W., Duan, Q., Li, J., Wang, C., Di, Z., Dai, Y., Ye, A., and Miao, C.: Multi-objective parameter optimization of common land model using adaptive surrogate modeling, Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, 2015.
Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., Wu, J., and Zhou, R.: Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China, Water Resour. Res., 56, e2019WR025968, https://doi.org/10.1029/2019WR025968, 2020.
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, 80–91,2009.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., and Sohlberg, R.: Global land cover classification at 1 km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 1331–1364, 2000.
Holtzman, N. M., Pavelsky, T. M., Cohen, J. S., Wrzesien, M. L., and Herman, J. D.: Tailoring WRF and Noah-MP to improve process representation of Sierra Nevada runoff: Diagnostic evaluation and applications, J. Adv. Model. Earth Sy., 12, e2019MS001832, https://doi.org/10.1029/2019MS001832, 2020.
Huang, H., Fischella, M., Liu, Y., Ban, Z., Fayne, J., Li, D., Cavanaugh, K., and Lettenmaier, D. P.: Changes in mechanisms and characteristics of Western U.S. floods over the last sixty years, Geophys. Res. Lett., 49, e2021GL097022, https://doi.org/10.1029/2021GL097022, 2022.
Hussein, A.: Process-based calibration of WRF-hydro model in unregulated mountainous basin in Central Arizona, MS thesis, Ira A. Fulton Schools of Engineering, Arizona State University, 110 pp., https://keep.lib.asu.edu/items/158362 (last access: 1 December 2023), 2020.
Imhoff, R. O., Van Verseveld, W. J., Van Osnabrugge, B., and Weerts, A. H.: Scaling point-scale (pedo) transfer functions to seamless large-domain parameter estimates for high-resolution distributed hydrologic modeling: An example for the Rhine River, Water Resour. Res., 56, e2019WR026807, https://doi.org/10.1029/2019WR026807, 2020.
Fisher, R. A. and Koven, C. D.: Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020.
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.
Lahmers, T. M., Hazenberg, P., Gupta, H., Castro, C., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., and Wang, Y. H.: Evaluation of NOAA national water model parameter calibration in semiarid environments prone to channel infiltration, J. Hydrometeorol., 22, 2939–2969, 2021.
Lang, M., Ouarda, T. B., and Bobée, B.: Towards operational guidelines for over-threshold modeling, J. Hydrol., 225, 103–117, 1999.
Li, D., Lettenmaier, D. P., Margulis, S. A., and Andreadis, K.: Theroleofrain-on-snowinflooding over the conterminous United States, Water Resour. Res., 55, 8492–8513, https://doi.org/10.1029/2019WR024950, 2019.
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, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P., and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Climate, 26, 9384–9392, https://doi.org/10.1175/JCLI-D-12-00508.1, 2013 (data available at: http://livnehpublicstorage.colorado.edu:81/Livneh.2013.CONUS.Dataset/, last access: 1 October 2023).
Maidment, D. R.: Conceptual Framework for the National Flood Interoperability Experiment, J. Am. Water Resour. As., 53, 245–57, 2017.
Mascaro, G., Hussein, A., Dugger, A., and Gochis, D. J.: Process-based calibration of WRF-Hydro in a mountainous basin in southwestern US, J. Am. Water Resour. As., 59, 49–70, 2023.
Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen, B.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States, J. Climate, 15, 3237–3251, 2002.
Mendoza, P. A., Clark, M. P., Mizukami, N., Newman, A. J., Barlage, M., Gutmann, E. D., Rasmussen, R. M., Rajagopalan, B., Brekke, L. D., and Arnold, J. R.: Effects of hydrologic model choice and calibration on the portrayal of climate change impacts, J. Hydrometeorol., 16, 762–780, 2015.
Miller, D. A. and White, R. A.: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling, Earth Interact., 2, 1–26, 1998.
Natural Resources Conservation Service: SNOTEL (Snow Telemetry) Data, USDA, https://www.nrcs.usda.gov/wps/portal/wcc/home/ (last access: 1 January 2024), 2023.
Niu, G. Y., Yang, Z. L., Dickinson, R. E., and Gulden, L. E.: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models, J. Geophys. Res.-Atmos., 110, D21106, https://doi.org/10.1029/2005JD006111, 2005.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data, J. Geophys. Res., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., and Tewari, M.: 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 Oceanic and Atmospheric Administration): National Water Model: Improving NOAA's Water Prediction Services, https://water.noaa.gov/assets/styles/public/images/wrn-national-water-model.pdf (last access: 26 June 2024), 2016.
Prata, A. J.: A new long-wave formula for estimating downward clear-sky radiation at the surface, Q. J. Roy. Meteor. Soc., 122, 1127–1151, 1996.
Poissant, D., Arsenault, A., and Brissette, F.: Impact of parameter set dimensionality and calibration procedures on streamflow prediction at ungauged catchments, J. Hydrol. Reg. Stud., 12,220–237, https://doi.org/10.1016/j.ejrh.2017.05.005, 2017.
Qi, W. Y., Chen, J., Li, L., Xu, C.-Y., Xiang, Y.-H., Zhang, S.-B., and Wang, H.-M.: Impact of the number of donor catchments and the efficiency threshold on regionalization performance of hydrological models, J. Hydrol., 601, 126680, https://doi.org/10.1016/j.jhydrol.2021.126680, 2021.
Raff, D., Brekke, L., Werner, K., Wood, A., and White. K.: Short-Term Water Management Decisions: User Needs for Improved Climate, Weather, and Hydrologic Information, U.S. Bureau of Reclamation, https://water.noaa.gov/assets/styles/public/images/wrn-national-water-model.pdf (last access: 13 October 2023), 2013.
Razavi, T. and Coulibaly, P.: An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds, Can. Water Resour. J., 42,2–20, https://doi.org/10.1080/07011784.2016.1184590, 2017.
Schaake, J. C., Koren, V. I., Duan, Q.-Y., Mitchell, K., and Chen, F.: Simple water balance model for estimating runoff at different spatial and temporal scales, J. Geophys. Res., 101, 7461–7475, https://doi.org/10.1029/95JD02892, 1996.
Schaperow, J. R., Li, D., Margulis, S. A., and Lettenmaier D. P.: A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model, Scientific Data, 8, 216, https://doi.org/10.1038/s41597-021-00999-4, 2021.
Schweppe, R., Thober, S., Müller, S., Kelbling, M., Kumar, R., Attinger, S., and Samaniego, L.: MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models, Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, 2022.
Sharma, P. and Machiwal, D.: Chapter 1 – Streamflow forecasting: overview of advances in data-driven techniques, in: Advances in Streamflow Forecasting, Elsevier, 1–50, 9780128206737, https://doi.org/10.1016/B978-0-12-820673-7.00013-5, 2021.
Shi, X., Wood, A. W., and Lettenmaier, D. P.: How essential is hydrologic model calibration to seasonal streamflow forecasting?, J. Hydrometeorol., 9, 1350–1363, 2008.
Sofokleous, I., Bruggeman, A., Camera, C., and Eliades, M.: Grid-based calibration of the WRF-Hydro with Noah-MP model with improved groundwater and transpiration process equations, J. Hydrol., 617, 128991, https://doi.org/10.1016/j.jhydrol.2022.128991, 2023.
Su, L., Cao, Q., Xiao, M., Mocko, D. M., Barlage, M., Li, D., Peters-Lidard, C. D., and Lettenmaier, D. P.: Drought variability over the conterminous United States for the past century, J. Hydrometeorol., 22, 1153–1168, https://doi.org/10.1175/JHM-D-20-0158.1, 2021.
Su, L., Cao, Q., Xiao, M., Mocko, D. M., Barlage, M., Li, D.,Peters-Lidard, C. D., and Lettenmaier, D. P.: Drought variability over the conterminous United States for the past century, J. Hydrometeorol., 22, 1153–1168, https://doi.org/10.1175/JHM-D-20-0158.1, 2021 (data available at: ftp://livnehpublicstorage.colorado.edu/public/sulu, last access: 1 October 2023).
Su, L., Cao, Q., Shukla, S., Pan, M., and Lettenmaier, D. P.: Evaluation of Subseasonal Drought Forecast Skill over the Coastal Western United States, J. Hydrometeorol., 24, 709–726, 2023a.
Su, L.: Improving Runoff Simulation in the Western United States with Noah-MP and VIC, figshare [data set], https://figshare.com/s/66fe8305bff516e80f6f (last access: 1 June 2024), 2023b.
Tangdamrongsub, N.: Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era, Water, 15, 2456, https://doi.org/10.3390/w15132456, 2023.
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.
Troy, T. J., Wood, E. F., and Sheffield, J.: An efficient calibration method for continental-scale land surface modeling, Water Resour. Res., 44, W09411, https://doi.org/10.1029/2007WR006513, 2008.
USWRC: Guidelines for determining flood flow frequency, Bulletin 17B of the Hydrology Subcommittee, 183 pp., https://water.usgs.gov/osw/bulletin17b/dl_flow.pdf (last access: 19 October 2023), 1982.
Yang, Y., Pan, M., Beck, H. E., Fisher, C. K., Beighley, R. E., Kao, S. C., Hong, Y., and Wood, E. F.: In quest of calibration density and consistency in hydrologic modeling: Distributed parameter calibration against streamflow characteristics, Water Resour. Res., 55, 7784–7803, 2019.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins, Adv. Water Resour., 30, 1756–1774, https://doi.org/10.1016/j.advwatres.2007.01.005, 2007.
Zheng, H., Yang, Z.-L., Lin, P., Wei, J., Wu, W.-Y., Li, L., Zhao, L., and Wang, S.: On the sensitivity of the precipitation partitioning into evapotranspiration and runoff in land surface parameterizations, Water Resour. Res., 55, 95–111, https://doi.org/10.1029/2017WR022236, 2019.
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river...