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Evaluating surface and subsurface fluxes in hydrological models to advance basin-scale operational water supply forecasting

ORCID Icon, ORCID Icon, ORCID Icon, &
Received 28 Sep 2023, Accepted 13 Jun 2024, Published online: 01 Aug 2024

References

  • Abbaszadeh, P., Gavahi, K., and Moradkhani, H., 2020. Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting. Advances in Water Resources, 145, 103721. doi:10.1016/j.advwatres.2020.103721.
  • Abolafia‐Rosenzweig, R., et al., 2022. Evaluation and optimization of snow albedo scheme in Noah‐MP land surface model using in situ spectral observations in the Colorado Rockies. Journal of Advances in Modeling Earth Systems, 14 (10), e2022MS003141. doi:10.1029/2022MS003141.
  • Ala-aho, P., et al., 2017. Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation: a minimalist approach to parameterisation. Journal of Hydrology, 547, 664–677. doi:10.1016/j.jhydrol.2017.02.023.
  • Archfield, S.A., et al., 2015. Accelerating advances in continental domain hydrologic modeling. Water Resources Research, 51 (12), 10078–10091. doi:10.1002/2015WR017498.
  • Arsenault, R., Brissette, F., and Martel, J.L., 2018. The hazards of split-sample validation in hydrological model calibration. Journal of Hydrology, 566, 346–362. doi:10.1016/j.jhdrol.2018.09.027.
  • Bajracharya, A.R., et al., 2023. Process based calibration of a continental-scale hydrological model using soil moisture and streamflow data. Journal of Hydrology: Regional Studies, 47, 101391. doi:10.1016/j.ejrh.2023.101391.
  • Barlow, P.M., et al., 2015. U.S. Geological Survey groundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0): user guide for estimation of base flow, runoff, and groundwater recharge from streamflow data. Reston, VA: US Geological Survey. doi:10.3133/tm3B10.
  • Bingeman, A.K., Kouwen, N., and Soulis, E.D., 2006. Validation of the hydrological processes in a hydrological model. Journal of Hydrologic Engineering, 11 (5), 451–463. doi:10.1061/(ASCE)1084-0699(2006)11:5(451).
  • Biondi, D., et al., 2012. Validation of hydrological models: conceptual basis, methodological approaches and a proposal for a code of practice. Physics and Chemistry of the Earth, Parts A/B/C, 42–44, 70–76. doi:10.1016/J.PCE.2011.07.037.
  • Charusombat, U., et al., 2018. Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes. Hydrology and Earth System Sciences, 22 (10), 5559–5578. doi:10.5194/HESS-22-5559-2018.
  • Cho, K. and Kim, Y., 2022. Improving streamflow prediction in the WRF-Hydro model with LSTM networks. Journal of Hydrology, 605, 127297. doi:10.1016/j.jhydrol.2021.127297.
  • Clark, M.P., et al., 2015. Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51 (8), 5929–5956. doi:10.1002/2015WR017096.
  • Costa, D., Zhang, H., and Levison, J., 2021. Impacts of climate change on groundwater in the Great Lakes Basin: a review. Journal of Great Lakes Research, 47 (6), 1613–1625. doi:10.1016/J.JGLR.2021.10.011.
  • Croley, T.E., 1983. Great Lake basins (USA-Canada) runoff modeling. Journal of Hydrology, 64 (1–4), 135–158. doi:10.1016/0022-1694(83)90065-3.
  • Croley, T.E. and He, C., 2005. Distributed-parameter large basin runoff model. I: model Development. Journal of Hydrologic Engineering, 10 (3), 173–181. doi:10.1061/(asce)1084-0699(2005)10:3(173).
  • Devia, G.K., Ganasri, B.P., and Dwarakish, G.S., 2015. A review on hydrological models. Aquatic Procedia, 4, 1001–1007. doi:10.1016/J.AQPRO.2015.02.126.
  • EC and USEPA (Environment Canada and the United States Environmental Protection Agency). 2003. State of the Great Lakes 2003. Cat No. En40-11/35-2003E. EPA 905-R-03-004. Availbale from: https://archive.epa.gov/solec/web/pdf/state_of_the_great_lakes_2003_summary_report.pdf [Accessed 23 July 2024].
  • Ek, M.B., et al., 2003. Implementation of Noah land surface model advances in the National Centers for environmental prediction operational mesoscale Eta model. Journal of Geophysical Research: Atmospheres, 108 (D22), 8851. doi:10.1029/2002JD003296.
  • Engel, B., et al., 2007. A hydrologic/water quality model applicati1. Journal of the American Water Resources Association, 43 (5), 1223–1236. doi:10.1111/j.1752-1688.2007.00105.x.
  • EPA (United States Environmental Protection Agency). 2023. Facts and figures about the great lakes. https://www.epa.gov/greatlakes/facts-and-figures-about-great-lakes [ Accessed 30 May 2023].
  • Erler, A.R., et al., 2019. Evaluating climate change impacts on soil moisture and groundwater resources within a Lake-Affected Region. Water Resources Research, 55 (10), 8142–8163. doi:10.1029/2018WR023822.
  • Fatichi, S., et al., 2016. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology, 537, 45–60. doi:10.1016/j.jhydrol.2016.03.026.
  • Fry, L.M., Apps, D., and Gronewold, A.D., 2020. Operational Seasonal water supply and water level forecasting for the laurentian Great Lakes. Journal of Water Resources Planning and Management, 146 (9). doi:10.1061/(asce)wr.1943-5452.0001214.
  • Fry, L.M., et al., 2014. The Great Lakes Runoff Intercomparison Project phase 1: lake Michigan (GRIP-M). Journal of Hydrology, 519, 3448–3465. doi:10.1016/j.jhydrol.2014.07.021.
  • Gaborit, É., et al., 2017. Great Lakes Runoff Inter-comparison Project, phase 2: lake Ontario (GRIP-O). Journal of Great Lakes Research, 43 (2), 217–227. doi:10.1016/j.jglr.2016.10.004.
  • Garavaglia, F., et al., 2017. Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach. Hydrology and Earth System Sciences, 21 (8), 3937–3952. doi:10.5194/hess-21-3937-2017.
  • Gesch, D.B., et al., 2002. The national elevation data set. Photogrammetric Engineering & Remote Sensing, 68 (1), 5–11.
  • Gochis, D.J., et al., 2020. The NCAR WRF-hydro modeling system technical description. Boulder, CO, USA: University Corporation for Atmospheric Research.
  • Gochis, J. and Chen, F., 2003. Hydrological enhancements to the community Noah Land surface model. Boulder, CO, USA: University Corporation for Atmospheric Research. doi:10.5065/D60P0X00.
  • Gronewold, A.D., Anderson, E.J., and Smith, J., 2019. Evaluating operational hydrodynamic models for real-time simulation of evaporation from large Lakes. Geophysical Research Letters, 46 (6), 3263–3269. doi:10.1029/2019GL082289.
  • Gronewold, A.D., et al., 2011. An appraisal of the Great Lakes advanced hydrologic prediction system. Journal of Great Lakes Research, 37 (3), 577–583. doi:10.1016/j.jglr.2011.06.010.
  • Gronewold, A.D., et al., 2013. Coasts, water levels, and climate change: a Great Lakes perspective. Climatic Change, 120 (4), 697–711. doi:10.1007/S10584-013-0840-2/FIGURES/7.
  • Hersbach, H., et al., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999–2049. doi:10.1002/QJ.3803.
  • Holman, K.D., et al., 2012. Improving historical precipitation estimates over the Lake Superior basin. Geophysical Research Letters, 39 (3). doi:10.1029/2011GL050468.
  • Homer, C., et al., 2015. Completion of the 2011 national Land cover database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing, 81 (5), 345–354. doi:10.14358/PERS.81.5.345.
  • Hong, Y., et al., 2022. Evaluation of gridded precipitation datasets over international basins and large lakes. Journal of Hydrology, 607, 127507. doi:10.1016/J.JHYDROL.2022.127507.
  • Hrachowitz, M., et al., 2014. Process consistency in models: the importance of system signatures, expert knowledge, and process complexity. Water Resources Research, 50 (9), 7445–7469. doi:10.1002/2014WR015484.
  • Hunter, T.S., et al., 2015. Development and application of a North American Great Lakes hydrometeorological database — part I: precipitation, evaporation, runoff, and air temperature. Journal of Great Lakes Research, 41 (1), 65–77. doi:10.1016/J.JGLR.2014.12.006.
  • Institute of Hydrology, 1980. Low Flow Studies. Wallingford, UK: Institute of Hydrology.
  • Kirchner, J.W., 2006. Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research, 42 (3). doi:10.1029/2005WR004362.
  • Kult, J.M., et al., 2014. Regionalization of hydrologic response in the Great Lakes basin: considerations of temporal scales of analysis. Journal of Hydrology, 519, 2224–2237. doi:10.1016/J.JHYDROL.2014.09.083.
  • Kumar, A., et al., 2015. Identification of the best multi-model combination for simulating river discharge. Journal of Hydrology, 525, 313–325. doi:10.1016/j.jhydrol.2015.03.060.
  • Kumari, N., et al., 2021. Identification of suitable hydrological models for streamflow assessment in the Kangsabati River Basin, India, by using different model selection scores. Natural Resources Research, 30 (6), 4187–4205. doi:10.1007/S11053-021-09919-0/FIGURES/7.
  • Li, L., et al., 2017. Evaluating the present annual water budget of a Himalayan headwater river basin using a high-resolution atmosphere-hydrology model. Journal of Geophysical Research, 122 (9), 4786–4807. doi:10.1002/2016JD026279.
  • Liu, L., Menenti, M., and Ma, Y., 2022. Evaluation of albedo schemes in WRF coupled with Noah-MP on the Parlung No. 4 Glacier. Remote Sensing, 14 (16), 3934. doi:10.3390/rs14163934.
  • Liu, Y. and Gupta, H.V., 2007. Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resources Research, 43 (7), 7401. doi:10.1029/2006WR005756.
  • Lofgren, B.M., et al., 2013. Methodological approaches to projecting the hydrologic impacts of climate change. Earth Interactions, 17 (22), 1–19. doi:10.1175/2013EI000532.1.
  • Lofgren, B.M. and Gronewold, A.D., 2013. Reconciling alternative approaches to projecting hydrologic impacts of climate change. Bulletin of the American Meteorological Society, 94 (10), ES133–ES135. doi:10.1175/BAMS-D-13-00037.1.
  • Lofgren, B.M., Hunter, T.S., and Wilbarger, J., 2011. Effects of using air temperature as a proxy for potential evapotranspiration in climate change scenarios of Great Lakes basin hydrology. Journal of Great Lakes Research, 37, 744–752. doi:10.1016/j.jglr.2011.09.006.
  • Lofgren, B.M. and Rouhanaa, J., 2016. Physically plausible methods for projecting changes in Great Lakes Water Levels under climate change scenarios. Journal of Hydrometeorology, 17 (8), 2209–2223. doi:10.1175/JHM-D-15-0220.1.
  • López López, P., et al., 2017. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products. Hydrology and Earth System Sciences, 21 (6), 3125–3144. doi:10.5194/hess-21-3125-2017.
  • Mai, J., et al., 2021. Great Lakes Runoff intercomparison project phase 3: lake Erie (GRIP-E). Journal of Hydrologic Engineering, 26 (9). doi:10.1061/(asce)he.1943-5584.0002097.
  • Mai, J., et al., 2022. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL). Hydrology and Earth System Sciences, 26 (13), 3537–3572. doi:10.5194/hess-26-3537-2022.
  • Mai, J., 2023. Ten strategies towards successful calibration of environmental models. Journal of Hydrology, 620, 129414. doi:10.1016/J.JHYDROL.2023.129414.
  • Martens, B., et al., 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10, 1903–1925. doi:10.5194/gmd-10-1903-2017.
  • Mason, L.A., et al., 2019. New transboundary hydrographic data set for advancing regional hydrological modeling and water resources management. Journal of Water Resources Planning and Management, 145 (6). doi:10.1061/(asce)wr.1943-5452.0001073.
  • McKay, L., et al., 2012. US Environmental Protection Agency. Washington, DC: National Operational Hydrologic Remote Sensing Center.
  • Mei, Y., et al., 2023. Can hydrological models benefit from using global soil moisture, evapotranspiration, and Runoff products as calibration targets? Water Resources Research, 59 (2). doi:10.1029/2022WR032064.
  • Menne, M.J., et al., 2012. An overview of the global historical climatology network-daily database. Journal of Atmospheric and Oceanic Technology, 29 (7), 897–910. doi:10.1175/JTECH-D-11-00103.1.
  • Miller, D.A. and White, R.A., 1998. A conterminous united states multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interactions, 2 (2), 1–26. doi:10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2.
  • Milly, P.C.D. and Dunne, K.A., 2017. A hydrologic drying bias in water-resource impact analyses of anthropogenic climate change. Journal of the American Water Resources Association, 53 (4), 822–838. doi:10.1111/1752-1688.12538.
  • Montanari, A. and Koutsoyiannis, D., 2012. A blueprint for process-based modeling of uncertain hydrological systems. Water Resources Research, 48 (9). doi:10.1029/2011WR011412.
  • Monteith, J.L., 1965. Evaporation and environment. Symposia of the society for experimental biology, 19, 205–234.
  • Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50 (3), 885–900. doi:10.13031/2013.23153.
  • Moriasi, D.N., et al., 2015. Hydrologic and water quality models: performance measures and evaluation criteria. Transactions of the ASABE, 58 (6), 1763–1785. doi:10.13031/trans.58.10715.
  • Munyaneza, O., Wenninger, J., and Uhlenbrook, S., 2012. Identification of runoff generation processes using hydrometric and tracer methods in a meso-scale catchment in Rwanda. Hydrology and Earth System Sciences, 16 (7), 1991–2004. doi:10.5194/HESS-16-1991-2012.
  • Naabil, E., et al., 2017. Water resources management using the WRF-Hydro modelling system: case-study of the Tono dam in West Africa. Journal of Hydrology: Regional Studies, 12, 196–209. doi:10.1016/j.ejrh.2017.05.010.
  • Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6.
  • Neff, B.P., et al., 2005. Base flow in the Great Lakes Basin. Scientific Investigations Report. doi:10.3133/SIR20055217.
  • Niel, H., Paturel, J.-E., and Servat, E., 2003. Study of parameter stability of a lumped hydrologic model in a context of climatic variability. Journal of Hydrology, 278, 213–230. doi:10.1016/S0022-1694(03)00158-6.
  • Niu, G.Y., et al., 2011. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. Journal of Geophysical Research: Atmospheres, 116 (D12). doi:10.1029/2010JD015139.
  • NWS-OWP (National Weather Service Office of Water Prediction), 2021. Analysis of record for calibration: version 1.1; Sources, Methods, and Verification. Tuscaloosa, AL: National Weather Service Office of Water Prediction.
  • O’Neill, P.E., et al., 2023. Data from: SMAP enhanced L3 radiometer global and polar grid daily 9 km ease-grid soil moisture version 5 [dataset]. National Snow and Ice Data Center. doi:10.5067/M20OXIZHY3RJ [Accessed 23 July 2024].
  • Pal, S., et al., 2023. Projected changes in extreme streamflow and inland flooding in the mid-21st century over Northeastern United States using ensemble WRF-Hydro simulations. Journal of Hydrology: Regional Studies, 47, 101371. doi:10.1016/J.EJRH.2023.101371.
  • Pietroniro, A., et al., 2007. Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale. Hydrology and Earth System Sciences, 11 (4), 1279–1294. doi:10.5194/HESS-11-1279-2007.
  • Rajib, M.A., Merwade, V., and Yu, Z., 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. Journal of Hydrology, 536, 192–207. doi:10.1016/J.JHYDROL.2016.02.037.
  • Rummler, T., et al., 2022. Lateral terrestrial water fluxes in the LSM of WRF-Hydro: benefits of a 2D groundwater representation. Hydrological Processes, 36 (3), e14510. doi:10.1002/HYP.14510.
  • Running, S., Mu, Q., and Zhao, M., 2017. Data from: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4. Global 500m SIN Grid V006 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center. Availbale from: doi:10.5067/MODIS/MOD16A2.006 [Accessed 23 July 2024].
  • Running, S.W., et al., 2017. User’s guide MODIS global terrestrial evapotranspiration (ET) product (MOD16A2/A3 and Year-end Gap-filled MOD16A2GF/A3GF) NASA earth observing system MODIS Land algorithm (For Collection 6.1). https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MODIS_C61_Land_
  • Seiller, G., Anctil, F., and Perrin, C., 2012. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions. Hydrology and Earth System Sciences, 16 (4), 1171–1189. doi:10.5194/HESS-16-1171-2012.
  • Shen, C. and Phanikumar, M.S., 2010. A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling. Advances in Water Resources, 33 (12), 1524–1541. doi:10.1016/J.ADVWATRES.2010.09.002.
  • Shin, S., et al., 2023a. Climate change impacts on water quantity and quality of a watershed-lake system using a spatially integrated modeling framework in the Kissimmee River – lake Okeechobee system. Journal of Hydrology: Regional Studies, 47, 101408. doi:10.1016/J.EJRH.2023.101408.
  • Shin, S., et al., 2023b. Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling. Journal of Hydrology, 622, 129458. doi:10.1016/J.JHYDROL.2023.129458.
  • Sofokleous, I., et al., 2023. Grid-based calibration of the WRF-Hydro with Noah-MP model with improved groundwater and transpiration process equations. Journal of Hydrology, 617, 128991. doi:10.1016/j.jhydrol.2022.128991.
  • Srivastava, A., et al., 2017. Evaluation of variable-infiltration capacity model and MODIS-terra satellite-derived grid-scale evapotranspiration estimates in a River Basin with tropical Monsoon-Type climatology. Journal of Irrigation and Drainage Engineering, 143 (8), 04017028. doi:10.1061/(ASCE)IR.1943-4774.0001199.
  • Wilcox, D.A., et al., 2007. Lake-level variability and water availability in the Great Lakes. Reston, VA: US Geological Survey. doi:10.3133/CIR1311.
  • Wolock, D.M. (2003). Base-Flow Index Grid for the Conterminous United States. Open-File Report. 10.3133/OFR03263.
  • Xiang, T., et al., 2017. On the diurnal cycle of surface energy fluxes in the North American monsoon region using the WRF-Hydro modeling system. Journal of Geophysical Research: Atmospheres, 122 (17), 9024–9049. doi:10.1002/2017JD026472.
  • Xu, S., et al., 2021. Investigating groundwater-lake interactions in the Laurentian Great Lakes with a fully-integrated surface water-groundwater model. Journal of Hydrology, 594, 125911. doi:10.1016/J.JHYDROL.2020.125911.
  • Yadav, M., Wagener, T., and Gupta, H., 2007. Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Advances in Water Resources, 30 (8), 1756. doi:10.1016/j.advwatres.2007.01.005.
  • Yang, K., et al., 2023. Optimization and validation of soil frozen‐thawing parameterizations in Noah‐MP. Journal of Geophysical Research: Atmospheres, 128 (23), e2022JD038217. doi:10.1029/2022JD038217.
  • Yucel, I., et al., 2015. Calibration and evaluation of a flood forecasting system: utility of numerical weather prediction model, data assimilation and satellite-based rainfall. Journal of Hydrology, 523, 49–66. doi:10.1016/j.jhydrol.2015.01.042.