Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1365-2021
© Author(s) 2021. 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-25-1365-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling
Department of Civil and Structural Engineering, University of Sheffield, Sheffield, United Kingdom
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, United States
Patrick M. Reed
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, United States
Danielle S. Grogan
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, United States
Shan Zuidema
Water Systems Analysis Group, University of New Hampshire, Durham, NH, United States
Alexander Prusevich
Water Systems Analysis Group, University of New Hampshire, Durham, NH, United States
Stanley Glidden
Water Systems Analysis Group, University of New Hampshire, Durham, NH, United States
Jonathan R. Lamontagne
Department of Civil and Environmental Engineering, Tufts University, Medford, MA, United States
Richard B. Lammers
Water Systems Analysis Group, University of New Hampshire, Durham, NH, United States
Related authors
Veysel Yildiz, Robert Milton, Solomon Brown, and Charles Rougé
Hydrol. Earth Syst. Sci., 27, 2499–2507, https://doi.org/10.5194/hess-27-2499-2023, https://doi.org/10.5194/hess-27-2499-2023, 2023
Short summary
Short summary
The proposed approach is based on the parameterisation of flow duration curves (FDCs) to generate hypothetical streamflow futures. (1) We sample a broad range of future climates with modified values of three key streamflow statistics. (2) We generate an FDC for each hydro-climate future. (3) The resulting ensemble is ready to support robustness assessments in a changing climate. Our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows.
Robert Reinecke, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
EGUsphere, https://doi.org/10.5194/egusphere-2025-1181, https://doi.org/10.5194/egusphere-2025-1181, 2025
Short summary
Short summary
Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Veysel Yildiz, Robert Milton, Solomon Brown, and Charles Rougé
Hydrol. Earth Syst. Sci., 27, 2499–2507, https://doi.org/10.5194/hess-27-2499-2023, https://doi.org/10.5194/hess-27-2499-2023, 2023
Short summary
Short summary
The proposed approach is based on the parameterisation of flow duration curves (FDCs) to generate hypothetical streamflow futures. (1) We sample a broad range of future climates with modified values of three key streamflow statistics. (2) We generate an FDC for each hydro-climate future. (3) The resulting ensemble is ready to support robustness assessments in a changing climate. Our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
Short summary
Short summary
This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Iman Haqiqi, Danielle S. Grogan, Thomas W. Hertel, and Wolfram Schlenker
Hydrol. Earth Syst. Sci., 25, 551–564, https://doi.org/10.5194/hess-25-551-2021, https://doi.org/10.5194/hess-25-551-2021, 2021
Short summary
Short summary
This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics focusing on corn in the United States during the 1981–2015 period. The findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Adam, J. C., Haddeland, I., Su, F., and Lettenmaier, D. P.: Simulation of reservoir influences on annual and seasonal streamflow changes for the Lena, Yenisei, and Ob' rivers, J. Geophys. Res.-Atmos., 112, D24114, https://doi.org/10.1029/2007JD008525, 2007. a, b, c
Ahmadalipour, A. and Moradkhani, H.: Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment, J. Hydrol., 555, 557–568, https://doi.org/10.1016/j.jhydrol.2017.10.059, 2017. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements – FAO Irrigation and drainage paper 56, FAO, Rome, 326 pp., 1998. a
Avisse, N., Tilmant, A., Müller, M. F., and Zhang, H.: Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas, Hydrol. Earth Syst. Sci., 21, 6445–6459, https://doi.org/10.5194/hess-21-6445-2017, 2017. a
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015. a
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L. E., David, C. H., de Roo, A., Döll, P., Drost, N., Famiglietti, J. S., Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R. M., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja, E. H., van de Giesen, N., Winsemius, H., and Wood, E. F.: Hyper-resolution global hydrological modelling: what is next?, Hydrol. Process., 29, 310–320, https://doi.org/10.1002/hyp.10391, 2015. a
Bierkens, M. F. P., Reinhard, S., de Bruijn, J. A., Veninga, W., and Wada, Y.: The Shadow Price of Irrigation Water in Major Groundwater-Depleting Countries, Water Resour. Res., 55, 4266–4287, https://doi.org/10.1029/2018WR023086, 2019. a
Burek, P. A., Roo, A. D., and van der Knijff, J.: LISFLOOD - Distributed Water Balance and Flood Simulation Model – Revised User Manual, Tech. Rep., Publications Office of the European Union, Directorate-General Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy, 2013. a, b
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007. a, b
Center For International Earth Science Information Network – CIESIN – Columbia University: Gridded Population of the World, Version 4 (GPWv4): Population Density, NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, USA, 2016. a
Coe, M. T.: Modeling Terrestrial Hydrological Systems at the Continental Scale: Testing the Accuracy of an Atmospheric GCM, J. Climate, 13, 686–704, https://doi.org/10.1175/1520-0442(2000)013<0686:MTHSAT>2.0.CO;2, 2000. a
Coerver, H. M., Rutten, M. M., and van de Giesen, N. C.: Deduction of reservoir operating rules for application in global hydrological models, Hydrol. Earth Syst. Sci., 22, 831–851, https://doi.org/10.5194/hess-22-831-2018, 2018. a, b
Dang, T. D., Chowdhury, A. F. M. K., and Galelli, S.: On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, 2020. a
Dieter, C. A., Maupin, M. A., Caldwell, R. R., Harris, M. A., Ivahnenko, T. I., Lovelace, J. K., Barber, N. L., and Linsey, K. S.: Estimated use of water in the United States in 2015, USGS Numbered Series 1441, iP-090439, US Geological Survey, Reston, VA, available at: http://pubs.er.usgs.gov/publication/cir1441, last access: 13 November 2018. a
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003. a
Döll, P., Fiedler, K., and Zhang, J.: Global-scale analysis of river flow alterations due to water withdrawals and reservoirs, Hydrol. Earth Syst. Sci., 13, 2413–2432, https://doi.org/10.5194/hess-13-2413-2009, 2009. a, b
Dynesius, M. and Nilsson, C.: Fragmentation and Flow Regulation of River Systems in the Northern Third of the World, Science, 266, 753–762, https://doi.org/10.1126/science.266.5186.753, 1994. a
Ehsani, N., Fekete, B. M., Vörösmarty, C. J., and Tessler, Z. D.: A neural network based general reservoir operation scheme, Stoch. Environ. Res. Risk A., 30, 1151–1166, https://doi.org/10.1007/s00477-015-1147-9, 2016. a, b, c
Ehsani, N., Vörösmarty, C. J., Fekete, B. M., and Stakhiv, E. Z.: Reservoir operations under climate change: Storage capacity options to mitigate risk, J. Hydrol., 555, 435–446, https://doi.org/10.1016/j.jhydrol.2017.09.008, 2017. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gilmore, S.: Assessing the Adaptive Capacity of Idaho's Magic Valley As a Complex Social-Ecological System, MS thesis, University of Idaho, Moscow, ID, 2019. a
Giuliani, M. and Herman, J. D.: Modeling the behavior of water reservoir operators via eigenbehavior analysis, Adv. Water Resour., 122, 228–237, https://doi.org/10.1016/j.advwatres.2018.10.021, 2018. a
Giuliani, M., Herman, J. D., Castelletti, A., and Reed, P.: Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management, Water Resour. Res., 50, 3355–3377, https://doi.org/10.1002/2013WR014700, 2014. a
Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., and Reed, P. M.: Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations, J. Water Resour. Plan. Manage., 142, 04015050, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000570, 2016. a
Grill, G., lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., Babu, S., Borrelli, P., l. Cheng, Crochetiere, H., Macedo, H. E., Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., Mcclain, M. E., Meng, J., Mulligan, M., Nilsson, C., Olden, J. D., Opperman, J. J., Petry, P., Liermann, C. R., l. S/'aenz, Salinas-Rodr/'iguez, S., Schelle, P., Schmitt, R. J. P., Snider, J., anf K. Tockner, F. T., Valdujo, P. H., van Soesbergen, A., and Zarfl, C.: Mapping the world's free-flowing rivers, Nature, 569, 215–221, https://doi.org/10.1038/s41586-019-1111-9, 2019. a
Grogan, D. S.: Global and regional assessments of unsustainable groundwater use in irrigated agriculture, PhD thesis, University of New Hampshire, USA, available at: http://scholars.unh.edu/dissertation/2 (last access: 21 May 2019), 2016. a
Grogan, D. S., Zhang, F., Prusevich, A., Lammers, R. B., Wisser, D., Glidden, S., Li, C., and Frolking, S.: Quantifying the link between crop production and mined groundwater irrigation in China, Sci. Total Environ., 511, 161–175, https://doi.org/10.1016/j.scitotenv.2014.11.076, 2015. a, b, c
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. a
Guse, B., Reusser, D. E., and Fohrer, N.: How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance, Hydrol. Process., 28, 2651–2670, https://doi.org/10.1002/hyp.9777, 2014. a
Haddeland, I., Skaugen, T., and Lettenmaier, D. P.: Anthropogenic impacts on continental surface water fluxes, Geophys. Res. Lett., 33, L08406, https://doi.org/10.1029/2006GL026047, 2006. a, b, c
Hamlet, A. F. and Lettenmaier, D. P.: Effects of Climate Change on Hydrology and Water Resources in the Columbia River Basin, J. Am. Water Resour. Assoc., 35, 1597–1623, https://doi.org/10.1111/j.1752-1688.1999.tb04240.x, 1999. a
Han, W., Yang, Z., Di, L., and Mueller, R.: CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support, Comput. Electron. Agric., 84, 111–123, https://doi.org/10.1016/j.compag.2012.03.005, 2012. a
Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y., and Tanaka, K.: An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing, Hydrol. Earth Syst. Sci., 12, 1007–1025, https://doi.org/10.5194/hess-12-1007-2008, 2008. a
Hanasaki, N., Fujimori, S., Yamamoto, T., Yoshikawa, S., Masaki, Y., Hijioka, Y., Kainuma, M., Kanamori, Y., Masui, T., Takahashi, K., and Kanae, S.: A global water scarcity assessment under Shared Socio-economic Pathways – Part 2: Water availability and scarcity, Hydrol. Earth Syst. Sci., 17, 2393–2413, https://doi.org/10.5194/hess-17-2393-2013, 2013. a
Hanasaki, N., Yoshikawa, S., Pokhrel, Y., and Kanae, S.: A Quantitative Investigation of the Thresholds for Two Conventional Water Scarcity Indicators Using a State-of-the-Art Global Hydrological Model With Human Activities, Water Resour. Res., 54, 8279–8294, https://doi.org/10.1029/2018WR022931, 2018. a
Hejazi, M. I., Cai, X., and Ruddell, B. L.: The role of hydrologic information in reservoir operation – Learning from historical releases, Adv. Water Resour., 31, 1636–1650, 2008. a
Hejazi, M. I., Voisin, N., Liu, L., Bramer, L. M., Fortin, D. C., Hathaway, J. E., Huang, M., Kyle, P., Leung, L. R., Li, H.-Y., Liu, Y., Patel, P. L., Pulsipher, T. C., Rice, J. S., Tesfa, T. K., Vernon, C. R., and Zhou, Y.: 21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating, P. Natl. Acad. Sci. USA, 112, 10635–10640, https://doi.org/10.1073/pnas.1421675112, 2015. a
Herbert, C. and Döll, P.: Global assessment of current and future groundwater stress with a focus on transboundary aquifers, Water Resour. Res., 55, 4760–4784, https://doi.org/10.1029/2018WR023321, 2019. a
Herman, J. and Usher, W.: SALib: An open-source Python library for Sensitivity Analysis, J. Open Source Softw., 2, 97, https://doi.org/10.21105/joss.00097, 2017. a
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, 2013a. a
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models, Hydrol. Earth Syst. Sci., 17, 5109–5125, https://doi.org/10.5194/hess-17-5109-2013, 2013b. a, b
Hoekema, D. J. and Sridhar, V.: Relating climatic attributes and water resources allocation: A study using surface water supply and soil moisture indices in the Snake River basin, Idaho, Water Resour. Res., 47, W07536, https://doi.org/10.1029/2010WR009697, 2011. a
Independent Panel To Review Cause of Teton Dam Failure: Report to US Department of Interior and State of Idaho on Failure of Teton Dam, Tech. Rep., United States Bureau of Reclamations, available at: https://www.usbr.gov/pn/snakeriver/dams/uppersnake/teton/1976failure.pdf
(last access: 20 May 2018), 1976. a
Iooss, B. and Lemaître, P.: A Review on Global Sensitivity Analysis Methods, Springer US, Boston, MA, 101–122, https://doi.org/10.1007/978-1-4899-7547-8_5, 2015. a
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, 2015. a
Jägermeyr, J., Gerten, D., Schaphoff, S., Heinke, J., Lucht, W., and Rockström, J.: Integrated crop water management might sustainably halve the global food gap, Environ. Res. Lett., 11, 025002, https://doi.org/10.1088/1748-9326/11/2/025002, 2016. a
Jeuland, M., Baker, J., Bartlett, R., and Lacombe, G.: The costs of uncoordinated infrastructure management in multi-reservoir river basins, Environ. Res. Lett., 9, 105006, https://doi.org/10.1088/1748-9326/9/10/105006, 2014. a
Kliskey, A., Abatzoglou, J., Alessa, L., Kolden, C., Hoekema, D., Moore, B., Gilmore, S., and Austin, G.: Planning for Idaho's waterscapes: A review of historical drivers and outlook for the next 50 years, Environ. Sci. Policy, 94, 191–201, https://doi.org/10.1016/j.envsci.2019.01.009, 2019. a
Kraucunas, I., Clarke, L., Dirks, J., Hathaway, J., Hejazi, M., Hibbard, K., Huang, M., Jin, C., Kintner-Meyer, M., van Dam, K. K., Leung, R., Li, H.-Y., Moss, R., Peterson, M., Rice, J., Scott, M., Thomson, A., Voisin, N., and West, T.: Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA), Climatic Change, 129, 573–588, https://doi.org/10.1007/s10584-014-1064-9, 2015. a
Lamontagne, J. R., Reed, P. M., Marangoni, G., Keller, K., and Garner, G. G.: Robust abatement pathways to tolerable climate futures require immediate global action, Nat. Clim. Change, 9, 290–294, https://doi.org/10.1038/s41558-019-0426-8, 2019. a
Latrubesse, E. M., Arima, E. Y., Dunne, T., Park, E., Baker, V. R., d'Horta, F. M., Wight, C., Wittmann, F., Zuanon, J., Baker, P. A., Ribas, C. C., Norgaard, R. B., Filizola, N., Ansar, A., Flyvbjerg, B., and Stevaux, J. C.: Damming the rivers of the Amazon basin, Nature, 546, 363–369, https://doi.org/10.1038/nature22333, 2017. a
Legates, D. R. and McCabe Jr., G. J.: Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, https://doi.org/10.1029/1998WR900018, 1999. a
Lehner, B., Verdin, K., and Jarvis, A.: New Global Hydrography Derived From Spaceborne Elevation Data, Eos Tran. AGU, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008. a
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011. a
Li, H.-Y., Leung, L. R., Getirana, A., Huang, M., Wu, H., Xu, Y., Guo, J., and Voisin, N.: Evaluating Global Streamflow Simulations by a Physically Based Routing Model Coupled with the Community Land Model, J. Hydrometeorol., 16, 948–971, https://doi.org/10.1175/JHM-D-14-0079.1, 2015. a
Liu, J., Hertel, T. W., Lammers, R. B., Prusevich, A., Baldos, U. L. C., Grogan, D. S., and Frolking, S.: Achieving sustainable irrigation water withdrawals: global impacts on food security and land use, Environ. Res. Lett., 12, 104009, https://doi.org/10.1088/1748-9326/aa88db, 2017. a, b
Liu, X., Liu, W., Yang, H., Tang, Q., Flörke, M., Masaki, Y., Müller Schmied, H., Ostberg, S., Pokhrel, Y., Satoh, Y., and Wada, Y.: Multimodel assessments of human and climate impacts on mean annual streamflow in China, Hydrol. Earth Syst. Sci., 23, 1245–1261, https://doi.org/10.5194/hess-23-1245-2019, 2019. a
Loucks, D. P. and van Beek, E.: Water Resources Systems Planning and Management, UNESCO, Paris, 2005. a
Marques, G. F. and Tilmant, A.: The economic value of coordination in large-scale multireservoir systems: The Parana River case, Water Resour. Res., 49, 7546–7557, https://doi.org/10.1002/2013WR013679, 2013. a
Masaki, Y., Hanasaki, N., Biemans, H., Schmied, H. M., Tang, Q., Wada, Y., Gosling, S. N., Takahashi, K., and Hijioka, Y.: Intercomparison of global river discharge simulations focusing on dam operation – multiple models analysis in two case-study river basins, Missouri-Mississippi and Green-Colorado, Environ. Res. Lett., 12, 055002, https://doi.org/10.1088/1748-9326/aa57a8, 2017. a, b
Mateo, C. M., Hanasaki, N., Komori, D., Tanaka, K., Kiguchi, M., Champathong, A., Sukhapunnaphan, T., Yamazaki, D., and Oki, T.: Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models, Water Resour. Res., 50, 7245–7266, https://doi.org/10.1002/2013WR014845, 2014. a
Maupin, M. A., Kenny, J. F., Hutson, S. S., Lovelace, J. K., Barber, N. L., and Linsey, K. S.: Estimated use of water in the United States in 2010, no. 1405 in Circular, US Geological Survey, Reston, Virginia, 2014. a
McGuire, M., Wood, A. W., Hamlet, A. F., and Lettenmaier, D. P.: Use of Satellite Data for Streamflow and Reservoir Storage Forecasts in the Snake River Basin, J. Water Resour. Plan. Manage., 132, 97–110, https://doi.org/10.1061/(ASCE)0733-9496(2006)132:2(97), 2006. a
Meigh, J. R., McKenzie, A. A., and Sene, K. J.: A Grid-Based Approach to Water Scarcity Estimates for Eastern and Southern Africa, Water Resour. Manag., 13, 85–115, https://doi.org/10.1023/A:1008025703712, 1999. a
Metin, A. D., Dung, N. V., Schröter, K., Guse, B., Apel, H., Kreibich, H., Vorogushyn, S., and Merz, B.: How do changes along the risk chain affect flood risk?, Nat. Hazards Earth Syst. Sci., 18, 3089–3108, https://doi.org/10.5194/nhess-18-3089-2018, 2018. a
Meza, I., Siebert, S., Döll, P., Kusche, J., Herbert, C., Eyshi Rezaei, E., Nouri, H., Gerdener, H., Popat, E., Frischen, J., Naumann, G., Vogt, J. V., Walz, Y., Sebesvari, Z., and Hagenlocher, M.: Global-scale drought risk assessment for agricultural systems, Nat. Hazards Earth Syst. Sci., 20, 695–712, https://doi.org/10.5194/nhess-20-695-2020, 2020. a
Mishra, V., Aaadhar, S., Shah, H., Kumar, R., Pattanaik, D. R., and Tiwari, A. D.: The Kerala flood of 2018: combined impact of extreme rainfall and reservoir storage, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2018-480, 2018. a
Monteith, J. L.: Evaporation and the Environment in the State and Movement of Water in Living Organisms, in: Proceedings of the Society for Experimental Biology, Symposium No. 19, Cambridge University Press, Cambridge, 205–234, 1965. a
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991. a, b
Müller, M. F., Yoon, J., Gorelick, S. M., Avisse, N., and Tilmant, A.: Impact of the Syrian refugee crisis on land use and transboundary freshwater resources, P. Natl. Acad. Sci. USA, 113, 14932–14937, https://doi.org/10.1073/pnas.1614342113, 2016. a
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C., Barlage, M., and Rasmussen, R.: Projected increases and shifts in rain-on-snow flood risk over western North America, Nat. Clim. Change, 8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018. a
Najibi, N., Devineni, N., and Lu, M.: Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin, Adv. Water Resour., 100, 153–167, https://doi.org/10.1016/j.advwatres.2016.12.004, 2017. a
National Oceanic and Atmospheric Administration: Flooding in Idaho, available at: https://www.weather.gov/safety/flood-states-id, last access: 13 April 2020. a
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in Earth system models – Part 2: Representation of water supply and allocation and opportunities for improved modeling, Hydrol. Earth Syst. Sci., 19, 63–90, https://doi.org/10.5194/hess-19-63-2015, 2015. a
Nilsson, C., Reidy, C. A., Dynesius, M., and Revenga, C.: Fragmentation and Flow Regulation of the World's Large River Systems, Science, 308, 405–408, https://doi.org/10.1126/science.1107887, 2005. a
Oyerinde, G. T., Wisser, D., Hountondji, F. C., Odofin, A. J., Lawin, A. E., Afouda, A., and Diekkrüger, B.: Quantifying Uncertainties in Modeling Climate Change Impacts on Hydropower Production, Climate, 4, 34, https://doi.org/10.3390/cli4030034, 2016. a
Pianosi, F. and Wagener, T.: Understanding the time-varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis, Hydrol. Process., 30, 3991–4003, https://doi.org/10.1002/hyp.10968, 2016. a
Pokhrel, Y., Hanasaki, N., Koirala, S., Cho, J., Yeh, P. J.-F., Kim, H., Kanae, S., and Oki, T.: Incorporating Anthropogenic Water Regulation Modules into a Land Surface Model, J. Hydrometeorol., 13, 255–269, https://doi.org/10.1175/JHM-D-11-013.1, 2012. a, b
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cy., 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010. a
Qiu, J., Yang, Q., Zhang, X., Huang, M., Adam, J. C., and Malek, K.: Implications of water management representations for watershed hydrologic modeling in the Yakima River basin, Hydrol. Earth Syst. Sci., 23, 35–49, https://doi.org/10.5194/hess-23-35-2019, 2019. a
Quinn, J., Reed, P., Giuliani, M., and Castelletti, A.: What is controlling our control rules? Opening the black box of multi-reservoir operating policies using time-varying sensitivity analysis, Water Resour. Res., 55, 5962–5984, https://doi.org/10.1029/2018WR024177, 2019. a, b, c
Quinn, J. D., Reed, P. M., Giuliani, M., and Castelletti, A.: Rival framings: A framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems, Water Resour. Res., 53, 7208–7233, https://doi.org/10.1002/2017WR020524, 2017. a
Rauscher, S. A., Pal, J. S., Diffenbaugh, N. S., and Benedetti, M. M.: Future changes in snowmelt-driven runoff timing over the western US, Geophys. Res. Lett., 35, L16703, https://doi.org/10.1029/2008GL034424, 2008. a
Reinecke, R., Foglia, L., Mehl, S., Herman, J. D., Wachholz, A., Trautmann, T., and Döll, P.: Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization, Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, 2019. a
Reusser, D. E. and Zehe, E.: Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity, Water Resour. Res., 47, W07550, https://doi.org/10.1029/2010WR009946, 2011. a
Rougé, C., Reed, P. M., Grogan, D. S., Zuidema, S., Prusevich, A., Glidden, S., Lamontagne, J. R., and Lammers, R. B.: UpperSnakeRiver_reservoirs_WBM, GitHub, available at: https://github.com/charlesrouge/, last access: 15 September 2020. a
Ruano, M., Ribes, J., Seco, A., and Ferrer, J.: An improved sampling strategy based on trajectory design for application of the Morris method to systems with many input factors, Environ. Model. Softw., 37, 103–109, https://doi.org/10.1016/j.envsoft.2012.03.008, 2012. a
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015. a
Schmitt, R. J. P., Bizzi, S., Castelletti, A., and Kondolf, G. M.: Improved trade-offs of hydropower and sand connectivity by strategic dam planning in the Mekong, Nat. Sustain., 1, 96–104, https://doi.org/10.1038/s41893-018-0022-3, 2018. a
Schumann, G. J.-P., Stampoulis, D., Smith, A. M., Sampson, C. C., Andreadis, K. M., Neal, J. C., and Bates, P. D.: Rethinking flood hazard at the global scale, Geophys. Res. Lett., 43, 10249–10256, https://doi.org/10.1002/2016GL070260, 2016. a
Shin, S., Pokhrel, Y., and Miguez-Macho, G.: High-Resolution Modeling of Reservoir Release and Storage Dynamics at the Continental Scale, Water Resour. Res., 55, 787–810, https://doi.org/10.1029/2018WR023025, 2019. a, b
Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., and de Haan, C.: Livestock's long shadow, Tech. Rep., Food and Agriculture Organisation of the United Nations (UN-FAO), available at: http://www.fao.org/docrep/010/a0701e/a0701e00.HTM (last access: 9 August 2018), 2006. a
Stewart, R., Wollheim, W., Miara, A., Vorosmarty, C., Fekete, B., Lammers, R., and Rosenzweig, B.: Horizontal Cooling Towers: Riverine Ecosystem Services and the Fate of Thermoelectric Heat in the Contemporary Northeast US, Environ. Res. Lett., 8, 025010, https://doi.org/10.1088/1748-9326/8/2/025010, 2013. a
Stewart, R. J., Wollheim, W. M., Gooseff, M. N., Briggs, M. A., Jacobs, J. M., Peterson, B. J., and Hopkinson, C. S.: Separation of river network-scale nitrogen removal among the main channel and two transient storage compartments, Water Resour. Res., 47, W00J10, https://doi.org/10.1029/2010WR009896, 2011. a
Thomas, C. A. and Lamke, R. D.: Floods of February 1962 in Southern Idaho and Northeastern Nevada, Tech. Rep., United States Geological Survey, Washington, D.C., 1962. a
Timpe, K. and Kaplan, D.: The changing hydrology of a dammed Amazon, Sci. Adv., 3, e1700611, https://doi.org/10.1126/sciadv.1700611, 2017. a
Turner, S. W. D., Xu, W., and Voisin, N.: Inferred inflow forecast horizons guiding reservoir release decisions across the United States, Hydrol. Earth Syst. Sci., 24, 1275–1291, https://doi.org/10.5194/hess-24-1275-2020, 2020. a
van Beek, L. P. H., Yoshihide, W., and P., B. M. F.: Global monthly water stress: 1. Water balance and water availability, Water Resour. Res., 47, W07517, https://doi.org/10.1029/2010WR009791, 2011. a
Veldkamp, T. I. E., Zhao, F., Ward, P. J., de Moel, H., Aerts, J. C. J. H., Schmied, H. M., Portmann, F. T., Masaki, Y., Pokhrel, Y., Liu, X., Satoh, Y., Gerten, D., Gosling, S. N., Zaherpour, J., and Wada, Y.: Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study, Environ. Res. Lett., 13, 055008, https://doi.org/10.1088/1748-9326/aab96f, 2018. a
Voisin, N., Li, H., Ward, D., Huang, M., Wigmosta, M., and Leung, L. R.: On an improved sub-regional water resources management representation for integration into earth system models, Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, 2013a. a, b, c, d
Voisin, N., Liu, L., Hejazi, M., Tesfa, T., Li, H., Huang, M., Liu, Y., and Leung, L. R.: One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest, Hydrol. Earth Syst. Sci., 17, 4555–4575, https://doi.org/10.5194/hess-17-4555-2013, 2013b. a
Vörösmarty, C. J., Sharma, K. P., Fekete, B. M., Copeland, A. H., Holden, J., Marble, J., and Lough, J. A.: The Storage and Aging of Continental Runoff in Large Reservoir Systems of the World, Ambio, 26, 210–219, 1997. a
Wada, Y., van Beek, L. P. H., Viviroli, D., Dürr, H. H., Weingartner, R., and Bierkens, M. F. P.: Global monthly water stress: 2. Water demand and severity of water stress, Water Resour. Res., 47, W07518, https://doi.org/10.1029/2010WR009792, 2011. a
Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources, Earth Syst. Dynam., 5, 15–40, https://doi.org/10.5194/esd-5-15-2014, 2014. a, b
Wada, Y., Bierkens, M. F. P., de Roo, A., Dirmeyer, P. A., Famiglietti, J. S., Hanasaki, N., Konar, M., Liu, J., Müller Schmied, H., Oki, T., Pokhrel, Y., Sivapalan, M., Troy, T. J., van Dijk, A. I. J. M., van Emmerik, T., Va Huijgevoort, M. H. J., Van Lanen, H. A. J., Vörösmarty, C. J., Wanders, N., and Wheater, H.: Human–water interface in hydrological modelling: current status and future directions, Hydrol. Earth Syst. Sci., 21, 4169–4193, https://doi.org/10.5194/hess-21-4169-2017, 2017. a
Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., and van Beek, L. P. H. R.: High-Resolution Global Water Temperature Modeling, Water Resour. Res., 55, 2760–2778, https://doi.org/10.1029/2018WR023250, 2019. a
Wang, K., Shi, H., Chen, J., and Li, T.: An improved operation-based reservoir scheme integrated with Variable Infiltration Capacity model for multiyear and multipurpose reservoirs, J. Hydrol., 571, 365–375, https://doi.org/10.1016/j.jhydrol.2019.02.006, 2019. a, b
Willmott, C. J., Rowe, C. M., and Mintz, Y.: Climatology of the terrestrial seasonal water cycle, J. Climatol., 5, 589–606, https://doi.org/10.1002/joc.3370050602, 1985. a
Wise, E. K.: Tree ring record of streamflow and drought in the upper Snake River, Water Resour. Res., 46, W11529, https://doi.org/10.1029/2010WR009282, 2010. a
Wise, E. K.: Hydroclimatology of the US Intermountain West, Prog. Phys. Geogr., 36, 458–479, https://doi.org/10.1177/0309133312446538, 2012. a
Wisser, D., Frolking, S., Douglas, E. M., Fekete, B. M., Vörösmarty, C. J., and Schumann, A. H.: Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets, Geophys. Res. Lett., 35, l24408, https://doi.org/10.1029/2008GL035296, 2008. a
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B., Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a, b
Wu, Y. and Chen, J.: An Operation-Based Scheme for a Multiyear and Multipurpose Reservoir to Enhance Macroscale Hydrologic Models, J. Hydrometeorol., 13, 270–283, https://doi.org/10.1175/JHM-D-10-05028.1, 2012. a, b
Yang, T., Gao, X., Sorooshian, S., and Li, X.: Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme, Water Resour. Res., 52, 1626–1651, https://doi.org/10.1002/2015WR017394, 2016. a
Yassin, F., Razavi, S., Elshamy, M., Davison, B., Sapriza-Azuri, G., and Wheater, H.: Representation and improved parameterization of reservoir operation in hydrological and land-surface models, Hydrol. Earth Syst. Sci., 23, 3735–3764, https://doi.org/10.5194/hess-23-3735-2019, 2019. a, b
Yates, D., Sieber, J., Purkey, D., and Huber-Lee, A.: WEAP21 – A Demand-, Priority-, and Preference-Driven Water Planning Model, Water Int., 30, 487–500, https://doi.org/10.1080/02508060508691893, 2005. a
Yoshikawa, S., Cho, J., Yamada, H. G., Hanasaki, N., and Kanae, S.: An assessment of global net irrigation water requirements from various water supply sources to sustain irrigation: rivers and reservoirs (1960–2050), Hydrol. Earth Syst. Sci., 18, 4289–4310, https://doi.org/10.5194/hess-18-4289-2014, 2014.
a, b, c
Zagona, E. A., Fulp, T. J., Shane, R., Magee, T., and Goranflo, H. M.: Riverware: A Generalized Tool for Complex Reservoir System Modeling, J. Am. Water Resour. Assoc., 37, 913–929, https://doi.org/10.1111/j.1752-1688.2001.tb05522.x, 2001. a
Zaherpour, J., Gosling, S. N., Mount, N., Schmied, H. M., Veldkamp, T. I. E., Dankers, R., Eisner, S., Gerten, D., Gudmundsson, L., Haddeland, I., Hanasaki, N., Kim, H., Leng, G., Liu, J., Masaki, Y., Oki, T., Pokhrel, Y., Satoh, Y., Schewe, J., and Wada, Y.: Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts, Environ. Res. Lett., 13, 065015, https://doi.org/10.1088/1748-9326/aac547, 2018. a
Zajac, Z., Revilla-Romero, B., Salamon, P., Burek, P., Hirpa, F. A., and Beck, H.: The impact of lake and reservoir parameterization on global streamflow simulation, J. Hydrol., 548, 552–568, https://doi.org/10.1016/j.jhydrol.2017.03.022, 2017. a, b
Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L., and Tockner, K.: A global boom in hydropower dam construction, Aquat. Sci., 77, 161–170, https://doi.org/10.1007/s00027-014-0377-0, 2015. a
Zaveri, E., Grogan, D. S., Fisher-Vanden, K., Frolking, S., Lammers, R. B., Wrenn, D. H., Prusevich, A., and Nicholas, R. E.: Invisible water, visible impact: groundwater use and Indian agriculture under climate change, Environ. Res. Lett., 11, 084005, https://doi.org/10.1088/1748-9326/11/8/084005, 2016. a, b, c, d
Zhao, G., Gao, H., Naz, B. S., Kao, S.-C., and Voisin, N.: Integrating a reservoir regulation scheme into a spatially distributed hydrological model, Adv. Water Resour., 98, 16–31, https://doi.org/10.1016/j.advwatres.2016.10.014, 2016. a, b, c
Zuidema, S., Grogan, D., Prusevich, A., Lammers, R., Gilmore, S., and Williams, P.: Interplay of changing irrigation technologies and water reuse: example from the upper Snake River basin, Idaho, USA, Hydrol. Earth Syst. Sci., 24, 5231–5249, https://doi.org/10.5194/hess-24-5231-2020, 2020. a
Short summary
Amid growing interest in using large-scale hydrological models for flood and drought monitoring and forecasting, it is important to evaluate common assumptions these models make. We investigated the representation of reservoirs as separate (non-coordinated) infrastructure. We found that not appropriately representing coordination and control processes can lead a hydrological model to simulate flood and drought events that would not occur given the coordinated emergency response in the basin.
Amid growing interest in using large-scale hydrological models for flood and drought monitoring...