Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-1071-2024
© 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-28-1071-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2024
Research article |
| 01 Mar 2024
| 01 Mar 2024
Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models
Department of Physical Geography, Utrecht University, Utrecht, 3584 CS, the Netherlands
Laura E. Condon
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85705, USA
Related authors
Jennie C. Steyaert, Edwin Sutanudjaja, Marc Bierkens, and Niko Wanders
EGUsphere, https://doi.org/10.5194/egusphere-2024-3658, https://doi.org/10.5194/egusphere-2024-3658, 2025
Short summary
Short summary
Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
Short summary
Short summary
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Jennie C. Steyaert, Edwin Sutanudjaja, Marc Bierkens, and Niko Wanders
EGUsphere, https://doi.org/10.5194/egusphere-2024-3658, https://doi.org/10.5194/egusphere-2024-3658, 2025
Short summary
Short summary
Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.
Benjamin D. West, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 29, 245–259, https://doi.org/10.5194/hess-29-245-2025, https://doi.org/10.5194/hess-29-245-2025, 2025
Short summary
Short summary
This article describes the addition of reservoirs to the hydrologic model ParFlow. ParFlow is particularly good at helping us understand some of the broader drivers behind different parts of the water cycle. By having reservoirs in such a model, we hope to be able to better understand both our impacts on the environment and how to adjust our management of reservoirs to changing conditions.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
Short summary
Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Amanda Triplett and Laura E. Condon
Hydrol. Earth Syst. Sci., 27, 2763–2785, https://doi.org/10.5194/hess-27-2763-2023, https://doi.org/10.5194/hess-27-2763-2023, 2023
Short summary
Short summary
Accelerated melting in mountains is a global phenomenon. The Heihe River basin depends on upstream mountains for its water supply. We built a hydrologic model to examine how shifts in streamflow and warming will impact ground and surface water interactions. The results indicate that degrading permafrost has a larger effect than melting glaciers. Additionally, warming temperatures tend to have more impact than changes to streamflow. These results can inform other mountain–valley system studies.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2023-666, https://doi.org/10.5194/egusphere-2023-666, 2023
Preprint archived
Short summary
Short summary
Long Short-Term Memory (LSTM) is a widely-used machine learning (ML) model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes analogous to a hydrological reservoir. Models using HydroLSTM perform similarly to LSTM but require fewer cell states. The learned parameters are informative about the dominant hydroclimatic characteristics of a catchment. Our results demonstrate how hydrological knowledge is encoded in the new structure.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345, https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
Short summary
Short summary
As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.
Jennie C. Steyaert and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2022-1051, https://doi.org/10.5194/egusphere-2022-1051, 2022
Preprint archived
Short summary
Short summary
All river systems in the US are impacted by dams, yet analyses are limited by a lack of data. We use the first national dataset of reservoir data to analyze reservoir storage trends from 1980–2019. We show that reservoir storage has decreased over the past 40 years. The range in monthly storage has increased over time in drier regions and decreased in wetter ones. Lastly, we find that most regions have reservoir storage that takes longer to recover from and are therefore more vulnerable.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, https://doi.org/10.5194/gmd-14-7223-2021, 2021
Short summary
Short summary
Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Jun Zhang, Laura E. Condon, Hoang Tran, and Reed M. Maxwell
Earth Syst. Sci. Data, 13, 3263–3279, https://doi.org/10.5194/essd-13-3263-2021, https://doi.org/10.5194/essd-13-3263-2021, 2021
Short summary
Short summary
Existing national topographic datasets for the US may not be compatible with gridded hydrologic models. A national topographic dataset developed to support physically based hydrologic models at 1 km and 250 m over the contiguous US is provided. We used a Priority Flood algorithm to ensure hydrologically consistent drainage networks and evaluated the performance with an integrated hydrologic model. Datasets and scripts are available for direct data usage or modification of processing as desired.
Cited articles
Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., and Silverii, F.: A Decade of Water Storage Changes Across the Contiguous United States From GPS and Satellite Gravity, Geophys. Res. Lett., 46, 13006–13015, https://doi.org/10.1029/2019GL085370, 2019.
Barnett, T. P. and Pierce, D. W.: When will Lake Mead go dry?, Water Resour. Res., 44, W03201, https://doi.org/10.1029/2007WR006704, 2008.
Benson, R.: Reviewing Reservoir Operations: Can Federal Water Projects Adapt to Change?, Columb. J. Environ. Law, 42, 75, https://doi.org/10.7916/cjel.v42i2.3739, 2017.
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W. A., Heinke, J., von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge and irrigation water supply during the 20th century, Water Resour. Res., 47, W03509, https://doi.org/10.1029/2009wr008929, 2011.
Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., Pekel, J. F., and Cottam, A.: A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry, Hydrol. Earth Syst. Sci., 23, 669–690, https://doi.org/10.5194/hess-23-669-2019, 2019.
Cayan, D. R., Das, T., Pierce, D. W., Barnett, T. P., Tyree, M., and Gershunov, A.: Future dryness in the southwest US and the hydrology of the early 21st century drought, P. Natl. Acad. Sci. USA, 107, 21271–21276, https://doi.org/10.1073/pnas.0912391107, 2010.
Chen, W. and Olden, J. D.: Designing flows to resolve human and environmental water needs in a dam-regulated river, Nat. Commun., 8, 2158, https://doi.org/10.1038/s41467-017-02226-4, 2017.
Collier, M., Webb, R. H., and Schmidt, J. C.: Dams and Rivers: A Primer on the Downstream Effects of Dams, USGS Numbered Series Circular 1126, 94, US Geological Survey, https://doi.org/10.3133/cir1126, 1997.
Crétaux, J. F., Arsen, A., Calmant, S., Kouraev, A., Vuglinski, V., Bergé-Nguyen, M., Gennero, M. C., Nino, F., Abarca Del Rio, R., Cazenave, A., and Maisongrande, P.: SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data, Adv. Space Res., 47, 1497–1507, https://doi.org/10.1016/j.asr.2011.01.004, 2011.
Di Baldassarre, G., Wanders, N., AghaKouchak, A., Kuil, L., Rangecroft, S., Veldkamp, T. I. E., Garcia, M., van Oel, P. R., Breinl, K., and Van Loon, A. F.: Water shortages worsened by reservoir effects, Nat. Sustainabil., 1, 617–622, https://doi.org/10.1038/s41893-018-0159-0, 2018.
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, 2003.
Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A., Rodell, M., Strassberg, G., and Scanlon, B. R.: Impact of water withdrawals from groundwater and surface water on continental water storage variations, J. Geodynam., 59–60, 143–156, https://doi.org/10.1016/j.jog.2011.05.001, 2012.
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.
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.
Graf, W. L.: Dam nation: A geographic census of American dams and their large-scale hydrologic impacts, Water Resour. Res., 35, 1305–1311, https://doi.org/10.1029/1999wr900016, 1999.
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Ehalt Macedo, H., 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., Reidy Liermann, C., Saenz, L., Salinas-Rodriguez, S., Schelle, P., Schmitt, R. J. P., Snider, J., Tan, F., Tockner, K., 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.
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.
Hanasaki, N., Kanae, S., and Oki, T.: A reservoir operation scheme for global river routing models, J. Hydrol., 327, 22–41, https://doi.org/10.1016/j.jhydrol.2005.11.011, 2006.
Ho, M., Lall, U., Allaire, M., Devineni, N., Kwon, H. H., Pal, I., Raff, D., and Wegner, D.: The future role of dams in the United States of America, Water Resour. Res., 53, 982–998, https://doi.org/10.1002/2016wr019905, 2017.
Hou, J., van Dijk, A. I. J. M., Beck, H. E., Renzullo, L. J., and Wada, Y.: Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale, Hydrol. Earth Syst. Sci., 26, 3785–3803, https://doi.org/10.5194/hess-26-3785-2022, 2022.
Johnson, P. T. J., Olden, J. D., and Vander Zanden, M. J.: Dam invaders: impoundments facilitate biological invasions into freshwaters, Front. Ecol. Environ., 6, 357–363, https://doi.org/10.1890/070156, 2008.
Keyantash, J.: Indices for Meteorological and Hydrological Drought, in: Hydrological Aspects of Climate Change, edited by: Pandey, A., Kumar, S., and Kumar, A., Springer Singapore, Singapore, 215–235, https://doi.org/10.1007/978-981-16-0394-5_11, 2021.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen–Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
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.
Moore, R. B., McKay, L. D., Rea, A. H., Bondelid, T. R., Price, C. V., Dewald, T. G., and Johnston, C. M.: User's guide for the National Hydrography Dataset plus (NHDPlus) High Resolution, Open-File Report, US Geological Survey, https://www.usgs.gov/national-hydrography/national-hydrography-dataset (last access: 10 November 2021), 2019.
Naz, B. S., Kao, S.-C., Ashfaq, M., Gao, H., Rastogi, D., and Gangrade, S.: Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States, J. Hydrol., 556, 359–370, https://doi.org/10.1016/j.jhydrol.2017.11.027, 2018.
Nilsson, C. and Berggren, K.: Alteration of Riparian Ecosystems Caused by River Regulation, BioScience, 50, 783–792, https://doi.org/10.1641/0006-3568(2000)050[0783:AORECB]2.0.CO;2, 2000.
Overpeck, J. T. and Udall, B.: Climate change and the aridification of North America, P. Natl. Acad. Sci. USA, 117, 11856–11858, https://doi.org/10.1073/pnas.2006323117, 2020.
Patterson, L. A. and Doyle, M. W.: A Nationwide Analysis of U.S. Army Corps of Engineers Reservoir Performance in Meeting Operational Targets, J. Am. Water Resour. Assoc., 54, 543–564, https://doi.org/10.1111/1752-1688.12622, 2018.
Patterson, L. A. and Doyle, M. W.: Managing rivers under changing environmental and societal boundary conditions, Part 1: National trends and U.S. Army Corps of Engineers reservoirs, River Res. Appl., 35, 327–340, https://doi.org/10.1002/rra.3418, 2019.
Prein, A. F., Holland, G. J., Rasmussen, R. M., Clark, M. P., and Tye, M. R.: Running dry: The US Southwest's drift into a drier climate state, Geophys. Res. Lett., 43, 1272–1279, 2016.
Randle, T. J., Morris, G. L., Tullos, D. D., Weirich, F. H., Kondolf, G. M., Moriasi, D. N., Annandale, G. W., Fripp, J., Minear, J. T., and Wegner, D. L.: Sustaining United States reservoir storage capacity: Need for a new paradigm, J. Hydrol., 602, 126686, https://doi.org/10.1016/j.jhydrol.2021.126686, 2021.
Steyaert, J.: jsteyaert/ResOpsUS_Analysis: ResOpsUS_Analysis_v1 (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10692477, 2024.
Steyaert, J. C., Condon, L. E., Turner, S. W. D., and Voisin, N.: ResOpsUS, a dataset of historical reservoir operations in the contiguous United States, Sci. Data, 9, 34, https://doi.org/10.1038/s41597-022-01134-7, 2022.
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.
Turner, S. W. D., Steyaert, J. C., Condon, L., and Voisin, N.: Water storage and release policies for all large reservoirs of conterminous United States, J. Hydrol., 603, 126843, https://doi.org/10.1016/j.jhydrol.2021.126843, 2021.
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, 2013.
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., Van 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.
Williams, A. P., Cook, B. I., and Smerdon, J. E.: Rapid intensification of the emerging southwestern North American megadrought in 2020–2021, Nat. Clim. Change, 12, 232–234, https://doi.org/10.1038/s41558-022-01290-z, 2022.
Wisser, D., Frolking, S., Hagen, S., and Bierkens, M. F. P.: Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs, Water Resour. Res., 49, 5732–5739, https://doi.org/10.1002/wrcr.20452, 2013.
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.
Zhao, G. and Gao, H.: Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches, Remote Sens. Environ., 226, 109–124, https://doi.org/10.1016/j.rse.2019.03.015, 2019.
Zou, H., Liu, D., Guo, S., Xiong, L., Liu, P., Yin, J., Zeng, Y., Zhang, J., and Shen, Y.: Quantitative assessment of adaptive measures on optimal water resources allocation by using reliability, resilience, vulnerability indicators, Stoch. Environ. Res. Risk A., 34, 103–119, https://doi.org/10.1007/s00477-019-01753-4, 2019.
Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty Russell, B., Menarguez Michael, A., Zhang, G., and Wang, J.: Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016, P. Natl. Acad. Sci. USA, 115, 3810–3815, https://doi.org/10.1073/pnas.1719275115, 2018.
Short summary
Reservoirs impact all river systems in the United States, yet their operations are difficult to quantify due to limited data. Using historical reservoir operations, we find that storage has declined over the past 40 years, with clear regional differences. We observe that active storage ranges are increasing in arid regions and decreasing in humid regions. By evaluating reservoir model assumptions, we find that they may miss out on seasonal dynamics and can underestimate storage.
Reservoirs impact all river systems in the United States, yet their operations are difficult to...
