Articles | Volume 30, issue 14
https://doi.org/10.5194/hess-30-4629-2026
© Author(s) 2026. 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-30-4629-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2026
Research article |
| 22 Jul 2026
| 22 Jul 2026
Benchmarking reservoir operation schemes for large-scale hydrological models
Jesús Casado-Rodríguez
CORRESPONDING AUTHOR
European Commission – Joint Research Centre, Ispra, Italy
now at: Kajo s.r.o., Banská Bystrica, Slovakia
Juliana Disperati
Fincons Group, Vimercate, Italy
Stefania Grimaldi
European Commission – Joint Research Centre, Ispra, Italy
European Commission – Joint Research Centre, Ispra, Italy
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Cited articles
Abeshu, G. W., Tian, F., Wild, T., Zhao, M., Turner, S., Chowdhury, A. F. M. K., Vernon, C. R., Hu, H., Zhuang, Y., Hejazi, M., and Li, H.-Y.: Enhancing the representation of water management in global hydrological models, Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, 2023. a
Baratgin, L., Polcher, J., Dumas, P., and Quirion, P.: Modeling hydropower operations at the scale of a power grid: a demand-based approach, Hydrol. Earth Syst. Sci., 28, 5479–5509, https://doi.org/10.5194/hess-28-5479-2024, 2024. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W., Heinke, J., Bloh, W. V., and Gerten, D.: Impact of reservoirs on river discharge and irrigation water supply during the 20th century, Water Resour. Res., 47, https://doi.org/10.1029/2009WR008929, 2011. a
Burek, P., van der Knijff, J., and de Roo, A.: LISFLOOD. Distributed Water Balance and Flood Simulation Model, Tech. rep., European Commission – Joint Research Centre, Luxembourg, ISBN 879-92-79-33190-9, https://doi.org/10.2788/24719, 2013. a, b
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. a, b
Casado Rodríguez, J.: casadoj/reservoirs-LSHM: Version used in the paper (Version v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.21370914, 2026. a
Casado Rodríguez, J., Disperati, J., and Salamon, P.: ResOpsUS+CARS: Reservoir Operations US and CAtchment and Reservoir Static attributes (Version 1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.15978041, 2025. a, b
Choulga, M., Moschini, F., Mazzetti, C., Grimaldi, S., Disperati, J., Beck, H., Salamon, P., and Prudhomme, C.: Technical note: Surface fields for global environmental modelling, Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, 2024. 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
Duan, Q., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution approach for effective and efficient global minimization, J. Optimiz. Theory App., 76, 501–521, 1993. a
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158, 265–284, 1994. a
Haddeland, I., Skaugen, T., and Lettenmaier, D. P.: Anthropogenic impacts on continental surface water fluxes, Geophys. Res. Lett., 33, https://doi.org/10.1029/2006GL026047, 2006. a
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. a, b
Hao, Z., Chen, F., Jia, X., Cai, X., Yang, C., Du, Y., and Ling, F.: GRDL: A new global reservoir area-storage-depth data set derived through deep learning-based bathymetry reconstruction, Water Resour. Res., https://doi.org/10.1029/2023WR035781, 2024. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hou, J., Van Dijk, A. I. J. M., Renzullo, L. J., and Larraondo, P. R.: GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging, Earth Syst. Sci. Data, 16, 201–218, https://doi.org/10.5194/essd-16-201-2024, 2024. a, b, c
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting model parameters using a ready-made python package, PLoS ONE, 10, 1–22, https://doi.org/10.1371/journal.pone.0145180, 2015. a
JRC: European Flood Awareness System, https://european-flood.emergency.copernicus.eu/en (last access: 5 January 2026), 2026a. a
JRC: Global Flood Awareness System, https://global-flood.emergency.copernicus.eu/ (last access: 5 January 2026), 2026b. a
JRC: Open Source Lisflood, https://ec-jrc.github.io/lisflood/ (last access: 5 January 2026), 2026c. a
Khandelwal, A., Karpatne, A., Ravirathinam, P., Ghosh, R., Wei, Z., Dugan, H. A., Hanson, P. C., and Kumar, V.: ReaLSAT, a global dataset of reservoir and lake surface area variations, Scientific Data, 9, https://doi.org/10.1038/s41597-022-01449-5, 2022. a, b
Khazaei, B., Read, L. K., Casali, M., Sampson, K. M., and Yates, D. N.: GLOBathy, the global lakes bathymetry dataset, Scientific Data, 9, https://doi.org/10.1038/s41597-022-01132-9, 2022. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A global community dataset for large-sample hydrology, Scientific Data, 10, https://doi.org/10.1038/s41597-023-01975-w, 2023. a, b
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013. a
Lehner, B., Liermann, C. R., Revenga, C., Vörömsmarty, 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, b
Lehner, B., Beames, P., Mulligan, M., Zarfl, C., De Felice, L., van Soesbergen, A., Thieme, M., Garcia de Leaniz, C., Anand, M., Belletti, B., Brauman, K. A., Januchowski-Hartley, S. R., Lyon, K., Mandle, L., Mazany-Wright, N., Messager, M. L., Pavelsky, T., Pekel, J.-F., Wang, J., Wen, Q., Wishart, M., Xing, T., Yang, X., and Higgins, J.: The Global Dam Watch database of river barrier and reservoir information for large-scale applications, Scientific Data, 11, 1069, https://doi.org/10.1038/s41597-024-03752-9, 2024. a, b, c, d, e
Liebe, J., van de Giesen, N., and Andreini, M.: Estimation of small reservoir storage capacities in a semi-arid environment, Phys. Chem. Earth, 30, 448–454, https://doi.org/10.1016/j.pce.2005.06.011, 2005. a, b
Liu, Y., Xie, X., Wang, Y., Tursun, A., Peng, D., Wu, X., and Xue, B.: Increased surface water evaporation loss induced by reservoir development on the Loess Plateau, Hydrol. Earth Syst. Sci., 30, 67–89, https://doi.org/10.5194/hess-30-67-2026, 2026. a
Mekonnen, M. M. and Hoekstra, A. Y.: The blue water footprint of electricity from hydropower, Hydrol. Earth Syst. Sci., 16, 179–187, https://doi.org/10.5194/hess-16-179-2012, 2012. a
Moreno-Rodenas, A., Mantilla-Jones, J. D., and Valero, D.: Age, climate and economic disparities drive the current state of global dam safety, Nature Water, https://doi.org/10.1038/s44221-025-00402-1, 2025. a, b, c, d
Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., and Matias, Y.: Global prediction of extreme floods in ungauged watersheds, Nature, 627, 559–563, https://doi.org/10.1038/s41586-024-07145-1, 2024. a
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016. a
Roo, A. P. D., Wesseling, C. G., and Deursen, W. P. V.: Physically based river basin modelling within a GIS: The LISFLOOD model, Hydrol. Process., 14, 1981–1992, https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<1981::aid-hyp49>3.0.co;2-f, 2000. a
Salamon, P. and the team of co-authors: Improving hydrological modelling and prediction at the European and Global scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8642, https://doi.org/10.5194/egusphere-egu25-8642, 2025. a
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, 1–25, https://doi.org/10.1029/2008WR007327, 2010. a
Scherer, L. and Pfister, S.: Global water footprint assessment of hydropower, Renew. Energ., 99, 711–720, https://doi.org/10.1016/j.renene.2016.07.021, 2016. a, b
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345–4364, https://doi.org/10.5194/hess-19-4345-2015, 2015. a
Schwatke, C., Dettmering, D., and Seitz, F.: Volume variations of small inland water bodies from a combination of satellite altimetry and optical imagery, Remote Sensing, 12, https://doi.org/10.3390/rs12101606, 2020. a, b
Shen, H., Tolson, B. A., and Mai, J.: Time to Update the Split-Sample Approach in Hydrological Model Calibration, Water Resour. Res., 58, https://doi.org/10.1029/2021WR031523, 2022. 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, c, d
Steyaert, J. C. and Condon, L. E.: Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models, Hydrol. Earth Syst. Sci., 28, 1071–1088, https://doi.org/10.5194/hess-28-1071-2024, 2024. a, b
Thober, S., Cuntz, M., Kelbling, M., Kumar, R., Mai, J., and Samaniego, L.: The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km, Geosci. Model Dev., 12, 2501–2521, https://doi.org/10.5194/gmd-12-2501-2019, 2019. a
US Army Corps of Engineers: National Inventory of Dams, https://nid.sec.usace.army.mil/#/ (last access: 13 June 2025), 2025. a
US Bureau of Reclamation: Projects & Facilities, https://www.usbr.gov/projects/facilities.php?type=Dam (last access: 13 June 2025), 2025. a
US Geological Survey: Water Data for the Nation, https://waterdata.usgs.gov/ (last access: 13 June 2025), 2025. a
van der Knijff, J. M., Younis, J., and de Roo, A. P.: LISFLOOD: A GIS-based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010. 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, c
Yigzaw, W., Li, H. Y., Demissie, Y., Hejazi, M. I., Leung, L. R., Voisin, N., and Payn, R.: A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models, Water Resour. Res., 54, 10372–10386, https://doi.org/10.1029/2017WR022040, 2018. a
Zajac, Z., Revilla-Romero, B., Salamon, P., Burek, P., Hirpa, F., 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, c
Zhao, G. and Gao, H.: Automatic Correction of Contaminated Images for Assessment of Reservoir Surface Area Dynamics, Geophys. Res. Lett., 6092–6099, https://doi.org/10.1038/nature20584, 2018. a
Short summary
Dams significantly change river flows, yet they are difficult to represent in global hydrological models. We tested four different modeling methods using data from 164 reservoirs to find the most effective approach. We found that tracking water storage is better than tracking outflow for predicting reservoir behavior. This discovery enables the use of satellite information for better water management. These improvements are being implemented in the European and Global Flood Awareness Systems.
Dams significantly change river flows, yet they are difficult to represent in global...
