{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:51:06Z","timestamp":1778323866974,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC18K0939"],"award-info":[{"award-number":["80NSSC18K0939"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA\u2019s new MODIS\/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data\u2014the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area\u2013Elevation (A\u2013E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS\/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R2 = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R2 values (0.71\u20130.96 for elevation, and 0.79\u20130.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08\u201319.34% for elevation, and 6.39\u201318.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.<\/jats:p>","DOI":"10.3390\/rs13040565","type":"journal-article","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T08:33:48Z","timestamp":1612514028000},"page":"565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["NASA\u2019s MODIS\/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8745-191X","authenticated-orcid":false,"given":"Yao","family":"Li","sequence":"first","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-0530","authenticated-orcid":false,"given":"Gang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8461-4162","authenticated-orcid":false,"given":"Deep","family":"Shah","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maosheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"},{"name":"Science Systems and Applications Inc., Lanham, MD 20706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0551-6543","authenticated-orcid":false,"given":"Sudipta","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"},{"name":"Science Systems and Applications Inc., Lanham, MD 20706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadashiva","family":"Devadiga","sequence":"additional","affiliation":[{"name":"Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0195-2030","authenticated-orcid":false,"given":"Bingjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5443-1146","authenticated-orcid":false,"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huilin","family":"Gao","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R., Heinke, J., Von Bloh, W., and Gerten, D. (2011). Impact of reservoirs on river discharge and irrigation water supply during the 20th century. Water Resour. Res., 47.","DOI":"10.1029\/2009WR008929"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/S0022-1694(02)00135-X","article-title":"Flood risk and flood management","volume":"267","author":"Plate","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cooke, G.D., Welch, E.B., Peterson, S., and Nichols, S.A. (2016). Restoration and Management of Lakes and Reservoirs, CRC Press.","DOI":"10.1201\/9781420032109"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15697","DOI":"10.1038\/ncomms15697","article-title":"Water scarcity hotspots travel downstream due to human interventions in the 20th and 21st century","volume":"8","author":"Veldkamp","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1073\/pnas.1222460110","article-title":"Multimodel assessment of water scarcity under climate change","volume":"111","author":"Schewe","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","unstructured":"Murdock, H.E., Gibb, D., Andr\u00e9, T., Appavou, F., Brown, A., Epp, B., Kondev, B., McCrone, A., Musolino, E., and Ranalder, L. (2019). Renewables 2019 Global Status Report, REN21 Secretariat. Available online: https:\/\/www.ren21.net\/wp-content\/uploads\/2019\/05\/gsr_2019_full_report_en.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7520","DOI":"10.1002\/2015JD023147","article-title":"A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand","volume":"120","author":"Mehran","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116265","DOI":"10.1016\/j.watres.2020.116265","article-title":"Exploring multidecadal changes in climate and reservoir storage for assessing nonstationarity in flood peaks and risks worldwide by an integrated frequency analysis approach","volume":"185","author":"Zhou","year":"2020","journal-title":"Water Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1175\/JHM-D-15-0002.1","article-title":"The contribution of reservoirs to global land surface water storage variations","volume":"17","author":"Zhou","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1029\/2017WR022040","article-title":"A New Global Storage-Area-Depth Dataset for Modeling Reservoirs in Land Surface and Earth System Models","volume":"54","author":"Yigzaw","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1175\/BAMS-D-15-00224.1","article-title":"Reservoir evaporation in the Western United States: Current science, challenges, and future needs","volume":"99","author":"Friedrich","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","unstructured":"Moreo, M. (2015). Evaporation data from Lake Mead and Lake Mohave, Nevada and Arizona, March 2010 through April 2015. US Geol. Surv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.rse.2011.02.019","article-title":"Improvements to a MODIS global terrestrial evapotranspiration algorithm","volume":"115","author":"Mu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.jhydrol.2017.10.007","article-title":"A remote sensing method for estimating regional reservoir area and evaporative loss","volume":"555","author":"Zhang","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"25179","DOI":"10.1029\/95JC02125","article-title":"The contribution of TOPEX\/POSEIDON to the global monitoring of climatically sensitive lakes","volume":"100","author":"Birkett","year":"1995","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Birkett, C., Reynolds, C., Beckley, B., and Doorn, B. (2011). From research to operations: The USDA global reservoir and lake monitor. Coastal Altimetry, Springer.","DOI":"10.1007\/978-3-642-12796-0_2"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.asr.2011.01.004","article-title":"SOLS: A lake database to monitor in the Neac r Real Time water level and storage variations from remote sensing data","volume":"47","author":"Jelinski","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-19-4345-2015","article-title":"DAHITI\u2013an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry","volume":"19","author":"Schwatke","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6092","DOI":"10.1029\/2018GL078343","article-title":"Automatic correction of contaminated images for assessment of reservoir surface area dynamics","volume":"45","author":"Zhao","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2017.05.039","article-title":"An approach for global monitoring of surface water extent variations in reservoirs using MODIS data","volume":"202","author":"Khandelwal","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1038\/nclimate3111","article-title":"Earth\u2019s surface water change over the past 30 years","volume":"6","author":"Donchyts","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111210","DOI":"10.1016\/j.rse.2019.111210","article-title":"Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery","volume":"232","author":"Yao","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isprsjprs.2020.08.008","article-title":"Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information","volume":"168","author":"Ling","year":"2020","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gao, H., Birkett, C., and Lettenmaier, D.P. (2012). Global monitoring of large reservoir storage from satellite remote sensing. Water Resour. Res., 48.","DOI":"10.1029\/2012WR012063"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8927","DOI":"10.1002\/2014WR015829","article-title":"Monitoring reservoir storage in South Asia from multisatellite remote sensing","volume":"50","author":"Zhang","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"669","DOI":"10.5194\/hess-23-669-2019","article-title":"A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry","volume":"23","author":"Busker","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2019.03.015","article-title":"Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches","volume":"226","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112104","DOI":"10.1016\/j.rse.2020.112104","article-title":"Estimating lake temperature profile and evaporation losses by leveraging MODIS LST data","volume":"251","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Althoff, D., Rodrigues, L.N., and da Silva, D.D. (2019). Evaluating evaporation methods for estimating small reservoir water surface evaporation in the Brazilian savannah. Water, 11.","DOI":"10.3390\/w11091942"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5321","DOI":"10.1080\/01431161.2020.1739354","article-title":"Evaporation rates in a vital lake: A 34-year assessment for the Karaoun Lake","volume":"41","author":"Mhawej","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Meng, X., Liu, H., Du, Q., Xu, L., and Liu, Y. (2020). Evaluation of the Performance of Different Methods for Estimating Evaporation over a Highland Open Freshwater Lake in Mountainous Area. Water, 12.","DOI":"10.3390\/w12123491"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111831","DOI":"10.1016\/j.rse.2020.111831","article-title":"A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry","volume":"244","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Lehner, B., Reidy Liermann, C., Revenga, C., Vorosmarty, C., Fekete, B., Crouzet, P., Doll, P., Endejan, M., Frenken, K., and Magome, J. (2011). Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01, NASA Socioeconomic Data and Applications Center (SEDAC)."},{"key":"ref_35","unstructured":"Vermote, E. (2021, February 01). MOD09Q1 MODIS\/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC 2015, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod09q1v006\/."},{"key":"ref_36","unstructured":"Vermote, E., Franch, B., and Claverie, M. (2021, February 01). VIIRS\/NPP Surface Reflectance 8-Day L3 Global 500m SIN Grid V001. NASA EOSDIS Land Process. DAAC 2016, Available online: https:\/\/lpdaac.usgs.gov\/products\/vnp09h1v001\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","unstructured":"Hulley, G., and Hook, S. (2021, February 01). MOD21 MODIS\/Terra Land Surface Temperature\/3-Band Emissivity 5-Min L2 1km V006. NASA EOSDIS Land Process. DAAC 2017, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod21v006\/."},{"key":"ref_39","unstructured":"Hulley, G., and Hook, S. (2021, February 01). VIIRS\/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V001. NASA EOSDIS Land Process. DAAC 2018, Available online: https:\/\/lpdaac.usgs.gov\/products\/vnp21v001\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","unstructured":"Zhao, G., Li, Y., Zhang, S., Shah, D., and Gao, H. (2021, February 01). Collection 6.1 MODIS Reservoir Product Algorithm Theoretical Basis Document (ATBD) Version 1.0. NASA GSFC 2021, Available online: https:\/\/modis-land.gsfc.nasa.gov\/modgwr.html."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"13603","DOI":"10.1038\/ncomms13603","article-title":"Estimating the volume and age of water stored in global lakes using a geo-statistical approach","volume":"7","author":"Messager","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s00704-009-0168-z","article-title":"Evaporation estimates from Nasser Lake, Egypt, based on three floating station data and Bowen ratio energy budget","volume":"100","author":"Elsawwaf","year":"2010","journal-title":"Theor. Appl. Climatol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jhydrol.2004.10.028","article-title":"Effects of climate variability on lake evaporation: Results from a long-term energy budget study of Sparkling Lake, northern Wisconsin (USA)","volume":"308","author":"Lenters","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12215","DOI":"10.3390\/rs70912215","article-title":"Quality assessment of S-NPP VIIRS land surface temperature product","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7883","DOI":"10.1109\/TGRS.2019.2917012","article-title":"Deriving High-Resolution Reservoir Bathymetry from ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gao, H., Zhang, S., Durand, M., and Lee, H. (2016). Satellite remote sensing of lakes and wetlands. Hydrologic Remote Sensing, CRC Press.","DOI":"10.1201\/9781315370392-5"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.envsoft.2011.11.017","article-title":"An area-dependent wind function for estimating open water evaporation using land-based meteorological data","volume":"31","author":"McJannet","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/S0380-1330(84)71808-9","article-title":"Estimation of overlake wind speed from overland wind speed: A comparison of three methods","volume":"10","author":"Schwab","year":"1984","journal-title":"J. Great Lakes Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lee, Z.P., Darecki, M., Carder, K.L., Davis, C.O., Stramski, D., and Rhea, W.J. (2005). Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods. J. Geophys. Res. Oceans, 110.","DOI":"10.1029\/2004JC002573"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1128\/MMBR.00003-16","article-title":"Marine bacterial and archaeal ion-pumping rhodopsins: Genetic diversity, physiology, and ecology","volume":"80","author":"Pinhassi","year":"2016","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Gao, H. (2020). Using the Digital Elevation Model (DEM) to improve the spatial coverage of the MODIS based reservoir monitoring network in South Asia. Remote Sens., 12.","DOI":"10.3390\/rs12050745"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, Y., Gao, H., Allen, G.H., and Zhang, Z. (2021). Constructing Reservoir Area-Volume-Elevation Curve from TanDEM-X DEM Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2021.3051103"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10712-015-9346-y","article-title":"The SWOT mission and its capabilities for land hydrology","volume":"37","author":"Biancamaria","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"138343","DOI":"10.1016\/j.scitotenv.2020.138343","article-title":"Hydropower dam operation strongly controls Lake Victoria\u2019s freshwater storage variability","volume":"726","author":"Getirana","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"12915","DOI":"10.1029\/2019JD031059","article-title":"Roles of Irrigation and Reservoir Operations in Modulating Terrestrial Water and Energy Budgets in the Indian Subcontinental River Basins","volume":"124","author":"Shah","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1002\/2016WR019638","article-title":"The twenty-first century Colorado River hot drought and implications for the future","volume":"53","author":"Udall","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1002\/wat2.1085","article-title":"Hydrological drought explained","volume":"2","year":"2015","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e2020JD032871","DOI":"10.1029\/2020JD032871","article-title":"Drought onset and termination in India","volume":"125","author":"Shah","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e2019WR026284","DOI":"10.1029\/2019WR026284","article-title":"Integrated Drought Index (IDI) for drought monitoring and assessment in India","volume":"56","author":"Shah","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e2018WR024620","DOI":"10.1029\/2018WR024620","article-title":"Quantifying climate and catchment control on hydrological drought in the continental United States","volume":"56","author":"Konapala","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jhydrol.2014.10.047","article-title":"Human and climate impacts on the 21st century hydrological drought","volume":"526","author":"Wanders","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_65","unstructured":"Tallaksen, L.M., and Van Lanen, H.A. (2004). Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, Elsevier."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e2019WR025843","DOI":"10.1029\/2019WR025843","article-title":"Contrasting Influences of Human Activities on Hydrological Drought Regimes Over ChinaBased on High-Resolution Simulations","volume":"56","author":"Yang","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4169","DOI":"10.5194\/hess-21-4169-2017","article-title":"Human-water interface in hydrological modelling: Current status and future directions","volume":"21","author":"Wada","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:50Z","timestamp":1760159990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,5]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040565"],"URL":"https:\/\/doi.org\/10.3390\/rs13040565","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,5]]}}}