{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:09:09Z","timestamp":1774631349009,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T00:00:00Z","timestamp":1566432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area\u2013elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSWMWH\u2013DEMmedian elevation combinations that showed a strong linear \u2018area (WA) \u2013 elevation (WH)\u2019 (R2 &gt; 0.75) hypsometry. Based on \u2018WA-WH\u2019 linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987\u20131992, 2000\u20132004, 2007\u20132009 and 2012\u20132015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12\u201318\u201324\u201336 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale.<\/jats:p>","DOI":"10.3390\/rs11171974","type":"journal-article","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T10:15:07Z","timestamp":1566555307000},"page":"1974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products"],"prefix":"10.3390","volume":"11","author":[{"given":"Tejas","family":"Bhagwat","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), M\u00fcnchener Stra\u00dfe 20, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0113-8637","authenticated-orcid":false,"given":"Igor","family":"Klein","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), M\u00fcnchener Stra\u00dfe 20, 82234 We\u00dfling, Germany"}]},{"given":"Juliane","family":"Huth","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), M\u00fcnchener Stra\u00dfe 20, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5571-800X","authenticated-orcid":false,"given":"Patrick","family":"Leinenkugel","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), M\u00fcnchener Stra\u00dfe 20, 82234 We\u00dfling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.advwatres.2018.01.028","article-title":"The blue water footprint of the world\u2019s artificial reservoirs for hydroelectricity, irrigation, residential and industrial water supply, flood protection, fishing and recreation","volume":"113","author":"Hogeboom","year":"2018","journal-title":"Adv. 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