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• W4291915062 abstract "PreviousNext You have accessSecond International Meeting for Applied Geoscience & EnergyIntegration of hydrologic remote sensing data to estimate changes in groundwater storage across multiple spatial scalesAuthors: Aakash AhamedRosemary KnightSarfaraz AlamAakash AhamedStanford UniversitySearch for more papers by this author, Rosemary KnightStanford UniversitySearch for more papers by this author, and Sarfaraz AlamStanford UniversitySearch for more papers by this authorhttps://doi.org/10.1190/image2022-3744433.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSatellite remote sensing data and models can provide accurate and timely estimates of hydrologic fluxes (precipitation, evapotranspiration, runoff), and states (soil moisture, snow water equivalent), and have recently emerged as powerful tools for numerous applications in water resources management. However, it remains unclear how effectively remotely sensed datasets can be integrated to close the water balance and estimate changes in groundwater storage at different spatial scales. Because many remote sensing platforms provide free near real-time and data at high spatial (<1km – 10km) and temporal resolutions (daily – monthly), the integration of these datasets has the potential to provide novel and actionable insights to water managers and decision-makers. In this study, we integrate multiple sources of remotely sensed hydrologic data to derive ensemble groundwater storage change estimates through a water balance approach applied at three spatial scales spanning three orders of magnitude. The available sources for each flux and state variable were used in an ensemble water balance framework to quantify the uncertainty in estimates of groundwater storage changes, and the uncertainty for each water balance component (i.e. the flux and state variables) was estimated through triple collocation. Results shows that estimates of changes in groundwater storage computed from remotely sensed water balances can capture seasonality, long-term trends, and extreme events, and compare favorably to independent estimates from groundwater wells, calibrated groundwater flow models, and gravimetric measurements across all three spatial scales. This data-driven approach, along with estimates of uncertainty among water balance components, can be used by water managers to assess area-integrated changes in groundwater storage at low latencies relative to traditional methods, and for areas that do not benefit from infrastructure such as monitoring wells or calibrated groundwater flow models.Keywords: remote sensing, hydrology, water resources, groundwaterPermalink: https://doi.org/10.1190/image2022-3744433.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Aakash Ahamed, Rosemary Knight, and Sarfaraz Alam, (2022), Integration of hydrologic remote sensing data to estimate changes in groundwater storage across multiple spatial scales, SEG Technical Program Expanded Abstracts : 3026-3028. https://doi.org/10.1190/image2022-3744433.1 Plain-Language Summary Keywordsremote sensinghydrologywater resourcesgroundwaterPDF DownloadLoading ..." @default.
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• W4291915062 date "2022-08-15" @default.
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• W4291915062 title "Integration of hydrologic remote sensing data to estimate changes in groundwater storage across multiple spatial scales" @default.
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