Estimation of Terrestrial Water Storage in the Western United States Using Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loading

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dc.contributor.advisorForman, Barton Aen_US
dc.contributor.authorYin, Gaohongen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-10-10T05:34:14Z
dc.date.available2020-10-10T05:34:14Z
dc.date.issued2020en_US
dc.description.abstractAccurate estimation of terrestrial water storage (TWS) is critically important for the global hydrologic cycle and the Earth's climate system. The space-based Gravity Recovery and Climate Experiment (GRACE) mission and land surface models (LSMs) have provided valuable information in monitoring TWS changes. In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth's surface to mass redistribution. All of these techniques have their own strengths and weaknesses in detecting TWS changes due to their unique uncertainties, error characteristics, and spatio-temporal resolutions. This dissertation investigated the potential of improving our knowledge in TWS changes via merging the information provided by ground-based GPS, GRACE, and LSMs. First, the vertical displacements derived from ground-based GPS, GRACE, and NASA Catchment Land Surface Model (Catchment) were compared to analyze the behavior and error characteristics of each data set. Afterwards, the ground-based GPS observations were merged into Catchment using a data assimilation (DA) framework in order to improve the accuracy of TWS estimates and mitigate hydrologic state uncertainty. To the best of our knowledge, this study is the first attempt to assimilate ground-based GPS observations into an advanced land surface model for the purpose of improving TWS estimates. TWS estimates provided by GPS DA were evaluated against GRACE TWS retrievals. GPS DA performance in estimating TWS constituent components (i.e., snow water equivalent and soil moisture) and hydrologic fluxes (i.e., runoff) were also examined using ground-based in situ measurements. GPS DA yielded encouraging results in terms of improving TWS estimates, especially during drought periods. Additionally, the findings suggest a multi-variate assimilation approach to merge both GRACE and ground-based GPS into the LSMs to further improve modeled TWS and its constituent components should be pursued as a new and novel research project.en_US
dc.identifierhttps://doi.org/10.13016/jqes-ssni
dc.identifier.urihttp://hdl.handle.net/1903/26598
dc.language.isoenen_US
dc.subject.pqcontrolledHydrologic sciencesen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledWater resources managementen_US
dc.subject.pquncontrolledGRACEen_US
dc.subject.pquncontrolledGround-based GPSen_US
dc.subject.pquncontrolledLand Surface Modelen_US
dc.subject.pquncontrolledSurface Deformationen_US
dc.subject.pquncontrolledTerrestrial Water Storageen_US
dc.titleEstimation of Terrestrial Water Storage in the Western United States Using Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loadingen_US
dc.typeDissertationen_US

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