Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

Abstract

Ground‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.

A Review of GNSS/GPS in Hydrogeodesy: Hydrologic Loading Applications and Their Implications for Water Resource Research

“…Recent studies take advantage of long GNSS position time series to study the interannual timescales associated with longer duration climatic drivers, such as drought and sustained years of high precipitation (e.g., Argus et al., 2017; Borsa et al., 2014; Chew & Small, 2014; V. G. Ferreira et al., 2018; Jin & Zhang, 2016). Additionally, many studies investigate both seasonal and interannual scale changes (e.g., Adusumilli et al., 2019; Argus et al., 2020; Elósegui, 2003; Fu et al., 2015; Hsu et al., 2020; Jiang et al., 2017; Wahr et al., 2013; Wang et al., 2017; Yin et al., 2020, 2021). However, only recently has attention turned to hydrologic loading at shorter, more dynamic temporal scales associated with individual storm events and subseasonal deformation (Adusumilli et al., 2019; Ferreira et al., 2019; Han & Razeghi, 2017; Jiang et al., 2021; Knowles et al., 2020; Milliner et al., 2018; Springer et al., 2019; Yin et al., 2021; Zhan et al., 2021).…”

Assimilation of SBAS-InSAR Based Vertical Deformation Into Land Surface Model to Improve the Estimation of Terrestrial Water Storage

“…The catchment deficit is defined as the average increase in water depth required to make the catchment to saturation. The perturbation parameters for model prognostic variables are shown in Table I following Yin et al [18].…”

“…However, EnKF is used to estimate the TWS by processing the time series deformation of the SBAS-InSAR for the first time. The model state and error covariance matrix based on the mean and spread of ensemble members are used in the Bayesian merging process of the land surface model and SBAS-InSAR time-series deformation [18].…”

“…The TWS estimates from OL, and SBAS-InSAR DA simulations were evaluated with the TWS by the GRACE inversion. In this study, to quantify the accuracy of the model estimation, Unbiased root-mean-square difference (ubRMSD) and correlation coefficient (R) were used [18].…”

“…In addition, using the DA to process the vertical deformation derived from the elastic response of the Earth's surface is another approach to estimate the TWS. The Global Positioning System (GPS) DA experiments demonstrated the ability of data fusion methods to estimate TWS variations using temporal vertical deformation [18]. Although using the vertical deformation @my.swjtu.edu.cn; rsgxliu@swjtu.edu.cn; xiangwei@my.swjtu.edu.cn; Sunta o@my.swjtu.edu.cn; qiankun@my.swjtu.edu.cn; caijiaxin@my.swjtu.edu.cn; sapirasteh@swjtu.edu.cn; XChen_RS@my.swjtu.edu.cn ) T observed by GPS to infer TWS changes can improve the temporal resolution, GPS observation stations are sparse and unevenly distributed in space.…”