Surface mass variations inferred from the Global Positioning System (GPS), and observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) complement each other in terms of spatial and temporal coverage. This paper presents an analysis of regional surface mass variations inverted from GPS vertical displacements under different density distributions of GPS stations, and compares the GPS-derived mass variations with GRACE/GFO inversion results in spatial and temporal domains. To this end, GPS vertical displacement data from a total of 85 permanent GPS stations of the Crustal Movement Observation Network of China (CMONOC), the latest GRACE/GFO RL06 spherical harmonic (SH) solutions and GRACE RL06 mascon solutions are used to investigate surface mass variations in four regions or basins, including the Yunnan Province (YNP), Min River Basin (MRB), Jialing River Basin (JLRB), and Wu River Basin (WRB) in Southwest China. Our results showed that the spatial distributions and seasonal characteristics of GPS-derived mass change time series agree well with those from GRACE/GFO observations, especially in regions with relatively dense distributions of GPS stations (e.g., in the YNP and MRB), but there are still obvious discrepancies between the GPS and GRACE/GFO results. Scale factor methods (both basin-scaled and pixel-scaled) were employed to reduce the amplitude discrepancies between GPS and GRACE/GFO results. The results also showed that the one-year gap between the GRACE and GFO missions can be bridged by scaled GPS-derived mass change time series in the four studied regions, especially in the YNP and MRB regions (with relatively dense distributions of GPS stations).

The surface displacements measured by the Global Navigation Satellite System (GNSS) provide a unique insight for studying terrestrial water storage (TWS) changes. In this study, we recovered the TWS changes from GNSS vertical displacements in Southwest China (SWC) using Slepian basis function (SBF) from January 2011 to December 2020. The performance of the TWS changes estimated by SBF was validated against the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow‐On (GFO) and the GNSS‐inverted TWS changes estimated by Green's function method. The results showed that the spatial patterns, seasonal, and linear trends of the TWS changes derived from GNSS using SBF agreed with the GRACE/GFO estimates. However, there are still evident differences in the local scope, and the GNSS‐derived TWS changes presented stronger amplitudes and more details in the spatio‐temporal domains than the GRACE/GFO estimates in SWC. The unconstrained GNSS inversion results using SBF also presented stronger signal amplitudes than those estimates with Green's function, and the TWS changes estimated by SBF were more reliable than Green's function method for regions with sparsely distributed GNSS stations in SWC. Additionally, the average distance between the GNSS stations can be considered as a reasonable filtering radius of SBF, and the SBF‐estimated TWS changes with different Gaussian filtering radii had comparable signal amplitudes and spatial patterns with the estimates of Green's function and GRACE/GFO.

Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry and Global Navigation Satellite System (GNSS) surface displacement measurements offer complementary advantages for monitoring terrestrial water storage (TWS) changes. We propose a new joint inversion model based on the combination of GRACE‐based geopotential difference (GPD) observations using the mascon method and GNSS vertical displacements through the Green's function method to obtain reliable TWS changes in Brazil. The performance of the jointly inverted TWS changes is assessed through closed‐loop simulations and comparisons with hydrometeorological data (precipitation‐P, evapotranspiration‐ET, and runoff‐R) using water budget closure (P‐ET‐R) and river water level observations from satellite altimetry. The simulation results indicate that the joint inversion results exhibit higher accuracy and reliability than GRACE GPD‐based mascon (GPD‐mascon) and GNSS solutions, and the standard deviations (STDs) of joint results decrease by ∼6.98 and ∼37.5 mm compared to those from GPD‐mascon and GNSS solutions. The joint inversion of real GRACE and GNSS data demonstrates notably lower uncertainty than that of GPD‐mascon solutions and exhibits significant improvement than GNSS‐only solutions. The STDs and correlation coefficients between monthly R time series derived from three inversion methods (joint inversion, GPD‐mascon and GNSS solutions, combined with P and ET through water budget closure) and in situ observations are 19.607 mm and 0.912, 20.879 mm and 0.904, and 31.370 mm and 0.778, respectively. The joint estimates also yield better correlation with river water level observations compared to GPD‐mascon and GNSS solutions. Furthermore, at the weekly time scale, the joint inversion results show better consistency with P‐ET‐R data than those from GPD‐mascon and GNSS solutions.

To suppress the correlated noise of Gravity Recovery and Climate Experiment (GRACE) spherical harmonic (SH) solutions, we developed a series of constrained monthly gravity field solutions named WHU‐GRACE‐GPD01s from August 2002 to July 2016 using GRACE‐based geopotential differences. The constrained solutions were estimated using Kaula regularization, and the optimal regularization parameters were adaptively determined from the GRACE data itself through variance component estimation. The performance of the constrained WHU‐GRACE‐GPD01s solutions was validated against the official SH solutions (GFZ, JPL, and CSR RL06) and mass concentration (mascon) solutions (CSR RL06M) at global and regional scales. The results demonstrate that mass changes derived from the constrained solutions and official SH solutions with DDK4 filtering are in good agreement, but the constrained solutions present weaker longitudinal stripes and have a lower noise level at regional scales (e.g., in the Middle Pacific Ocean and Sahara Desert). Furthermore, regional mass changes (e.g., major river basins, Greenland, and Antarctic ice sheet) inferred from the constrained solutions agree with the ones derived from the official SH solutions with DDK4 filtering. The constrained solutions also have a higher signal intensity and smaller spatial leakages as compared to the CSR RL06 SH solutions with spatial filtering (Gaussian filtering plus de‐striping). For the problematic months, the constrained solutions are more reliable than the official SH solutions using spatial filtering or DDK4 filtering, and are closer to CSR RL06M mascon solutions. These validations demonstrate that our constrained solutions are comparable to the official SH solutions and can be used without postprocessing.

The space geodetic technologies (e.g., Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO)) provide effective tools to infer terrestrial water storage (TWS) change, which is an important indicator of the hydrological cycle and climate change. This study investigated the optimization approaches of the Slepian basis function (SBF) method in recovering TWS changes in the Southwest River Basin of China (SWRB) with sparse data and conducted a comprehensive analysis of TWS changes in SWRB using GNSS and GRACE/GFO. The results show that using the Shannon number criterion and even-distributed GNSS observations can obtain reliable TWS changes based on the SBF method. The GNSS-inverted TWS changes generally maintain good consistency with GRACE/GFO and hydrological model results in SWRB, but the ground-based daily GNSS estimates present stronger signal amplitudes and more high-frequency information. In the upper SWRB, the GRACE/GFO well reveals the continuous water loss (-8.19±1.76 mm/yr) induced by human activity, while the GNSS does not reveal the long-term linear changes of TWS, because it is difficult to separate them from the GNSS vertical displacement time series. In the lower SWRB, both GNSS and GRACE/GFO can reveal the increasing and decreasing trends of TWS changes within the study period, and precipitation is the dominant driving factor that causes the secular changes in TWS. Additionally, the ENSO generally presents relatively stronger effects on TWS changes than PDO in SWRB. Our results contribute to understanding the regional hydrological cycle and managing the water resources in SWRB using geodetic data such as GNSS and GRACE/GFO.