Satellite geodetic technologies, such as the Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), have complementary advantages in inferring terrestrial water storage (TWS) changes at regional and basin scales. We introduced a joint inversion strategy to infer TWS changes using GNSS- and GRACE/GFO-derived vertical displacements based on Green's function theory in the Yangtze River Basin (YRB) from January 2011 to December 2020. Additionally, we investigated the performance of variance component estimation (VCE) and Akaike's Bayesian Information Criterion (ABIC) to determine the optimal relative weights of different observation data. The performance of our joint inversion strategy was verified through a closed-loop simulation and multi-source hydrometeorological data (i.e. the time derivative of TWS changes (dS/dt) from precipitation (P), evapotranspiration (ET), and runoff (R) based on the water balance equation, called P-ET-R). The closed‐loop simulation shows that the TWS changes from joint inversion have better consistencies with the synthetic signals than those of GNSS- and GRACE-only estimates, and the corresponding root mean square errors (RMSE) decreased 1.43 − 6.28 mm and correlation coefficients (CC) increased 3 per cent−10 per cent. The ABIC was more suitable for the joint inversion of measured GRACE/GFO and GNSS data for TWS changes in the YRB. Analysis from the measured data shows that the spatial patterns and seasonal characteristics in TWS changes derived from GNSS, GRACE/GFO, and their joint inversion are in good agreement in the YRB. The contribution of GNSS observations to the joint inversion in the upstream of the YRB is greater than that of GRACE/GFO due to the relatively densely distributed GNSS stations, but the opposite is true in the downstream. Furthermore, the joint inversion results have better agreements with P and P-ET-R compared to GNSS- and GRACE/GFO-only estimates in the upstream, and the corresponding CCs increased 5 per cent−7 per cent (for P) and 2 per cent−5 per cent (for P-ET-R), respectively, which further demonstrates the effectiveness of our joint inversion strategy. Our estimation strategy provides a new insight for joint inversion of GNSS and GRACE/GFO data to obtain more reliable TWS changes.

During temporal gravity field model determination, the kinematic empirical parameters are mainly designed to remove the strong bias, drift, and 1‐cycle per revolution variations in range‐rates. In practice, two different strategies are commonly used to process these empirical parameters. One is to determine the empirical parameters before solving spherical harmonic coefficients, called Pure Predetermined Strategy (PPS). The other is to simultaneously determine the empirical parameters and spherical harmonic coefficients, called Pure Simultaneous Strategy (PSS). In this study, apart from these two strategies, a novel processing strategy called Filter Predetermined Strategy (FPS) is also discussed. These different processing strategies may result in different solutions. With the Gravity Recovery and Climate Experiment Level 1B data spanning 2005 to 2010, the impacts of different kinematic empirical parameters processing strategies were assessed in detail. The numerical results indicate that (1) using three different processing strategies and their hybrids can determine the temporal gravity field model, while (2) the solutions via PPS present apparent temporal signal attenuation, which is approximately 15% lower in annual amplitude in Amazon River Basin, and 15% lower in yearly trend in Greenland, and (3) the signal‐to‐noise ratios of the solutions via PPS are generally smaller than those of the solutions via FPS and PSS, and (4) the performance of FPS is superior in terms of postfit range‐rates, but compatible with PSS in terms of other cross comparisons. According to comprehensive comparison results in terms of temporal signals and noise, the performance of our Huazhong University of Science and Technology models determined via FPS is in excellent accordance with other representative temporal gravity field models, such as CSR RL05, GFZ RL05a, and JPL RL05.

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.