Evaluation of GRACE mascon solutions using in-situ geodetic data: The case of hydrologic-induced crust displacement in the Yangtze River Basin

Since the Gravity Recovery and Climate Experiment (GRACE) satellite mission was started in 2002, a variety of spatial products have been made available to further understanding of mass redistribution in the Earth system. Two such mascon (mass concentration) solutions were developed by the Center for Space Research (CSR-M) and the NASA Jet Propulsion Laboratory (JPL-M), which offers significantly improved spatial localization and more accurate amplitude measurements of changes in recovered terrestrial Total Water Storage (TWS). However, it is difficult to validate GRACE-derived TWS mascons due to the lack of independent measurements of water storage in various forms at larger scales.In this study, we present a simple framework to evaluate GRACE mascon products based on in-situ GPS measurements from the Yangtze River Basin (YRB) in China. We found that the mascons show a more pronounced spatial difference in TWS distribution and highlight more details as compared to smoother results from empirical post-processing filtering applied to spherical harmonics (SH) data. The prediction of vertical displacements from CSR-M and JLP-M is closer to GPS than that from SH. The residual analysis showed the reductions in WRMS (weighted root-mean-squares) from the GPS minus the CSR-M average were greater than those for JPL-M in 41 GPS stations, and the scaling factors from CLM4.0 used in JPL-M-sf had few improvements with respect to agreement with GPS measurements. Our findings indicated CSR-M solutions were more consistent with in-situ observations and more in line with actual surface mass transport in the YRB. These findings also suggested that when using GRACE mascons to detect local TWS changes or when combining GRACE-derived data with GPS-observed displacement to estimate crustal response to loadings, users should note the contributions from effects of load signal sources from atmospheric, non-tidal ocean, and difference sensitivity kernels on differences between TWS from satellite-based and in-situ observations.