# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 2 Data and Methodology
### Data  
#### 2.2.1 GRACE Data and Land Surface Models  
Monthly GRACE release 5 (RL05) data products, generated by the University of Texas at Austin's Center for Space Research (CSR), were used to obtain TWS changes. The products were provided in the form of spherical harmonic (SH) coefficients, i.e., Stokes coefficients, up to degree and order 60. The GRACE data were processed as follows. The degree 1 coefficients and the zonal degree 2 coefficients were replaced by more reliable estimates as suggested by [PERSON] et al. [58] and [PERSON] et al. [59]. Long-term non-hydroological mass change signal due to the Glacial Isostatic Adjustment (GIA) process was removed based on the model from [PERSON] et al. [60]. In addition, we applied the DDK4 filter to reduce correlated errors and high-frequency noises in the original SH coefficients [61].  
To further estimate GWS changes using GRACE, changes in SMS and SWES were estimated using four versions of LSMs from the Global Land Data Assimilation System (GLDAS), i.e., Noah [62], CLM [63], MOSAIC [64] and VIC [65]. By integrating ground- and space-based observations and using data assimilation techniques, the GLDAS LSMs can simulate optimal fields of land surface water/energy fluxes. The average values of the four versions of LSMs were computed as the final estimates of \(\Delta\)SMS and \(\Delta\)SWES. Then, gridded \(\Delta\)SMS + \(\Delta\)SWES data are filtered in the same way as the GRACE data and removed from GRACE-derived TWS changes to derive filtered GWS changes, as indicated in Equation (3). Based on the statistics of surface water reservoir storages from the China Water Resources Bulletins (CWRB) published by the Ministry of Water Resources of China (MWR) [66], the \(\Delta\)RESS trend in the NCP is \(\sim\)0.3 km\({}^{3}\)/yr during 2002-2014, which is removed from the GRACE-based GWS rate estimate; while the \(\Delta\)RESS trends in the other two main aquifers of China are negligible and not considered in the following analysis. To obtain undamped time series of GWS changes from filtered GWS changes based on GRACE and LSMs, we further estimated the scaling factors for recovering actual GWS signals. Gridded GWS changes from the average of two global hydrological models, i.e., the WaterGAP Global Hydrological Model (WGHM), and the PCRaster Global Water Balance (PCR-GLOBWB) model, were used to estimate the scaling factors in the three aquifers of China. These scaling factors were used to derive final time series of GWS changes in our study regions. Considering that Aquifer 1 and 2 (Figure 1) are close to the ocean, the leakage effects due to ocean mass changes might be significant in these two aquifers. We calculated the ocean leakage effects based on the ocean bottom pressure data from the Estimating the Circulation and Climate of the Ocean (ECCO) model and found that the leakage effects on the GWS changes and on the GWS rates are negligible. Thus, leakage is not considered for these aquifers in the following analysis. The uncertainties in GRACE-derived time series of GWS changes were estimated considering the GRACE measurement errors and uncertainties in LSMs. More details on uncertainty estimation can be found in [22].