Satellite gravimetry (Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow‐On (GFO)) has solved the challenge of monitoring global and basin‐scale terrestrial water storage anomalies (TWSA) at monthly intervals and spatial scales of ∼330 km. However, this spatial resolution limits its ability to capture small‐scale basin water variations, and GRACE/GFO cannot independently distinguish different vertical water components, which further restricts its application in hydrological studies. To address these challenges, we propose a joint inversion downscaling method that combines hydrological simulations and GRACE/GFO observations. Our approach effectively inherits the high spatial resolution patterns from hydrological models and preserves the large‐scale accuracy from the GRACE/GFO measurements. It also allows for the flexible inclusion of mascon groups, reducing dependence on hydrological models and improving the performance in glacier regions, where traditional downscaling methods struggle. We reconstructed high‐resolution (i.e., 0.5°) TWSA and its components in China, for example, groundwater and glacier, revealing their finer spatial trends and localized water mass changes. From 2002 to 2022, our study details TWSA and flux changes in different China's basins, demonstrating marked regional disparities. Groundwater depletion rate in the North China Plain is 2.43 ± 0.18 cm/yr, equivalent to 3.35 ± 0.25 Gt/yr, and glacier mass loss rate in the High Mountain Asia (HMA) is 34.10 ± 1.55 Gt/yr. We also map the spatiotemporal patterns of glaciers in HMA and surface water changes in the endorheic Tibetan Plateau. This work has successfully extracted high‐resolution signals for different vertical water components in China, providing valuable insights for regional water resource management and disaster mitigation.

Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC > 0.60) and low root-mean-square errors (RMSE < 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management.

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions.

The Surface Water and Ocean Topography (SWOT) mission, initially designed to observe oceans and inland waters, proves valuable for mapping intertidal flat topography. This study presents a fusion method that integrates freely available SWOT interferometric altimetry data with Sentinel‐2 imagery. The density‐based spatial clustering of applications with noise algorithm is employed to accurately identify observation data located in the intertidal zone. Intertidal topography with a 10 m resolution were generated from L2_HR_PIXC data collected during SWOT's science phase (July 2023–October 2024) in the intertidal region along the coast of Jiangsu Province, China. These Digital Elevation Models were compared with laser altimetry observations from the ICESat‐2. SWOT observations show high accuracy, with RMSEs of 0.24 m (multi‐cycle) and 0.41 m (single‐cycle), confirming their potential for intertidal monitoring. This study presents a data processing strategy that does not rely on ground‐based observations, demonstrating potential for application in a broader range of regions.

To calibrate airborne gravity gradiometers currently in development in China, it is urgent to build an airborne gravity gradiometer test site. The site’s selection depends on the preknowledge of high-resolution gravity and gradient structures. The residual terrain modelling (RTM) technique is generally applied to recover the short-scale gravity field signals. However, due to limitations in the quality and resolution of density models, RTM terrain generally assumes a constant density. This assumption can introduce significant errors in areas with substantial density anomalies and of reggued terrain, such as volcano areas. In this study, we promote a method to determine a high-resolution gravity field by integrating long-wavelength signals generated by EGM2008 with short-wavelength signals from terrain relief and shallow density anomalies. These short wavelength signals are recovered using the RTM technique with both constant density and density anomalies obtained through the equivalent source layer (ESL) method, utilizing sparse terrestrial gravity measurements. Compared to the recovery rate of $54.62\%$ using the classical RTM method, the recovery rate increases to $86.22\%$ after involving density anomalies. With this method, we investigate the gravity field signals over the Wudalianchi Volcano Field (WVF) both on the Earth’s surface and at a flight height of 100 m above the terrain. The contribution of each part and their attenuation characters are studied. In particular, the 5 km × 5 km area surrounding Bijiashan (BJS) and Wohushan (WHS) volcanos shows a strong gravity signature, making it a good candidate for the test site location. This study gives the location of the airborne gravity gradiometer test site which is an essential step in the instruments’ development. Furthermore, the method presented in this study offers a foundational framework for future data processing within the test site.