Accurate terrestrial water storage (TWS) estimation is important to evaluate the situation of the water resources over the Yangtze River Basin (YRB). This study exploits the TWS observation from the new temporal gravity field model, HUST-Grace2016 (Huazhong University of Science and Technology), which is developed by a new low-frequency noise processing strategy. A novel GRACE (Gravity Recovery and Climate Experiment) post-processing approach is proposed to enhance the quality of the TWS estimate, and the improved TWS is used to characterize the drought and flood events over the YRB. The HUST-Grace2016-derived TWS presents good agreement with the CSR (Center for Space Research) mascon solution as well as the PCR-GLOBWB (PCRaster Global Water Balance) hydrological model. Particularly, our solution provides remarkable performance in identifying the extreme climate events e.g., flood and drought over the YRB and its sub-basins. The comparison between GRACE-derived TWS variations and the MODIS-derived (Moderate Resolution Imaging Spectroradiometer) inundated area variations is then conducted. The analysis demonstrates that the terrestrial reflectance data can provide an alternative way of cross-comparing and validating TWS information in Poyang Lake and Dongting Lake, with a correlation coefficient of 0.77 and 0.70, respectively. In contrast, the correlation is only 0.10 for Tai Lake, indicating the limitation of cross-comparison between MODIS and GRACE data. In addition, for the first time, the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) vertical velocity data is incorporated with GRACE TWS in the exploration of the climate-induced hydrological activities. The good agreement between non-seasonal NCEP/NCAR vertical velocities and non-seasonal GRACE TWSs is found in flood years (2005, 2010, 2012 and 2016) and drought years (2006, 2011 and 2013). The evidence shown in this study may contribute to the analysis of the mechanism of climate impacts on the YRB.

The Poyang Lake, the largest freshwater lake in China, is famous for its ecological and economic importance as well as frequent flood characteristics. In this study, multiple satellite remote sensing observations (e.g., GRACE, MODIS, Altimetry, and TRMM), hydrological models, and in situ data are used to characterize the flood phenomena over the Poyang Lake basin between 2003 and 2016. To improve the accuracy of the terrestrial water storage (TWS) estimates over the Poyang Lake basin, a modified forward-modeling method is introduced in the GRACE processing. The method is evaluated using the contaminated noise onboard observations for the first time. The results in both spectral and spatial domains infer a good performance of the method on the suppression of high-frequency noise while reducing the signal loss. After applying forward-modeling method, the TWS derived from the GRACE spherical harmonic coefficients presents a comparable performance with the solution derived from the newly released CSR Release05 mascon product over the Poyang Lake basin. The flood events in 2010 and 2016 are identified from the positive anomalies in non-seasonal TWSs derived by GRACE and hydrological models. The flood signatures also coincide with the largest inundated areas estimated from MODIS data, and the observed areas in 2010 and 2016 are 3370.3 km² (30% higher than the long-term mean) and 3445.0 km² (33% higher), respectively. The water levels in the Hukou station exceed the warning water level for 25 days in 2010 and 28 days in 2016. These continuous warning-exceeded water levels also imply the severe flood events, which are primarily driven by the local plenteous precipitation in the rainy season (1528 mm in 2010, 1522 mm in 2016).

A multilayer approach is set up for local gravity field recovery within the framework of multi-resolution representation, where the gravity field is parameterized as the superposition of multiple layers of Poisson wavelets located at different depths beneath the Earth's surface. The layers are designed to recover gravity signals at different scales, where the shallow and deep layers mainly capture the short- and long-wavelength signals, respectively. The depths of these layers are linked to the locations of different anomaly sources beneath the Earth's surface, which are estimated by wavelet decomposition and power spectrum analysis. For testing the performance of this approach, a gravimetric quasi-geoid model over the North Sea, QGNSea V1.0, is modeled and validated against independent control data. The results show that the multilayer approach fits the gravity data better than the traditional single-layer approach, particularly in regions with topographical variation. An Akaike information criterion (AIC) test shows that the multilayer model obtains a smaller AIC value and achieves a better balance between the goodness of fit of data and the simplicity of the model. Further, an evaluation using independent GPS/leveling data tests the ability of regional models computed from different approaches towards realistic extrapolation, which shows that the accuracies of the QGNSea V1.0 derived from the multilayer approach are better by 0.4, 0.9, and 1.1 cm in the Netherlands, Belgium, and parts of Germany, respectively, than that using the single-layer approach. Further validation with existing models shows that QGNSea V1.0 is superior with respect to performance and may be beneficial for studying ocean circulation between the North Sea and its neighboring waters.