A Data-Driven Method for Enhancing Spatial Resolution in Estimating Terrestrial Water Storage Changes From Satellite Gravimetry

Understanding terrestrial water storage (TWS) changes is crucial for water management and hydrological applications. TWS changes are accurately observed by the Gravity Recovery and Climate Experiment and its Follow-On (GRACE/-FO) mission. However, the low spatial resolution limits the knowledge of water storage distribution. This study proposes a novel constrained point-mass modeling (CPM) approach, introducing a data-driven regularization matrix to improve spatial resolution. We derived point-mass solutions over the Amazon River basin from April 2002 to December 2019. Then, we evaluated our method using the WaterGAP global hydrology model (WGHM). Compared to results from traditional point-mass method (TPM) and three state-of-the-art GRACE/-FO solutions, the annual amplitudes in TWS changes from our method agree better with that from WGHM (slope =0.80 and $R<^>{2} =0.69$ ). Besides, the 179-month TWS changes also show a better consistency between our results and WGHM, indicating that our method effectively improves spatial resolution over the Amazon River basin. Moreover, benefiting from the improved spatial resolution of our method, our results, based solely on GRACE/-FO data, reveal spatial patterns in TWS changes that generally correspond with the major river channels of the basin.