SYSU TWSA v1.0: Global High-Resolution Terrestrial Water Storage Anomalies via Satellite Gravimetry

Abstract. Publicly available global high-resolution terrestrial water storage anomaly (TWSA) datasets derived from satellite gravimetry remain scarce. Many existing global downscaling products rely heavily on hydrological models. Consequently, their performance can degrade in regions where key mass variations observed by the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GFO) are poorly represented in the models, notably those associated with mountain glaciers and large lakes. Here we provide SYSU TWSA, a global monthly 0.5° TWSA dataset spanning April 2002 to December 2022, generated using a joint-inversion spatial downscaling framework that integrates large-scale constraints from GRACE/GFO, high-resolution spatial patterns from the WaterGAP Global Hydrological Model (WGHM), and additional mascon groups that explicitly represent mountain glaciers and selected large or rapidly changing lakes. The dataset helps alleviate the current shortage of global high-resolution products and explicitly strengthens the representation of glacier- and lake-related signals. We assess SYSU TWSA through four complementary evaluations: (1) basin-wise consistency with raw GRACE/GFO estimates, (2) a basin water-balance consistency check, (3) independent evaluation against in situ groundwater well observations, and (4) comparisons with representative downscaled products in both the spectral and spatial domains. SYSU TWSA shows strong agreement with GRACE/GFO at the basin scale, with coefficients of determination (R²) exceeding 0.85 across basin-size classes. In small basins, consistency with terrestrial water fluxes derived from the basin water-balance equation improves substantially, with NSE increasing by 17.1 % relative to raw GRACE/GFO across 1,200 basins. Agreement with groundwater wells also improves, with correlations increasing at 67.7 % of 28,248 wells. Comparisons with representative assimilation-based and deep-learning downscaled products further indicate that SYSU TWSA achieves competitive overall accuracy while strengthening the representation of glacier- and lake-related signals.