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SYSU TWSA v1.0: global high-resolution terrestrial water storage anomalies via satellite gravimetry
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DOI：10.5194/essd-18-4537-2026.png)
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Abstract
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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) an 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 generally good 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 1200 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 demonstrates competitive overall accuracy while strengthening the representation of glacier- and lake-related signals. The SYSU TWSA dataset is openly available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Terre.tpdc.303322; Xiong et al.; 2026).
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