MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
Daily grace satellite data evaluate short-term hydro-meteorological fluxes from global atmospheric reanalyses
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
citing papers explorer
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Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
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D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.