Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
arXiv:2606.23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle. The GRACE and GRACE-FO satellite missions provide the only direct, globally consistent observations of TWS change, but their record only begins in 2002 which is too short for many climate-scale analyses. We present a deep learning application that reconstructs monthly GRACE-like TWS anomalies (TWSA) back to 1940 by learning the relationship between daily
The increasing maturity of spatio-temporal graph neural networks, combined with the urgent need for long-term water storage data, enables this breakthrough in hydrological reconstruction.
This development provides a critical tool for understanding historical water trends, improving climate models, and informing policy decisions regarding water resource management over a much longer timescale.
The ability to reconstruct detailed, high-resolution terrestrial water storage data back to 1940 significantly enhances our capacity to analyze climate variability, human impact on water cycles, and predict future water scarcity events.
- · Climate scientists
- · Water resource managers
- · Agricultural sector planners
- · Governments in water-stressed regions
- · Regions unprepared for increased water volatility
Improved historical context for current hydrological anomalies.
Better predictive models for droughts and floods, leading to more resilient infrastructure planning.
Enhanced geopolitical stability in regions prone to water-related resource conflicts due to proactive management.
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Read at arXiv cs.LG