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This research introduces GraphRiverCast, a neural model that leverages graph topology to predict global river hydrodynamics without requiring initial state data or dense gauge networks. By treating the entire river network as a unified system, the approach eliminates intrinsic skill decay over lead times and significantly outperforms traditional isolated basin models. The study offers critical insights for applying machine learning to other data-scarce Earth systems where connectivity and dissipative dynamics are central to the problem.
Topology enables learning-based hydrodynamic prediction of the global river system
arXiv:2602.22293v2 Announce Type: replace Abstract: Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning has transformed Earth-system modeling, but a system-level advance for river prediction lags for lack of reliable data. Exploiting the connectivity and dissipative dynamics of rivers, we introduce GraphRiverCast, a neural model for global river systems that predicts daily multivariate hydrodynamics at every reach of a 0.25{\deg}network with only sparse gauges and no initial state. It shows no intrinsic skill decay with lead time, outperforms leading global river models by 25% in accuracy, and robustly generalizes to ungauged reaches and finer resolutions. GraphRiverCast lifts learning-based river prediction from isolated basins to a unified global system and offers insights for machine learning in data-scarce Earth systems.
Abstract:Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning has transformed Earth-system modeling, but a system-level advance for river prediction lags for lack of reliable data. Exploiting the connectivity and dissipative dynamics of rivers, we introduce GraphRiverCast, a neural model for global river systems that predicts daily multivariate hydrodynamics at every reach of a 0.25°network with only sparse gauges and no initial state. It shows no intrinsic skill decay with lead time, outperforms leading global river models by 25% in accuracy, and robustly generalizes to ungauged reaches and finer resolutions. GraphRiverCast lifts learning-based river prediction from isolated basins to a unified global system and offers insights for machine learning in data-scarce Earth systems.
21 pages, 4 figures. Main text only; the supplementary materials accompany the journal version
Machine Learning (cs.LG); Geophysics (physics.geo-ph)
arXiv:2602.22293 [cs.LG]
(or arXiv:2602.22293v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.22293 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words
