GraphRiverCast: A Topology-Informed Foundation Model for Global River Hydrodynamics

Accurate and rapid river forecasting is essential for global water cycle management but faces a persistent dichotomy: physics-based models offer structural consistency but are computationally intensive and difficult to calibrate efficiently, while data-driven approaches offer efficiency but often lack physical interpretability and struggle in data-scarce regions. To bridge this gap, we introduce GraphRiverCast (GRC), a topology-informed AI foundation model that forecasts multivariate river hydrodynamics at a global scale. Unlike conventional raster-based AI approaches, GRC explicitly encodes river network topology into a graph neural architecture. This design underpins a novel "pretrain-finetune" paradigm: the model first learns generalizable river hydrodynamic mechanisms from global physics-based simulations (pre-training), and then adapts to specific basins using sparse in-situ observations (fine-tuning). Our results demonstrate that topological awareness is essential for maintaining predictive accuracy and stability in "ColdStart" mode where initial states are unavailable. Furthermore, we show that fine-tuning with local data propagates observational constraints through the network topology, systematically improving performance even in ungauged river reaches. GRC thus establishes a scalable, physics-aligned framework that effectively synthesizes global hydrodynamic knowledge with local data applicability.