用图神经网络实现全球河流系统日尺度水文预测，仅需稀疏观测站数据。
Topology enables learning-based hydrodynamic prediction of the global river system
- 基于河流连通性与耗散动力学构建图神经网络模型
- 在0.25°全球河网上达25%精度提升且无预报时效衰减
- 适用于无观测河段和更高分辨率，适合地球系统建模研究
准确的河流预测对水资源、粮食和能源安全至关重要，但在整个河流网络中仍具挑战性。机器学习已改变地球系统建模，但因缺乏可靠数据，系统级河流预测进展滞后。本文利用河流的连通性与耗散动力学，提出GraphRiverCast模型，可在仅依赖稀疏观测站且无初始状态条件下，对0.25°全球河网中每个河段进行日尺度多变量水文动态预测。该模型预报性能不随提前期衰减，在精度上比现有领先全球河流模型高出25%，且能稳健泛化至未观测河段和更细分辨率。GraphRiverCast将学习型河流预测从孤立流域推进至统一全球系统，为数据稀缺地球系统中的机器学习提供了新思路。
原文摘要 · Abstract (English)
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.
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