发表机构
City University of Hong Kong (Dongguan); A*STAR; Singapore Management University（香港城市大学（东莞）; 新加坡科研局; 新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对受管控海岸带复合洪水预报，提出锚定预报框架，结合多源动态图学习，提升了持续高水位平台预报可靠性，为洪水预警与水资源管理提供支持。
AI 中文摘要
受管控海岸带系统的复合洪水受多个监测站观测到的水文条件和水资源管理活动影响。当前预报模型可捕捉时间依赖关系，平均误差较低，但全局误差指标可能掩盖与洪水预警相关的长时间高水位平台的不良重现。由于水文气象和运行信号分布在异构监测站，单站点记录无法完全表征高水位动态。不过，无约束的跨站点信号融合会降低局部时间预报的稳定性。本研究提出一种锚定预报框架，通过依赖状态和预见期的有界残差修正纳入跨站点信息。由水文气象和运行观测构建的多源状态表征可自适应校准跨站点关系和修正尺度，在保留局部时间预报作为稳定锚点的同时实现针对性跨站点调整。除传统全局误差统计外，我们通过预报与观测高水位过程的时间对齐评估事件尺度高水位特征。实验表明，选择性整合多站点动态条件可提升持续高水位平台的预报可靠性，同时在常规水文条件下保持高准确性，为洪水预警和水资源管理决策提供支持。
英文摘要
Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations. Current forecasting models can capture temporal dependencies with low average errors, but global error metrics may conceal poor reproduction of prolonged high-water plateaus that are relevant to flood early warning. Because hydrometeorological and operational signals are distributed across heterogeneous gages, single-site records do not fully represent high-water dynamics. Nevertheless, unconstrained fusion of cross-site signals can degrade the stability of local temporal forecasts. This work proposes an anchored forecasting framework that incorporates cross-site information through state- and lead-dependent bounded residual corrections. A multi-source regime representation constructed from hydrometeorological and operational observations adaptively calibrates inter-site relationships and correction scales, enabling targeted cross-site adjustment while preserving the local temporal forecast as a stable anchor. Beyond conventional global error statistics, we evaluate event-scale high-water characteristics through the temporal alignment of forecasted and observed high-water processes. Experiments demonstrate that selectively integrating multi-station dynamic conditions improves the prediction reliability of sustained high-water plateaus while maintaining high accuracy during routine hydrological conditions, supporting flood early warning and water-management decision support.
