用物理约束的机器学习模型，评估洪水预报在数据延迟时的稳定性。
Physics-guided Emulators Reveal Resilience and Fragility under Operational Latencies and Outages
- 结合长短时记忆网络与松弛水文平衡约束，保持物理一致性。
- 在5000多个流域测试中，数据质量下降时性能平滑退化。
- 适合需要高可靠性实时洪水预报的水利部门使用。
可靠的水文与洪水预报需要模型在输入数据延迟、缺失或不一致时仍保持稳定。然而，当前大多数降雨-径流预测研究均在理想数据条件下评估，侧重准确性而非运行韧性。本文开发了一种面向实际应用的全球洪水预警系统（GloFAS）模拟器，将长短期记忆网络与松弛的水平衡约束相结合，以维持物理一致性。五种架构覆盖从完整历史与预报强迫到存在数据延迟和中断的多种场景，支持对鲁棒性的系统评估。模型在美国管理较少的流域中训练，并在超过5000个流域（包括印度受高度调控的河流）中测试，能复现GloFAS的水文核心特征，在信息质量下降时性能平滑退化。跨不同水文气候与管理背景的迁移表现出降低但物理一致的性能，揭示了数据稀缺与人类影响下的泛化极限。该框架将运行韧性确立为水文机器学习可量化的属性，推动了可靠实时预报系统的设计。
原文摘要 · Abstract (English)
Reliable hydrologic and flood forecasting requires models that remain stable when input data are delayed, missing, or inconsistent. However, most advances in rainfall-runoff prediction have been evaluated under ideal data conditions, emphasizing accuracy rather than operational resilience. Here, we develop an operationally ready emulator of the Global Flood Awareness System (GloFAS) that couples long- and short-term memory networks with a relaxed water-balance constraint to preserve physical coherence. Five architectures span a continuum of information availability: from complete historical and forecast forcings to scenarios with data latency and outages, allowing systematic evaluation of robustness. Trained in minimally managed catchments across the United States and tested in more than 5,000 basins, including heavily regulated rivers in India, the emulator reproduces the hydrological core of GloFAS and degrades smoothly as information quality declines. Transfer across contrasting hydroclimatic and management regimes yields reduced yet physically consistent performance, defining the limits of generalization under data scarcity and human influence. The framework establishes operational robustness as a measurable property of hydrological machine learning and advances the design of reliable real-time forecasting systems.
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