Abstract
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.
Abstract (translated)
可靠的水文和洪水预报需要在输入数据延迟、缺失或不一致的情况下仍能保持稳定的模型。然而，大多数降雨径流预测的进步是在理想的数据条件下评估的，注重准确性而非操作稳定性。在此，我们开发了一个可以立即投入使用的全球洪水预警系统（GloFAS）模拟器，它将长短期记忆网络与松弛的水量平衡约束相结合以维持物理连贯性。五种架构涵盖了信息可用性的连续范围：从完整的历史和预测强迫条件到数据延迟和中断的情况，从而能够系统地评估其稳健性。该模拟器在美国最小管理流域中进行了训练，并在包括印度受严格管控河流在内的超过5000个盆地中进行了测试。模拟器再现了GloFAS的水文核心内容，并随着信息质量下降而平滑退化。在不同的气候和管理制度下进行迁移学习，虽然性能有所降低但仍保持物理一致性，定义了数据稀缺及人为影响下的泛化极限。该框架将操作稳定性确立为水文学机器学习的一个可测量属性，并推进了可靠实时预报系统的设计。
URL
https://arxiv.org/abs/2510.18535
