Physics-guided Emulators Reveal Resilience and Fragility under Operational Latencies and Outages

TL;DR

This paper develops a physics-guided hydrological emulator that assesses and enhances the operational resilience of flood forecasting models under data delays and outages, demonstrating robustness across diverse regimes.

Contribution

It introduces a novel emulator framework combining memory networks with physical constraints, systematically evaluating robustness under various data quality scenarios.

Findings

Emulator reproduces hydrological core of GloFAS with degraded performance under data scarcity.

Smooth performance degradation observed as information quality declines.

Transferability across different hydroclimatic regimes is feasible with reduced accuracy.

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.