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

2025-10-21

AI Summary

Existing hydrological models are typically evaluated under idealized data conditions, neglecting pervasive real-world operational challenges—such as data latency, missing observations, and temporal-spatial inconsistencies—leading to insufficient operational robustness. To address this, we propose a physics-informed surrogate modeling framework that integrates LSTM-based sequence learning with relaxed water balance constraints, and systematically constructs a five-tier architectural continuum to quantify model robustness under varying degrees of information availability. Leveraging cross-basin transfer learning, the model trained on minimally regulated U.S. basins successfully generalizes to highly regulated rivers in India. Validation across over 5,000 basins demonstrates smooth performance degradation under deteriorating data quality, sustained physical consistency, and significantly enhanced predictive stability and generalizability under non-ideal conditions. This work establishes a quantifiable, physics-aware robustness evaluation paradigm for real-time flood forecasting systems.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Evaluating hydrological model resilience under data delays and outages
• Developing operationally robust flood forecasting systems with machine learning
• Testing model generalization across diverse hydroclimatic and managed basins

Innovation

• Emulator couples memory networks with water-balance constraint
• Architectures span continuum of information availability scenarios
• Framework establishes measurable operational robustness in hydrology