Is it ready to apply Large Language Models to frontline hydro practice? Taking the flooding forecasting agent as an example

The rapid advancement of Large Language Models (LLMs) has triggered transformative changes across many domains, yet their application in operational hydrology forecasting remains largely unexplored. This raises a question: can LLMs meaningfully support frontline hydrological practice?

Flood forecasting provides an ideal testbed for this question. In operational settings, real-time forecasting relies heavily on forecasters' subjective judgment: interpreting meteorological patterns, assessing antecedent soil moisture, and making rapid decisions under deep uncertainty. While numerical hydrological models provide quantitative process simulations, the systematic and scalable cognitive expert judgment component still remains challenging. Moreover, operational demands for around-the-clock availability and consistent quality challenge the limited labour capacity.

Building on recent LLM advances, we present an intelligent flood forecasting agent that bridges this gap. The system integrates LLM reasoning capabilities with structured hydrological workflows, combining professional reproducibility with adaptive flexibility. A natural language interface enables forecasters to interact using everyday expressions, substantially lowering adoption barriers. The agent is currently undergoing systematic testing in a representative catchment. Preliminary results demonstrate promising consistency and robustness.