Research
From research to operational forecasting practice.
We study whether large language model agents can support frontline flood forecasting without weakening scientific accountability.
Why We Do This
Why an agent for flood forecasting?
Climate change is driving more extreme floods, and forecasting is one of the first defenses. Operational forecasting starts with a hydrological model, but the final bulletin rarely comes straight from the model. Experienced forecasters stay in the loop, combining rainfall and water-regime information with local experience to revise the output. That judgment is often a major part of forecast quality.
This layer is tacit: hard to express, hard to audit, and slow to train. Machine learning scales, but often stays difficult to inspect. LLMs bring language, planning, and tool use, but most current uses stop at chat interfaces and miss the full operational process that real forecasting requires.
HydroAgent is built around that gap: the forecaster's work needs to be captured, reviewed, and run in the tools people actually use.
LLM Agent × Hydrology
Exploring how large language model agents can interface with hydrological models and operational data.
Forecaster-in-the-loop
Keeping human expertise central while automating routine steps in the forecast workflow.
Workflow automation
End-to-end orchestration from data ingestion to bulletin generation and review.
Papers
Research papers
Our first papers will be listed here soon.
This page will list each paper with its core question, method, key figures, and a preprint link. Follow us and we'll let you know the moment the first one is out.
Next Step
Start a focused discussion about product fit, workflow design, or research collaboration.
HydroAgent-Lab works with institutions, forecasting teams, and research partners that need operationally credible hydrologic systems.