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Large Language Model (LLM) Agent in Hydrology

This tutorial introduces AQUAH, the first language-based agent specifically designed for hydrologic modeling. With simple natural-language prompts (e.g., 'simulate floods for a specific basin'), AQUAH autonomously retrieves data, configures hydrologic models, executes simulations, and produces analyst-ready reports, simplifying complex environmental modeling tasks.
Zhi Li
Written by Zhi Li

We are excited to present AQUAH, the first end-to-end language-based agent purpose-built for hydrologic modeling. AQUAH allows users to start with a simple, natural-language prompt, such as “simulate floods for the Little Bighorn basin from 2020 to 2022”, and from there, it autonomously handles the entire modeling workflow—from retrieving necessary terrain, weather, and gauge data, to configuring a hydrologic model, running simulations, and generating an analyst-ready PDF report. Early feedback from hydrologists highlights AQUAH’s ability to lower the barrier between Earth observation data, physics-based tools, and practical decision-making. While further calibration and validation are needed for operational adoption, AQUAH illustrates the promise of LLM-centered, vision-grounded agents to streamline and transform complex environmental modeling tasks.

Zhi Li
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Zhi Li

Dr. Zhi Li is an Assistant Professor at the University of Colorado Boulder. Prior to this, he was the Stanford Doerr School of Sustainability Dean’s Postdoc Fellow (23′-25′). He earned his doctoral degree (2022) from the University of Oklahoma/Hydrometeorology and Remote Sensing Laboratory. He obtained Master’s and Bachelor’s degrees at the National University of Singapore (2019) and Hohai University (2017), respectively. He had prior experience working as a Data Scientist in the private sector in Singapore to develop opportunistic sensors for rainfall monitoring. His research focuses on 1. improving flood forecasts and 2. assessing flood impacts on agriculture, ecology, and public health.

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