Embracing large language model (LLM) technologies in hydrology research
Zewei Ma, Bin Peng, Zewei Yue, Huimin Zeng, Ming Pan, Xiaocui Wu, Jie Yang, Liting Mai, Kaiyu Guan · Environmental Research Water · 2025
The growing complexity of hydrological systems necessitates innovative approaches to data management, knowledge management, and model development. Large language models (LLMs) have great potential to accelerate hydrological research by unifying and advancing these three critical aspects. In this perspective work, we review recent advances and applications of LLMs and exemplify using LLMs in hydrology studies. We demonstrate that LLMs can enhance data accessibility by efficiently extracting and organizing information from diverse sources and formats. LLMs also facilitate comprehensive knowledge management through knowledge retrieval and synthesis, enabling the integration of various datasets. Furthermore, LLMs, combined with modular development, Chain-of-Thought reasoning, and the intent-based network framework, hold immense promise for transforming physical model development and fostering model unification across scales. LLMs are powerful tools for integrating domain hydrological knowledge and advances in machine learning. Their potential in hydrological studies and the mitigation of their risks will require rigorous assessment, domain-specific regulations and guidelines, and significant contributions from hydrologists. We envision LLMs become indispensable resources for meeting the evolving demands of transdisciplinary hydrological research.