Embracing Large Language Model (LLM) Technologies in Hydrology Research

Zewei Ma , Bin Peng, Zhenrui Yue, Huimin Zeng, Ming Pan, xiaocui wu, Jie Yang, Liting Mai, Kaiyu Guan

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 revolutionize 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. Moreover, LLMs 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. We highlight that LLMs are powerful tools for integrating domain hydrological knowledge and advances in machine learning, ultimately serving as an indispensable resource to meet the evolving demands of transdisciplinary hydrological research.