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This perspective paper explores the potential of Large Language Models (LLMs) to revolutionize hydrology research by addressing challenges in data management, knowledge management, and model development. The authors demonstrate how LLMs can improve data accessibility through information extraction, facilitate knowledge management via retrieval and synthesis, and transform physical model development using techniques like Chain-of-Thought reasoning. The paper argues that LLMs can integrate hydrological domain knowledge with machine learning advances, making them essential for interdisciplinary hydrological research.
LLMs could be the key to unlocking unified hydrological models by bridging data silos, synthesizing knowledge, and enabling modular, reasoning-driven development.
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.
