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Generative artificial intelligence, with its ability to process and generate human-like text based on vast datasets, offers significant potential to enhance the interpretation of hydrological models, predict water cycle dynamics, and understand complex environmental interactions. By leveraging large language models (LLMs), researchers can optimize data processing, automate the extraction of relevant information from the extensive scientific literature, receive informed support in model programming, and generate descriptions of phenomena in natural language—extracting insights from vast datasets that go beyond the feasibility of manual analysis. Moreover, LLMs facilitate interdisciplinary collaboration by translating complex hydrological concepts into accessible language for diverse audiences. This seminar provides a brief introduction for selected high school students to generative model concepts, explores current applications, and examines the benefits and challenges of integrating LLMs in hydrology and related fields, highlighting their potential to advance scientific understanding and practical water resource management.
