将大模型与水文模型结合,可提高水文模型的适应性与拓展性,降低模型使用门槛,加速水文科学的智能化进程。在搜集整理水文模型资料并分类阐述其特点的基础上,指出水文模型存在可移植性差、对数据质量敏感、极端事件预测能力弱等不足;明确其智慧化需融合知识辅助决策、优化交互方式以增强多模型协同解决复杂问题的需求;结合当前大模型的研究应用现状,指出水文模型与大模型具备优势互补特性。提出了大模型与水文模型单向耦合和双向耦合的协同方式;结合协同方式,基于自然下垫面与城市下垫面的湖泊入流复合水文过程,构建大模型与水文模型耦合的思路框架。这种耦合不仅以智能接口形式降低了专业水文模型的应用门槛,更通过动态参数优化与协同计算机制,提升了复杂水文过程模拟的效率和应急决策的精准性。

[Objective] Traditional hydrological models are mostly designed for specific scenarios, representing simplifications of complex physical processes or fitting of historical data. They require extensive modeling and driving data, and have high technical threshold for use, which largely limits their widespread application. Large language models, with their powerful understanding and generative capabilities, demonstrate superiority in the framework design and interaction processes of complex systems. Integrating large language models with hydrological model systems can provide an intelligent engine for the data collection, model construction and calibration, and simulation result analysis required by hydrological models. This enhances the adaptability and extensibility of hydrological models and lowers the model usage threshold. Therefore, this study explores the synergistic application paths for large language models and hydrological models and provides a prospect for future research priorities in their synergy. It aims to provide support and technical reference for the synergistic development of the two models, reduce the usage threshold of hydrological models, and accelerate the intelligent transformation of hydrological science. [Methods] Starting from hydrological models, based on the collection and organization of current hydrological model information and the classified elaboration of their characteristics, this study identifies problems such as poor portability, sensitivity to data quality, and weak predictive capability for extreme events. Additionally, it clarifies three requirements for their intelligent transformation: integrating knowledge-assisted decision-making, optimizing interaction methods to lower the usage threshold, and enhancing multi-model synergy to address complex problems. Combining the current research and application status of large language models, this study reviews the technical trends of mainstream large language models and analyzes the challenges in their practical application, including a lack of domain-specific knowledge, weak business reasoning capability, and difficulties in collaborating with professional tools. Finally, the study points out that hydrological models and large language models have complementary advantages, making their coupling inevitable. An application example is provided, focusing on the composite hydrological process of lake inflow from natural and urban underlying surfaces. [Results] This study proposes synergistic approaches for unidirectional and bidirectional coupling between large language models and hydrological models. This enables the effective utilization of large language models across various application scenarios of hydrological models, provides support and technical reference for their synergistic development, and contributes to lowering the usage threshold of hydrological models and accelerating the intelligent transformation of hydrological science. Combining the synergistic approaches for unidirectional coupling and bidirectional coupling, and based on the composite hydrological process of lake inflow from natural and urban underlying surfaces, a conceptual framework for coupling large language models and hydrological models that is universal and innovative is constructed. Additionally, specific implementation procedures are provided, offering a reusable methodological reference for studying complex hydrological processes. [Conclusion] The coupling technology of large language models with hydrological models, through continuous exploration and research, has preliminarily demonstrated the application potential of integrating large language models with hydrological simulation and prediction. Large language models, leveraging their advantages in semantic parsing, dynamic parameter calibration, and multi-model synergistic scheduling, have expanded the functional boundaries of hydrological models in aspects such as real-time interactive response, cross-scale coupled simulation, and emergency decision-making support. This integration not only lowers the application threshold of professional hydrological models through intelligent interfaces, but also enhances the efficiency of simulating complex hydrological processes and the accuracy of emergency decision-making through dynamic parameter optimization and synergistic computing mechanisms. The coupling of these two models demonstrates rapid adaptability to novel environmental conditions and the characteristics of autonomous reasoning and optimization, paving an innovative path for establishing a “perception-simulation-decision” full-chain integrated digital twin watershed system.