AI-driven innovations in watershed management: development and prospects from the SWAT model to AI+SWAT

Background Under the dual pressures of global climate change and intensifying anthropogenic activities, watershed ecosystems face critical challenges, including soil erosion, non-point source pollution, and water scarcity. The Soil and Water Assessment Tool (SWAT), a physically based hydrological model, has established a comprehensive simulation system covering the water cycle, sediment, and non-point source pollution, becoming one of the most widely used tools. However, the traditional SWAT model is increasingly constrained by high dependencies on high-quality input data, complex parameter calibration processes, and significant technical barriers for non-expert users, limiting its potential in regions with limited data and rapid decision-making scenarios. Methods Based on a systematic review of the development history and application limitations of the SWAT model, this paper focuses on the paradigm shift of SWAT model development driven by Artificial Intelligence (AI) technology. The research framework categorizes the convergence of AI and SWAT into specific technological pathways, evaluating how data-driven techniques address the limitations of physically based mechanisms. Results The analysis identifies two distinct yet complementary pathways for AI-SWAT integration: “Embedded Fusion” and “External Empowerment.” The Embedded Fusion pathway utilizes deep learning and Physics-Informed Neural Networks (PINNs) to construct hybrid models, optimizing parameter calibration and process simulation accuracy through data-driven methods. Conversely, the External Empowerment pathway, exemplified by the recently released SWAT VEXA system, utilizes generative AI and Large Language Models (LLMs) to build the user interaction interface. This innovation transforms complex professional tools into conversational intelligent assistants, significantly lowering the technical threshold for widespread application. Conclusions This paper compares the development of digital twin watershed technology in China, pointing out that the SWAT model is in a critical transition period from a solitary reliance on physical mechanisms to a paradigm integrating physical mechanisms and data intelligence. Although this transformation faces new challenges such as model interpretability and interaction accuracy, it provides a feasible technological paradigm for building smart watershed management decision support systems.