The rapid advancement of artificial intelligence, particularly large language models (LLMs), has become a central catalyst for the digital transformation across multiple sectors. In the water conservancy field, the efficient circulation of data elements is critical for advancing water resource management, optimizing infrastructure operations, and enhancing disaster resilience. However, current LLM-enabled data circulation faces multiple challenges, including security vulnerabilities, weak cross-domain coordination, latency in data processing, and limited interpretability. Leveraging LLM technologies, this study starts by identifying critical challenges in the water conservancy domain, which include inadequate data sharing, subpar monitoring capabilities, poor data quality, constrained analytical abilities, underutilized data values, and elevated operational and maintenance expenses. Guided by the principles of open access, elastic scalability, full-chain security assurance, and standards-based interoperability, this study proposes a six-layer, data-centric architecture, encompassing multi-source data acquisition, hybrid storage, intelligent data governance, agile service orchestration, scenario-driven applications, and endogenous security. Furthermore, it analyzes the coupling mechanisms and collaborative requirements across five systemic dimensions: regulatory frameworks, data and model standards, platform technologies, talent ecosystems, and pilot-scale experimentation. Building on these insights, a development pathway is proposed for constructing an LLM-enabled ecosystem for the market-oriented, secure, and intelligent circulation of water conservancy data, providing theoretical support and practical guidance for facilitating the market-oriented allocation and efficient circulation of water conservancy data. The proposed architecture can significantly enhance data usability, interpretability, and circulation efficiency, thereby propelling the water conservancy sector toward high-quality digital transformation, and providing a solid theoretical and technical foundation for the establishment of a smart water conservancy system.