Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins: the iwmi-doc-plugin, which enables semantic search over indexed documents using Azure AI Search, and the iwmi-api-plugin, which queries live databases to deliver dynamic insights such as environmental-flow alerts, rainfall trends, reservoir levels, water accounting, and irrigation data. The system features guided multilingual interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities. Evaluated using the RAGAS framework, WaterCopilot achieves an overall score of 0.8043, with high answer relevancy (0.8571) and context precision (0.8009). Key innovations include automated threshold-based alerts, integration with the LRB Digital Twin, and a scalable deployment pipeline hosted on AWS. While limitations in processing non-English technical documents and API latency remain, WaterCopilot establishes a replicable AI-augmented framework for enhancing water governance in data-scarce, transboundary contexts. The study demonstrates the potential of this AI assistant to support informed, timely decision-making and strengthen water security in complex river basins.

翻译：跨界河流流域的可持续水资源管理面临着数据碎片化、实时访问受限以及整合多样化信息源复杂性的挑战。本文介绍了WaterCopilot——一款由国际水资源管理研究所（IWMI）与微软研究院为林波波河流域（LRB）合作开发的人工智能驱动虚拟助手，旨在通过一个统一的交互式平台来弥合这些差距。该系统基于检索增强生成（RAG）和工具调用架构构建，通过两个定制插件整合静态政策文档和实时水文数据：iwmi-doc-plugin利用Azure AI搜索实现对索引文档的语义搜索；iwmi-api-plugin则查询实时数据库，提供环境流量预警、降雨趋势、水库水位、水资源核算和灌溉数据等动态洞察。该系统具备引导式多语言交互（英语、葡萄牙语、法语）、透明的来源引用、自动化计算及可视化功能。使用RAGAS框架进行评估，WaterCopilot综合得分为0.8043，其中答案相关性（0.8571）和上下文精确度（0.8009）表现优异。主要创新包括基于阈值的自动化预警、与LRB数字孪生系统的集成，以及部署在AWS上的可扩展部署流水线。尽管在处理非英语技术文档和API延迟方面仍存在局限，但WaterCopilot为在数据稀缺的跨界环境中加强水资源治理，建立了一个可复制的AI增强框架。本研究证明了该人工智能助手在支持复杂河流流域中及时、科学的决策制定以及增强水安全方面的潜力。