The rapid expansion of artificial intelligence (AI) infrastructure across the United States presents challenges for water resource management. AI infrastructure requires a large water supply for cooling. This project will develop three new AI models for water resources. The models will identify where water resources exist that can reliably support the growth of AI infrastructure. First, a large-scale AI model will provide insights for water availability to choose sites. Second, digital twins will be created to reveal hazards that may disrupt local water supply. Finally, an AI method will help predict impacts of wastewater and thermal pollution. The broader impacts include practical guidance on water management and sustainability. The project’s primary scientific goal is to develop a multi-scale hydrologic modeling framework that integrates physics-informed AI and hierarchical digital twin technologies to inform water management for AI infrastructure development. The project consists of three main technical components for analyzing water resource sustainability and identifying optimal sites for AI infrastructure. First, an AI-driven hydrologic model will analyze geospatial data across the contiguous United States to identify regions with adequate water resources. Second, digital twins will be created for selected sites, enabling scenario analysis to understand the potential impacts of natural hazards and water availability fluctuations. Third, a fractional-calculus-based m