Research

Understanding AI Data Center Water Demand 

University of Minnesota researchers are leading work on a predictive decision-support tool for data center water circularity and governance, with Minnesota nonprofit Freshwater as a partner organization. 

Learn more at "AI's Hidden Thirst" on the Institute on the Environment website — or read the project synopsis below.

The Challenge
The rapid expansion of hyperscale data centers has created a data gap between data center developers and local municipalities: regulators and communities lack the tools to verify complex infrastructure impacts, while data center developers need to predict how dynamic AI workloads translate into physical water stress.

The Impact Goal
Leverage predictive modeling to build a transparent decision-support dashboard that empowers regulators, communities and data center operators to collaboratively optimize water circularity and mitigate cumulative chemical and thermal pollution across Minnesota’s watersheds.

The Project Goals

• Co-design the decision-support dashboard’s metrics and compliance thresholds with the Freshwater Society and the UMN Morris Benson Center for Community Partnerships
• Develop a deterministic, micro-to-macro water quantity model that translates algorithmic GPU workloads into facility-level water consumption and evaporation rates
• Develop chemical concentration and thermal models to predict water quality impacts and inform the protection of local aquatic ecosystems
• Design and prototype a decision-support dashboard that integrates the deterministic water quantity and quality models, which utilizes Large Language Models (LLMs) strictly as a secure translation layer, to provide private optimization tools for data center operators, alongside transparent compliance metrics for regulators and communities
• Develop a community report on data transparency to empower civic groups to educate residents
• Design high school educational modules deployed in rural Minnesota