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    2026 National Competitive Grant Program

    2026 National Competitive Grant Program supports researchers studying industrial water use with AI and machine learning to improve water-supply assessments and inform national and local decisions.

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    This grant was recently updated

    Applications are now open, replacing the September 30, 2025 deadline with October 26, 2026 for complete packages to VWRLSC and November 12, 2026 for Grants.gov submission. The award cap fell from $310,000 to $120,000; priorities shifted to industrial water use and America250, and other details also changed.

    Funder: Vermont Water Resources and Lake Studies Center

    Due Dates: October 26, 2026 (Completed package to VWRLSC) | November 12, 2026, before 5:00 p.m. Eastern Time (Institute submission to Grants.gov)

    Funding Amounts: Up to $120,000 for one to three years; $462,500 estimated program funding for four awards

    Summary: Supports water-supply and availability research, emphasizing AI-enabled methods for measuring and predicting industrial water use.

    Key Information: Researchers must apply through a Water Resources Research Institute established under the Water Resources Research Act.


    Description

    The U.S. Geological Survey’s FY2026 National Competitive Grants 104g General program supports research to improve the nation’s water supply and availability and explore new approaches to water problems. The Vermont Water Resources and Lake Studies Center coordinates submissions on behalf of eligible researchers. The program emphasizes methods to observe, estimate, and validate water use by data centers and other water-intensive industries drawing from public supplies. America250 projects recognizing the nation’s founding, history, and cultural heritage are also encouraged.

    Research must integrate diverse data sources and apply artificial intelligence and machine learning to improve transparency, traceability, and identification of bias or uncertainty. Potential topics include water reuse, self-supplied withdrawals, seasonal demand, prediction uncertainty, and future-use scenarios. Methods should support national assessments and local decisions, with stakeholder engagement informing practical, credible results.


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