AI-powered Hydrology Research to Alaska's Remote Rivers (Note: This is a previously awarded grant, but indicates a relevant funding area) is sponsored by National Water Prediction Service. This opportunity supports mission-aligned projects and measurable outcomes.

New $700K Award will take AI-powered Hydrology Research to Alaska’s Remote Rivers - The John and Marcia Price College of Engineering at the University of Utah New $700K Award will take AI-powered Hydrology Research to Alaska’s Remote Rivers | In John and Marcia Price College of Engineering News Alaska: Famed for its dramatic landscapes, stunning glaciers, colossal mountains, and abundant wildlife.

Yet equally striking is the sheer scale of its river systems. With more than 365,000 miles of rivers , Alaska is home to over 40% of the nation’s surface water. These rivers do far more than move water—they sustain entire ecosystems.

From salmon that nourish forests and wildlife to communities that depend on predictable seasonal flows, Alaska’s rivers shape life far beyond their banks. Yet despite their importance, they remain some of the least monitored and modeled waterways in the United States. Thanks to a new research grant, Ryan Johnson , Assistant Professor in the Department of Civil & Environmental Engineering, and his students are helping change that.

The ~$700K grant awarded from the National Water Prediction Service will support a hands-on research effort in Southcentral and Southeastern Alaska, where Johnson and his team will work directly with local communities, fisheries managers, and federal partners to develop next-generation tools for predicting streamflow and water temperature in data-scarce regions.

The work aims to improve ecosystem health, fisheries management, infrastructure protection, and emergency response planning—helping decision-makers anticipate change rather than react to it. At the heart of Johnson’s research is a deceptively simple question: How do you make reliable predictions when there’s very little data?

Traditional hydrologic models depend on long-term monitoring records—something Alaska often lacks due to its extreme remoteness and expansive terrain. Johnson’s team is tackling this challenge by combining machine learning (ML) and artificial intelligence (AI) with observational data from similar environments around the planet to bypass the limited regional data, a technique known as transfer learning.

“Transfer learning allows us to train our ML models to regions with similar hydrology and apply it to places where observations are limited, as in Southcentral and Southeast Alaska,” Johnson explained. “It’s a way to expand predictive capability without waiting decades to collect new data.

” The research will contribute to expanding the National Water Model (NWM) into Alaska—a much needed integration, yet one that presents major scientific and operational hurdles. Continue reading at the Department of Civil & Environmental Engineering .