Continuous Snow Monitoring for Improved Water Supply Management

Applied Research Team, Inc. proposes an innovative decision support tool that provides continuous snow monitoring for improved water supply management. More than 40 million people in the Western US and over a billion people globally rely on water stored high in the mountains as snowpack that melts in spring and summer. This precious resource is threatened by climate change and associated extreme weather patterns, increased demands and less dependable availability. Drinking water, irrigation and hydropower depend on the decisions by water managers for allocation and release of water when needed. Our proposal integrates ground-based weather radar and synthetic aperture radar with advanced machine learning techniques to quantify snow water equivalent accumulation. This approach offers advantages over existing methods by delivering all-weather operations, basin-wide coverage, frequent updates, cost-effectiveness, and reduced need for field collection in harsh environments. This proposal leverages NASA's unique observational assets to address critical water resource challenges while enabling commercialization opportunities in climate technology and water management sectors. It builds upon extensive snow remote sensing research and collaboration with stakeholders, incorporating robust data validation, and it aligns with NASA's strategic objectives, supporting the agency's Earth science mission. Comprehensive validation against airborne lidar, ground measurements, and streamflow data will enable diligent performance tracking, ensuring measurable achievement of objectives. The project will demonstrate technical feasibility, provide a clear pathway to an operational commercial product, conduct rigorous validation against authoritative data sources, and quantify a multi-million-dollar market opportunity with water agencies. The project goals will be realized through a subscription-based decision support tool for water managers.

This proposal could directly support multiple NASA directives and strategic objectives with the development of an innovative commercial product for continuous snow monitoring and improved water supply management. NASA observations, integrated with cutting-edge techniques in machine learning and data fusion builds upon extensive research in snow remote sensing, directly transitioning these capabilities into an operational decision support application benefiting society. Effective management of freshwater resources, especially from mountain snowpack in the western U.S., is a critical national priority given climate change impacts and increasing demands. Our technology provides a breakthrough solution for monitoring this vital water source, squarely addressing grand challenges facing the nation as directed. With a clear path towards an operational commercial product, rigorous validation against authoritative data sources, and a quantified multi-million dollar market opportunity with water agencies, this proposal aligns with directives on facilitating commercialization of agency-developed innovations. Close partnerships with entities like the Bureau of Reclamation, state agencies, and water forecasters/managers who represent the end-users of this tool are central to our stakeholder-driven approach, supporting directives on developing solutions meeting specific stakeholder needs. Comprehensive validation of product accuracy against airborne lidar, ground measurements, and streamflow data enables diligent performance tracking as required by directives, ensuring objectives are quantifiably achieved. By leveraging NASA's unique observational assets, addressing critical water resource challenges, enabling commercialization, collaborating with stakeholders, and incorporating robust performance validation, this proposal directly supports NASA's directives while delivering an innovative solution aligned with the agency's strategic objectives. The proposed technology for remote snow monitoring may transform existing markets. Currently the market is served by a sole-source provider of airborne lidar. This product could open new opportunities to commercialize through public-private partnerships or contracted services with government and private entities. Company R&D, partially supported by an NSF Phase 1 SBIR, uncovered a powerful method for placing SWE, measured aloft by dual-polarized weather radar, onto the ground. Machine learning is used to transform these atmospheric measurements into accurate terrestrial maps of SWE. The proposed investigation will test feasibility to extend our geographic applicability where terrestrial SWE maps are derived from NASA satellite data and Sythetic Aperture Radar. Snowmelt forecasters at the National Weather Service River Forecast Centers and the National Resources Conservation Service use SWE estimates in their models. The US Bureau of Reclamation needs their forecasts for management of reservoirs and other water management facilities. Colorado, California, and other states in the Western US have interstate and international water compacts that are difficult to comply with when SWE estimates are inaccurate. Within states, water conservation/conservancy districts, irrigation and agricultural entities, hydroelectric facilities, and municipal water supply and planning organizations are making increasing investments in improved SWE monitoring. Our innovative combination of NASA observations with other data sources positions this as a potentially disruptive technology compared to current sparse or infrequent SWE monitoring techniques. A continuous, basin-wide decision support tool would enable water managers with a commercially supported service, providing substantial value across the water management ecosystem.

This is early/mid-stage (TRL 6) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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