SWEdar - an Automated Low-Power Snow Water Equivalent Sensor

Intellectual Merit: This Small Business Innovation Research Phase I project will assist in producing the next-generation approach for monitoring critical water resources stored in the seasonal snowpack. Agriculture is a significant user of ground and surface water in the United States, accounting for approximately 80 percent of the Nation's consumptive water use and over 90 percent in many Western States. Seasonal snow provides 50-80% of the available water in the Western U.S., but is poorly resolved with a sparse network of sites that use technology that has not changed in over 40 years. Snow researchers have used microwave radar since 1979 and proven its great advantages for snow measurement, however commercially available radar systems are not suited to continuous monitoring of snow due to design and power requirements. Commercially available radar sensors used for snow are designed for short surveys, require an experienced user, and manual input. Post-processing is required, with limited information in near-realtime. Current system cost prohibit widespread use.Technical hurdles: The proposed R&D in this SBIR project will address what we feel is our final technical hurdle; resolving SWE in late season wet snow conditions. For the past several years, our research has focused on penetrating deep into the snow pack to detect the underlying ground and developing SWE related algorithms. These efforts resulted in reliable and accurate SWE measurements in the early to mid season dry snow conditions. A typical season can begin in October and end in late May to early June; we have produced accurate SWE values through the middle of March, or roughly 75% of the season. Through this past research we have concluded that an accurate measurement to the surface of the snow pack is critical in resolving the last inaccuracies related to late season wet snow conditions. This Phase I research will focus on accurate ranging to the snow surface by incorporating an additional higher frequency UWB low powered radar sensor. The new range data will be integrated into our existing SWE algorithms to improve the automated detection of the snow surface, and to allow analysis of frequency-dependent attenuation to allow SWE inversion in wet snow. Goals: Resolve the inability to accurately determine SWE in late season wet snow conditions. The result will be a sensing solution capable of estimating and monitoring the critical state variables of the snowpack (depth, SWE, density, liquid water content) autonomously in real-time. The goal is to be well positioned for a Phase II award where we can begin the prototyping and commercialization of our SWE sensor.Summary of plan: A new K/W-band radar will be integrated with the current snow radar design. Prototype multi-band radar systems will be installed at two locations that are coincident with standard snow observations. Automated processing will be improved and uncertainties in snow estimates defined. Broader/Commercial Impact: The commercialization of a Flat Earth SWE sensor would allow government and private organizations to expand their networks into these critical higher elevations, and cost effectively increase the number of sensors in their networks, improving the accuracy of forecasting for snow related water resources. Sustainable management of water in agriculture is critical to increase agricultural production, ensure water can be shared with other users and maintain the environmental and social benefits of water systems. ?