Creating an advanced, economical, eco-friendly measurement of snow water equivalence using observed GNSS signals beneath the snow

The purpose for this research is to develop a Snow Water Equivalency (SWE) sensor that will broaden our understanding/management of snow and the water we receive from it.Importance of the ProblemSnowpack is very important to the United States' water budget with 85% of freshwater runoff originating as snowmelt in the west, and nine significant floods of the 20th century were directly related to snowmelt in the east[1]. Case studies conducted by the NRCS in 2010 estimated the value of snowpack data to industries in the Western US at over $1 billion[2]. Snowpack is deviating from historical norms and this deviation threatens today's society. There has been a shift toward early fall snow, an increase in spring rain, and an earlier spring melt[3], [4]. The frequency of extreme snow events has also increased. Snowfall variability can cause geographical regions that are not typically large contributors to rivers and streams to become large sources of runoff. For example, the record snowfall on the northern plains was a contributing factor to the Missouri River flooding that caused major economic damage in 2011[5].In the Northern Hemisphere, nearly 2 billion people live in watersheds dependent on snowpack[6], [7]. An additional 1.4 billion live in watersheds where snowpack no longer meets current human water needs, 36.5 million of these people are within the US[8]. In a letter the Director of Public Works for the city of Bozeman, MT illustrated the community's reliance on snow. He stated Bozeman's water supply is dependent on snow water from three drainages, only one has snowpack monitoring in place. In addition, downstream agriculture, hydropower, and other industries rely on the same waters, and the entire Upper Missouri basin is closed to appropriations for new water rights. Bozeman is experiencing a 5% annual growth, and current water supplies can only sustain this growth for 10-15 years, with a margin for safe operation diminishing annually.Precise water management is crucial to our nation's food security. Agriculture accounts for nearly 80% of the water consumption in the U.S., and much of the utilized water is sourced from mountain snowpack. Appropriately the USDA Research, Education, and Economics action plan states a future sustainable agriculture relies on "watershed-scale management encouraging market-driven water reallocations and real water conservation"[9]. To achieve better watershed-scale management, higher density spatial sampling and improved technology is required. This call for improved water and snow measurement infrastructure by the USDA has been echoed at the state level by both Montana and Colorado[10], [11].Current methods used to measure SWE are labor intensive, utilize hazardous antifreezes, are economically prohibitive, and/or are vulnerable to the environment. These limitations greatly restrict the number of locations where SWE measurements are made. The limitation and errors in SWE measurements lends to inaccurate stream flow forecasts which result in great economic losses due to floods or droughts that could have been mitigated. The largest snowpack monitoring network in the world is operated by the USDA Natural Resource Conservation Service (NRCS) Snow Survey program, and has an immediate need for new measurement technology. The program's automated SNOTEL sites are in deep mountain snowpack locations that drive stream flows, but extreme staffing restraints are severely hampering the program ability to continue to operate the labor-intensive snow measurement techniques used.The Research ProjectThe SWE sensor that NWB Sensors proposes to develop will monitor the interactions between the snowpack and signals from the Global Navigation Satellite Systems (GNSS) (like the United States operated GPS or the Russian operated GLONAS). This research will develop algorithms relating these GNSS signals to actual SWE values. Recently low-cost GNSS receivers have reached the market making thi