Snow is an important component of both regional and global climate systems, as well as a critical storage component in the hydrologic cycle. Of particular interest is the snow water equivalent (SWE) which considers both depth and density. However, the methods and sensors currently available to measure SWE, or even just snow depth, are either restricted to a single point or expensive (e.g., aircraft based methods.) An emerging option for remotely determining snow depth is the utilization of existing networks of GPS base stations, as is currently being investigated by CU professors Kristine M. Larson (PI), Eric E. Small (co-PI), Mark W. Williams and Dennis M. Akos under NSF grants. Multipath, the reflection of signals off the ground and other nearby surfaces, is here used to estimate the snow depth over a comparably large area. However, this method has two drawbacks. A survey-grade basestation including receivers and antennas is expensive, around $20k, and since geodesy-type antennas are designed to mitigate low elevation signals the resulting quality of the estimates suffers compared to what could be offered by a custom solution. To overcome this, this project proposes the development and marketing of a snow sensor based on a mass-market GPS receiver coupled with a custom antenna, that potentially can be produced at low cost in reasonable quantities. The sensor also has the potential to be used to estimate soil moisture content and vegetation growth.
At present, available snow depth information is temporally and/or spatially limited, the knowledge about the current state of the snow water equivalent potentially available for runoff is similarly limited. Thus, a widely-deployed network of affordable and accurate wide area snow sensors will increase the knowledge and understanding of the hydrologic cycle and potentially improve both climate and weather models. Furthermore, it may also prove valuable for water management facilities where snow levels are an important source of information when predicting droughts. Also, farmers and the tourism industry are also likely to benefit from the information this sensor can provide. It is possible that soil moisture and vegetation growth can also be measured with the snow sensor, however additional research effort will be required to assess the full capabilities. The prototypes and production units will "call home" with the raw data rather than provide measurements directly. The users can then access processed estimates online. This has several benefits; it offers a convenient method of accessing data, it allows for refinement of the algorithms as more data and insight becomes available and it is also possible, at the users' discretion, to make the data available to the scientific community.
This project explored the commercial potential of snow sensing instrument using GPS reflections. The potential for snow measurements is well understand and being exploited using the network of GPS reference stations. This project explored using mass market GPS engines to achieve snow depth measurements. Focus for this particular project was the commercial interest in such technology and the requirements which would be placed on the sensor itself. A number of potential customers and partners were interviewed and helped to refine the design and marketing of the technology. There is interest in using such technology in the commercial marketplace but there still exist technical questions which need to be addressed. The most significant of these is the performance. The sensor has been shown to work and is an accurate measure of snow depth. Further, the low cost component is quite evident from the effort (and is a critical component for the marketplace). However, more extensive testing is required for different types (densities) of snow as well as corresponding terrain. Current efforts are involved with long term testing of the sensor in various regions/environments to get a better understand of the potential and, consequently, the resulting market for the technology. If tests are successful, then it is expected a commercial prototype will be available in 2015 with a refined variant in 2016.
