The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 5 0 and 30 0 . A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g . cm -3 ) overestimates the in situ measurements (0.08 g . cm -3 ± 0.02 g . cm -3 ). Consequently, the average inferred SWE (2.33 g . cm -2 ) also overestimates the in situ calculations (1.38 g . cm -2 ± 0.36 g . cm -2 ).
KeywordsGlobal Positioning System (GPS)GPS Interferometric Reflectometry (GPS-IR)Snow DepthSnow DensitySnow Water Equivalent (SWE)MultipathSpecular Reflection
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Snow Water Equivalent Estimation for a Snow-Covered Prairie Grass Field by GPS Interferometric Reflectometry — Oak Academic Publishing