Snow Water Equivalent Estimation for a Snow-Covered Prairie Grass Field by GPS Interferometric Reflectometry

Mark D Jacobson

doi:10.4236/pos.2012.33005

Abstract

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 50 and 300. 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).

Highlights

Snow water equivalent (SWE) measurements are necessary for the management of water supply and flood control systems in seasonal snow-covered regions
The average inferred snow density is greater than the average in situ measurement by approximately 60%
This suggests that there is potential for estimating snow density and snow water equivalent (SWE) using global positioning system (GPS)-IR provided the theory incorporates a realistic model of the prairie grass field and includes the near field effects of the reflected signals

Summary

Introduction

Snow water equivalent (SWE) measurements are necessary for the management of water supply and flood control systems in seasonal snow-covered regions. Its basic mechanism is the interference between the direct (line-of-sight) signal and the multipath signals, reflected from near-ground surfaces such as snow, bare soil, etc It was shown by Jacobson [18] that it may be possible to estimate SWE by GPS-IR. A simple model depicting a frozen soil surface covered by a prairie grass layer and a snow layer, as described by Jacobson [20], is used as the fitting function in a quasiNewton algorithm (QNA) [42] This algorithm is used to minimize the error between theory and measurement in aleast-squares sense. The antenna gain decreases by approximately 10 dB at 90 ̊ away from the maximum of the main lobe These low elevation angles provide the greatest effect on the reflected signals because the electrical path length of the GPS signal in the snow increases as the elevation angle decreases.

SWE Estimation

Method

Findings

Conclusions and Future Research