Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS Data Layers in the Elbow River Watershed, Alberta

Abstract

This study illustrates the potential to combine LiDAR remote sensing and GIS techniques for the purpose of estimating instantaneous winter snowpack volume within the mountainous Elbow River Watershed (ERW) upstream of Calgary, Alberta. Two LiDAR (Light Detection and Ranging) datasets, one during snow-free and the other during late winter were used to evaluate a procedure for snow depth sampling. These data were also used to classify terrain and canopy cover attributes to enable snow depth estimation in areas that were not directly sampled but for which equivalent land classifications could be derived via other means. The mean snow depth from 1675 field measurements collected coincident with the winter LiDAR survey (late March, 2008) in snow-covered areas only was 0.28 m ( = 0.27 m). The mean LiDAR-based snow depth in snow-covered areas was comparable with the field values at 0.26 m ( = 1.2 m), or 0.18 m when averaged across both snow-covered and snow-free areas. Using field measurements of snow density, a GIS routine was employed to estimate total watershed snow water equivalent (SWE) from ten snow accumulation units (SAUs) using elevation, aspect and canopy cover. The total watershed SWE estimate was 46.0 106 m3. This volume of water can also be expressed as 0.058 m of water depth across the entire basin, or approximately 18% of the total 2008 runoff yield. Further work is needed to improve LiDAR-based snow depth estimation in areas of shallow snowpack where the influence of noise in the data is highest and to optimize the methods of sampling and extrapolation. At the present level of airborne LiDAR sophistication, positional uncertainties in LiDAR data (though small) are such that high confidence in the watershed snowpack volume estimate, would only be achieved during deep snowpack years; which also tend to be the years where accurate data are least required. However, given the availability of LiDAR base maps is ever growing, and the accuracy and costs associated with the technology are constantly improving, this approach to snow depth sampling has the potential to become a useful tool to support headwater snowpack resource assessment in water-stressed regions of Canada.