Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range

Abstract

Mountain snowpacks are important water supplies that are susceptible to climate change, yet snow measurements are sparse relative to snowpack heterogeneity. We used remote sensing to derive a spatiotemporal index of snow climatology that reveals patterns in snow accumulation, persistence, and ablation. Then we examined how this index relates to climate, terrain, and vegetation. Analyses were based on Moderate Resolution Imaging Spectroradiometer eight-day snow cover from 2000 to 2010 for a mountain watershed in the Colorado Front Range, USA. The Snow Cover Index (SCI) was calculated as the fraction of years that were snow covered for each pixel. The proportion of SCI variability explained by independent variables was evaluated using regression analysis. Independent variables included elevation, northing, easting, slope, aspect, northness, solar radiation, precipitation, temperature, and vegetation cover. Elevation was the dominant control on SCI patterns, due to its influence on both temperature and precipitation. Grouping SCI values by elevation, we identified three distinct snow zones in the basin: persistent, transitional, and intermittent. The transitional snow zone represents an area that is sensitive to losing winter snowpack. The SCI can be applied to other basins or regions to identify dominant controls on snow cover patterns and areas sensitive to snow loss.