LiDAR illuminates the influence of elevation, aspect, and vegetation on seasonal snowpack: case studies from four western Critical Zone Observatories

Abstract:

Warming can alter hydrologic regimes in snow dominated areas by increasing the proportion of rain to snow and by expanding the extent, density, and activity of vegetation. We evaluate the present-day influence of elevation, aspect, and forest cover on the spatial distribution of seasonal snow accumulation using five snow-on and snow-off Light Detection and Ranging (LiDAR) datasets from four Critical Zone Observatories (CZO) across the western U.S. All sites exhibit increases in snow depth with elevation; however, the relationship between snow depth and elevation is not monotonic and varies from site to site. Most sites demonstrate a decline in snowpack at the highest elevations, suggesting either orographic exhaustion, wind scour or low retention on high slopes. The elevation distributions in each dataset generally predict snow volume distributions with high accuracy, implying that hypsometry alone provides a useful measure of a watershed’s coarse-scale sensitivity to warming-driven snowpack loss. When analyzed at finer scales, we find that elevation weakly predicts snow storage at Boulder and Reynolds CZOs where wind transport, aspect and vegetation dependent snow storage are significant. Though mean snow depths on northern aspects in open areas in most sites are two to five times greater than in forested areas, Reynolds Creek’s sparse patches of forest are attractors, maintaining snow depths seven times greater than in open areas. Our results emphasize that the relation between elevation and snow depth is robust when working at basin-averaged or regional scales but at finer scales, topographic complexity and vegetation produce a diverse array of local snow accumulation patterns.