Individual Storm Event Influence on Seasonal Snow Depth Distribution in Complex Mountain Terrain

Abstract:

Understanding of the fine-scale distribution of seasonal snow is essential for resource managers and their downstream communities, as well as for avalanche risk assessment for infrastructure, practitioners, and recreationalists alike. Snow depth can be difficult to assess due to its variability across different spatial and temporal scales, which is inherently complex in mountainous regions. This research compares the snow depth evolution and spatial variability of complex, steep, avalanche-prone mountain terrain with an adjacent flat, protected mountain meadow on a sub-weekly basis over the course of an entire winter. Unmanned Aerial Systems (UASs) and Structure-from-Motion (SfM) photogrammetry show potential for quantifying snowpack variability at the slope scale. Additionally, these methods provide the opportunity to explore how individual storm events influence seasonal snow depth distribution in a variety of topographies. Recent studies, using similar techniques, have been constrained to relatively simple terrain and have largely focused on comparing single time steps. Our project seeks to adapt those methods to more complex mountain terrain and at a higher temporal frequency to gain new insight into seasonal snow accumulation patterns. We utilize overlapping imagery collected by a consumer-available UAS equipped with on-board real-time-kinematic (RTK) global navigation satellite systems (GNSS) and SfM photogrammetry to create 3D digital surface models of the evolving snow surface and measure change in the sub-decimeter scale. All UAS-collected data has been validated by analyzing real-time collected weather data at three nearby remote weather stations, in-situ measured snow depth data, and monthly snow-surveys at the study site. Results indicate that individual meteorological events can have significant influence on the entire winter’s snow depth distribution in complex mountain terrain. This project has the potential to reduce uncertainty currently associated with snowpack and snow water resource analysis by documenting and quantifying snow depth variability and snowpack evolution on relatively inaccessible slopes at detailed spatial and temporal resolutions.