Comment on tc-2022-96

Abstract

Dynamic natural processes govern snow distribution in mountainous environments throughout the world. Interactions of these different processes create spatially variable patterns of snow depth across a landscape. Variations in accumulation and redistribution occur at a variety of spatial scales, which are well established for moderate mountain terrain. However, spatial patterns of snow depth variability in steep, complex mountain terrain have not been fully explored due to insufficient spatial resolutions of snow depth measurement. Recent advances in uncrewed aerial systems (UAS) and structure from motion (SfM) photogrammetry provide an opportunity to map spatially continuous snow depths at high resolution in these environments. Using UAS and SfM photogrammetry, we captured 12 snow depth maps at a steep couloir site in the Bridger Range of Montana, USA, during the 2019–2020 winter. We quantified the scale breaks of snow depth distribution in this complex mountain terrain at a variety of resolutions over two orders of magnitude (0.02 m to 20 m) and time steps (4 to 58 days) using variogram analysis in a high-performance computing environment. We found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability within complex mountain terrain and that snow depths are autocorrelated within horizontal distances of 15 m. The results of this research have 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 in complex terrain at high spatial and temporal resolutions.