Assessing the seasonal evolution of snow depth spatial variability andscaling in complex mountain terrain

Dynamic natural processes govern snow distribution inmountainous environments throughout the world. Interactions between thesedifferent processes create spatially variable patterns of snow depth acrossa landscape. Variations in accumulation and redistribution occur at avariety of spatial scales, which are well established for moderate mountainterrain. However, spatial patterns of snow depth variability in steep,complex mountain terrain have not been fully explored due to insufficientspatial resolutions of snow depth measurement. Recent advances in uncrewedaerial systems (UASs) and structure from motion (SfM) photogrammetry providean opportunity to map spatially continuous snow depths at high resolutions inthese environments. Using UASs and SfM photogrammetry, we produced 11 snowdepth maps at a steep couloir site in the Bridger Range of Montana, USA,during the 2019-2020 winter. We quantified the spatial scales of snow depthvariability in this complex mountain terrain at a variety of resolutionsover 2 orders of magnitude (0.02 to 20 m) and time steps (4 to 58 d)using variogram analysis in a high-performance computing environment. Wefound that spatial resolutions greater than 0.5 m do not capture thecomplete patterns of snow depth spatial variability within complex mountainterrain and that snow depths are autocorrelated within horizontal distancesof 15 m at our study site. The results of this research have the potentialto reduce uncertainty currently associated with snowpack and snow waterresource analysis by documenting and quantifying snow depth variability andsnowpack evolution on relatively inaccessible slopes in complex terrain athigh spatial and temporal resolutions.