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

Abstract. Dynamic natural processes govern snow distribution in
mountainous environments throughout the world. Interactions between 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 (UASs) and structure from motion (SfM) photogrammetry provide
an opportunity to map spatially continuous snow depths at high resolutions in
these environments. Using UASs and SfM photogrammetry, we produced 11 snow
depth 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 depth
variability in this complex mountain terrain at a variety of resolutions
over 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. 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 at our study site. 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.
