Preprints
https://doi.org/10.5194/tc-2022-96
https://doi.org/10.5194/tc-2022-96
 
03 Jun 2022
03 Jun 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Assessing the Seasonal Evolution of Snow Depth Spatial Variability and Scaling in Complex Mountain Terrain

Zachary S. Miller1, Erich H. Peitzsch1, Eric A. Sproles2, Karl W. Birkeland3, and Ross T. Palomaki2 Zachary S. Miller et al.
  • 1US Geological Survey Northern Rocky Mountain Science Center, West Glacier, Montana, MT 59936, USA
  • 2Geographic Snow, Water, and Ice Resources Lab, Department of Earth Sciences, Montana State University, Bozeman, Montana, MT 59717, USA
  • 3USDA Forest Service National Avalanche Center, Bozeman, Montana, MT 59771, USA

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.

Zachary S. Miller et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-96', Yves Bühler, 06 Jul 2022
    • AC1: 'Reply to RC1 Comments', Zachary Miller, 28 Jul 2022
  • RC2: 'Comment on tc-2022-96', Anonymous Referee #2, 06 Jul 2022
    • AC2: 'Reply to RC2 Comments', Zachary Miller, 28 Jul 2022

Zachary S. Miller et al.

Zachary S. Miller et al.

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Short summary
Snow depth varies across steep, complex mountain landscapes due to interactions of dynamic natural processes. Our study found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability from a winter timeseries of high-resolution snow depth maps of a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.