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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-277
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-277
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Oct 2020

23 Oct 2020

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This preprint is currently under review for the journal TC.

Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

Ahmad Hojatimalekshah1, Zach Uhlmann1, Nancy F. Glenn1, Christopher A. Hiemstra2, Christopher J. Tennant3, Jake D. Graham1, Lucas Spaete4, Art Gelvin2, Hans-Peter Marshall1, James McNamara1, and Josh Enterkine1 Ahmad Hojatimalekshah et al.
  • 1Department of Geosciences, Boise State University, Boise, 83725, USA
  • 2Cold Regions Research and Engineering Laboratory, Fort Wainwright, 99703, USA
  • 3US Army Corps of Engineers, Sacramento, CA, 95814, USA
  • 4Minnesota Department of Natural Resources, Division of Forestry, Resource Assessment, Grand Rapids, 55744, USA

Abstract. Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts, and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA, to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We apply the Marker-controlled watershed algorithm for individual tree segmentation and measure the snow depth using the Multi-scale Model to Model Cloud Comparison algorithm. Canopy, topography and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity) along with local scale processes such as wind are highly influential on snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results emphasize the importance of tree species and distribution on snow depth patterns. Fine scale analysis from TLS provides information on local scale controls, and provides an opportunity to be readily coupled with airborne or spaceborne lidar to investigate larger-scale controls on snow depth.

Ahmad Hojatimalekshah et al.

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Data sets

SnowEx17 Boise State University Terrestrial Laser Scanner (TLS) Point Cloud, Version 1 N. Glenn, L. Spaete, Z. Uhlmann, C. Merriman, A. Raymondi, and C. Tennant https://doi.org/10.5067/IWGD4WFMCQNW

SnowEx17 CRREL Terrestrial Laser Scanner (TLS) Point Cloud, Version 1 C. Hiemstra https://doi.org/10.5067/YOIPYEWCZOD5

Ahmad Hojatimalekshah et al.

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Short summary
Utilizing terrestrial laser scanning data, we quantitatively describe the relationships between snow depth, vegetation canopy, and local scale processes during the snow accumulation period. In addition to topography and wind, our findings suggest the importance of fine scale tree structure, species type, and distributions on snow depth. Terrestrial laser scanning provides information in and under the vegetation canopy, complementary to wide area airborne lidar surveys.
Utilizing terrestrial laser scanning data, we quantitatively describe the relationships between...
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