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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 1
The Cryosphere, 10, 257–269, 2016
https://doi.org/10.5194/tc-10-257-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 10, 257–269, 2016
https://doi.org/10.5194/tc-10-257-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Jan 2016

Research article | 28 Jan 2016

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

Z. Zheng1, P. B. Kirchner2,3, and R. C. Bales1,4 Z. Zheng et al.
  • 1Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA, USA
  • 2Joint Institute for Regional Earth System Science and Engineering, Pasadena, CA, USA
  • 3Southwest Alaska Network, National Park Service, Anchorage, AK, USA
  • 4Sierra Nevada Research Institute, UC Merced, Merced, CA, USA

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

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By analyzing high-resolution lidar products and using statistical methods, we quantified the snow depth dependency on elevation, slope and aspect of the terrain and also the surrounding vegetation in four catchment size sites in the southern Sierra Nevada during snow peak season. The relative importance of topographic and vegetation attributes varies with elevation and canopy, but all these attributes were found significant in affecting snow distribution in mountain basins.
By analyzing high-resolution lidar products and using statistical methods, we quantified the...
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