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The Cryosphere An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/tc-2016-188
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
07 Sep 2016
Review status
A revision of this discussion paper for further review has not been submitted.
Subgrid snow depth coefficient of variation within complex mountainous terrain
Graham A. Sexstone1, Steven R. Fassnacht2,3,4, Juan Ignacio López-Moreno5, and Christopher A. Hiemstra6 1EASC-Watershed Science, Colorado State University, Fort Collins, Colorado 80523-1476, USA
2ESS-Watershed Science, Colorado State University, Fort Collins, Colorado 80523-1476, USA
3Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado 80523-1375, USA
4Geospatial Centroid, Colorado State University, Fort Collins, Colorado 80523-1476, USA
5Instituto Pirenaico de Ecología, CSIC, Campus de Aula Dei, P.O. Box 202, Zaragoza, 50080, Spain
6U.S. Army Cold Regions Research and Engineering Laboratory, Fort Wainwright, Alaska 99703-0170, USA
Abstract. Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets from mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was an important factor of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influences seasonal snow variability. Subgrid CVds was also correlated with topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two simple statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for parameterizing CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

Citation: Sexstone, G. A., Fassnacht, S. R., López-Moreno, J. I., and Hiemstra, C. A.: Subgrid snow depth coefficient of variation within complex mountainous terrain, The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-188, in review, 2016.
Graham A. Sexstone et al.
Graham A. Sexstone et al.

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Short summary
Seasonal snowpacks vary spatially within mountainous environments and the representation of this variability by modeling can be a challenge. This study uses high-resolution airborne lidar data to evaluate the variability of snow depth within a grid size common for modeling applications. Results suggest that snow depth coefficient of variation is well correlated with ecosystem type, depth of snow, and topography and forest characteristics, and can be parameterized using airborne lidar data.
Seasonal snowpacks vary spatially within mountainous environments and the representation of this...
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