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Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters

Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D 0 , which was used to delineate sites of intense redeposition on lee slopes. Based on 504 snow depth samples from a May 1999 survey of the upper Green Lakes Valley, Colorado, the correlation of the developed parameters to the observed snow distribution and the effect of their inclusion in a spatial snow distribution model were quantified. The parameter was found to be a significant predictor, accounting for more of the variance in the observed snow depth than could be explained by elevation, solar radiation, or slope. Samples located in D 0 -delineated drift zones were shown to have significantly greater depths than samples located in nondrift zones. A regression tree model of snow distribution based on a predictor variable set of , D 0 , elevation, solar radiation, and slope explained 8%%––23%% more variance in the observed snow distribution, and performed noticeably better in unsampled areas of the basin, compared to a regression tree model based on only the latter three predictors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters

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References (51)

Publisher
American Meteorological Society
Copyright
Copyright © 2001 American Meteorological Society
ISSN
1525-7541
DOI
10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D 0 , which was used to delineate sites of intense redeposition on lee slopes. Based on 504 snow depth samples from a May 1999 survey of the upper Green Lakes Valley, Colorado, the correlation of the developed parameters to the observed snow distribution and the effect of their inclusion in a spatial snow distribution model were quantified. The parameter was found to be a significant predictor, accounting for more of the variance in the observed snow depth than could be explained by elevation, solar radiation, or slope. Samples located in D 0 -delineated drift zones were shown to have significantly greater depths than samples located in nondrift zones. A regression tree model of snow distribution based on a predictor variable set of , D 0 , elevation, solar radiation, and slope explained 8%%––23%% more variance in the observed snow distribution, and performed noticeably better in unsampled areas of the basin, compared to a regression tree model based on only the latter three predictors.

Journal

Journal of Hydrometeorology – American Meteorological Society

Published: Jul 18, 2001

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