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

    Source: Journal of Hydrometeorology:;2002:;Volume( 003 ):;issue: 005::page 524
    Author:
    Winstral, Adam
    ,
    Elder, Kelly
    ,
    Davis, Robert E.
    DOI: 10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2
    Publisher: American Meteorological Society
    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, D0, 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 D0-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 , D0, 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.
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      Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4206232
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    • Journal of Hydrometeorology

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    contributor authorWinstral, Adam
    contributor authorElder, Kelly
    contributor authorDavis, Robert E.
    date accessioned2017-06-09T16:17:16Z
    date available2017-06-09T16:17:16Z
    date copyright2002/10/01
    date issued2002
    identifier issn1525-755X
    identifier otherams-65050.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4206232
    description abstractWind 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, D0, 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 D0-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 , D0, 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.
    publisherAmerican Meteorological Society
    titleSpatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters
    typeJournal Paper
    journal volume3
    journal issue5
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2
    journal fristpage524
    journal lastpage538
    treeJournal of Hydrometeorology:;2002:;Volume( 003 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian