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    Projections of Snow Water Equivalent Using a Process-Based Energy Balance Snow Model in Southwestern British Columbia

    Source: Journal of Applied Meteorology and Climatology:;2022:;volume( 061 ):;issue: 001
    DOI: 10.1175/JAMC-D-20-0260.1
    Abstract: Information about snow water equivalent in southwestern British Columbia, Canada, is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1-km resolution and evaluated using a reanalysis SWE product [Snow Data Assimilation System (SNODAS)], manual snow-survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed interannual variability well (R 2 > 0.8 for annual maximum SWE), but peak SWE biases of 20%–40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modeled SWE displays lower bias relative to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and are used to illustrate the impacts of climate change on SWE at 1°, 2°, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort and the sensitivity of annual peak SWE in the Metropolitan Vancouver municipal watersheds to moderate temperature increases. The results both illustrate the potential utility of a process-based snow model and identify areas where the input meteorological variables could be improved.
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      Projections of Snow Water Equivalent Using a Process-Based Energy Balance Snow Model in Southwestern British Columbia

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4286085
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    date accessioned2022-05-09T01:00:39Z
    date available2022-05-09T01:00:39Z
    date copyright31 Jan 2022
    date issued2022
    identifier otherJAMC-D-20-0260.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286085
    description abstractInformation about snow water equivalent in southwestern British Columbia, Canada, is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1-km resolution and evaluated using a reanalysis SWE product [Snow Data Assimilation System (SNODAS)], manual snow-survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed interannual variability well (R 2 > 0.8 for annual maximum SWE), but peak SWE biases of 20%–40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modeled SWE displays lower bias relative to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and are used to illustrate the impacts of climate change on SWE at 1°, 2°, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort and the sensitivity of annual peak SWE in the Metropolitan Vancouver municipal watersheds to moderate temperature increases. The results both illustrate the potential utility of a process-based snow model and identify areas where the input meteorological variables could be improved.
    titleProjections of Snow Water Equivalent Using a Process-Based Energy Balance Snow Model in Southwestern British Columbia
    typeJournal Paper
    journal volume61
    journal issue1
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-20-0260.1
    page77–95
    treeJournal of Applied Meteorology and Climatology:;2022:;volume( 061 ):;issue: 001
    contenttypeFulltext
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