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Climate change impacts on maritime mountain snowpack in the Oregon Cascades

机译:气候变化对俄勒冈喀斯喀特山脉海上积雪的影响

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This study investigates the effect of projected temperature increases on maritime mountain snowpack in the McKenzie River Basin (MRB; 3041 kmsup2/sup) in the Cascades Mountains of Oregon, USA. We simulated the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with SnowModel, a spatially-distributed, process-based model (Liston and Elder, 2006b). Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model then evaluated the inter- and intra-year sensitivity of basin wide snowpack to projected temperature increases (2 °C) and variability in precipitation (±10%). Results show that a 2 °C increase in temperature would shift the average date of peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 2000 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. The results of this study are regionally relevant as melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. While this research focused on one watershed, it serves as a case study examining the effects of climate change on maritime snow, which comprises 10% of the Earth's seasonal snow cover.
机译:本研究调查了预计温度升高对美国俄勒冈州喀斯喀特山脉麦肯齐河流域(MRB; 3041 km 2 )海上山脉积雪的影响。我们使用空间分布,基于过程的模型SnowModel模拟了1989-2009年期间MRB中雪水当量(SWE)的空间分布(Liston和Elder,2006b)。使用基于点的SWE,降水和温度测量结果对模拟进行了评估,结果显示Nash-Sutcliffe效率系数分别为0.83、0.97和0.80。使用Landsat Thematic Mapper的积雪覆盖范围显示空间精度为82%。经过验证的模型随后评估了盆地范围积雪对年内预计温度升高(2°C)和降水变化(±10%)的年际和年内敏感性。结果表明,温度每升高2°C,峰值积雪的平均日期就会提前12天,并使整个盆地范围内的积雪量减少56%。 1000至2000 m海拔之间的积雪对温度升高最敏感。较高的海拔也受到影响,但程度较小。温度升高是积雪减少的主要驱动力,但是降水的变化会在积雪的时间和体积存储方面产生明显的变化。这项研究的结果与地区相关,因为MRB雪堆中的融水为整个地区的农业,生态系统和市政提供了关键的水供应,尤其是在夏季水需求高的时候。尽管这项研究集中在一个分水岭上,但它是一个案例研究,研究了气候变化对海上积雪的影响,该积雪占地球季节性积雪的10%。

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