Snowpack variability across various spatio‐temporal resolutions
Snowpack variability across various spatio‐temporal resolutions
复制标题
不同时空分辨率下的积雪变化
DOI：
10.1002/hyp.10245
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发表时间：
2015
影响因子：
3.2
通讯作者：
G. Sexstone
中科院分区：
文献类型：
--
作者：
J. López‐Moreno;J. Revuelto;S. Fassnacht;C. Azorín;S. Vicente‐Serrano;E. Morán;G. Sexstone
在比利牛斯山脉的一个小流域（0.55平方公里）中，通过地面激光扫描仪测量获得的高分辨率雪深（SD）图（1 × 1 m）用于评估积雪在流域和子网格尺度上的小尺度变化。在两个雪季（2011-2012年和2012-2013年）的不同样地分辨率（5 × 5、25 × 25、49 × 49和99 × 99 m）和8个不同天数下比较了变异系数。我们还研究了小尺度积雪变率与SD、地形变量、地形变量小尺度变率之间的关系。结果表明，SD值具有显著的变异性，且随尺度的增大而增大。季节性最大积雪日数显示出最小的小尺度变化，但随着融化的开始，这种变化急剧增加。积雪深度的变异系数（CV）在统计学上表现出显着的一致性之间的各种空间分辨率的研究，虽然它逐渐下降，网格大小之间的差异越来越大进行比较。SD最好地解释了亚网格变异性的空间分布。地形变量，包括坡度、挡风、子网格高度变化和潜在的入射太阳辐射也与积雪的CV显著相关，最大的相关性发生在99 × 99 m分辨率下。在此分辨率下，逐步多元回归模型解释了70%以上的方差，而在25 × 25 m分辨率下，它们解释了略高于50%的方差。结果强调了考虑SD的小尺度变异性对于从可用的准时信息中全面表示积雪分布的重要性，以及使用SD和其他预测因子设计优化调查以获取分布式SD数据的潜力。版权所有© 2014约翰威利父子有限公司.
High‐resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km2) in the Pyrenees were used to assess small‐scale variability of the snowpack at the catchment and sub‐grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small‐scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small‐scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub‐grid variability. Topographic variables including slope, wind sheltering, sub‐grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small‐scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.