Statistical modelling of the snow depth distribution in open alpine terrain
Statistical modelling of the snow depth distribution in open alpine terrain
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DOI：
10.5194/hess-17-3005-2013
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发表时间：
2013-01-01
影响因子：
6.3
通讯作者：
Lehning, M.
中科院分区：
文献类型：
--
作者：
Gruenewald, T.;Stoetter, J.;Lehning, M.
高山积雪的空间分布特点是变异性大。考虑到这一变化对水文学、冰川学、生态学或自然灾害等许多任务都很重要。统计模型经常用于评估积雪的空间变异性。在这项研究中，我们收集了来自世界各地不同山区的七组高分辨率雪深测量数据。所有数据都是在季节性积雪最多的时候从机载激光扫描中获得的。地形参数被用来模拟积雪深度分布的流域尺度上应用多元线性回归。我们发现，通过平均出大量的空间异质性在米尺度，即个人漂移和聚集积雪在景观或水文响应单元尺度（单元格大小400米），30%至91%的雪深变化可以解释的模型，校准到当地条件在单一的研究领域。由于所有网站都是稀疏植被，只有少数地形变量被列为解释变量，包括海拔，坡度，从北（北）方面的偏差，和避风参数。在大多数情况下，海拔、坡度和北移是积雪分布的很好的预测因子。模型的比较表明，参数及其系数的重要性不同的集水区。一个“全球”模型，结合所有调查领域的所有数据，只能解释23%的变异。地方统计模型似乎无法转移到不同区域。然而，在一个高峰雪季开发的模型是很好的预测其他高峰雪季。
The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.