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首页> 外文期刊>Journal of hydrometeorology >Fractal distribution of snow depth from lidar data
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Fractal distribution of snow depth from lidar data

机译：基于激光雷达数据的积雪深度的分形分布

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Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15-40 m, depending on the site, which indicates a process change at that scale. Terrain has a fractal distribution over nearly the entire range of scales available in the data. Directional differences in the fractal dimensions for each parameter are also present at multiple scales, and are related to the wind direction frequency distributions at each site. The results indicate that different sampling resolutions may yield different results and allow rescaling in specific scale ranges. Resolutions of 10 m and finer are consistently self-similar, as are resolutions greater than 30 m, though the coarser resolutions show nearly random distributions.
机译：从晶体的微观结构到区域性的降雪气候，积雪的性质在很大的空间范围内都有很大的不同。风，能量平衡和降水的驱动力与地形和植被相互作用，在1至1000 m的水平尺度上主导着雪深的变化。这项研究使用了在科罗拉多州中北部三个地点使用机载激光雷达测量的地面高程，植被表面高程和积雪深度数据。分形维数是根据对数变换后的变异函数的斜率估算的，并显示了海拔，植被和积雪深度数据集中的比例不变，分形行为。雪深和植被地形在不同的尺度范围内均表现出两种不同的分形分布（多重分形行为），所有位置的短程分形维数均在2.5附近，长程分形维数在2.9附近。这些分形范围在15-40 m处被水垢破坏所分隔，具体取决于位置，这表明该规模的过程发生了变化。地形几乎在数据中可用的整个比例范围内都有分形分布。每个参数的分形维数的方向差异也存在于多个尺度上，并且与每个站点的风向频率分布有关。结果表明，不同的采样分辨率可能会产生不同的结果，并允许在特定比例范围内重新缩放。分辨率为10 m和更细的分辨率始终是自相似的，大于30 m的分辨率也一样，尽管较粗糙的分辨率几乎显示出随机分布。

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