资源环境科技发展态势分析平台

Understanding and characterizing the spatial distribution of snow is critical to represent the energy balance and runoff production in mountain environments. In this study, we investigate the inter‐annual and seasonal variability in snow depth scaling behavior at the Izas experimental catchment of the Spanish Pyrenees (2000 to 2300 m above sea level). We conduct variogram analyses of 24 snow depth maps derived from terrestrial lidar scans, acquired during six consecutive snow seasons (2011‐2017) that span a range of hydroclimatic conditions. We complement our analyses with bare ground topography data and wind speed and direction measurements. Our results show temporal consistency in the spatial variability of snow depth, with short‐range fractal behavior and scale break lengths that are similar to the optimal search distance (25 m) previously reported for the Topographic Position Index (TPI), a terrain‐based predictor of snow depth. Beyond the 25‐m scale break, there is little to no fractal structure. We report a long‐range scale break of the order of 185‐300 m for most dates – aligned with the dominant wind direction –, and patterns between anisotropies in scale break lengths of shallow snow‐cover and directional terrain scaling behavior. The temporal consistency of snow depth scaling patterns suggests that, in addition to guiding the spatial configuration of physically‐based models, fractal analysis could be used to inform the design of independent variables for statistical models used to predict snow depth and its variability.