Characterization of the scale dependence and scale invariance of the spatial organization of snow depth fields, and the corresponding topographic, meteorologic, and canopy controls - Trujillo-Gómez, Ernesto, author; Ramírez, Jorge A., advisor (Colorado State University. Libraries, 2009)
The spatial organization of snow cover properties and its dependence on scale are determined by precipitation patterns and the interaction of the snow pack with topography, winds, vegetation and radiative fluxes, among others factors. The objectives of this research are to characterize the spatial scaling properties and spatial organization of snow depth fields in several environments at scales between 1 m and 1000 m, and to determine how these properties are related to topography, vegetation, and winds. These objectives are accomplished through (a) the analysis of LIDAR elevation contours, and snow depth contours, (b) the analysis of synthetically generated profiles and fields of snow depth, and (c) simulations performed using a new cellular automata model for redistribution of snow by wind. The analyses of the power spectral densities of snow depth show the existence of two distinct scaling regimes separated by a scale break located at scales of the order of meters to tens of meters depending on the environment. The breaks separate a highly variable larger-scales interval from a highly correlated smaller-scales interval. Complementary analyses support the conclusion that the scaling behavior of snow depth is controlled by the scaling characteristics of the spatial distribution of vegetation height when snow redistribution by wind is minimal and canopy interception is dominant, and by the interaction of winds with features such as surface concavities and vegetation when snow redistribution by wind is dominant. Using these observations together with synthetic snow depth profiles and fields, we show that the scale at which the break occurs increases with the separation distance between snow depth maxima. Finally, the cellular automata model developed here is used to show that the correlation structure of the snow depth fields becomes stronger as the amount of snow transported increases, while the probability distributions of the fields progress from a Gaussian distribution for small transport rates to positively skewed probabilities for high transport rates. These simulation results are used to illustrate the controls that topography, vegetation, and winds have on the spatial organization of snow depth in wind-dominanted environments. Implications of the results from the different analysis are discussed.
Keywords: fractals; self-affinity; snow; snow depth; snow hydrology; hydrologic sciences; civil engineering