HR: 0800h
AN: C31A-0300    [Abstracts]
TI: Fractal Dimension as a Measure of Snow Depth Complexity
AU: * Deems, J S
EM: deems@cnr.colostate.edu
AF: Department of Geosciences, Colorado State University, Fort Collins, CO 80523-1482 United States
AU: Elder, K J
EM: kelder@fs.fed.us
AF: USDA Forest Service Rocky Mountain Research Station, 240 West Prospect Road, Fort Collins, CO 80526-2098 United States
AU: Fassnacht, S R
EM: serf@cnr.colostate.edu
AF: Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523-1472 United States
AU: Reich, R M
EM: robin@cnr.colostate.edu
AF: Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523-1472 United States
AB: Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The variability in snow depth in subalpine and alpine environments has importance in understanding and modeling hydrologic, ecologic, and avalanche processes. The driving forces of wind and energy balance interact with topographic and vegetation roughness elements to dominate the observed variability in snow depth at scales from 1 to 1000 meters. Despite this apparent and intuitive relation to topography, efforts to relate topographic parameters to variability in snowpack properties have been largely unsatisfactory. The measure of the fractal dimension of surfaces, i.e. land surface, vegetation, and snow cover, shows promise as a tool for assessing the relative complexity of the interrelated surface morphologies. Fractal dimension is an index of the roughness and self-similarity of an object derived from its scaling properties. This study uses LiDAR-derived land surface elevation, vegetation surface elevation, and snow depth data collected at the Buffalo Pass, Walton Creek, and Alpine Intensive Study Areas as part of the NASA Cold Lands Processes Experiment (CLPX) in April and September, 2003. Fractal dimensions are estimated from the slope of a log-transformed variogram, and demonstrate scale-invariant, fractal behavior over several orders of magnitude in the elevation, vegetation, and snow depth datasets. Directional differences in the snow depth fractal dimension are examined relative to the prevailing wind direction.
DE: 3250 Fractals and multifractals
DE: 1863 Snow and ice (1827)
DE: 1894 Instruments and techniques
SC: Cryosphere [C]
MN: 2004 AGU Fall Meeting