Snow Depth Variability in Sagebrush Drifts in High Altitude Rangelands, North Park, Colorado
M.S. (Watershed Science) 2010 Colorado State University, Fort Collins, CO, USA 80523-1472
B.S. (Civil Engineering Technology - Environmental Science) 2004 Rochester Institute of Technology, Rochester, NY 14623
In high altitude rangelands, such as those in Colorado, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, thus enabling the formation of snowdrifts. Sagebrush management techniques could have significant effects on snow accumulation patterns. Snow that potentially could have been trapped by the plants may return to the atmosphere through sublimation. Soil moisture and subsequent plant growth may be affected by this sublimation. Measurement of snow depth and the spatial variability of these measurements might be important information for understanding snowdrift formation processes. Determination of the most effective measurement scale for understanding important ecologic and hydrologic processes in this environment is therefore essential. Directional variogram analyses and Moran's I statistics are two efficient methods for representing the spatial variability of snow depth at different measurement scales in shallow rangeland snow packs.
The three following hypotheses are tested to determine the nature of snow depth spatial variability in the high altitude plateau rangeland of North Park, Colorado, using directional variogram analyses and Moran's I statistical methods: (1) Sagebrush plant dimensions (microtopography) are less spatially autocorrelated than the variations in snow depth measurements in resultant snowdrifts around an individual plant; (2) As winter progresses and the voids within sagebrush plants are filled with wind-distributed snow, the resultant surface evolves into a progressively less spatially variable microtopography; (3) When measuring a shallow rangeland snow pack, smaller scale measurements produce a progressively more spatially variable dataset of snow depths and a therefore less spatially autocorrelated snow surface texture.
Results of both the variogram and Moran's I analyses indicate that the first hypothesis may be supported. Variogram gamma values and fractal dimensions for the sagebrush canopy microtopography tend to be larger than for the corresponding snow depth measurements. This specifies more spatial variability in the sagebrush surface than in snow depths. The Moran's I values also indicate that there is less spatial autocorrelation within sagebrush plant geometry than there is among snow depth measurements in resultant snowdrifts.
The second hypothesis is also supported by the results of both variogram analyses and Moran's I statistics. Variogram analyses indicate that snow depth becomes less spatially variable (with lower sill values) as the winter progresses. There is also evidence of a "leveling-off" of the spatial variability occurring later in the season. Variogram coefficients of variation and fractal dimensions are also very close in value. The Moran's I values also indicate more positive spatial autocorrelation among snow depths throughout the winter season.
Results of the variogram analyses for the multiple scale snow depth datasets do not support the third hypothesis. The results actually indicate that smaller scale snow depth measurements produce a more spatially autocorrelated dataset in shallow rangeland snow packs. As scale in snow depth measurements increases, both lag distances and gamma values increase slightly, as well. The Moran's I values are more supportive of the third hypothesis, indicating that mid-range small-scale snow depth measurements may be the least spatially variable.
Advisor: Steven Fassnacht
Paul Meiman (Range Science)
Alan Knapp (Biology)