Mon, March 20, 2017, noon:
Dean Yang, Taken by Storm
Geddes, C., Daniel G. Brown, and D.B. Fagre. 2005. "Topography and vegetation as predictors of snow water equivalent (swe) across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana." Arctic, Antarctic, and Alpine Research, 37(2): 197-205.
We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R-2 = 0.93) and GIS-based models (R-2 = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its environment and could affect the ability of additional vegetation to become established.
Country of focus: United States of America.