Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Geronimus says black-white differences in mortality "help silence black voices in the electorate"

Do universities need more conservative thinkers?

Starr critical of risk assessment scores for sentencing

Highlights

Presentation on multilevel modeling using Stata, July 26th, noon, 6050 ISR

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

Topography and vegetation as predictors of snow water equivalent (swe) across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana

Publication Abstract

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.

DOI:10.1657/1523-0430(2005)037[0197:TAVAPO]2.0.CO;2 (Full Text)

Country of focus: United States of America.

Browse | Search : All Pubs | Next