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Keywords:

  • snow water equivalent;
  • binary tree regression;
  • terrain factors

[1] The nature of the snowpack has the potential to strongly influence the patterns of alpine plant productivity and composition by governing soil moisture levels, growing season duration and the thermal regime of alpine soils. This study evaluates these relationships by modeling the interrelationships of snow depth, snow water equivalent (SWE), snow disappearance rate, soil moisture, attributes of the alpine plant community and selected terrain factors using decision-tree techniques at Niwot Ridge, Colorado Front Range. The modeling results showed a strong correlation (r2 > 0.9, P < 0.001) between the snow disappearance rate and SWE and terrain factors that control the degree of shelter and exposure of a given local and elevation. The model was sufficiently robust to predict the spatial distribution of the snowpack for 12 years that exhibited average snow fall (r2 = 0.8, P < 0.001), but yielded lower correlation (r2 = 0.2, P < 0.001) in drought years. Soil moisture was significantly correlated (r2 = 0.7, P < 0.001) with snow-fall amounts and terrain factors; however, meltwater and summer rain offset the potential soil moisture deficit in windward sites. Annual plant biomass did not correlate well with snow attributes and soil moisture because the cascading impact of topography on snowpack and soil moisture was not well captured by measurements of aboveground biomass. In contrast, the species richness index was significantly correlated with snow depth and soil moisture (r2 = 0.7, P < 0.001), thereby demonstrating the importance of snow on some attributes of the alpine plant community.