Quantifying the effects of forest vegetation on snow accumulation, ablation, and potential meltwater inputs, Valles Caldera National Preserve, NM, USA

• Main Author: Musselman, Keith N.
• Other Authors: Brooks, Paul D.
• Language: en_US
• Published: The University of Arizona. 2006
• Online Access: http://hdl.handle.net/10150/626930; http://arizona.openrepository.com/arizona/handle/10150/626930

I quantified the competing effects of forest vegetation on snow accumulation and ablation in a lower mid-latitude montane environment where solar radiation dominates winter snow-atmosphere energy fluxes and limited work has been focused. Detailed snowpit analyses and ultrasonic snow depth sensors indicated forest vegetation affected snowcover in three ways; canopy interception and sloughing, enhanced snowpack metamorphism and ablation, and shading of direct solar radiation. Competing accumulation and melt processes determine the snow cover duration, SWE yield, and potential meltwater inputs. On average, canopy interception resulted in 44% less SWE accumulating beneath the canopy. I observed an inverse correlation between snowpack density and grain size with distance from the tree bole at maximum accumulation. Larger grains and lower densities near the bole indicated enhanced metamorphism of the near tree snowpack. Snow surveys around 15 trees at max accumulation indicated that the north sides of trees had 24.6% (p=0.01) more SWE than south tree sides. Micro- to tree scale observations support our stand and catchment-scale finding that a shaded snowpack experiences increased SWE accumulation, decreased ablation and melt rates, and prolonged seasonal snow cover. Specifically, we found that vegetative shading may delay the basin average maximum SWE accumulation by up to three weeks and greatly increase snow cover duration by minimizing snowmelt rates. Data point to compelling differences in forest ablation and melt processes in this lower mid-latitude where enhanced insolation augments the physical processes observed elsewhere. A binary regression tree model indicated strong correlation (R 2 = 0.54) between micro-scale (i.e. 10-cm resolution) canopy structure indices and snow depth, suggesting that future remotely sensed vegetation data may improve snow distribution models. A better understanding of the effects of forest cover on a basin's snowpack will prepare us to more accurately predict the potentially wide-ranging hydrologic impacts of climate, land cover, and land use change in these seasonally snow covered forested environments.