Abstract

In mountains with seasonal snow cover, the effects of climate change on snowpack will be constrained by landscape-vegetation interactions with the atmosphere. Airborne lidar surveys used to estimate snow depth, topography, and vegetation were coupled with reanalysis climate products to quantify these interactions and to highlight potential snowpack sensitivities to climate and vegetation change across the western U.S. at Rocky Mountain (RM), Northern Basin and Range (NBR), and Sierra Nevada (SNV) sites. In forest and shrub areas, elevation captured the greatest amount of variability in snow depth (16–79%) but aspect explained more variability (11–40%) in alpine areas. Aspect was most important at RM sites where incoming shortwave to incoming net radiation (SW:NetR↓) was highest (∼0.5), capturing 17–37% of snow depth variability in forests and 32–37% in shrub areas. Forest vegetation height exhibited negative relationships with snow depth and explained 3–6% of its variability at sites with greater longwave inputs (NBR and SNV). Variability in the importance of physiography suggests differential sensitivities of snowpack to climate and vegetation change. The high SW:NetR↓ and importance of aspect suggests RM sites may be more responsive to decreases in SW:NetR↓ driven by warming or increases in humidity or cloud cover. Reduced canopy-cover could increase snow depths at SNV sites, and NBR and SNV sites are currently more sensitive to shifts from snow to rain. The consistent importance of aspect and elevation indicates that changes in SW:NetR↓ and the elevation of the rain/snow transition zone could have widespread and varied effects on western U.S. snowpacks.

Citation

Christopher J. Tennant, Adrian A. Harpold, Kathleen Ann Lohse, Sarah E. Godsey, Benjamin T. Crosby, Laurel G. Larsen, Paul D. Brooks, Robert W. Van Kirk, Nancy F. Glenn (2017): Regional sensitivities of seasonal snowpack to elevation, aspect, and vegetation cover in western North America. Water Resources Research 53. DOI: 10.1002/2016WR019374

This Paper/Book acknowledges NSF CZO grant support.