@article{643145738c8a41218a4e495aa7183095,
title = "Regional sensitivities of seasonal snowpack to elevation, aspect, and vegetation cover in western North America",
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.",
keywords = "aspect, climate sensitivity, elevation, lidar, radiation, snow depth, vegetation, wind speed",
author = "Tennant, {Christopher J.} and Harpold, {Adrian A.} and Lohse, {Kathleen Ann} and Godsey, {Sarah E.} and Crosby, {Benjamin T.} and Larsen, {Laurel G.} and Brooks, {Paul D.} and {Van Kirk}, {Robert W.} and Glenn, {Nancy F.}",
note = "Funding Information: During the preparation of this manuscript, C. J. Tennant was supported by NSF RC CZO Cooperative Agreement EAR 1331872, USDA ARS and a NSF CZO SAVI Award 1445246. A Gordon and Betty Moore Foundation Data-Driven Discovery Investigator award provided additional support to L. G. Larsen and C. J. Tennant. Lidar data were collected by NCALM under awards EAR-0922307 (Jemez), EPS- 0447689 (Reynolds), EAR-0922307 (Sierra). A. A. Harpold was supported by USDA NIFA NEV05293 and NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Cooperative Agreement NNX14AN24A. Additional support was provided by NSF EPSCoR award OIA-1208732 and NSF EAR-1331408. The climate metrics used in this study were downloaded from NASA{\textquoteright}s Giovanni data portal (http://giovanni.gsfc.nasa.gov/gio vanni/) and the snow depth, terrain, and canopy-height models for BCW, JRB, KREW, and WOLV were obtained from http://www.opentopography.org/ . KREW is labeled Providence Creek on OpenTopography. The snow depth, terrain, and canopy-height models for RCEW were obtained from Nancy Glenn and are available at http:// scholarworks.boisestate.edu/bcal_ data/4/. We thank Adam Winstral for a careful review that allowed us to correct errors in an initial data set and for his thoughtful comments which greatly improved the study. We also thank associate editor Charlie Luce and two anonymous reviewers for insightful comments that significantly increased the quality of the manuscript. Publisher Copyright: {\textcopyright} 2017. American Geophysical Union. All Rights Reserved.",
year = "2017",
month = aug,
doi = "10.1002/2016WR019374",
language = "English (US)",
volume = "53",
pages = "6908--6926",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",
number = "8",
}