4.6 Article

Scaling-up permafrost thermal measurements in western Alaska using an ecotype approach

期刊

CRYOSPHERE
卷 10, 期 5, 页码 2517-2532

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/tc-10-2517-2016

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资金

  1. US Fish and Wildlife Service and the Selawik National Wildlife Refuge through Cooperative Ecosystem Studies Unit Agreement [F11AC00613]
  2. NSF OPP grants [ARC-0856864, ARC-1304271]
  3. Next Generation Ecosystem Experiment (NGEE- Arctic) project
  4. Office of Biological and Environmental Research in the DOE Office of Science
  5. Russian Science Foundation [RNF 16-17-00102]
  6. Directorate For Geosciences
  7. Office of Polar Programs (OPP) [1304271] Funding Source: National Science Foundation
  8. Division Of Environmental Biology
  9. Direct For Biological Sciences [1026415] Funding Source: National Science Foundation
  10. Russian Science Foundation [16-17-00102] Funding Source: Russian Science Foundation

向作者/读者索取更多资源

Permafrost temperatures are increasing in Alaska due to climate change and in some cases permafrost is thawing and degrading. In areas where degradation has already occurred the effects can be dramatic, resulting in changing ecosystems, carbon release, and damage to infrastructure. However, in many areas we lack baseline data, such as subsurface temperatures, needed to assess future changes and potential risk areas. Besides climate, the physical properties of the vegetation cover and subsurface material have a major influence on the thermal state of permafrost. These properties are often directly related to the type of ecosystem overlaying permafrost. In this paper we demonstrate that classifying the landscape into general ecotypes is an effective way to scale up permafrost thermal data collected from field monitoring sites. Additionally, we find that within some ecotypes the absence of a moss layer is indicative of the absence of near-surface permafrost. As a proof of concept, we used the ground temperature data collected from the field sites to re-code an ecotype land cover map into a map of mean annual ground temperature ranges at 1 m depth based on analysis and clustering of observed thermal regimes. The map should be useful for decision making with respect to land use and understanding how the landscape might change under future climate scenarios.

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