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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 2
The Cryosphere, 11, 857–875, 2017
https://doi.org/10.5194/tc-11-857-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 11, 857–875, 2017
https://doi.org/10.5194/tc-11-857-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Apr 2017

Research article | 03 Apr 2017

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Haruko M. Wainwright et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Haruko Wainwright on behalf of the Authors (10 Feb 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (11 Feb 2017) by Guillaume Chambon
RR by Anonymous Referee #2 (17 Feb 2017)
ED: Publish subject to technical corrections (20 Feb 2017) by Guillaume Chambon
AR by Haruko Wainwright on behalf of the Authors (26 Feb 2017)  Author's response    Manuscript
Publications Copernicus
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Short summary
Snow has a profound impact on permafrost and ecosystem functioning in the Arctic tundra. This paper aims to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. In addition, we develop a Bayesian geostatistical method to integrate multiscale observational platforms (a snow probe, ground penetrating radar, unmanned aerial system and airborne lidar) for estimating snow depth in high resolution over a large area.
Snow has a profound impact on permafrost and ecosystem functioning in the Arctic tundra. This...
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