Articles | Volume 11, issue 2
The Cryosphere, 11, 857–875, 2017
The Cryosphere, 11, 857–875, 2017

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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Cited articles

Anderson, B. T., McNamara, J. P., Marshall, H. P., and Flores, A. N.: Insights into the physical processes controlling correlations between snow distribution and terrain properties, Water Resour. Res., 50, 4545–4563, 2014.
Anderson, K. and Gaston, K. J.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Front. Ecol. Environ., 11, 138–146, 2013.
Annan, A. P.: Ground penetrating radar, in near surface geophysics, in: Investigations in Geophysics, edited by: Butler, D. K., Society of Exploration Geophysicists, Tulsa, OK, USA, 13, 357–438, 2005.
Benson, C. S. and Sturm, M.: Structure and wind transport of seasonal snow on the Arctic slope of Alaska, Ann. Glaciol., 18, 261–267, 1993.
Berezovskaya, S. and Kane, D. L.: Measuring snow water equivalent for hydrological applications: part 1, accuracy of observations, in: 16th International Northern Research Basins Symposium and Workshop Petrozavodsk, Russia, 27 August–2 September 2007, available at: (last access: 12 March 2017), 2007.
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.