Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
doi:10.5194/tc-2016-168
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
30 Aug 2016
Review status
A revision of this discussion paper was accepted for the journal The Cryosphere (TC) and is expected to appear here in due course.
Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
Haruko M. Wainwright1, Anna K. Liljedahl2, Baptiste Dafflon1, Craig Ulrich1, John E. Peterson1, and Susan S. Hubbard1 1Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126
2University of Alaska Fairbanks, 306 Tanana Loop, Fairbanks, AK 99775-5860, USA
Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes, and estimated using ground penetrating radar (GPR) surveys and the Photogrammetric Detection and Ranging (PhoDAR) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. UAS-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm by 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free LiDAR digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free LiDAR DEM and multi-scale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high precision estimates of snow depth (RMSE = 6.0 cm), at 0.5-meter resolution and over the LiDAR domain (750 m by 700 m).

Citation: Wainwright, H. M., Liljedahl, A. K., Dafflon, B., Ulrich, C., Peterson, J. E., and Hubbard, S. S.: Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods, The Cryosphere Discuss., doi:10.5194/tc-2016-168, in review, 2016.
Haruko M. Wainwright et al.
Haruko M. Wainwright et al.

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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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