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Mapping Snow Depth Within a Tundra Ecosystem Using Multiscale Observations and Bayesian Methods
Published: April 03, 2017
Posted: May 10, 2017

Bayesian approach developed to integrate multiscale measurements for estimating snow depth over the landscape.

The Science 
This project developed a Bayesian approach to integrate a variety of state-of-art snow sensing techniques—in situ measurements, ground-penetrating radar, phodar on unmanned aerial system (UAS), and airborne lidar—for mapping highly heterogeneous snow depth over ice-wedge polygonal tundra. Project analysis also showed that the end-of-winter snow depth was highly variable in several-meter distances, influenced by microtopography.

The Impact
Snow plays a critical role in Arctic ecosystem functioning, as it influences permafrost thaw, water delivery, and carbon exchange. Snow depth is, however, extremely heterogeneous and traditionally difficult to map in sufficient resolution using conventional point measurements. Although there have been significant technical advances in measuring snow depth (e.g., geophysics and remote sensing), it is still challenging to integrate all these state-of-art data in a harmonized manner due to their different scales and accuracy. The developed Bayesian approach will be an integrating framework for these advanced datasets, allowing the measurement of snow depth at high resolution over a large area.

This paper aims to develop an effective strategy to characterize heterogeneous snow depth over the Arctic tundra, using state-of-art techniques (ground-penetrating radar and UAS phodar) and also to quantify the relationship between snow depth and topography. All the techniques provided fairly accurate estimates of snow depth, while they have different characteristics in term of acquisition time and accuracy. The team of researchers then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the wavelet approach. The researchers found that the end-of-winter snow depth was highly variable over several-meter distances, affected primarily by microtopography. In addition, the team developed and implemented a Bayesian approach to integrate multiscale measurements for estimating snow depth over the landscape.

BER Program Manager
Daniel Stover
Terrestrial Ecosystem Science, SC-23.1 (301-903-0289)

Principal Investigator
Stan D. Wullschleger
Oak Ridge National Laboratory
Oak Ridge, TN 37831

The Next-Generation Ecosystem Experiments (NGEE)–Arctic project is supported by the Office of Biological and Environmental Research within the U.S. Department of Energy's Office of Science. This NGEE–Arctic research is supported through contract number DE-AC0205CH11231 to Lawrence Berkeley National Laboratory.

Wainwright, H.M., Liljedahl, A.K., Dafflon, B., Ulrich, C., Peterson, J.E., Gusmeroli, A., and Hubbard, S.S. “Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods.” The Cryosphere 11(2), 857–875 (2017). [DOI:10.5194/tc-11-857-2017].

Topic Areas:

  • Research Area: Terrestrial Ecosystem Science

Division: SC-33.1 Earth and Environmental Sciences Division, BER


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