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

• Published in: The Cryosphere
• Main Authors: Wainwright, Haruko M., Liljedahl, Anna K., Dafflon, Baptiste, Ulrich, Craig, Peterson, John E., Gusmeroli, Alessio, Hubbard, Susan S.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2017
• Subjects: article; Verlagsveröffentlichung; Arctic; The Cryosphere; Tundra; Alaska
• Online Access: https://doi.org/10.5194/tc-11-857-2017; https://noa.gwlb.de/receive/cop_mods_00010412; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00010369/tc-11-857-2017.pdf; https://tc.copernicus.org/articles/11/857/2017/tc-11-857-2017.pdf

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. Phodar-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 × 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 multiscale 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 m resolution and over the lidar domain (750 m × 700 m).