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

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=2.9cm), with a spatial sampling of 10cm 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=6.0cm) and a fine spatial sampling (4cm×4cm). 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) asmore » well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less
Authors:
 [1] ; ORCiD logo [2] ;  [1] ;  [1] ;  [1] ;  [2] ;  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of Alaska, Fairbanks, AK (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
The Cryosphere (Online)
Additional Journal Information:
Journal Name: The Cryosphere (Online); Journal Volume: 11; Journal Issue: 2; Journal ID: ISSN 1994-0424
Publisher:
European Geosciences Union
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1379796

Wainwright, Haruko M., Liljedahl, Anna K., Dafflon, Baptiste, Ulrich, Craig, Peterson, John E., Gusmeroli, Alessio, and Hubbard, Susan S.. Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods. United States: N. p., Web. doi:10.5194/tc-11-857-2017.
Wainwright, Haruko M., Liljedahl, Anna K., Dafflon, Baptiste, Ulrich, Craig, Peterson, John E., Gusmeroli, Alessio, & Hubbard, Susan S.. Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods. United States. doi:10.5194/tc-11-857-2017.
Wainwright, Haruko M., Liljedahl, Anna K., Dafflon, Baptiste, Ulrich, Craig, Peterson, John E., Gusmeroli, Alessio, and Hubbard, Susan S.. 2017. "Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods". United States. doi:10.5194/tc-11-857-2017. https://www.osti.gov/servlets/purl/1379796.
@article{osti_1379796,
title = {Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods},
author = {Wainwright, Haruko M. and Liljedahl, Anna K. and Dafflon, Baptiste and Ulrich, Craig and Peterson, John E. and Gusmeroli, Alessio and Hubbard, Susan S.},
abstractNote = {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=2.9cm), with a spatial sampling of 10cm 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=6.0cm) and a fine spatial sampling (4cm×4cm). 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=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).},
doi = {10.5194/tc-11-857-2017},
journal = {The Cryosphere (Online)},
number = 2,
volume = 11,
place = {United States},
year = {2017},
month = {4}
}