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 (G...

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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: Text
Language:English
Published: 2018
Subjects:
Online Access:https://doi.org/10.5194/tc-11-857-2017
https://tc.copernicus.org/articles/11/857/2017/
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spelling ftcopernicus:oai:publications.copernicus.org:tc53571 2023-05-15T15:11:07+02:00 Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods Wainwright, Haruko M. Liljedahl, Anna K. Dafflon, Baptiste Ulrich, Craig Peterson, John E. Gusmeroli, Alessio Hubbard, Susan S. 2018-10-24 application/pdf https://doi.org/10.5194/tc-11-857-2017 https://tc.copernicus.org/articles/11/857/2017/ eng eng doi:10.5194/tc-11-857-2017 https://tc.copernicus.org/articles/11/857/2017/ eISSN: 1994-0424 Text 2018 ftcopernicus https://doi.org/10.5194/tc-11-857-2017 2020-07-20T16:23:47Z 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). Text Arctic Tundra Alaska Copernicus Publications: E-Journals Arctic The Cryosphere 11 2 857 875
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description 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).
format Text
author Wainwright, Haruko M.
Liljedahl, Anna K.
Dafflon, Baptiste
Ulrich, Craig
Peterson, John E.
Gusmeroli, Alessio
Hubbard, Susan S.
spellingShingle 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
author_facet Wainwright, Haruko M.
Liljedahl, Anna K.
Dafflon, Baptiste
Ulrich, Craig
Peterson, John E.
Gusmeroli, Alessio
Hubbard, Susan S.
author_sort Wainwright, Haruko M.
title Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
title_short Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
title_full Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
title_fullStr Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
title_full_unstemmed Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
title_sort mapping snow depth within a tundra ecosystem using multiscale observations and bayesian methods
publishDate 2018
url https://doi.org/10.5194/tc-11-857-2017
https://tc.copernicus.org/articles/11/857/2017/
geographic Arctic
geographic_facet Arctic
genre Arctic
Tundra
Alaska
genre_facet Arctic
Tundra
Alaska
op_source eISSN: 1994-0424
op_relation doi:10.5194/tc-11-857-2017
https://tc.copernicus.org/articles/11/857/2017/
op_doi https://doi.org/10.5194/tc-11-857-2017
container_title The Cryosphere
container_volume 11
container_issue 2
container_start_page 857
op_container_end_page 875
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