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 groundpenetrating radar (GP...

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Main Authors: Wainwright, HM, Liljedahl, AK, Dafflon, B, Ulrich, C, Peterson, JE, Gusmeroli, A, Hubbard, SS
Format: Article in Journal/Newspaper
Language:unknown
Published: eScholarship, University of California 2017
Subjects:
Online Access:https://escholarship.org/uc/item/7mj9q82f
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spelling ftcdlib:oai:escholarship.org/ark:/13030/qt7mj9q82f 2023-05-15T15:10:38+02:00 Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods Wainwright, HM Liljedahl, AK Dafflon, B Ulrich, C Peterson, JE Gusmeroli, A Hubbard, SS 857 - 875 2017-04-03 application/pdf https://escholarship.org/uc/item/7mj9q82f unknown eScholarship, University of California qt7mj9q82f https://escholarship.org/uc/item/7mj9q82f public Cryosphere, vol 11, iss 2 Meteorology & Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience article 2017 ftcdlib 2020-03-20T23:54:55Z 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 groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS).We found that GPR data provided highprecision estimates of snow depth (RMSE D2.9 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 (RMSED6.0 cm) 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 (RMSED6.0 cm), at 0.5m resolution and over the lidar domain (750m × 700m). Article in Journal/Newspaper Arctic Tundra Alaska University of California: eScholarship Arctic
institution Open Polar
collection University of California: eScholarship
op_collection_id ftcdlib
language unknown
topic Meteorology & Atmospheric Sciences
Oceanography
Physical Geography and Environmental Geoscience
spellingShingle Meteorology & Atmospheric Sciences
Oceanography
Physical Geography and Environmental Geoscience
Wainwright, HM
Liljedahl, AK
Dafflon, B
Ulrich, C
Peterson, JE
Gusmeroli, A
Hubbard, SS
Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
topic_facet Meteorology & Atmospheric Sciences
Oceanography
Physical Geography and Environmental Geoscience
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 groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS).We found that GPR data provided highprecision estimates of snow depth (RMSE D2.9 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 (RMSED6.0 cm) 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 (RMSED6.0 cm), at 0.5m resolution and over the lidar domain (750m × 700m).
format Article in Journal/Newspaper
author Wainwright, HM
Liljedahl, AK
Dafflon, B
Ulrich, C
Peterson, JE
Gusmeroli, A
Hubbard, SS
author_facet Wainwright, HM
Liljedahl, AK
Dafflon, B
Ulrich, C
Peterson, JE
Gusmeroli, A
Hubbard, SS
author_sort Wainwright, HM
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
publisher eScholarship, University of California
publishDate 2017
url https://escholarship.org/uc/item/7mj9q82f
op_coverage 857 - 875
geographic Arctic
geographic_facet Arctic
genre Arctic
Tundra
Alaska
genre_facet Arctic
Tundra
Alaska
op_source Cryosphere, vol 11, iss 2
op_relation qt7mj9q82f
https://escholarship.org/uc/item/7mj9q82f
op_rights public
_version_ 1766341629444096000