Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
Abstract
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 »
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1349549
- Alternate Identifier(s):
- OSTI ID: 1379796
- Grant/Contract Number:
- AC0205CH11231; AC02-05CH11231
- Resource Type:
- Published Article
- 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:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
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. Germany: N. p., 2017.
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. Germany. https://doi.org/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. Mon .
"Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods". Germany. https://doi.org/10.5194/tc-11-857-2017.
@article{osti_1349549,
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 = {Germany},
year = {Mon Apr 03 00:00:00 EDT 2017},
month = {Mon Apr 03 00:00:00 EDT 2017}
}
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https://doi.org/10.5194/tc-11-857-2017
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