A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography
Abstract During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughn...
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crcambridgeupr:10.1017/jog.2021.12 2024-03-03T08:46:06+00:00 A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography Lang, Shinan Xu, Ben Cui, Xiangbin Luo, Kun Guo, Jingxue Tang, Xueyuan Cai, Yiheng Sun, Bo Siegert, Martin J. 2021 http://dx.doi.org/10.1017/jog.2021.12 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021000125 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology volume 67, issue 263, page 560-568 ISSN 0022-1430 1727-5652 Earth-Surface Processes journal-article 2021 crcambridgeupr https://doi.org/10.1017/jog.2021.12 2024-02-08T08:34:58Z Abstract During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The ‘Self-Adaptive Two-Parameter’ system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models. Article in Journal/Newspaper Journal of Glaciology Cambridge University Press Journal of Glaciology 67 263 560 568 |
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Cambridge University Press |
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crcambridgeupr |
language |
English |
topic |
Earth-Surface Processes |
spellingShingle |
Earth-Surface Processes Lang, Shinan Xu, Ben Cui, Xiangbin Luo, Kun Guo, Jingxue Tang, Xueyuan Cai, Yiheng Sun, Bo Siegert, Martin J. A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
topic_facet |
Earth-Surface Processes |
description |
Abstract During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The ‘Self-Adaptive Two-Parameter’ system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models. |
format |
Article in Journal/Newspaper |
author |
Lang, Shinan Xu, Ben Cui, Xiangbin Luo, Kun Guo, Jingxue Tang, Xueyuan Cai, Yiheng Sun, Bo Siegert, Martin J. |
author_facet |
Lang, Shinan Xu, Ben Cui, Xiangbin Luo, Kun Guo, Jingxue Tang, Xueyuan Cai, Yiheng Sun, Bo Siegert, Martin J. |
author_sort |
Lang, Shinan |
title |
A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
title_short |
A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
title_full |
A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
title_fullStr |
A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
title_full_unstemmed |
A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
title_sort |
self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography |
publisher |
Cambridge University Press (CUP) |
publishDate |
2021 |
url |
http://dx.doi.org/10.1017/jog.2021.12 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021000125 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology volume 67, issue 263, page 560-568 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2021.12 |
container_title |
Journal of Glaciology |
container_volume |
67 |
container_issue |
263 |
container_start_page |
560 |
op_container_end_page |
568 |
_version_ |
1792501999062745088 |