A Bayesian ice thickness estimation model for large-scale applications
Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present a new ice thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The forward model calculates flu...
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International Glaciological Society
2020
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ftunivzuerich:oai:www.zora.uzh.ch:195739 2024-09-30T14:37:52+00:00 A Bayesian ice thickness estimation model for large-scale applications Werder, Mauro A Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel 2020-02-01 application/pdf https://www.zora.uzh.ch/id/eprint/195739/ https://www.zora.uzh.ch/id/eprint/195739/1/2020_Paul_a-bayesian-ice-thickness-estimation-model-for-large-scale-applications.pdf https://doi.org/10.5167/uzh-195739 https://doi.org/10.1017/jog.2019.93 eng eng International Glaciological Society https://www.zora.uzh.ch/id/eprint/195739/1/2020_Paul_a-bayesian-ice-thickness-estimation-model-for-large-scale-applications.pdf doi:10.5167/uzh-195739 doi:10.1017/jog.2019.93 urn:issn:0022-1430 info:eu-repo/semantics/openAccess Creative Commons: Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/ Werder, Mauro A; Huss, Matthias; Paul, Frank; Dehecq, Amaury; Farinotti, Daniel (2020). A Bayesian ice thickness estimation model for large-scale applications. Journal of Glaciology, 66(255):137-152. Institute of Geography 910 Geography & travel Earth-Surface Processes Journal Article PeerReviewed info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2020 ftunivzuerich https://doi.org/10.5167/uzh-19573910.1017/jog.2019.93 2024-09-11T00:49:03Z Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present a new ice thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The forward model calculates flux in an elevation-band flow-line model, and translates this into ice thickness and surface ice speed using a shallow ice formulation. Both ice thickness and speed are then extrapolated to the map plane. The model assimilates observations of ice thickness and speed using a Bayesian scheme implemented with a Markov chain Monte Carlo method, which calculates estimates of ice thickness and their error. We illustrate the model's capabilities by applying it to a mountain glacier, validate the model using 733 glaciers from four regions with ice thickness measurements, and demonstrate that the model can be used for large-scale studies by fitting it to over 30 000 glaciers from five regions. The results show that the model performs best when a few thickness observations are available; that the proposed scheme by which parameter-knowledge from a set of glaciers is transferred to others works but has room for improvements; and that the inferred regional ice volumes are consistent with recent estimates. Article in Journal/Newspaper Journal of Glaciology University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
institution |
Open Polar |
collection |
University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
op_collection_id |
ftunivzuerich |
language |
English |
topic |
Institute of Geography 910 Geography & travel Earth-Surface Processes |
spellingShingle |
Institute of Geography 910 Geography & travel Earth-Surface Processes Werder, Mauro A Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel A Bayesian ice thickness estimation model for large-scale applications |
topic_facet |
Institute of Geography 910 Geography & travel Earth-Surface Processes |
description |
Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present a new ice thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The forward model calculates flux in an elevation-band flow-line model, and translates this into ice thickness and surface ice speed using a shallow ice formulation. Both ice thickness and speed are then extrapolated to the map plane. The model assimilates observations of ice thickness and speed using a Bayesian scheme implemented with a Markov chain Monte Carlo method, which calculates estimates of ice thickness and their error. We illustrate the model's capabilities by applying it to a mountain glacier, validate the model using 733 glaciers from four regions with ice thickness measurements, and demonstrate that the model can be used for large-scale studies by fitting it to over 30 000 glaciers from five regions. The results show that the model performs best when a few thickness observations are available; that the proposed scheme by which parameter-knowledge from a set of glaciers is transferred to others works but has room for improvements; and that the inferred regional ice volumes are consistent with recent estimates. |
format |
Article in Journal/Newspaper |
author |
Werder, Mauro A Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel |
author_facet |
Werder, Mauro A Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel |
author_sort |
Werder, Mauro A |
title |
A Bayesian ice thickness estimation model for large-scale applications |
title_short |
A Bayesian ice thickness estimation model for large-scale applications |
title_full |
A Bayesian ice thickness estimation model for large-scale applications |
title_fullStr |
A Bayesian ice thickness estimation model for large-scale applications |
title_full_unstemmed |
A Bayesian ice thickness estimation model for large-scale applications |
title_sort |
bayesian ice thickness estimation model for large-scale applications |
publisher |
International Glaciological Society |
publishDate |
2020 |
url |
https://www.zora.uzh.ch/id/eprint/195739/ https://www.zora.uzh.ch/id/eprint/195739/1/2020_Paul_a-bayesian-ice-thickness-estimation-model-for-large-scale-applications.pdf https://doi.org/10.5167/uzh-195739 https://doi.org/10.1017/jog.2019.93 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Werder, Mauro A; Huss, Matthias; Paul, Frank; Dehecq, Amaury; Farinotti, Daniel (2020). A Bayesian ice thickness estimation model for large-scale applications. Journal of Glaciology, 66(255):137-152. |
op_relation |
https://www.zora.uzh.ch/id/eprint/195739/1/2020_Paul_a-bayesian-ice-thickness-estimation-model-for-large-scale-applications.pdf doi:10.5167/uzh-195739 doi:10.1017/jog.2019.93 urn:issn:0022-1430 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons: Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.5167/uzh-19573910.1017/jog.2019.93 |
_version_ |
1811640654951874560 |