A Bayesian ice thickness estimation model for large-scale epplications
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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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/386048 2023-05-15T16:57:29+02:00 A Bayesian ice thickness estimation model for large-scale epplications Werder, Mauro Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel 2020-02 application/application/pdf https://hdl.handle.net/20.500.11850/386048 https://doi.org/10.3929/ethz-b-000386048 en eng International Glaciological Society info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2019.93 info:eu-repo/semantics/altIdentifier/wos/000509742700012 http://hdl.handle.net/20.500.11850/386048 doi:10.3929/ethz-b-000386048 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International CC-BY Journal of Glaciology, 66 (255) Glacier modelling glacier volume glacier flow info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2020 ftethz https://doi.org/20.500.11850/386048 https://doi.org/10.3929/ethz-b-000386048 https://doi.org/10.1017/jog.2019.93 2022-04-25T14:01:55Z 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. ISSN:0022-1430 ISSN:1727-5652 Article in Journal/Newspaper Journal of Glaciology ETH Zürich Research Collection |
institution |
Open Polar |
collection |
ETH Zürich Research Collection |
op_collection_id |
ftethz |
language |
English |
topic |
Glacier modelling glacier volume glacier flow |
spellingShingle |
Glacier modelling glacier volume glacier flow Werder, Mauro Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel A Bayesian ice thickness estimation model for large-scale epplications |
topic_facet |
Glacier modelling glacier volume glacier flow |
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. ISSN:0022-1430 ISSN:1727-5652 |
format |
Article in Journal/Newspaper |
author |
Werder, Mauro Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel |
author_facet |
Werder, Mauro Huss, Matthias Paul, Frank Dehecq, Amaury Farinotti, Daniel |
author_sort |
Werder, Mauro |
title |
A Bayesian ice thickness estimation model for large-scale epplications |
title_short |
A Bayesian ice thickness estimation model for large-scale epplications |
title_full |
A Bayesian ice thickness estimation model for large-scale epplications |
title_fullStr |
A Bayesian ice thickness estimation model for large-scale epplications |
title_full_unstemmed |
A Bayesian ice thickness estimation model for large-scale epplications |
title_sort |
bayesian ice thickness estimation model for large-scale epplications |
publisher |
International Glaciological Society |
publishDate |
2020 |
url |
https://hdl.handle.net/20.500.11850/386048 https://doi.org/10.3929/ethz-b-000386048 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology, 66 (255) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2019.93 info:eu-repo/semantics/altIdentifier/wos/000509742700012 http://hdl.handle.net/20.500.11850/386048 doi:10.3929/ethz-b-000386048 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/20.500.11850/386048 https://doi.org/10.3929/ethz-b-000386048 https://doi.org/10.1017/jog.2019.93 |
_version_ |
1766049037271695360 |