Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images
Abstract Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are n...
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Online Access: | http://dx.doi.org/10.1017/jog.2020.11 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143020000118 |
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crcambridgeupr:10.1017/jog.2020.11 2024-05-19T07:43:13+00:00 Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel 2020 http://dx.doi.org/10.1017/jog.2020.11 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143020000118 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology volume 66, issue 257, page 373-385 ISSN 0022-1430 1727-5652 journal-article 2020 crcambridgeupr https://doi.org/10.1017/jog.2020.11 2024-05-02T06:51:15Z Abstract Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow. Article in Journal/Newspaper Journal of Glaciology Cambridge University Press Journal of Glaciology 66 257 373 385 |
institution |
Open Polar |
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Cambridge University Press |
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crcambridgeupr |
language |
English |
description |
Abstract Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow. |
format |
Article in Journal/Newspaper |
author |
Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel |
spellingShingle |
Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
author_facet |
Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel |
author_sort |
Heredia, María Belén |
title |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_short |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_full |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_fullStr |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_full_unstemmed |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_sort |
bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
publisher |
Cambridge University Press (CUP) |
publishDate |
2020 |
url |
http://dx.doi.org/10.1017/jog.2020.11 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143020000118 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology volume 66, issue 257, page 373-385 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2020.11 |
container_title |
Journal of Glaciology |
container_volume |
66 |
container_issue |
257 |
container_start_page |
373 |
op_container_end_page |
385 |
_version_ |
1799482940015509504 |