Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images
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 increa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2020-06-01
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| Series: | Journal of Glaciology |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0022143020000118/type/journal_article |
| _version_ | 1827994424959827968 |
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| author | María Belén Heredia Nicolas Eckert Clémentine Prieur Emmanuel Thibert |
| author_facet | María Belén Heredia Nicolas Eckert Clémentine Prieur Emmanuel Thibert |
| author_sort | María Belén Heredia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T04:40:43Z |
| format | Article |
| id | doaj.art-656568619f0146d288323f4ac5d2bf41 |
| institution | Directory Open Access Journal |
| issn | 0022-1430 1727-5652 |
| language | English |
| last_indexed | 2024-04-10T04:40:43Z |
| publishDate | 2020-06-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Glaciology |
| spelling | doaj.art-656568619f0146d288323f4ac5d2bf412023-03-09T12:40:55ZengCambridge University PressJournal of Glaciology0022-14301727-56522020-06-016637338510.1017/jog.2020.11Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric imagesMaría Belén Heredia0Nicolas Eckert1Clémentine Prieur2Emmanuel Thibert3Univ. Grenoble Alpes, INRAE, UR ETGR, Grenoble, FranceUniv. Grenoble Alpes, INRAE, UR ETGR, Grenoble, FranceUniv. Grenoble Alpes, CNRS, Inria, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), LJK, 38000Grenoble, FranceUniv. Grenoble Alpes, INRAE, UR ETGR, Grenoble, FrancePhysically-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.https://www.cambridge.org/core/product/identifier/S0022143020000118/type/journal_articleAvalanchesglaciological instruments and methodsglaciological natural hazards |
| spellingShingle | María Belén Heredia Nicolas Eckert Clémentine Prieur Emmanuel Thibert Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images Journal of Glaciology Avalanches glaciological instruments and methods glaciological natural hazards |
| title | 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_short | 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 |
| topic | Avalanches glaciological instruments and methods glaciological natural hazards |
| url | https://www.cambridge.org/core/product/identifier/S0022143020000118/type/journal_article |
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