Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model
While performing statistical dynamical simulations for avalanche predetermination, a propagation model must reach a compromise between precise description of the avalanche flow and computation times. Crucial problems are the choice of appropriate distributions describing the variability of the diffe...
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ftinraparis:oai:prodinra.inra.fr:180595 2023-05-15T16:57:09+02:00 Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model Naaim, M. Parent, Éric Ecker, Nicolas 2010 application/pdf http://prodinra.inra.fr/ft/74F4F7C9-2265-41AC-A3E5-E5E765EAB28E http://prodinra.inra.fr/record/180595 https://doi.org/10.3189/002214310793146331 eng eng http://creativecommons.org/licenses/by-nd-nc/1.0/ CC-BY-ND-NC Journal of Glaciology 198 (56), 563-586. (2010) avalanche predetermination;algorithm;impact pressure ARTICLE 2010 ftinraparis https://doi.org/10.3189/002214310793146331 2015-10-30T07:35:35Z While performing statistical dynamical simulations for avalanche predetermination, a propagation model must reach a compromise between precise description of the avalanche flow and computation times. Crucial problems are the choice of appropriate distributions describing the variability of the different inputs/outputs and model identifiability. In this study, a depth-averaged propagation model is used within a hierarchical Bayesian framework. First, the joint posterior distribution is estimated using a sequential Metropolis Hastings algorithm. Details for tuning the estimation algorithm are provided, as well as tests to check convergence. Of particular interest is the calibration of the two coefficients of a Voellmy friction law, with model identifiability ensured by prior information. Second, the point estimates are used to predict the joint distribution of different variables of interest for hazard mapping. Recent developments are employed to compute pressure distributions taking into account the rheology of snow. The different steps of the method are illustrated with a real case study, for which all possible decennial scenarios are simulated. It appears that the marginal distribution of impact pressures is strongly skewed, with possible high values for avalanches characterized by low Froude numbers. Model assumptions and results are discussed. Article in Journal/Newspaper Journal of Glaciology Institut National de la Recherche Agronomique: ProdINRA Hastings ENVELOPE(-154.167,-154.167,-85.567,-85.567) Journal of Glaciology 56 198 563 586 |
institution |
Open Polar |
collection |
Institut National de la Recherche Agronomique: ProdINRA |
op_collection_id |
ftinraparis |
language |
English |
topic |
avalanche predetermination;algorithm;impact pressure |
spellingShingle |
avalanche predetermination;algorithm;impact pressure Naaim, M. Parent, Éric Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
topic_facet |
avalanche predetermination;algorithm;impact pressure |
description |
While performing statistical dynamical simulations for avalanche predetermination, a propagation model must reach a compromise between precise description of the avalanche flow and computation times. Crucial problems are the choice of appropriate distributions describing the variability of the different inputs/outputs and model identifiability. In this study, a depth-averaged propagation model is used within a hierarchical Bayesian framework. First, the joint posterior distribution is estimated using a sequential Metropolis Hastings algorithm. Details for tuning the estimation algorithm are provided, as well as tests to check convergence. Of particular interest is the calibration of the two coefficients of a Voellmy friction law, with model identifiability ensured by prior information. Second, the point estimates are used to predict the joint distribution of different variables of interest for hazard mapping. Recent developments are employed to compute pressure distributions taking into account the rheology of snow. The different steps of the method are illustrated with a real case study, for which all possible decennial scenarios are simulated. It appears that the marginal distribution of impact pressures is strongly skewed, with possible high values for avalanches characterized by low Froude numbers. Model assumptions and results are discussed. |
author2 |
Ecker, Nicolas |
format |
Article in Journal/Newspaper |
author |
Naaim, M. Parent, Éric |
author_facet |
Naaim, M. Parent, Éric |
author_sort |
Naaim, M. |
title |
Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
title_short |
Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
title_full |
Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
title_fullStr |
Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
title_full_unstemmed |
Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
title_sort |
long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
publishDate |
2010 |
url |
http://prodinra.inra.fr/ft/74F4F7C9-2265-41AC-A3E5-E5E765EAB28E http://prodinra.inra.fr/record/180595 https://doi.org/10.3189/002214310793146331 |
long_lat |
ENVELOPE(-154.167,-154.167,-85.567,-85.567) |
geographic |
Hastings |
geographic_facet |
Hastings |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology 198 (56), 563-586. (2010) |
op_rights |
http://creativecommons.org/licenses/by-nd-nc/1.0/ |
op_rightsnorm |
CC-BY-ND-NC |
op_doi |
https://doi.org/10.3189/002214310793146331 |
container_title |
Journal of Glaciology |
container_volume |
56 |
container_issue |
198 |
container_start_page |
563 |
op_container_end_page |
586 |
_version_ |
1766048431333179392 |