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...
Published in: | Journal of Glaciology |
---|---|
Main Authors: | , , |
Other Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2010
|
Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600/document https://hal.archives-ouvertes.fr/hal-01197600/file/Eckert.N._1.pdf https://doi.org/10.3189/002214310793146331 |
id |
ftccsdartic:oai:HAL:hal-01197600v1 |
---|---|
record_format |
openpolar |
spelling |
ftccsdartic:oai:HAL:hal-01197600v1 2023-05-15T16:57:28+02:00 Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model Eckert, Nicolas Naaim, Mohamed Parent, Éric Érosion torrentielle, neige et avalanches (UR ETGR (ETNA)) Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF) Mathématiques et Informatique Appliquées (MIA-Paris) AgroParisTech-Institut National de la Recherche Agronomique (INRA) 2010 https://hal.archives-ouvertes.fr/hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600/document https://hal.archives-ouvertes.fr/hal-01197600/file/Eckert.N._1.pdf https://doi.org/10.3189/002214310793146331 en eng HAL CCSD International Glaciological Society info:eu-repo/semantics/altIdentifier/doi/10.3189/002214310793146331 hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600/document https://hal.archives-ouvertes.fr/hal-01197600/file/Eckert.N._1.pdf doi:10.3189/002214310793146331 PRODINRA: 180595 WOS: 000283027700001 info:eu-repo/semantics/OpenAccess ISSN: 0022-1430 Journal of Glaciology https://hal.archives-ouvertes.fr/hal-01197600 Journal of Glaciology, International Glaciological Society, 2010, 56 (198), pp.563-586. ⟨10.3189/002214310793146331⟩ algorithm impact pressure avalanche predetermination [SDV]Life Sciences [q-bio] info:eu-repo/semantics/article Journal articles 2010 ftccsdartic https://doi.org/10.3189/002214310793146331 2021-10-24T01:17:02Z 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 Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Hastings ENVELOPE(-154.167,-154.167,-85.567,-85.567) Journal of Glaciology 56 198 563 586 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
algorithm impact pressure avalanche predetermination [SDV]Life Sciences [q-bio] |
spellingShingle |
algorithm impact pressure avalanche predetermination [SDV]Life Sciences [q-bio] Eckert, Nicolas Naaim, Mohamed Parent, Éric Long-term avalanche hazard assessment with a bayesian depth-averaged propagation model |
topic_facet |
algorithm impact pressure avalanche predetermination [SDV]Life Sciences [q-bio] |
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 |
Érosion torrentielle, neige et avalanches (UR ETGR (ETNA)) Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF) Mathématiques et Informatique Appliquées (MIA-Paris) AgroParisTech-Institut National de la Recherche Agronomique (INRA) |
format |
Article in Journal/Newspaper |
author |
Eckert, Nicolas Naaim, Mohamed Parent, Éric |
author_facet |
Eckert, Nicolas Naaim, Mohamed Parent, Éric |
author_sort |
Eckert, Nicolas |
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 |
publisher |
HAL CCSD |
publishDate |
2010 |
url |
https://hal.archives-ouvertes.fr/hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600/document https://hal.archives-ouvertes.fr/hal-01197600/file/Eckert.N._1.pdf 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 |
ISSN: 0022-1430 Journal of Glaciology https://hal.archives-ouvertes.fr/hal-01197600 Journal of Glaciology, International Glaciological Society, 2010, 56 (198), pp.563-586. ⟨10.3189/002214310793146331⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3189/002214310793146331 hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600 https://hal.archives-ouvertes.fr/hal-01197600/document https://hal.archives-ouvertes.fr/hal-01197600/file/Eckert.N._1.pdf doi:10.3189/002214310793146331 PRODINRA: 180595 WOS: 000283027700001 |
op_rights |
info:eu-repo/semantics/OpenAccess |
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_ |
1766049025577975808 |