Aplicación de modelos numéricos de "aguas someras" para el análisis de peligros de flujos volcánicos: El caso de Titan2D y volcán Turrialba (Costa Rica)

Charbonnier, Sylvain; Palma, José Luis; Ogburn, S. E.

doi:10.15517/rgac.v0i52.19021

Cited by 2 publications

(2 citation statements)

References 34 publications

(73 reference statements)

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“…(H/L) also informs computational flow models that use a Coulomb friction law, including TITAN2D (Patra et al, 2005), which have built upon the work of Savage & Hutter (1989), who used Coulomb friction laws in conjunction with depth-averaged equations for mass and momentum. The Heim coefficient can therefore provide a guideline for choosing appropriate basal friction input angles for different flow volumes for TITAN2D (Ogburn, 2008(Ogburn, , 2014Charbonnier & Gertisser, 2012;Charbonnier et al, 2015).…”

Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

Ogburn

Berger²,

Calder³

et al. 2016

SIV

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In volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

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Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

Ogburn

Berger²,

Calder³

et al. 2016

SIV

View full text Add to dashboard Cite

show abstract

“…The Heim coefficient can therefore provide a guideline for choosing appropriate basal friction input angles for different flow volumes for TITAN2D (Ogburn, 2008(Ogburn, , 2014Charbonnier & Gertisser, 2012;Charbonnier et al, 2015).…”

Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

Untitled

2016

SIV

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Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics, Statistics in Volcanology 2.1 : 1 − 26. DOI: http://dx.doi.org/10.5038/2163-338X.2.1Authors retain copyright of their material under a Creative Commons Non-Commercial Attribution 3.0 License. Ogburn et al.Pooling strength among limited data using hierarchical Bayesian analysis 2 AbstractIn volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

show abstract

Aplicación de modelos numéricos de "aguas someras" para el análisis de peligros de flujos volcánicos: El caso de Titan2D y volcán Turrialba (Costa Rica)

Cited by 2 publications

References 34 publications

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

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