Calibration strategies of PDC kinetic energy models and their application to the construction of hazard maps

Aravena, Álvaro; Bevilacqua, Andrea; Vitturi, Mattia de’ Michieli; Ongaro, T. Esposti; Neri, Augusto; Cioni, Raffaello

doi:10.1007/s00445-022-01538-8

Cited by 7 publications

(9 citation statements)

References 116 publications

(128 reference statements)

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“…In contrast to Dade and Huppert (1996), in this study we assume the instantaneous release of a fixed volume of pyroclastic material, as in Neri et al (2015), Bevilacqua (2016), Bevilacqua et al (2017) and Aravena et al (2020Aravena et al ( , 2022. This assumption is considered reasonable because the time scale of the eruption of the PDC volume is shorter than the emplacement time.…”

Section: Model 2: Density Current Dynamics With Particle Depositionmentioning

confidence: 99%

“…More detailed input schemes and calibration approaches could be attempted in further research (Bevilacqua et al, 2019;Patra et al, 2020;Aravena et al, 2022), but these approaches would not be straightforward because of the lack of well-preserved distal deposits. Note that we assume the model inputs consist of an array of independent variables.…”

Section: Input Ranges Based On Expert Judgmentmentioning

confidence: 99%

“…In fact, the dense underflow is thought to be formed by segregation of particles and so it is essentially unknown how initial potential energy is partitioned between the dense and less dense components of the PDC: the effective height H is not likely to be related to overall column height. There would be a similar issue with the H variable in the energy cone formulation, which was not implemented in this study (Bevilacqua et al, 2022;Aravena et al, 2022).…”

Section: Expert Judgment Approach and The Input Sampling Strategymentioning

confidence: 99%

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Assessing minimum pyroclastic density current mass to impact critical infrastructures: example from Aso caldera (Japan)

Bevilacqua

Aravena

Aspinall

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. We describe a method for calculating the probability that a distal geographic location is impacted by a pyroclastic density current (PDC) of a given size, considering the key related uncertainties. Specifically, we evaluate the minimum volume and mass of a PDC generated at the Aso caldera (Japan) that might affect each of five distal infrastructure (marker) sites, with model input parameter uncertainties derived from expert judgment. The 5 marker sites are all located 115–145 km from the caldera; as these lie in well-separated directions, we can test the effects of the different topographic shielding effects in each case. To inform our probabilistic analysis, we apply alternative kinetic energy assessment approaches, i.e., rock avalanche and density current dynamics. In the latter formulation, the minimum mass needed to reach the markers ranges between median values of ∼153×1012 and ∼465×1012 kg (M 7.2–7.7), depending on the site. Rock avalanche dynamics modeling indicates that a ∼3-times greater mass would be required to reach the marker sites with 50 % probability, while the hypothetical scenario of a relatively dilute distal ash cloud would require ∼3-times less mass. We compare our results with the largest recorded Aso eruption, showing that a catastrophic eruption, similar to Aso-4, ≈ M8, would present a significant conditional probability of PDCs reaching the marker sites, in the density current formulation and contingent on uncertainty in the erupted mass and on marker site direction.

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Section: Model 2: Density Current Dynamics With Particle Depositionmentioning

confidence: 99%

Section: Input Ranges Based On Expert Judgmentmentioning

confidence: 99%

Section: Expert Judgment Approach and The Input Sampling Strategymentioning

confidence: 99%

See 1 more Smart Citation

Assessing minimum pyroclastic density current mass to impact critical infrastructures: example from Aso caldera (Japan)

Bevilacqua

Aravena

Aspinall

et al. 2022

Nat. Hazards Earth Syst. Sci.

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show abstract

“…More detailed input schemes and calibration approaches could be attempted in further research (Bevilacqua et al, 2019;Patra et al, 2020;Aravena et al, 2022), but these approaches would not be straightforward because of the lack of well-preserved distal deposits. Note that we assume the model inputs are formed of an array of independent variables.…”

Section: Input Ranges Based On Expert Judgmentmentioning

confidence: 99%

“…In fact, the dense underflow is thought to be formed by segregation of particles and so it is essentially unknown how initial potential energy is partitioned between the dense and less dense components of the PDC: the effective height H is not likely to be related to overall column height. There would be a similar issue with the H variable in the energy cone formulation, which was not implemented in this 415 study (Bevilacqua et al, 2022;Aravena et al, 2022).…”

Section: Expert Judgement Approach and The Input Sampling Strategy 400mentioning

confidence: 99%

Comment on nhess-2022-100

2022

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Influence of the topography of stratovolcanoes on the propagation and channelization of dense pyroclastic density currents analyzed through numerical simulations

Aravena

Roche

2022

Bull Volcanol

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We applied systematically the branching energy cone model to a large (𝑁 = 50) set of stratovolcanoes around the world in order to evaluate the main topographic characteristics that may control the propagation of dense pyroclastic density currents (PDCs). Results indicate that channelization efficiency of a PDC is strongly controlled by the relative scale between flow size and volcano topographic features. Most of the studied stratovolcanoes topographies are able to induce significant PDC channelization in proximal domains, while strong channelization in distal zones is mainly observed for volcanoes with steep flanks, with long, uninterrupted valleys, and with catchments zones of pyroclastic material (i.e. the valleys heads) located near the source. From the statistical analysis of numerical results, we recognize five groups of stratovolcanoes in terms of the mode of interaction between their topographies and dense PDCs: (1) intense channelization through different valleys up to distal domains (e.g. Colima and Peteroa); (2) intense channelization through a single, dominant valley up to distal domains (e.g. Reventador and Mt. St. Helens); (3) intense channelization near the source and moderate distal channelization, frequently involving intertwined drainage networks (e.g. Tungurahua and El Misti); (4) potentially intense channelization only near the source, typically involving flat distal topographies (e.g. Sinabung and Mayon); and (5) weak channelization in proximal domains, resulting in efficient early energy dissipation and thus reduced PDC run-out distance (e.g. Kelut and Akagi). The relevance of this classification lies on the possibility of defining volcanic analogues (defined here as volcanoes that share a suite of topographic characteristics and may be considered comparable to a certain extent) and identifying the main processes that may affect PDC propagation in specific topographic contexts. These aspects are useful for studying poorly documented volcanic edifices and for volcanic hazard assessment. Additionally, we compare this classification with published morphometric characteristics of volcanoes, showing that morphometric parameters such as mean slope of the low flank, irregularity index, ratio of volcano height and basal width, and ratio of crater width and basal width, are useful variables for recognizing the groups we defined. These parameters can be used as rough indicators of the expected interaction patterns between the topography of a given volcano and dense PDCs.

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Calibration strategies of PDC kinetic energy models and their application to the construction of hazard maps

Abstract: Questa versione della pubblicazione è conforme a quanto richiesto dalle politiche dell'editore in materia di copyright. This version of the publication conforms to the publisher's copyright policies.

Cited by 7 publications

References 116 publications

Assessing minimum pyroclastic density current mass to impact critical infrastructures: example from Aso caldera (Japan)

Assessing minimum pyroclastic density current mass to impact critical infrastructures: example from Aso caldera (Japan)

Comment on nhess-2022-100

Influence of the topography of stratovolcanoes on the propagation and channelization of dense pyroclastic density currents analyzed through numerical simulations

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