The frequency of explosive volcanic eruptions in Southeast Asia

Whelley, P. L.; Newhall, Christopher G.; Bradley, Kyle

doi:10.1007/s00445-014-0893-8

Cited by 85 publications

(124 citation statements)

References 28 publications

(38 reference statements)

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“…Map of Southeast Asia showing the location of Holocene active volcanoes (grey triangles; GVP), large, >5 km diameter calderas (black dots; Whelley at al ., ), the sources of VEI 6–8 eruptions for which geochemical data are available (coloured symbols), and marine cores discussed in the text (circles refer to those studied by Ninkovich (), the star is ODP‐758, pluses are those studied by Salisbury et al . (), and triangles are cores around the Philippines).…”

Section: Introductionmentioning

confidence: 97%

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Timing, magnitude and geochemistry of major Southeast Asian volcanic eruptions: identifying tephrochronologic markers

Maisonneuve

Bergal‐Kuvikas

2019

J Quaternary Science

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We review the current knowledge about Southeast Asian volcanoes and their eruption histories, and focus on identifying tephrochronologic markers representing major explosive eruptions in order to further future palaeoclimate and volcanological studies. Forty-one volcanic edifices in Southeast Asia have been classified as large calderas by Whelley et al. (2015) and thus have, or are likely to have, produced large explosive eruptions with a Volcanic Explosivity Index (VEI) of 6-8. Unfortunately, only 20 such eruptions have known ages, spanning from 1.2 Ma to 1991 AD, and fewer have geochemical data that can be used for tephrostratigraphic correlations. Volcanic products from different geodynamic regions and different sources can generally be distinguished on major element plots (e.g. K 2 O versus CaO) of matrix glass composition. However, the distinction of multiple eruptions from the same source often requires additional data such as trace element compositions of matrix glass and/or mineral compositions. Biotite, but also magnetite compositions (MgO and TiO 2 content in particular) appear to be very discriminating. Up to nine tuffs in addition to the three to four Toba tuffs can be utilised as widespread tephrochronologic markers and span a range from 1.2 to 1.6 Ma to recent. As only a few Holocene major eruptions have been well characterised and dated, many large calderas are still unstudied, and many distal tephra layers are still lacking a source, more tephrochronologic markers can certainly be defined in the future.

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Section: Introductionmentioning

confidence: 97%

“…Whelley et al . () identified 41 large, >5 km diameter calderas in Southeast Asia, suggesting that more such markers could be defined (Fig. ).…”

Section: Introductionmentioning

confidence: 99%

Timing, magnitude and geochemistry of major Southeast Asian volcanic eruptions: identifying tephrochronologic markers

Maisonneuve

Bergal‐Kuvikas

2019

J Quaternary Science

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“…3A) than for stratovolcanoes (a = 2.63; Fig. 3B), which is expected given that calderas are associated with larger volcanic eruptions (Brown et al, 2014;Cashman and Giordano, 2014;Whelley et al, 2015). This gives us confidence that the methodology correctly characterizes the volcanic record, and allows us to investigate different volcanic arcs (Figs.…”

Section: For Explanation Of Observation Window and Parameters) A: Fomentioning

confidence: 55%

Regional variability in the frequency and magnitude of large explosive volcanic eruptions

Sheldrake

Caricchi

2017

Geology

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Quantifying the frequency at which volcanic eruptions of different size occurs is important for hazard assessment. Volcanic records can be used to estimate the recurrence rate of large-magnitude eruptions (magnitude ≥4), but recording biases that impact data completeness complicate analysis. To overcome these biases, we conceptualize the volcanic record as a series of individual and unique time series associated by a common behavior. Thus, we approach issues of completeness on a volcano-by-volcano basis and use a hierarchical Bayesian approach to characterize the common frequency-magnitude (f-M) behavior for different groups of volcanoes. We identify variations in the f-M relationship between different volcano types and between different volcanic arcs. By accounting for systematic under-recording in the volcanic record, we also calculate the global recurrence rates for large-magnitude eruptions during the Holocene, which are similar to previous estimates. However, higher recurrence rates for smaller-magnitude events are observed, which is a result of our adjustments for data completeness. Quantifying how the f-M relationship varies between different groups of volcanoes provides an opportunity to understand how the tectonic setting influences f-M behavior, which is important to quantify long-term regional volcanic hazard.

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“…Indeed, Siebert et al (2010) posit that poorly known, thickly vegetated, long-quiescent volcanoes that have had no historical activity ... may be the most dangerous of all. The record of activity at any given volcano may be incomplete or heavily biased due to inadequate or differential preservation and exposure of the deposits, or the history of nearby human settlement (e.g., Crosweller et al, 2012;Brown et al, 2014;Kiyosugi et al, 2015;Whelley et al, 2015). Other practical issues, such as accessibility and remoteness (e.g., Whelley et al, 2015), also hinder investigation and therefore influence data completeness.…”

Section: Introductionmentioning

confidence: 99%

“…The record of activity at any given volcano may be incomplete or heavily biased due to inadequate or differential preservation and exposure of the deposits, or the history of nearby human settlement (e.g., Crosweller et al, 2012;Brown et al, 2014;Kiyosugi et al, 2015;Whelley et al, 2015). Other practical issues, such as accessibility and remoteness (e.g., Whelley et al, 2015), also hinder investigation and therefore influence data completeness. In many cases, scientific interest in a given system is driven by significant observable volcanic activity, while small magnitude or effusive activity is often poorly recorded (Deligne et al, 2010;Furlan, 2010;Siebert et al, 2010;Crosweller et al, 2012;Brown et al, 2014).…”

Section: Introductionmentioning

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.

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The frequency of explosive volcanic eruptions in Southeast Asia

Cited by 85 publications

References 28 publications

Timing, magnitude and geochemistry of major Southeast Asian volcanic eruptions: identifying tephrochronologic markers

Timing, magnitude and geochemistry of major Southeast Asian volcanic eruptions: identifying tephrochronologic markers

Regional variability in the frequency and magnitude of large explosive volcanic eruptions

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