Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano

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dc.citation.volume432
dc.contributor.authorScott E
dc.contributor.authorBebbington M
dc.contributor.authorWilson T
dc.contributor.authorKennedy B
dc.contributor.authorLeonard G
dc.date.accessioned2023-11-02T22:16:38Z
dc.date.accessioned2023-11-03T04:55:00Z
dc.date.available2022-10-12
dc.date.available2023-11-02T22:16:38Z
dc.date.available2023-11-03T04:55:00Z
dc.date.issued2022-12
dc.description.abstractTaupō volcano, located within the Taupō Volcanic Zone (TVZ) in the central North Island of Aotearoa-New Zealand, is one of the world's most active silicic caldera systems. Silicic calderas such as Taupō are capable of a broad and complex range of volcanological activity, ranging from minor unrest episodes to large destructive supereruptions. A critical tool for volcanic risk management is eruption forecasting. The Bayesian Event Tree for Eruption Forecasting (BET_EF) is one probabilistic eruption forecasting tool that can be used to produce short-term eruption forecasts for any volcano worldwide. A BET_EF model is developed for Taupō volcano, informed by geologic and historic data. Monitoring parameters for the model were obtained through a structured expert elicitation workshop with 30 of Aotearoa-New Zealand's volcanologists and volcano monitoring scientists. The eruption probabilities output by the BET_EF model for Taupō volcano's 17 recorded unrest episodes (between 1877 and 2019) were examined. We found time-inhomogeneity in the probabilities stemming from both the changes over time in the monitoring network around Taupō volcano and increasing level of past data (number of non-eruptive unrest episodes). We examine the former issue through the lens of the latest episodes, and the latter by re-running the episodes assuming knowledge of all 16 other episodes (calibration to 2021 data). The time variable monitoring network around Taupō volcano and parameter weights had a substantial impact on the estimated probabilities of magmatic unrest and eruption. We also note the need for improved monitoring and data processing at Taupō volcano, the existence of which would prompt updates and therefore refinements in the BET_EF model.
dc.description.confidentialfalse
dc.edition.editionDecember 2022
dc.identifier.citationScott E, Bebbington M, Wilson T, Kennedy B, Leonard G. (2022). Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano. Journal of Volcanology and Geothermal Research. 432.
dc.identifier.doi10.1016/j.jvolgeores.2022.107687
dc.identifier.eissn1872-6097
dc.identifier.elements-typejournal-article
dc.identifier.issn0377-0273
dc.identifier.number107687
dc.identifier.piiS0377027322002189
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69008
dc.languageEnglish
dc.publisherElsevier BV
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0377027322002189
dc.relation.isPartOfJournal of Volcanology and Geothermal Research
dc.rights(c) 2022 The Author/s
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEruption forecasting
dc.subjectBayesian event tree
dc.subjectExpert elicitation
dc.subjectCaldera unrest
dc.subjectTaupō Volcanic Zone
dc.titleDevelopment of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano
dc.typeJournal article
pubs.elements-id457726
pubs.organisational-groupOther
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