Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand.
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| Title: | Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand. |
|---|---|
| Authors: | Dempsey, D. E.1 (AUTHOR) david.dempsey@canterbury.ac.nz, Kempa-Liehr, A. W.2 (AUTHOR), Ardid, A.1 (AUTHOR), Li, A.2 (AUTHOR), Orenia, S.2 (AUTHOR), Singh, J.2 (AUTHOR), Tyler, A. J.2 (AUTHOR), Cronin, S. J.2 (AUTHOR) |
| Source: | Bulletin of Volcanology. Oct2022, Vol. 84 Issue 10, p1-26. 26p. |
| Subject Terms: | *Time series analysis, *Emergency management, *Observatories, *Forecasting, *Volcanoes, *Flood warning systems |
| Geographic Terms: | New Zealand |
| Abstract: | Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 159723948 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dempsey%2C+D%2E+E%2E%22">Dempsey, D. E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> david.dempsey@canterbury.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Kempa-Liehr%2C+A%2E+W%2E%22">Kempa-Liehr, A. W.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ardid%2C+A%2E%22">Ardid, A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+A%2E%22">Li, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Orenia%2C+S%2E%22">Orenia, S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+J%2E%22">Singh, J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tyler%2C+A%2E+J%2E%22">Tyler, A. J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cronin%2C+S%2E+J%2E%22">Cronin, S. J.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Bulletin+of+Volcanology%22">Bulletin of Volcanology</searchLink>. Oct2022, Vol. 84 Issue 10, p1-26. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br />*<searchLink fieldCode="DE" term="%22Observatories%22">Observatories</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Volcanoes%22">Volcanoes</searchLink><br />*<searchLink fieldCode="DE" term="%22Flood+warning+systems%22">Flood warning systems</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+Zealand%22">New Zealand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00445-022-01600-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Time series analysis Type: general – SubjectFull: Emergency management Type: general – SubjectFull: Observatories Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Volcanoes Type: general – SubjectFull: Flood warning systems Type: general – SubjectFull: New Zealand Type: general Titles: – TitleFull: Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dempsey, D. E. – PersonEntity: Name: NameFull: Kempa-Liehr, A. W. – PersonEntity: Name: NameFull: Ardid, A. – PersonEntity: Name: NameFull: Li, A. – PersonEntity: Name: NameFull: Orenia, S. – PersonEntity: Name: NameFull: Singh, J. – PersonEntity: Name: NameFull: Tyler, A. J. – PersonEntity: Name: NameFull: Cronin, S. J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 02588900 Numbering: – Type: volume Value: 84 – Type: issue Value: 10 Titles: – TitleFull: Bulletin of Volcanology Type: main |
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