Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes
Abstract Forecasting future destructive eruptions from re‐awakening volcanoes remains a challenge, mainly due to a lack of previous event data. This sparks a search for similar volcanoes to provide additional information, especially those with better compiled and understood event records. However, w...
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2021GL096715 |
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| _version_ | 1832591229362110464 |
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| author | Ting Wang Mark Bebbington Shane Cronin Joel Carman |
| author_facet | Ting Wang Mark Bebbington Shane Cronin Joel Carman |
| author_sort | Ting Wang |
| collection | DOAJ |
| description | Abstract Forecasting future destructive eruptions from re‐awakening volcanoes remains a challenge, mainly due to a lack of previous event data. This sparks a search for similar volcanoes to provide additional information, especially those with better compiled and understood event records. However, we show that some of the most obviously geologically comparable volcanoes have differing statistical occurrence patterns. Using such matches produces large forecasting uncertainties. We created a statistical tool to identify and test the compatibility of potential analogue volcanoes based on repose‐time characteristics from world‐wide datasets. Selecting analogue volcanoes with compatible behavior for factors being forecast, such as repose time, significantly reduces forecasting uncertainties. Applying this tool to Tongariro volcano (NZ), there is a 5% probability for a Volcanic Explosivity Index (VEI) ≥ 3 explosive eruption in the next 50 years. Using pre‐historic geological records of a smaller available set of analogs, we forecast a 1% probability of a VEI ≥ 4 eruption in the next 50 years. |
| format | Article |
| id | doaj-art-506f69697d2242558bc20f04403024e2 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-506f69697d2242558bc20f04403024e22025-01-22T14:38:16ZengWileyGeophysical Research Letters0094-82761944-80072022-06-014912n/an/a10.1029/2021GL096715Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal ProcessesTing Wang0Mark Bebbington1Shane Cronin2Joel Carman3Department of Mathematics and Statistics University of Otago Dunedin New ZealandSchool of Mathematical and Computational Sciences, and School of Agriculture and Environment Massey University Palmerston North New ZealandSchool of Environment University of Auckland Auckland New ZealandDepartment of Mathematics and Statistics University of Otago Dunedin New ZealandAbstract Forecasting future destructive eruptions from re‐awakening volcanoes remains a challenge, mainly due to a lack of previous event data. This sparks a search for similar volcanoes to provide additional information, especially those with better compiled and understood event records. However, we show that some of the most obviously geologically comparable volcanoes have differing statistical occurrence patterns. Using such matches produces large forecasting uncertainties. We created a statistical tool to identify and test the compatibility of potential analogue volcanoes based on repose‐time characteristics from world‐wide datasets. Selecting analogue volcanoes with compatible behavior for factors being forecast, such as repose time, significantly reduces forecasting uncertainties. Applying this tool to Tongariro volcano (NZ), there is a 5% probability for a Volcanic Explosivity Index (VEI) ≥ 3 explosive eruption in the next 50 years. Using pre‐historic geological records of a smaller available set of analogs, we forecast a 1% probability of a VEI ≥ 4 eruption in the next 50 years.https://doi.org/10.1029/2021GL096715 |
| spellingShingle | Ting Wang Mark Bebbington Shane Cronin Joel Carman Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes Geophysical Research Letters |
| title | Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes |
| title_full | Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes |
| title_fullStr | Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes |
| title_full_unstemmed | Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes |
| title_short | Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes |
| title_sort | forecasting eruptions at poorly known volcanoes using analogs and multivariate renewal processes |
| url | https://doi.org/10.1029/2021GL096715 |
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