Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/58239
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dc.contributor.authorWang, Binen
dc.contributor.authorSpessa, Allan Cen
dc.contributor.authorFeng, Puyuen
dc.contributor.authorHou, Xinen
dc.contributor.authorYue, Chaoen
dc.contributor.authorLuo, Jing-Jiaen
dc.contributor.authorCiais, Philippeen
dc.contributor.authorWaters, Cathyen
dc.contributor.authorCowie, Annetteen
dc.contributor.authorNolan, Rachael Hen
dc.contributor.authorNikonovas, Tadasen
dc.contributor.authorJin, Huidongen
dc.contributor.authorWalshaw, Henryen
dc.contributor.authorWei, Jinghuaen
dc.contributor.authorGuo, Xiaoweien
dc.contributor.authorLiu, De Lien
dc.contributor.authorYu, Qiangen
dc.date.accessioned2024-04-10T02:38:43Z-
dc.date.available2024-04-10T02:38:43Z-
dc.date.issued2022-03-30-
dc.identifier.citationScience Bulletin, 67(6), p. 655-664en
dc.identifier.issn2095-9281en
dc.identifier.issn2095-9273en
dc.identifier.urihttps://hdl.handle.net/1959.11/58239-
dc.description.abstract<p>In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25<sup>0</sup> grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.</p>en
dc.languageenen
dc.publisherZhongguo Kexue Zazhishe,Science in China Pressen
dc.relation.ispartofScience Bulletinen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleExtreme fire weather is the major driver of severe bushfires in southeast Australiaen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.scib.2021.10.001en
dcterms.accessRightsUNE Greenen
dc.subject.keywordsSoutheast Australiaen
dc.subject.keywordsForest firesen
dc.subject.keywordsClimate driversen
dc.subject.keywordsBurnt area modellingen
dc.subject.keywordsMachine learningen
dc.subject.keywordsMultidisciplinary Sciencesen
dc.subject.keywordsScience & Technology - Other Topicsen
dc.subject.keywordsRemote sensingen
local.contributor.firstnameBinen
local.contributor.firstnameAllan Cen
local.contributor.firstnamePuyuen
local.contributor.firstnameXinen
local.contributor.firstnameChaoen
local.contributor.firstnameJing-Jiaen
local.contributor.firstnamePhilippeen
local.contributor.firstnameCathyen
local.contributor.firstnameAnnetteen
local.contributor.firstnameRachael Hen
local.contributor.firstnameTadasen
local.contributor.firstnameHuidongen
local.contributor.firstnameHenryen
local.contributor.firstnameJinghuaen
local.contributor.firstnameXiaoweien
local.contributor.firstnameDe Lien
local.contributor.firstnameQiangen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailacowie4@une.edu.auen
local.profile.emaildliu@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeChinaen
local.format.startpage655en
local.format.endpage664en
local.peerreviewedYesen
local.identifier.volume67en
local.identifier.issue6en
local.access.fulltextYesen
local.contributor.lastnameWangen
local.contributor.lastnameSpessaen
local.contributor.lastnameFengen
local.contributor.lastnameHouen
local.contributor.lastnameYueen
local.contributor.lastnameLuoen
local.contributor.lastnameCiaisen
local.contributor.lastnameWatersen
local.contributor.lastnameCowieen
local.contributor.lastnameNolanen
local.contributor.lastnameNikonovasen
local.contributor.lastnameJinen
local.contributor.lastnameWalshawen
local.contributor.lastnameWeien
local.contributor.lastnameGuoen
local.contributor.lastnameLiuen
local.contributor.lastnameYuen
dc.identifier.staffune-id:acowie4en
dc.identifier.staffune-id:dliuen
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local.identifier.unepublicationidune:1959.11/58239en
dc.identifier.academiclevelAcademicen
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local.title.maintitleExtreme fire weather is the major driver of severe bushfires in southeast Australiaen
local.relation.fundingsourcenoteThis work was supported by the National Natural Science Foundation of China (42088101 and 42030605). Allan Spessa and Tadas Nikonovas appreciate support from the research project: Towards an Operational Fire Early Warning System for Indonesia (TOFEWSI). The TOFEWSI project was funded from October 2017–October 2021 through the UK’s National Environment Research Council/Newton Fund on behalf of the UK Research & Innovation (NE/P014801/1) (UK Principal Investigator: Allan Spessa) (https://tofewsi.github.io/). Xiaowei Guo receives financial support from the Natural Science Foundation of Qinghai (2021-HZ-811).en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorWang, Binen
local.search.authorSpessa, Allan Cen
local.search.authorFeng, Puyuen
local.search.authorHou, Xinen
local.search.authorYue, Chaoen
local.search.authorLuo, Jing-Jiaen
local.search.authorCiais, Philippeen
local.search.authorWaters, Cathyen
local.search.authorCowie, Annetteen
local.search.authorNolan, Rachael Hen
local.search.authorNikonovas, Tadasen
local.search.authorJin, Huidongen
local.search.authorWalshaw, Henryen
local.search.authorWei, Jinghuaen
local.search.authorGuo, Xiaoweien
local.search.authorLiu, De Lien
local.search.authorYu, Qiangen
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2022en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/31240d20-9fa8-412e-8cae-9c3d2a79c9f8en
local.subject.for20204101 Climate change impacts and adaptationen
local.subject.seo2020TBDen
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Appears in Collections:Journal Article
School of Environmental and Rural Science
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