Extreme fire weather is the major driver of severe bushfires in southeast Australia
极端火灾天气是导致澳大利亚东南部森林大火的主要原因作者机构：State Key Laboratory of Soil Erosion and Dryland Farming on the Loess PlateauNorthwest A&F UniversityYangling 712100China New South Wales Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga Wagga 2650Australia Department of GeographyCollege of ScienceSwansea UniversitySingleton ParkSwansea SA28PPUK College of Land Science and TechnologyChina Agricultural UniversityBeijing 100193China College of Natural Resources and EnvironmentNorthwest A&F UniversityYangling 712100China Institute for Climate and Application Research(ICAR)/Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)Nanjing University of Information Science and TechnologyNanjing 210044China Laboratoire des Sciences du Climat et de l'EnvironnementCEA-CNRS-UVSQGif sur Yvette F-91191France New South Wales Department of Primary IndustriesDubbo 2830Australia New South Wales Department of Primary IndustriesArmidale 2351Australia School of Environmental and Rural ScienceUniversity of New EnglandArmidale 2351Australia Hawkesbury Institute for the EnvironmentWestern Sydney UniversityPenrith 2751Australia CSIRO Data61Canberra 2601Australia Python Charmers Pty LtdHawthorn 3122Australia Key Laboratory of Adaptation and Evolution of Plateau BiotaNorthwest Institute of Plateau BiologyChinese Academy of SciencesXining 810008China Climate Change Research CentreUniversity of New South WalesSydney 2052Australia College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 100049China
出 版 物：《Science Bulletin》 (科学通报（英文版）)
年 卷 期：2022年第67卷第6期
页 面：655-664,M0004页
核心收录：
基 金：supported by the National Natural Science Foundation of China(42088101 and 42030605) 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 InvestigatorAllan Spessa)(https//tofewsi.github.io/) financial support from the Natural Science Foundation of Qinghai(2021-HZ-811)
主 题：Remote sensing Forest fires Climate drivers Burnt area modelling Machine learning Southeast Australia
摘 要：In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation ***,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian 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 ***,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°grid based on several biophysical parameters,notably fire weather and vegetation *** model explained over 80%of the variation in the burnt *** 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.
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