In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegeta-tion 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 poten-tially 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 for-ests for 2001-2020 on a 0.25° grid based on several biophysical parameters,notably fire weather and veg-etation 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 Nino Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmen-tal 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.

Bin Wang;Allan C.Spessa;Puyu Feng;Xin Hou;Chao Yue;Jing-Jia Luo;Philippe Ciais;Cathy Waters;Annette Cowie;Rachael H.Nolan;Tadas Nikonovas;Huidong Jin;Henry Walshaw;Jinghua Wei;Xiaowei Guo;De Li Liu;Qiang Yu

State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Northwest A&F University,Yangling 712100,China;New South Wales Department of Primary Industries,Wagga Wagga Agricultural Institute,Wagga Wagga 2650,Australia;Department of Geography,College of Science,Swansea University,Singleton Park,Swansea SA2 8PP,UK;College of Land Science and Technology,China Agricultural University,Beijing 100193,China;College of Natural Resources and Environment,Northwest A&F University,Yangling 712100,China;Institute for Climate and Application Research(ICAR)/Key Laboratory of Meteorological Disaster of Ministry of Education(KLME),Nanjing University of Information Science and Technology,Nanjing 210044,China;Laboratoire des Sciences du Climat et de l'Environnement,CEA-CNRS-UVSQ,Gif sur Yvette F-91191,France;New South Wales Department of Primary Industries,Dubbo 2830,Australia;New South Wales Department of Primary Industries,Armidale 2351,Australia;School of Environmental and Rural Science,University of New England,Armidale 2351,Australia;Hawkesbury Institute for the Environment,Western Sydney University,Penrith 2751,Australia;CSIRO Data61,Canberra 2601,Australia;Python Charmers Pty Ltd,Hawthorn 3122,Australia;Key Laboratory of Adaptation and Evolution of Plateau Biota,Northwest Institute of Plateau Biology,Chinese Academy of Sciences,Xining 810008,China;Climate Change Research Centre,University of New South Wales,Sydney 2052,Australia;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China

科学通报（英文版）

[1]Bin Wang;Allan C.Spessa;Puyu Feng;Xin Hou;Chao Yue;Jing-Jia Luo;Philippe Ciais;Cathy Waters;Annette Cowie;Rachael H.Nolan;Tadas Nikonovas;Huidong Jin;Henry Walshaw;Jinghua Wei;Xiaowei Guo;De Li Liu;Qiang Yu-.Extreme fire weather is the major driver of severe bushfires in southeast Australia)[J].科学通报（英文版）,2022(06):655-664

bushfires

Extreme,weather,major,driver,severe,southeast,proportion,area,that,burns,typical,season,less,than,other,vegeta,types,However,austral,spring,summer,was,exception,over,four,times,previous,maximum,burnt,Australian,temperate,forests,Temperate,have,extensive,socio,economic,human,health,greenhouse,gas,emissions,biodiversity,impacts,due,high,intensities,robust,model,identifies,driving,factors,relates,thresholds,activity,regional,scales,would,help,land,managers,fighting,agencies,prepare,poten,tially,hazardous,Here,developed,machine,learning,diagnostic,quantify,nonlinear,relationships,between,monthly,biophysical,grid,several,parameters,notably,etation,productivity,Our,explained,variation,We,identified,dynamics,were,primarily,controlled,by,extreme,which,mainly,linked,fluctuations,Southern,Annular,Mode,SAM,Indian,Ocean,Dipole,IOD,relatively,smaller,contribution,from,central,Pacific,El,Nino,Oscillation,ENSO,environmen,tal,covariates,can,provide,useful,guidance,decision,makers,who,preparations,upcoming,developers,working,improved,early,warning,systems

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