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Extreme fire weather is the major driver of severe bushfires in southeast Australia

Science Bulletin 2022年第6期67卷 655-664,M0004页

作者： 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 UniversityYangling 712100China New South Wales Department of Primary Industries Wagga Wagga Agricultural InstituteWagga Wagga 2650Australia Department of Geography College of ScienceSwansea UniversitySingleton ParkSwansea SA28PPUK College of Land Science and Technology China Agricultural UniversityBeijing 100193China College of Natural Resources and Environment Northwest 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'Environnement CEA-CNRS-UVSQGif sur Yvette F-91191France New South Wales Department of Primary Industries Dubbo 2830Australia New South Wales Department of Primary Industries Armidale 2351Australia School of Environmental and Rural Science University of New EnglandArmidale 2351Australia Hawkesbury Institute for the Environment Western Sydney UniversityPenrith 2751Australia CSIRO Data61 Canberra 2601Australia Python Charmers Pty Ltd Hawthorn 3122Australia Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810008China Climate Change Research Centre University of New South WalesSydney 2052Australia College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China

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.

关键词： Remote sensing Forest fires Climate drivers burnt area modelling Machine learning Southeast Australia

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