No Cover Image

Journal article 643 views 123 downloads

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

Bin Wang, Allan 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

Science Bulletin, Volume: 67, Issue: 6, Pages: 655 - 664

Swansea University Authors: Allan Spessa, Tadas Nikonovas

  • 58376.pdf

    PDF | Version of Record

    This is an open access article under the CC BY license

    Download (1.75MB)

Check full text

DOI (Published version): 10.1016/j.scib.2021.10.001

Abstract

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 fir...

Full description

Published in: Science Bulletin
ISSN: 2095-9273
Published: Elsevier BV 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58376
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2021-10-18T09:01:24Z
last_indexed 2022-04-07T03:31:11Z
id cronfa58376
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-04-06T15:44:17.9954258</datestamp><bib-version>v2</bib-version><id>58376</id><entry>2021-10-18</entry><title>Extreme fire weather is the major driver of severe bushfires in southeast Australia</title><swanseaauthors><author><sid>4cd392e40ebc82bd13c0117d07b28d81</sid><firstname>Allan</firstname><surname>Spessa</surname><name>Allan Spessa</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>940b37dbdcb6896884af0887808b089c</sid><firstname>Tadas</firstname><surname>Nikonovas</surname><name>Tadas Nikonovas</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-10-18</date><deptcode>FGSEN</deptcode><abstract>In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019&#x2212;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&#x2212;2020 on a 0.25&#xB0; 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 linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the Central Pacific El Ni&#xF1;o 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 to model developers working on improved early warning systems for forest fires.</abstract><type>Journal Article</type><journal>Science Bulletin</journal><volume>67</volume><journalNumber>6</journalNumber><paginationStart>655</paginationStart><paginationEnd>664</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2095-9273</issnPrint><issnElectronic/><keywords>Remote sensing; Forest fires; Climate drivers; Burnt area modelling; Machine learning; Southeast Australia</keywords><publishedDay>30</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-03-30</publishedDate><doi>10.1016/j.scib.2021.10.001</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>National Natural Science Foun-dation of China (42088101 and 42030605)</funders><lastEdited>2022-04-06T15:44:17.9954258</lastEdited><Created>2021-10-18T09:59:27.0510989</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Geography</level></path><authors><author><firstname>Bin</firstname><surname>Wang</surname><order>1</order></author><author><firstname>Allan</firstname><surname>Spessa</surname><order>2</order></author><author><firstname>Puyu</firstname><surname>Feng</surname><order>3</order></author><author><firstname>Xin</firstname><surname>Hou</surname><order>4</order></author><author><firstname>Chao</firstname><surname>Yue</surname><order>5</order></author><author><firstname>Jing-Jia</firstname><surname>Luo</surname><order>6</order></author><author><firstname>Philippe</firstname><surname>Ciais</surname><order>7</order></author><author><firstname>Cathy</firstname><surname>Waters</surname><order>8</order></author><author><firstname>Annette</firstname><surname>Cowie</surname><order>9</order></author><author><firstname>Rachael H.</firstname><surname>Nolan</surname><order>10</order></author><author><firstname>Tadas</firstname><surname>Nikonovas</surname><order>11</order></author><author><firstname>Huidong</firstname><surname>Jin</surname><order>12</order></author><author><firstname>Henry</firstname><surname>Walshaw</surname><order>13</order></author><author><firstname>Jinghua</firstname><surname>Wei</surname><order>14</order></author><author><firstname>Xiaowei</firstname><surname>Guo</surname><order>15</order></author><author><firstname>De Li</firstname><surname>Liu</surname><order>16</order></author><author><firstname>Qiang</firstname><surname>Yu</surname><order>17</order></author></authors><documents><document><filename>58376__23788__1e0a72734b1145d1bd4a24bb8914718e.pdf</filename><originalFilename>58376.pdf</originalFilename><uploaded>2022-04-06T15:38:03.8402074</uploaded><type>Output</type><contentLength>1839067</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This is an open access article under the CC BY license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-04-06T15:44:17.9954258 v2 58376 2021-10-18 Extreme fire weather is the major driver of severe bushfires in southeast Australia 4cd392e40ebc82bd13c0117d07b28d81 Allan Spessa Allan Spessa true false 940b37dbdcb6896884af0887808b089c Tadas Nikonovas Tadas Nikonovas true false 2021-10-18 FGSEN 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° 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 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 to model developers working on improved early warning systems for forest fires. Journal Article Science Bulletin 67 6 655 664 Elsevier BV 2095-9273 Remote sensing; Forest fires; Climate drivers; Burnt area modelling; Machine learning; Southeast Australia 30 3 2022 2022-03-30 10.1016/j.scib.2021.10.001 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University National Natural Science Foun-dation of China (42088101 and 42030605) 2022-04-06T15:44:17.9954258 2021-10-18T09:59:27.0510989 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Bin Wang 1 Allan Spessa 2 Puyu Feng 3 Xin Hou 4 Chao Yue 5 Jing-Jia Luo 6 Philippe Ciais 7 Cathy Waters 8 Annette Cowie 9 Rachael H. Nolan 10 Tadas Nikonovas 11 Huidong Jin 12 Henry Walshaw 13 Jinghua Wei 14 Xiaowei Guo 15 De Li Liu 16 Qiang Yu 17 58376__23788__1e0a72734b1145d1bd4a24bb8914718e.pdf 58376.pdf 2022-04-06T15:38:03.8402074 Output 1839067 application/pdf Version of Record true This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title Extreme fire weather is the major driver of severe bushfires in southeast Australia
spellingShingle Extreme fire weather is the major driver of severe bushfires in southeast Australia
Allan Spessa
Tadas Nikonovas
title_short Extreme fire weather is the major driver of severe bushfires in southeast Australia
title_full Extreme fire weather is the major driver of severe bushfires in southeast Australia
title_fullStr Extreme fire weather is the major driver of severe bushfires in southeast Australia
title_full_unstemmed Extreme fire weather is the major driver of severe bushfires in southeast Australia
title_sort Extreme fire weather is the major driver of severe bushfires in southeast Australia
author_id_str_mv 4cd392e40ebc82bd13c0117d07b28d81
940b37dbdcb6896884af0887808b089c
author_id_fullname_str_mv 4cd392e40ebc82bd13c0117d07b28d81_***_Allan Spessa
940b37dbdcb6896884af0887808b089c_***_Tadas Nikonovas
author Allan Spessa
Tadas Nikonovas
author2 Bin Wang
Allan 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
format Journal article
container_title Science Bulletin
container_volume 67
container_issue 6
container_start_page 655
publishDate 2022
institution Swansea University
issn 2095-9273
doi_str_mv 10.1016/j.scib.2021.10.001
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description 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° 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 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 to model developers working on improved early warning systems for forest fires.
published_date 2022-03-30T04:14:51Z
_version_ 1763754001809014784
score 11.014067

Similar Items