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
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...
| Published in: | Science Bulletin |
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| ISSN: | 2095-9273 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58376 |
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| 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 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. |
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| Keywords: |
Remote sensing; Forest fires; Climate drivers; Burnt area modelling; Machine learning; Southeast Australia |
| College: |
Faculty of Science and Engineering |
| Funders: |
National Natural Science Foun-dation of China (42088101 and 42030605) |
| Issue: |
6 |
| Start Page: |
655 |
| End Page: |
664 |