CONTACT US: Contact info
|
Citation |
Wang B, Spessa AC, Feng P, Hou X, Yue C, Luo JJ, Ciais P, Waters C, Cowie A, Nolan RH, Nikonovas T, Jin H, Walshaw H, Wei J, Guo X, Liu DL, Yu Q. Sci. Bull. (Beijing) 2022; 67(6): 655-664. |
|
Copyright |
(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group) |
|
DOI |
|
PMID |
36546127 |
|
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 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. Language: en |
|
Keywords |
Australia; Humans; Weather; Machine learning; *Fires; *Wildfires; Burnt area modelling; Climate drivers; Forest fires; Forests; Remote sensing; Southeast Australia |