Climate change, fire return intervals and the growing risk of permanent forest loss in boreal Eurasia

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Arden L Burrell, Qiaoqi Sun, Robert Baxter, Elena A. Kukavskaya, Sergey Zhila, Tatiana Shestakova, Brendan M. Rogers, Kirsten Barrett


Climate change has driven an increase in the frequency and severity of fires in Eurasian boreal forests. A growing number of field studies have linked the change in fire regime to post-fire recruitment failure and permanent forest loss. In this study we used four burnt area and two forest loss datasets to calculate the landscape-scale fire return interval (FRI) and associated risk of permanent forest loss. We then used machine learning to predict how the FRI will change under a high emissions scenario (SSP3-7.0) by the end of the century. We found that there is currently 133 000 km2 at high, or extreme, risk of fire-induced forest loss, with a further 3 M km2 at risk by the end of the century. This has the potential to degrade or destroy some of the largest remaining intact forests in the world, negatively impact the health and economic wellbeing of people living in the region, as well as accelerate global climate change.



Climate, Environmental Indicators and Impact Assessment, Environmental Monitoring, Other Environmental Sciences, Physical and Environmental Geography, Remote Sensing, Spatial Science


Burnt Area, Recruitment Failure, boreal forest, Siberia, machine learning, remote sensing, wildfire, Forest Loss


Published: 2021-11-15 11:48

Last Updated: 2021-11-15 16:48


CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
The authors declare no competing interests.

Data Availability (Reason not available):
All datasets used in this study are publicly available and can be accessed from their original creators.

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