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 burned 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 are currently 133,000 km2 forest 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.
Keywords: Boreal forest; Fire return interval; Forest loss; Machine learning; Post fire recruitment failure; Wildfire.
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