Abstract
Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia, and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly 2001–2019 satellite-based fire occurrence dataset at a 0.25° spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian fire weather index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This improvement shows the potential of developing reliable data-driven prediction models for regions with a high quality fire occurrence record, and the limitation of using satellite-based fire occurrence data in regions subject to small fires not picked up by satellites. We conclude that data-driven fire prediction models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and its compound predictors can further be used to assess potential changes in fire danger probability under future climate scenarios.
Highlights
IntroductionBoreal ecosystems, covering large parts of northern North America and Eurasia, comprise one of the world’s most extensive biomes (Balshi et al, 2009)
A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly 2001–2019 satellite-based fire occurrence dataset at a 0.25◦ spatial grid as the target variable
Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This improvement shows the potential of developing reliable data-driven prediction models for regions with a high quality fire occurrence record, and the limitation of using satellite-based fire occurrence data in regions subject to small fires not picked up by satellites
Summary
Boreal ecosystems, covering large parts of northern North America and Eurasia, comprise one of the world’s most extensive biomes (Balshi et al, 2009). In the boreal region, which stores approx. Boreal fires emit up to 9% of global fire carbon emissions and 15% of global fire methane emissions (Van der Werf et al, 2017; Flannigan et al, 2009). The fire season length, fire frequency and burned area have increased in many parts of the boreal region, and these changes have been linked to climate change (Tomshin and Solovyev, 2021; Feurdean et al, 2020; Hanes et al, 2019; Balshi et al, 2009; Kasischke and Turetsky, 2006). Improved knowledge about boreal fires and their occurrence is of 25 high importance, both in the current and in future projected climates.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
