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Wildfires in the Arctic and tropical biomes: what is the relative role of climate?

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Abstract

This study seeks to use machine learning to investigate the role of meteorological and climate variables on wildfire occurrence in the Arctic and the global tropical forests biomes. Using monthly fire counts observed by the MODIS satellites in combination with temperature and precipitation data, indices of large-scale atmospheric and oceanic anomalies (teleconnections), and global temperature anomalies (GT), a combined wavelet-machine learning approach is developed to identify drivers of variability in global wildfire counts on a monthly scale. Precipitation and temperature are found to be the most important predictors of wildfire count in all regions. In addition, the Atlantic Multidecadal Oscillation and GT are identified as the two major drivers of wildfire occurrence. Results indicate that the record-breaking 2019 wildfires in the Asian Arctic can be attributed to unusually warm and dry weather, while the recent increase in wildfires in Amazonia rather can be attributed to regional human activities.

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Data used in this research are publically available through references listed throughout the manuscript.

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Authors and Affiliations

  1. Department of Geography, University of Florida, 3141 Turlington Hall, 330 Newell Dr., Gainesville, FL, 32611, USA

    Johanna Engström & David Keellings

  2. Center for Complex Hydrosystems Research (CCHR), The University of Alabama, Tuscaloosa, USA

    Johanna Engström, Peyman Abbaszadeh, Proloy Deb & Hamid Moradkhani

  3. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, USA

    Peyman Abbaszadeh, Proloy Deb & Hamid Moradkhani

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  1. Johanna Engström
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Engström, J., Abbaszadeh, P., Keellings, D. et al. Wildfires in the Arctic and tropical biomes: what is the relative role of climate?. Nat Hazards 114, 1901–1914 (2022). https://doi.org/10.1007/s11069-022-05452-2

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