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Quantifying the drivers and predictability of seasonal changes in African fire

Author

Listed:
  • Yan Yu

    (Princeton University)

  • Jiafu Mao

    (Environmental Sciences Division and Climate Change Science Institute)

  • Peter E. Thornton

    (Environmental Sciences Division and Climate Change Science Institute)

  • Michael Notaro

    (University of Wisconsin-Madison)

  • Stan D. Wullschleger

    (Environmental Sciences Division and Climate Change Science Institute)

  • Xiaoying Shi

    (Environmental Sciences Division and Climate Change Science Institute)

  • Forrest M. Hoffman

    (Computational Sciences and Engineering Division and Climate Change Science Institute
    University of Tennessee)

  • Yaoping Wang

    (University of Tennessee)

Abstract

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

Suggested Citation

  • Yan Yu & Jiafu Mao & Peter E. Thornton & Michael Notaro & Stan D. Wullschleger & Xiaoying Shi & Forrest M. Hoffman & Yaoping Wang, 2020. "Quantifying the drivers and predictability of seasonal changes in African fire," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16692-w
    DOI: 10.1038/s41467-020-16692-w
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    Cited by:

    1. Yan Yu & Jiafu Mao & Stan D. Wullschleger & Anping Chen & Xiaoying Shi & Yaoping Wang & Forrest M. Hoffman & Yulong Zhang & Eric Pierce, 2022. "Machine learning–based observation-constrained projections reveal elevated global socioeconomic risks from wildfire," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Zhangwen Su & Lujia Zheng & Sisheng Luo & Mulualem Tigabu & Futao Guo, 2021. "Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1317-1345, August.

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