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Title: Quantifying the drivers and predictability of seasonal changes in African fire

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.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2];  [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [4]; ORCiD logo [5]
  1. Princeton Univ., NJ (United States);
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Wisconsin, Madison, WI (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  5. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1633166
Grant/Contract Number:  
AC05-00OR22725; SC0012534
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Climate sciences; environmental sciences; fire ecology; projection and prediction

Citation Formats

Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., and Wang, Yaoping. Quantifying the drivers and predictability of seasonal changes in African fire. United States: N. p., 2020. Web. doi:10.1038/s41467-020-16692-w.
Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., & Wang, Yaoping. Quantifying the drivers and predictability of seasonal changes in African fire. United States. doi:https://doi.org/10.1038/s41467-020-16692-w
Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., and Wang, Yaoping. Tue . "Quantifying the drivers and predictability of seasonal changes in African fire". United States. doi:https://doi.org/10.1038/s41467-020-16692-w. https://www.osti.gov/servlets/purl/1633166.
@article{osti_1633166,
title = {Quantifying the drivers and predictability of seasonal changes in African fire},
author = {Yu, Yan and Mao, Jiafu and Thornton, Peter E. and Notaro, Michael and Wullschleger, Stan D. and Shi, Xiaoying and Hoffman, Forrest M. and Wang, Yaoping},
abstractNote = {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.},
doi = {10.1038/s41467-020-16692-w},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {2020},
month = {6}
}

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