Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Quantifying the Drivers and Predictability of Seasonal Changes in African Fire

TitleQuantifying the Drivers and Predictability of Seasonal Changes in African Fire
Publication TypeJournal Article
Year of Publication2020
JournalNature Communications
Volume11
Number2893
Abstract / Summary

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.

URLhttp://dx.doi.org/10.1038/s41467-020-16692-w
DOI10.1038/s41467-020-16692-w
Journal: Nature Communications
Year of Publication: 2020
Volume: 11
Number: 2893
Publication Date: 06/2020

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
Citation:
Yu, Y, J Mao, P Thornton, M Notaro, S Wullschleger, X Shi, F Hoffman, and Y Wang.  2020.  "Quantifying the Drivers and Predictability of Seasonal Changes in African Fire."  Nature Communications 11(2893).  https://doi.org/10.1038/s41467-020-16692-w.