Informal Lunchtime Seminar

Mon, Dec 16, 2019, 12:00 pm to 1:00 pm

The tropical and subtropical regions of Africa contain some of the most vulnerable ecosystems to fires. Successful prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on an in-depth understanding of various driving mechanisms that underlie fire evolution. Here, we assess the seasonal environmental drivers and predictability of fire variability across Africa using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and Machine Learning Techniques (MLTs). The slowly evolving sea–surface temperatures, soil moisture, and leaf area index were quantified and, when compared with previously identified climatic and socioeconomic predictors, found to be significant contributors to seasonal predictability of African fire variability. The dominant role of major oceanic and terrestrial drivers was associated with their evident influence on the regional burning conditions and fuel supply at seasonal-to-interannual timescales. Our results indicate that land-ocean-atmosphere interactions are key contributors to seasonal predictability of African fire activity, and the combined SGEFA-MLT approach can be further generalized to provide seasonal overviews of regional and global fire risk.

Location: 
Sayre Hall Conference Room 312
Speaker(s): 

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