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量化非洲火灾季节性变化的驱动因素和可预测性。

Quantifying the drivers and predictability of seasonal changes in African fire.

机构信息

Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA.

Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, TN, USA.

出版信息

Nat Commun. 2020 Jun 9;11(1):2893. doi: 10.1038/s41467-020-16692-w.

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.

摘要

非洲拥有一些对火灾最脆弱的生态系统。在这些火灾多发地区,成功地对火灾活动进行季节性预测仍然是一个挑战,这严重依赖于对火灾演变背后各种驱动机制的深入理解。在这里,我们使用逐步广义平衡反馈评估(SGEFA)和机器学习技术(MLTs)的分析框架,评估非洲火灾的季节性环境驱动因素和可预测性。我们量化了海表温度、土壤湿度和叶面积指数的影响,发现它们通过调节区域燃烧条件和燃料供应来主导火灾季节性变化。与先前确定的大气和社会经济预测因子相比,这些缓慢演变的海洋和陆地预测因子进一步确定了非洲火灾活动的季节性可预测性。我们的 SGEFA-MLT 联合方法能够提前一个月对非洲火灾进行熟练预测,并可推广用于提供区域和全球火灾风险的季节性估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb79/7283213/bc3c597e9cde/41467_2020_16692_Fig1_HTML.jpg

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