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Prescribed burning effects on savanna fire spread, intensity, and predictability

By Aristides Moustakas and Orestis Davlias

Abstract

Fire is an integral part of the Earth for millennia. Recent wildfires exhibited an unprecedented spatial and temporal extend and their control is beyond national firefighting capabilities. Prescribed or controlled burning treatments are debated as a potential measure for ameliorating the spread and intensity of wildfires. Machine learning analysis using random forests was performed in a spatio-temporal data set comprising a large number of savanna fires across 22 years. Results indicate that controlled fire return interval accounts of 3.5% of fire spread and 3.5% of fire intensity. Manipulating burn seasonality accounted for 5% of fire spread and 6% of fire intensity. While manipulated fire return interval and seasonality moderated both fire spread and intensity, their overall effects were low in comparison with hydrological and climatic variables. Predicting fire spread and intensity has been a poor endeavour thus far and we show that more data of the variables already monitored would not result in higher predictive accuracy. Given that the main driving factors of fire spread are related to hydrological and climatic variables, we suggest investigating further the use of climatic refugia against wildfire

Topics: Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods, Statistics - Applications
Year: 2020
OAI identifier: oai:arXiv.org:2005.07593

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