Prescribed burning effects on savanna fire spread, intensity, and predictability
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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 seasonality accounted for 5 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 wildfires
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