Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems
Preprint
Abstract
Fire is an integral part of the Earth for millennia. Several recent wildfires have exhibited an unprecedented spatial and temporal extent 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 exhibited a feature importance of 3.5% regarding fire spread rate and 3.5% regarding fire intensity. Manipulating burn seasonality showed a feature importance of 5% regarding fire spread rate and 6% regarding fire intensity. While manipulated fire return interval and seasonality moderated both fire spread rate and intensity, their overall effects were low in comparison with meteorological (hydrological and climatic) variables. Predicting fire spread rate 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.Keywords
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