Quantifying the Importance of Antecedent Fuel-Related Vegetation Properties for Burnt Area using Random Forests

Abstract

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month, and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. We showed that the inclusion of antecedent vegetation conditions on timescales > 1 yr had no impact on burnt area, but inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.567 to 0.686. The inclusion of antecedent moisture conditions also improved the simulation of burnt area through its influence on fuel build-up, which is additional to the influence of current moisture levels on fuel drying. The length of the period which needs to be considered to account for fuel build-up varies across biomes; fuel-limited regions are sensitive to antecedent conditions over longer time periods (~4 months) and moisture-limited regions are more sensitive to current conditions.

Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

“…They do not require physical parameterization [19], which is particularly beneficial when the number of considered variables is extensive. Random forest (RF) ML models are highly suitable for investigating emergent fire relationships, mainly because of their ability to evaluate non-linear relationships [7,[20][21][22][23].…”

Reconstructing burnt area during the Holocene: an Iberian case study

“…Analyses of present-day fire relationships using satellite-derived data have shown that vegetation properties determining fuel availability are the strongest determinants of fire occurrence (Bistinas et al, 2014;Forkel et al, 2019aForkel et al, , 2019bKuhn-Régnier et al, 2020). This suggests that palaeo-vegetation data could provide a way of reconstructing burnt area in the past, particularly at times when human influences on land cover were less important.…”