Wed, Aug 04, 2021:On Demand
Background/Question/Methods
Fire spread can be modelled like an infectious disease: a burning patch of fuel infects, or ignites, a neighbouring patch of fuel, which infects another neighbor, and so on. Emergent from such an infection model are fire spread thresholds, whereby a minimum threshold value of fuel connectivity and infection probability is needed for fire to spread. This non-linear, process-based approach to modelling fire is favoured by theoreticians, but many large-scale fire models apply a linear, quasi-empirical approach instead. In this study, we seek to resolve the apparent conflict between theoretical and applied fire models by addressing whether we should account for fire’s threshold dynamics when modelling fire spread at larger scales. We built an infection model to simulate fire spread and compared our simulations to over one thousand real savanna fires.
Results/Conclusions Our simulations recapitulated observed patterns in real fires better than competing linear or non-linear models. Furthermore, real fires occurring at the fire spread threshold had a near perfect (slope=0.99) correlation with model predictions. Having established that fire spread threshold do govern fire spread at the landscape scale, we parameterized these thresholds using measured fuel and weather data. The fuel connectivity threshold was predicted by grass biomass while the infection probability threshold was predicted by fuel moisture content and, to a lesser extent, vapour pressure deficit. Altogether, results emphasize that modelling fire spread as a linear process is not appropriate, although patterns may appear linear when averaged over spatial and temporal heterogeneity.
Results/Conclusions Our simulations recapitulated observed patterns in real fires better than competing linear or non-linear models. Furthermore, real fires occurring at the fire spread threshold had a near perfect (slope=0.99) correlation with model predictions. Having established that fire spread threshold do govern fire spread at the landscape scale, we parameterized these thresholds using measured fuel and weather data. The fuel connectivity threshold was predicted by grass biomass while the infection probability threshold was predicted by fuel moisture content and, to a lesser extent, vapour pressure deficit. Altogether, results emphasize that modelling fire spread as a linear process is not appropriate, although patterns may appear linear when averaged over spatial and temporal heterogeneity.