Abstract
Operational forest fire danger rating systems rely on the recent evolution of meteorological variables to estimate dead fuel condition. Further combining the latter with meteorological and environmental variables, they predict fire occurrence and spread. In this study we retrieved live fuel condition from MODIS multispectral measurements in the near infrared and shortwave infrared. Next, we combined these retrievals with an extensive dataset on actual forest fires in Campania (13,595 km2), Italy, to determine how live fuel condition affects the probability distribution functions of fire characteristics. Accordingly, the specific objective of this study was to develop and evaluate a new approach to estimate the probability distribution functions of fire burned area, duration and rate of spread as a function of the Perpendicular Moisture Index (PMI), whose value decreases with decreasing live fuel moisture content (LFMC). To this purpose, available fire data was intersected with MODIS 8-day composited reflectance data so to associate each fire event with the corresponding pre-fire PMI observation. Fires were then grouped in ten decile bins of PMI, and the conditional probability distribution functions of burned area, fire duration and rate of spread were determined in each bin. Distributions of burned area and rate of spread vary across PMI decile bins, while no significant difference was observed for fire duration. Further testing this result with a likelihood ratio test confirmed that PMI is a covariate of burned area and rate of spread, but not of fire duration. We defined an extreme event as a fire whose burned area (respectively rate of spread) exceeds the 95th percentile of the frequency distribution of all observed fire events. The probability distribution functions in the ten decile bins of PMI were combined to obtain a conditional probability distribution function, which was then used to predict the probability of extreme fires, as defined. It was found that the probability of extreme events steadily increases with decreasing PMI. Overall, at the end of the dry season the probability of extreme events is about the double than at the beginning. These results may be used to produce frequently (e.g. daily) updated maps of the probability of extreme events given a PMI map retrieved from e.g. MODIS reflectance data.
Highlights
Wildfires are a widespread factor of ecosystem disturbance (Bond et al, 2005), causing invaluable human casualties, negative effects on carbon sequestration and substantial economic loss (FAO, 2007; Montagné-Huck and Brunette, 2018; Pellegrini et al, 2018)
The specific objective of this study was to develop and evaluate a new approach to estimate the probability distribution functions of fire burned area, duration and rate of spread as a function of the Perpendicular Moisture Index (PMI), whose value decreases with decreasing live fuel moisture content (LFMC)
Fires were grouped in ten decile bins of PMI, and the conditional probability distribution functions of burned area, fire duration and rate of spread were determined in each bin
Summary
Wildfires are a widespread factor of ecosystem disturbance (Bond et al, 2005), causing invaluable human casualties, negative effects on carbon sequestration and substantial economic loss (FAO, 2007; Montagné-Huck and Brunette, 2018; Pellegrini et al, 2018). Scientific evidence supports the hypothesis that climate change may alter fire dynamics through the direct and indirect effects it exerts on fuel moisture and availability (Pausas and Ribeiro, 2013; Seidl et al, 2017; Williams and Abatzoglou, 2016) and on the probability distribution of dependent variables such as fire occurrence, burned area and rate of spread (Flannigan et al, 2016; Podschwit et al, 2018; Syphard et al, 2018). Fire danger models rely on meteorological input to process indicators of fuel water content and assess fire behaviour. The National Fire Danger Rating System used in the United States is a collection of fuel conditions and fire behaviour indicators computed from meteorological measurements, fuel models, climate class and
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