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First, a normal distribution model is fitted to the 32-year sample of decimal logarithms of monthly burned area. The model is improved by introducing two covariates:(1) the top-down meteorological factor (DSRtd) which consists of daily cumulated values of DSR since April 1 to July 31 and may be viewed as the cumulated stress on vegetation due to meteorological conditions during the pre-fire season; (2) the bottom-up factor (DSRbu) which consists of the square root of the mean of the squared daily deviations (restricted to days with positive departures of DSR from the corresponding long term mean) and may be viewed as the contribution of days characterized by extreme weather conditions favoring the onset and spreading of wildfires. Three different statistical models are then developed: the “climate anomaly” model, using DSRtd as covariate, the “weather anomaly”, using DSRbu as covariate, and the “combined” model using both variables as covariates. These models are used to define background fire danger, fire weather danger and combined fire danger, respectively quantifying the contribution of DSRtd, DSRbu and both covariates to increasing or decreasing the probability of having extremely high/low values of burned area in August.

Anticipating the severity of the fire season in Northern Portugal using statistical models based on meteorological indices of fire danger