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Abstract. Portugal is recurrently affected by large wildfire events that have serious impacts at the socio-economic and environmental levels and dramatic consequences associated with the loss of lives and the destruction of the landscape. Accordingly, seasonal forecasts are required to assist fire managers, thus contributing to alter the historically based purely reactive response. In this context, we present and discuss a statistical model to estimate the probability that the total burned area during summer will exceed a given threshold. The statistical model uses meteorological information that rates the accumulation of thermal and vegetation stress. Outlooks for the 39-year study period (1980–2018) show that, when the statistical model is applied from 26 May to 30 June, out of the six severe years, only one year is not anticipated as potentially severe and, out of the six weak years, only one is not anticipated as potentially weak. The availability of outlooks of wildfire potential with an anticipation of up to 1 month before the starting of the fire season, such as the one proposed here, may serve to provide clear directions for the fire community when planning prevention and combating fire events.

No abstract

Wildfire susceptibility maps are a well-known tool for optimizing available means to plan for prevention, early detection, and wildfire suppression in Portugal, especially regarding the critical fire season (1 July À 30 September). These susceptibility maps typically disregard seasonal weather conditions on each given year, being based on predisposing variables that remain constant on the long-term, such as elevation. We employ logistic regression for combining wildfire susceptibility with a meteorological index representing spring conditions (the Seasonal Severity Rating), with the purpose of predicting, for any given year and ahead of the critical fire season, which areas will burn. Results show that the combination of the index with wildfire susceptibility slightly increases the capability to predict which areas will burn, when compared with susceptibility alone. Spring meteorological context was found better suited for predicting if the following summer wildfire season will be more severe, rather than predicting where wildfires will effectively occur. The model can be updated yearly after the critical wildfire season and can be applied to optimize the allocation of human and material resources regarding the prevention, early detection and suppression activities, required to reduce the severity of wildfires in the country.

Wildfire susceptibility and hazard models based on drivers that change only on a multiyear timescale are considered of a structural nature. They ignore specific short-term conditions in any year and period within the year, especially summer, when most wildfire damage occurs in southern Europe. We investigate whether the predictive capacity of structural wildfire susceptibility and hazard models can be improved by integrating a seasonal dimension, expressed by three variables with yearly to seasonal timescales: (1) a meteorological index rating fuel flammability at the onset of summer; (2) the scarcity of fuel associated with the burned areas of the previous year, and (3) the excessive abundance of fuel in especially fire-prone areas that have not been burned in the previous ten years. We describe a new methodology for combining the structural maps with the seasonal variables, producing year-specific seasonal susceptibility and hazard maps. We then compare the structural and seasonal maps as to their capacity to predict burnt areas during the summer period in a set of eight independent years. The seasonal maps revealed a higher predictive capacity in 75% of the validation period, both for susceptibility and hazard, when only the highest class was considered. This percentage was reduced to 50% when the two highest classes were considered together. In some years, structural factors and other unconsidered variables probably exert a strong influence over the spatial pattern of wildfire incidence. These findings can complement existing structural data and improve the mapping tools used to define wildfire prevention and mitigation actions.