Predicting burnt areas during the summer season in Portugal by combining wildfire susceptibility and spring meteorological conditions

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