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

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 − 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.

Keywords

• burnt area model
• logistic regression
• meteorological wildfire index
• seasonal severity rating
• wildfire susceptibility