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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.

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 May 26 to June 30, 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 one 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.

The Fire Weather Index (FWI) is used to assess meteorological fire danger worldwide. It has been argued that it lacks an atmospheric instability term. A new enhanced FWI (FWIe) was recently developed incorporating atmospheric instability in the form of the Continuous Haines Index (CHI). Here, the first climatological and evolution analysis of these indexes was performed using ERA5 data for the 1980–2020 period. There was a prevalence of higher values over central Iberia; these were heavily modulated by the climate types, topography, and land cover. Southwest and east Iberia suffered the greatest decadal increases in all three indexes. Relating both indexes to occurrences detected by satellite, through fire radiative power (FRP), showed that FWIe provided an improved meteorological fire danger assessment in higher-risk conditions. This showed that greater-risk observations were more prone to be affected by atmospheric instability than lower-danger observations. Case studies for the 2017 central Portugal and 2003 and 2018 Monchique wildfires were additionally conducted to verify these conclusions. This work points to the usefulness of FWIe when/where atmospheric instability may play a critical role in the development of wildfires, which may contribute to a more focused deployment of suppression mechanisms by the authorities.

Forest fires are increasingly affecting forest ecosystems, with severe ecological and socio-economic impacts on neighboring communities. In this context, evaluating the risk of fires at the fireshed level is considered a crucial step towards improving knowledge about fire risk management, therefore, minimizing potential damages of wildfires on people, properties, and natural resources. The aim of this study was to assess forest fire risk perception of communities at two firesheds in Lebanon. In-person surveys were conducted in areas of high fire risk within each fireshed. The analyzed data showed variability in opinions and challenges about fire risk management. Most of the provided recommendations included advocating for the increase of awareness about fire risk and safety, inducing training about fire-fighting and creating networks to facilitate communication within communities at risk.