Assessing spatial uncertainty of the Portuguese fire risk through direct sequential simulation

Forest fires have a significant economic, social, and environmental impact in Portugal. For that its fire risk was assessed through Bayes Formalism, where the main component of the risk of fire was assessed by the conditional probability of fire I(u,t) given a class of the daily severity rating (DSR) for a specific period of time—P[I(u,t)|R(u,t)]. The evaluation of this a posterior probability, P[I(u,t)|R(u,t)], was based on the update of marginal local probability of fire in each chosen region u (Durão, 2006).DSR values were used to calculate fire's risk, taking into account historical data, I(s,t), in a given region s, and also to define DSR's local thresholds in order to have P [I(u,t)|R(u,t)] ≥ 0.65.In this paper we characterize these posterior probabilities using direct sequential simulation models (DSS models) to obtain the spatial distribution of these probabilities over the entire Portugal, in order to assess the risk of fire and associated spatial uncertainty. Local probability density functions (pdfs) and spatial uncertainty are evaluated by a set of equiprobable simulated images of these posterior probabilities.Results are presented and discussed for the Portuguese fire seasons of the 2-year period, 2003–2004. The conditional probabilities reproduced reasonably well what was officially published for the studied fire seasons. We expect that a better understanding of both spatial and temporal patterns of fire in Portugal together with uncertainty measures constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and fire fighting resources allocation in critical social and environmental areas.