Spatial extremes of wildfire sizes: Bayesian hieralquical models for extremes

• Main Author: Mendes, Jorge M.
• Publication Date: 2010
• Other Authors: Bermudez, Patricia Cortés de Zea, Pereira, J.M.C., Turkman, K.F., Vasconcelos, M.J.P.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.5/5956
• topic: Bayesian hierarchical models; generalized Pareto distribution; spatial and temporal processes; MCMC

In Portugal, due to the combination of climatological and ecological factors, large wildfires are a constant threat and due to their economic impact, a big policy issue. In order to organize efficient fire fighting capacity and resource management, correct quantification of the risk of large wildfires are needed. In this paper, we quantify the regional risk of large wildfire sizes, by fitting a Generalized Pareto distribution to excesses over a suitably chosen high threshold. Spatio-temporal variations are introduced into the model through model parameters with suitably chosen link functions. The inference on these models are carried using Bayesian Hierarchical Models and Markov chain Monte Carlo methods.