Spatial extremes of wildfire sizes: bayesian hierarchical models for extremes

Jorge M. Mendes, Patrícia Cortés de Zea Bermudez, José Pereira, K. F. Turkman, M. J.P. Vasconcelos

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-28
Number of pages28
JournalEnvironmental And Ecological Statistics
Issue number1
Publication statusPublished - 1 Feb 2010


  • Bayesian hierarchical models
  • Generalized Pareto distribution
  • MCMC
  • Spatial and temporal processes


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