Generating Annual Fire Risk Maps Using Bayesian Hierarchical Models

Abstract

Vegetation fires are an important environmental and socioeconomic problem, and large budgets are spent in fire prevention and fire fighting. Detailed knowledge of spatiotemporal patterns of fire occurrence is required for effective and efficient fire management, and annual fire risk maps can be an important tool to support strategic decisions relating to location–allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence, without addressing the loss component. We propose and evaluate two alternative approaches to the development of annual fire risk maps, using an atlas of annual burned area maps of Portugal (1975–2009), derived from the classification of satellite imagery, and a set of environmental maps representing vegetation, climatic, and topographic covariates. We look at current approaches for producing annual fire risk maps, and suggest improvements by incorporating the strong spatial and temporal dependence that exists in the data. This is accomplished using two different modeling strategies. The first strategy consists of modeling interarrival times between fires using a discrete version of the Weibull model. The second strategy consists of modeling annual fire occurrences using a first-order nonhomogeneous Markov model. These two distinct strategies accommodate different possibilities to introduce time-dependent covariates and make complementary probabilistic statements.

AMS Subject Classification:

• 62M30
• 62P12

Keywords:

• Fire frequency data
• Discrete Weibull model
• Non-homogeneous Markov models
• Spatio-temporal models

Acknowledgments

We thank the two referees and the associate editor for their constructive suggestions and careful reading of the article, which improved the first version. We also thank Alan Gelfand for his many useful comments and suggestions regarding modeling strategies for these types of data.

Funding

Research was partially funded by FCT Fundação para a Ciência e a Tecnologia, Portugal, through the projects PEst-OE/MAT/UI0006/2011 and PTDC/MAT/118335/2010. J. M. C. Pereira participated in this research under the framework of research projects “Forest fire under climate, social and economic changes in Europe, the Mediterranean and other fire-affected areas of the world (FUME),” EC FP7 grant agreement 243888, and “Fire–Land–Atmosphere Inter-Relationships: understanding processes to predict wildfire regimes in Portugal” (FLAIR), PTDC/AAC/AMB/104702/2008.

Notes

1. 1. Initially, two co-regionalized dependent ICAR models (Banerjee et al. 2004) were considered, but no evidence was given by the data about this dependence structure.