Human-caused fire occurrence modelling in perspective: a review
Sergi Costafreda-Aumedes A D , Carles Comas B and Cristina Vega-Garcia A C E
+ Author Affiliations
- Author Affiliations
A Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198, Lleida, Spain.
B Department of Mathematics, University of Lleida, Agrotecnio Center, Carrer Jaume II 69, 25001, Lleida, Spain.
C Forest Sciences Center of Catalonia, Crta. Sant Llorenç de Morunys, km 2, 25280, Solsona, Lleida, Spain.
D Department of Agri-food Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy.
E Corresponding author. Email: cvega@eagrof.udl.cat
International Journal of Wildland Fire 26(12) 983-998 https://doi.org/10.1071/WF17026
Submitted: 2 August 2016 Accepted: 27 September 2017 Published: 8 December 2017
Journal Compilation © CSIRO 2017 Open Access CC BY-NC-ND
Abstract
The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.
Additional keywords: meta-analysis, predictive models, space–time patterns, wildfire.
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