On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain
M. Padilla A B and C. Vega-García A
+ Author Affiliations
- Author Affiliations
A Agriculture and Forest Engineering Department, University of Lleida, 191 Alcalde Rovira Roure Avenue, E-25198 Lleida, Spain.
B Corresponding author. Email: padilla.marc@gmail.com
International Journal of Wildland Fire 20(1) 46-58 https://doi.org/10.1071/WF09139
Submitted: 1 December 2009 Accepted: 17 April 2010 Published: 14 February 2011
Abstract
Human-caused forest fires are common in Mediterranean countries. Forest fire management agencies customarily estimate daily fire loads by using meteorological fire danger rating indices, based on variables registered daily by weather stations. This paper is focussed on the evaluation of the relative performance of a comprehensive set of commonly used fire weather indices by developing holistic daily fire occurrence models in Spain involving also other topographic, fuel and human-related geographic factors. The data consisted of historical records of daily fire occurrences, daily weather data and geographic characteristics for the peninsular territory of Spain in a 10-km-spatial resolution grid, for the period from 2002 to 2005. The prediction units were 10 × 10-km-grid cells but in order to take into account the spatial variation in relationships between explanatory variables and historical occurrences, Spain was divided into 53 ecoregions and a logistic regression model was developed for each one of these regions. The explanatory variables included in the models illustrated which weather and geographic factors primarily affected daily human-caused fires in the ecoregions. The validation of the estimated ignition probabilities with the fire occurrences registered during 2005, reserved for independently testing the model’s predictive capability, resulted in values of total percentage correctly predicted varying from 47.4 to 82.6%.
Additional keywords: fire risk, fire weather, human factors, logistic regression, regional analysis, validation.
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