References

NHESS

Natural Hazards and Earth System Sciences

NHESS

Nat. Hazards Earth Syst. Sci.

1684-9981

Copernicus Publications

Göttingen, Germany

10.5194/nhess-13-311-2013

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

Martínez-Fernández

¹ Chuvieco

https://orcid.org/0000-0001-5618-4759

² Koutsias

INIA-CIFOR, Forest Research Centre, Department of Forest Ecology and Genetics, Carretera de La Coruña, Km. 7,5 28040, Madrid, Spain

Department of Geography and Geology, University of Alcalá, Calle Colegios 2, 28801 Alcalá de Henares, Spain

Department of Environmental and Natural Resources Management, University of Western Greece, G. Seferi 2, 30100 Agrinio, Greece

11 02 2013

13 2 311 327

2013

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://nhess.copernicus.org/articles/13/311/2013/nhess-13-311-2013.html

The full text article is available as a PDF file from https://nhess.copernicus.org/articles/13/311/2013/nhess-13-311-2013.pdf

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983–2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.

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