Regional variability and driving forces behind forest fires in Portugal an overview of the last three decades (1980–2009)

Nunes, Adélia

doi:10.1016/j.apgeog.2012.03.002

Cited by 84 publications

(92 citation statements)

References 24 publications

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“…The effect of weather on ignition confirmed our expectations and previous literature (e.g., Vilar et al, 2010;Narayarnaraj and Wimberly, 2012;Nunes, 2012), with summer temperature and spring precipitations driving summer ignitions, and temperature and precipitation of the driest quarter (i.e., often winter), together with aspect (i.e., stands located on drier, sunnier slopes), driving winter fires. The main driver of fire density distribution in southern Europe is precipitation, both off-season (which affects vegetation growth and fuel accumulation) and during the fire season, which limits fire ignition and spread (Oliveira et al, 2012).…”

Section: Natural Drivers Of Fire Ignitionsupporting

confidence: 80%

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Vacchiano

Foderi

Berretti

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Modeling and assessing the factors that drive forest fire ignitions is critical for fire prevention and sustainable ecosystem management. In southern Europe, the anthropogenic component of wildland fire ignitions is especially relevant. In the Alps, however, the role of fire as a component of disturbance regimes in forest and grassland ecosystems is poorly known. The aim of this work is to model the probability of fire ignition for an Alpine region in Italy using a regional wildfire archive (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and MaxEnt modeling. We analyzed separately (i) winter forest fires, (ii) winter fires on grasslands and fallow land, and (iii) summer fires. Predictors were related to morphology, climate, and land use; distance from infrastructures, number of farms, and number of grazing animals were used as proxies for the anthropogenic component. Collinearity among predictors was reduced by a principal component analysis. Regarding ignitions, 30 % occurred in agricultural areas and 24 % in forests. Ignitions peaked in the late winter-early spring. Negligence from agrosilvicultural activities was the main cause of ignition (64 %); lightning accounted for 9 % of causes across the study time frame, but increased from 6 to 10 % between the first and second period of analysis. Models for all groups of fire had a high goodness of fit (AUC 0.90-0.95). Temperature was proportional to the probability of ignition, and precipitation was inversely proportional. Proximity from infrastructures had an effect only on winter fires, while the density of grazing animals had a remarkably different effect on summer (positive correlation) and winter (negative) fires. Implications are discussed regarding climate change, fire regime changes, and silvicultural prevention. Such a spatially explicit approach allows us to carry out spatially targeted fire management strategies and may assist in developing better fire management plans.

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Section: Natural Drivers Of Fire Ignitionsupporting

confidence: 80%

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Vacchiano

Foderi

Berretti

et al. 2018

Nat. Hazards Earth Syst. Sci.

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“…Population and agricultural area decrease are also closely related with the population potential (POT DEN), a similar concept of population density or human presence, which is further associated with the probability of fire ignition and area burned. This has a positive influence in many studies (Cardille et al, 2001;Maingi and Henry, 2007;RomeroCalcerrada, 2008;Catry et al, 2009;Sebastian-Lopez et al, 2008;Martínez et al, 2009;Marques et al, 2011;Nunes, 2012) or a negative relationship for some areas in other studies (Narayanaraj and Wimberly, 2012; Sá et al, 2011). Additionally, the previously mentioned variables were also related with the CORINE land use class "agriculture but with significant areas of natural vegetation" (CL 21 PM), showing that fire occurrence was more likely in municipalities where agricultural and forest areas are intermixed, similar to what has been reported by Ortega et al (2012).…”

Section: Driving Factors Of Long-term Fire Presencementioning

confidence: 99%

“…Mean summer temperature and mean annual precipitation are important factors, especially in the warmer areas of the E and SE. Many studies (Shyphard et al, 2008;Drever et al, 2008;Padilla and Vega-García et al, 2011;Oliveira et al, 2012, Sá et al, 2011Narayarnaraj and Wimberly, 2012;Nunes, 2012) selected several climatic variables as very significant in their fire models -some related to precipitation, such as fire-season and off-season precipitation, precipitation seasonality, soil water storage and soil moisture anomaly -and others related to temperature, especially the maximum temperature in the driest season.…”

Section: Driving Factors Of Long-term Fire Presencementioning

confidence: 99%

“…The same conclusions about how topography reflects the locations of human activities in relation to fire ignitions are indicated for a region of China by Xu et al (2006), showing that the anthropogenic factors are closely related to fires when altitudes of forests are lower than 900 m. However, at higher elevations their influence is much lower. In other studies the topography effect has been related with fires, using variables related with roughness or terrain shape index (Dickson et al, 2006, Nunes, 2012Padilla and Vega-Garcia, 2011;Narayanaraj and Wimberly, 2012).…”

Section: Driving Factors Of Long-term Fire Densitymentioning

confidence: 99%

“…Although interest in accounting for regional variations in wildfire occurrence factors has been shown recently in some studies Carmo et al, 2011;GonzalezOlabarria et al, 2011;Padilla and Vega-García, 2011;Nunes, 2012), except for Koutsias et al (2005Koutsias et al ( , 2010, this has only begun to be addressed very recently by using local geographically weighted regression (Tulbure et al 2011;Poudyal et al, 2012;Avila-Flores et al, 2010;Sá et al, 2011;Rodrigues and De la Riva, 2012). In our study, similar to those of Koutsias et al (2010) and Sá et al (2011), GWR is considered as a complement to the "global" regression modelling approach, with which it is compared in order to better understand particular processes at the regional scale, but at the same time recognizing its own local characteristics and patterns (Fotheringham et al, , 1997(Fotheringham et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%

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

Fernández¹,

Chuvieco

Koutsias³

2013

Nat. Hazards Earth Syst. Sci.

127

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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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Comparative Analysis of Policies to Deal With Wildfire Risk

et al. 2014

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Fires are the main driver of land degradation in forest areas in Mediterranean sub‐humid regions and are likely to increase as a result of climate and other global changes. To prevent deleterious processes induced by fire, several policies and strategies have been implemented at national and regional scales. We perform a comparative study of policies and strategies of Portuguese and Spanish (Comunitat Valenciana) cases in order to assess the differences between them and identify their roles in forest fire prevention and in combating and mitigating impacts. To this end, we analyse the sustainability objectives stated in the legislation of each country to identify the strategies used to deal with forest fires and the extent to which they are integrated to achieve the sustainability objectives they pursue. The comparative analysis includes an assessment of sustainability, evaluated by the explicitness of the objectives, and identification of how the lines of action contribute to reach these objectives. We found different levels of complexity and that the adoption or rejection of some of the techniques is closely related to the tradition and the experience of local communities. This analysis highlights the importance of local characteristics and the stakeholders, involvement in designing effective strategies to reduce fire risk. Copyright © 2014 John Wiley & Sons, Ltd.

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Regional variability and driving forces behind forest fires in Portugal an overview of the last three decades (1980–2009)

Cited by 84 publications

References 24 publications

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

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

Comparative Analysis of Policies to Deal With Wildfire Risk

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