Wildfire risk estimation in the Mediterranean area

Abstract

We analyzed wildland fire occurrence and size data from Sardinia, Italy, and Corsica, France, to examine spatiotemporal patterns of fire occurrence in relation to weather, land use, anthropogenic features, and time of year. Fires on these islands are largely human caused and can be attributed to negligence, agro‐pastoral land use, and arson. Of particular interest was the predictive value of a fire weather index (FWI) that is widely used by fire managers to alert suppression crews. We found that an increase in the FWI from 30 to 60 produced on average an approximate eightfold increase in the odds of a large fire, regardless of the time of year during the fire season or land use type. Total area burned per fire season was positively correlated with the number of days with FWI > 40 over the period studied. Strong interactions between time of year and land type were also observed for both the probability of ignition and large fire. For example, the estimated odds of a large fire on agricultural lands in southern Sardinia was approximately 10 times larger than the forest and shrubland land type for areas close to roads, early (May) in the fire season. Conversely, toward the end of the fire season (September), we estimated the odds of a large fire in these same areas at about half the value estimated for the forest land classes. Of the explanatory variables analyzed, only FWI had an effect on the probability of a large fire (P < 0.1). The results of the study can be used in several ways including the following: (1) allocating fire detection and suppression resources to specific locations during the fire season; (2) prioritizing fuel breaks along specific road segments that have high predicted ignition rates; (3) refining the current fire danger indices; and (4) parameterizing wildfire simulation models to test how changing land use and climate change may affect spatial patterns in burn probability and intensity. Copyright © 2014 John Wiley & Sons, Ltd.

A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain

“…Fire occurrence analysis in Mediterranean areas is a prerequisite for fire modeling, since most fires are associated with anthropic activities (Martínez et al 2009;Padilla and Vega-García 2011;Ager et al 2014b; e.g., lightning caused just *2 % fires in the study area) and exhibit spatial-temporal ignition patterns that must be taken into account for accurate fire modeling (Bar-Massada et al 2011;Salis et al 2014Salis et al , 2015. The anthropic causes of fire ignition are often unknown (45 % in the study area; Fig.…”

“…In the Mediterranean areas, fire ignition location alone is a poor estimator of burned area, but fire spread modeling must account for historical ignition occurrence since anthropic activities are responsible for spatial-temporal ignition patterns (Bar-Massada et al 2011;Ager et al 2014b;Salis et al 2014Salis et al , 2015. Wildfires can be accurately and massively replicated using fire modeling programs such as FlamMap, FSim or Randig (Finney 2006), which are built on the computationally feasible and efficient minimum travel time (MTT) fire spread algorithm (Finney 2002).…”

Human-caused fire occurrence modelling in perspective: a review

“…central Africa) or differ seasonally (e.g. United Kingdom, Albertson et al 2009) -there is a well-defined seasonality in some regions with a high peak of fire occurrence in summer (Ager et al 2014), whereas in others there are two well-defined peaks of fire frequency, for example in early winter and summer (Martell et al 1989). Accordingly, some models only consider fire observations recorded during a fire season defined by the period with the highest number of fires (Haines et al 1970;Vega-Garcia et al 1995, 1996Dickson et al 2006;Vilar et al 2010).…”

Emergency logistics for wildfire suppression based on forecasted disaster evolution

“…Therefore, employing suitable tools and techniques to model this stochasticity in the decision-making process for effective preparedness and response to disasters is essential. Regarding wildfires, the need for such tools has led to the development of several prediction models (Martínez et al 2009;Thompson and Calkin 2011;Ager et al 2014) that focus on explaining spatial-temporal patterns with regard to certain variables (physiographic, infrastructural, socio-economic and weather-related) relating to the ignition of wildfires. However, in the literature, few studies incorporating wildfire propagation and logistics planning for disaster relief, revealing a research gap concerning appropriate solutions for these types of logistic problems.…”