Bachisio Arca

Assessing exposure of human and ecological values to wildfire in Sardinia, Italy

We used simulation modelling to analyse spatial variation in wildfire exposure relative to key social and economic features on the island of Sardinia, Italy. Sardinia contains a high density of urban interfaces, recreational values and highly valued agricultural areas that are increasingly being threatened by severe wildfires. Historical fire data and wildfire simulations were used to estimate burn probabilities, flame length and fire size. We examined how these risk factors varied among and within highly valued features located on the island. Estimates of burn probability excluding non-burnable fuels, ranged from 0-1.92 × 10-3, with a mean value of 6.48 × 10-5. Spatial patterns in modelled outputs were strongly related to fuel loadings, although topographic and other influences were apparent. Wide variation was observed among the land parcels for all the key values, providing a quantitative approach to inform wildfire risk management activities. Language: en

Evaluation of FARSITE simulator in Mediterranean maquis

In the last two decades, several models were developed to provide temporal and spatial variations of fire spread and behaviour. The most common models (i.e. BEHAVE and FARSITE) are based on Rothermels original fire spread equation and describe fire spread and behaviour taking into account the influences of fuels, terrain and weather conditions. The use of FARSITE on areas different from those where the simulator was originally developed requires a local calibration to produce reliable results. This is particularly true for Mediterranean ecosystems, where plant communities are characterised by high specific and structural heterogeneity and complexity. To perform FARSITE calibration, an appropriate fuel model or the development of a specific custom fuel model is needed. In this study, FARSITE was employed to simulate three fire events in Mediterranean areas using different fuel models and meteorological input data, and the accuracy of results was analysed. A custom fuel model designed and developed for shrubland vegetation (maquis) provided realistic values of rate of spread, when compared with estimated values obtained using standard fuel models. Our results confirm that the use of both wind field data and appropriate custom fuel models are crucial to obtain reasonable simulations of wildfire events occurring on Mediterranean vegetation during the drought season.

Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy.

In this paper, we applied landscape scale wildfire simulation modeling to explore the spatiotemporal patterns of wildfire likelihood and intensity in the island of Sardinia (Italy). We also performed wildfire exposure analysis for selected highly valued resources on the island to identify areas characterized by high risk. We observed substantial variation in burn probability, fire size, and flame length among time periods within the fire season, which starts in early June and ends in late September. Peak burn probability and flame length were observed in late July. We found that patterns of wildfire likelihood and intensity were mainly related to spatiotemporal variation in ignition locations, fuel moisture, and wind vectors. Our modeling approach allowed consideration of historical patterns of winds, ignition locations, and live and dead fuel moisture on fire exposure factors. The methodology proposed can be useful for analyzing potential wildfire risk and effects at landscape scale, evaluating historical changes and future trends in wildfire exposure, as well as for addressing and informing fuel management and risk mitigation issues.

Assessing climate change impacts on wildfire exposure in Mediterranean areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981-2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981-2010, 2011-2040, and 2041-2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041-2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

GPU-accelerated multi-objective optimization of fuel treatments for mitigating wildfire hazard

Abstract Fuel treatment is considered a suitable way to mitigate the hazard related to potential wildfires on a landscape. However, designing an optimal spatial layout of treatment units represents a difficult optimization problem. In fact, budget constraints, probabilistic nature of fire behaviour and complex interactions among the different fuel treatment patches, give rise to challenging search spaces on typical landscapes. In this study, we formulate the design problem in terms of a bi-objective optimization: minimizing both the extension of land characterized by high fire hazard and the cost of treatment. Then, we propose a computational approach that leads to a Pareto approximation set by exploiting an adapted version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) together with General-Purpose computing on Graphics Processing Units (GPGPU). Using an application example based on a real landscape, we also show that the proposed methodology has the potential to effectively support the design of a suitable fuel treatment for a landscape.

An optimal Cellular Automata algorithm for simulating wildfire spread

Abstract Raster-based methods for simulating wildfire spread are computationally more efficient than vector-based approaches. In spite of this, their success has been limited by the distortions that affect the fire shapes. This work presents a Cellular Automata (CA) approach that is able to mitigate the problem of distorted fire shapes thanks to a redefinition of the spread velocity, where the equations generally used in vector-based approaches are modified by means of some correction factors. A numerical optimization approach is used to find the optimal values for the correction factors. The results are compared to the ones given by two Cellular Automata simulators from the literature under homogeneous conditions. According to this work, the proposed approach provides better results, in terms of accuracy, at a comparable computational cost. The proposed approach has then been compared to Farsite, a vector-based fire-spread simulator, under realistic slope and wind conditions, producing equivalent results in a reduced computational time.

Predicting wildfire spread and behaviour in Mediterranean landscapes

The use of spatially explicit fire spread models to assess fire propagation and behaviour has several applications for fire management and research. We used the FARSITE simulator to predict the spread of a set of wildfires that occurred along an east–west gradient of the Euro-Mediterranean countries. The main purpose of this work was to evaluate the overall accuracy of the simulator and to quantify the effects of standard vs custom fuel models on fire simulation performance. We also analysed the effects of different fuel models and slope classes on the accuracy of FARSITE predictions. To run the simulations, several input layers describing each study area were acquired, and their effect on simulation outputs was analysed. Site-specific fuel models and canopy inputs were derived either from existing vegetation information and field sampling or through remote-sensing data. The custom fuel models produced an increase in simulation accuracy, and results were nearly unequivocal for all the case studies examined. We suggest that spatially explicit fire spread simulators and custom fuel models specifically developed for the heterogeneous landscapes of Mediterranean ecosystems can help improve fire hazard mapping and optimise fuel management practices across the Euro-Mediterranean region.

GPU-accelerated Optimization of Fuel Treatments for Mitigating Wildfire Hazard☆

Fuel treatment is considered a suitable way to mitigate the hazard related to potential wildfires on a landscape. However, designing an optimal spatial layout of treatment units represents a difficult optimization problem. In fact, budget constraints, the probabilistic nature of fire spread and interactions among the different area units composing the whole treatment, give rise to challenging search spaces on typical landscapes. In this paper we formulate such optimization problem with the objective of minimizing the extension of land characterized by high fire hazard. Then, we propose a computational approach that leads to a spatially-optimized treatment layout exploiting Tabu Search and General-Purpose computing on Graphics Processing Units (GPGPU). Using an application example, we also show that the proposed methodology can provide high-quality design solutions in low computing time.

An Improved Cellular Automata for Wildfire Spread

Despite being computationally more efficient than vector-based approaches, the use of raster-based techniques for simulating wildfire spread has been limited by the distortions that affect the fire shapes. This work presents a Cellular Automata approach that is able to mitigate this problem with a redefinition of the spread velocity, where the equations generally used in vector-based approaches are modified by means of a number of correction factors. A numerical optimization approach is used to find the optimal values for the correction factors. The results are compared to the ones given by two well-known Cellular Automata simulators. According to this work, the proposed approach provides better results, at a comparable computational cost.

Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area

Wildfire spread and behavior can be limited by fuel treatments, even if their effects can vary according to a number of factors including type, intensity, extension, and spatial arrangement. In this work, we simulated the response of key wildfire exposure metrics to variations in the percentage of treated area, treatment unit size, and spatial arrangement of fuel treatments under different wind intensities. The study was carried out in a fire-prone 625 km2 agro-pastoral area mostly covered by herbaceous fuels, and located in Northern Sardinia, Italy. We constrained the selection of fuel treatment units to areas covered by specific herbaceous land use classes and low terrain slope (<10%). We treated 2%, 5% and 8% of the landscape area, and identified priority sites to locate the fuel treatment units for all treatment alternatives. The fuel treatment alternatives were designed create diverse mosaics of disconnected treatment units with different sizes (0.5-10 ha, LOW strategy; 10-25 ha, MED strategy; 25-50 ha, LAR strategy); in addition, treatment units in a 100-m buffer around the road network (ROAD strategy) were tested. We assessed pre- and post-treatment wildfire behavior by the Minimum Travel Time (MTT) fire spread algorithm. The simulations replicated a set of southwestern wind speed scenarios (16, 24 and 32 km h-1) and the driest fuel moisture conditions observed in the study area. Our results showed that fuel treatments implemented near the existing road network were significantly more efficient than the other alternatives, and this difference was amplified at the highest wind speed. Moreover, the largest treatment unit sizes were the most effective in containing wildfire growth. As expected, increasing the percentage of the landscape treated and reducing wind speed lowered fire exposure profiles for all fuel treatment alternatives, and this was observed at both the landscape scale and for highly valued resources. The methodology presented in this study can support the design and optimization of fuel management programs and policies in agro-pastoral areas of the Mediterranean Basin and herbaceous type landscapes elsewhere, where recurrent grassland fires pose a threat to rural communities, farms and infrastructures.