This research analyzes the spatiotemporal patterns of wildfire regime attributes (e.g., seasonality, size, frequency, and burn rate) across the Golestan National Park (GNP), northeast Iran over the ...last two decades. We used a variety of data, including existing vegetation data, current vegetation survey, and historical wildfire data, and then data were processed through ArcMap. We also predicted fire exposure profiles (burn probability (BP), conditional flame length (CFL (m)), and fire size (FS (ha)) by the application of the minimum travel time (MTT) fire spread algorithm. The kernel density estimation (KDE) method was used to estimate wildfire likelihood, based on recent wildfires (2000–2020) that occurred in the GNP. Finally, we developed a logistic regression model to investigate how independent variables such as weather, fuel, and topographic data influence wildfires in the park. Wildfires in the landscape have not been constant in either space or time. Their extent, seasonality, frequency, and other wildfire regime characters varied considerably across the landscape. Our results highlighted that shrublands in the southern part of the park showed, in general, the highest values in terms of the wildfire regime attributes. Large fires (10–100 ha, 51%) and very large fires (>100 ha, 24%), fire intervals greater than 10 years (90%), and high burn rates (>1% y−1, 35%) are all characteristics that contribute to high wildfire activity in shrublands. Similarly, areas predicted to have high wildfire exposure levels (average BP = 0.004; average CFL = 1.60 m; average FS = 840 ha) are found in the fuel models of high-load grass and medium-load shrub. Finally, the regression model results revealed that weather and fuel were the most influential parameters (R2 ≥ 0.2), while topography had comparatively less influence in the study area. In light of these results, we suggest proactively incorporating this information into fire and fuel management which can help develop a fire prevention plan, predict fire ignition probability and frequency, and finally address altered fire regimes threatening the park.
We assessed potential economic losses and transmission to residential houses from wildland fires in a rural area of central Navarra (Spain). Expected losses were quantified at the individual ...structure level (n = 306) in 14 rural communities by combining fire model predictions of burn probability and fire intensity with susceptibility functions derived from expert judgement. Fire exposure was estimated by simulating 50,000 fire events that replicated extreme (97th percentile) historical fire weather conditions. Spatial ignition probabilities were used in the simulations to account for non-random ignitions, and were estimated from a fire occurrence model generated with an artificial neural network. The results showed that ignition probability explained most of spatial variation in risk, with economic value of structures having only a minor effect. Average expected loss to residential houses from a single wildfire event in the study area was 7955€, and ranged from a low of 740 to the high of 28,725€. Major fire flow-paths were analyzed to understand fire transmission from surrounding municipalities and showed that incoming fires from the north exhibited strong pathways into the core of the study area, and fires spreading from the south had the highest likelihood of reaching target residential structures from the longest distances (>5 km). Community firesheds revealed the scale of risk to communities and extended well beyond administrative boundaries. The results provided a quantitative risk assessment that can be used by insurance companies and local landscape managers to prioritize and allocate investments to treat wildland fuels and identify clusters of high expected loss within communities. The methodological framework can be extended to other fire-prone southern European Union countries where communities are threatened by large wildland fires.
Effective landscape-scale fuel management strategies are essential for reducing wildfire risk in Mediterranean fire-prone areas. In this study, the minimum travel time (MTT) fire-spread algorithm as ...implemented in FlamMap was applied to assess the potential of alternative fuel treatments for lowering wildfire losses in a 5,740-ha study area in eastern Sardinia, Italy. Twenty-seven wildfires at 10-m resolution were simulated considering three wind speeds (15, 18, and 21 km h
−1
) to compare fuel treatments: no treatment (NT), irrigated agroforestry areas with shrub clearing (T1), prescribed fire in eucalyptus stands (T2), and irrigated grasslands (T3). The simulations replicated a recent large wildfire that occurred in the study area (Orrì wildfire, 2019) and considered the weather and fuel moisture conditions associated with this event. The average wildfire exposure outputs (burned area, probability of burning, conditional flame length, potential crown fire occurrence, and surfaces withflame lengths above 2.5 m) decreased after fuel treatments, compared to no treatment. T1 was the most effective strategy in mitigating wildfire hazards and provided the most significant performance for several wildfire exposure indicators. Treating only 0.5% of the study area (~ 30 ha) resulted in a decrease in all wildfire exposure metrics to ~ 10% within the study area. In addition, the total surface characterized by high flame length (average > 2.5 m) was the lowest in the T1 treatment. This study can help land and fire managers optimize fuel treatment opportunities and wildfire risk mitigation strategies in Mediterranean areas.
We applied a geographical information system analysis to reclassify and characterize anthropic buildings based on structure density and area covered, land type, and proximity to wildlands able to ...originate intense wildfires and spot fires. The methodology was carried out in the 93,000 km2 Italy-France Maritime cooperation area (which includes the Regions of Sardinia, Tuscany, and Liguria, in Italy, and Corsica, and Provence-Alpes-Côte d'Azur, in France). We produced a 100-m raster dataset that characterizes and maps medium-high anthropic presence, wildland-anthropic areas, dispersed anthropic areas, and non-anthropic zones, in the whole study area. The study allowed to highlight variations in wildland anthropic interfaces among and within Regions as a function of anthropic presence and types and the surrounding wildlands. The spatial dataset provided with this work represents a valuable contribution to support landscape and urban planning and inform strategies to limit wildfire impacts nearby anthropic areas.
The growing threats posed by wildfires in Southern Europe are calling for the development of comprehensive and sound management and risk assessment strategies. In this work, we present the ...application of wildfire simulation modeling based on the minimum travel time (MTT) algorithm to assess fine-scale (100-m resolution) wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria and Provence-Alpes-Côte d’Azur), which cover about 72,000 km
2
of land. We simulated thousands of wildfires considering the current landscape and characterized and measured fine-scale wildfire risk factors and profiles by taking into account historical fire regimes, fuels, winds and fuel moisture conditions associated with the occurrence of the largest wildfires (>100 ha) that affected the study area in the last 20 years. Individual fires were simulated at 100-m resolution, consistent with the input files. Modeled annual burn probability and ignition probability revealed that Sardinia was the Region most affected by wildfires. The wildfire simulation outputs were then combined with main land uses and building footprint locations to characterize wildfire transmission and exposure to communities, and were summarized for main vegetation types and Regions. This study presents a cross-boundary and standardized approach based on wildfire spread modeling to analyze and quantify wildfire risk profiles in Southern Europe. The stochastic wildfire modeling systems we implemented used harmonized sets of data for a vast, fire-prone Mediterranean area, where previous similar studies were conducted at coarser resolutions and covered lower extent of lands. The approach presented in this work can be used as a reference pillar for the development and implementation of a common wildfire risk monitoring, management, and governance plan in the study area. The methods and findings of this study can be replicated in neighboring Mediterranean and other regions threatened by wildfires.
In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on ...wildfire risk in fire-prone areas, a fire-spread modeling approach to evaluate the variations in wildfire potential induced by different spatial patterns and percentages of land abandonment was applied. The study was carried out in a 1200 km2 agropastoral area located in north-western Sardinia (Italy) mostly covered by herbaceous fuels. We compared nine land abandonment scenarios, which consisted of the control conditions (NA) and eight scenarios obtained by combining four intensity levels (10, 20, 30, 40%) and two spatial patterns of agropastoral land abandonment. The abandonment scenarios hypothesized a variation in dead fuel load and fuel depth within abandoned polygons with respect to the control conditions. For each abandonment scenario, wildfire hazard and likelihood at the landscape scale was assessed by simulating over 17,000 wildfire seasons using the minimum travel time (MTT) fire spread algorithm. Wildfire simulations replicated the weather conditions associated with the largest fires observed in the study area and were run at 40 m resolution, consistent with the input files. Our results highlighted that growing amounts of land abandonment substantially increased burn probability, high flame length probability and fire size at the landscape level. Considering a given percentage of abandonment, the two spatial patterns of abandonment generated spatial variations in wildfire hazard and likelihood, but at the landscape scale the average values were not significantly different. The average annual area burned increased from about 2400 ha of the control conditions to about 3100 ha with 40% land abandonment. The findings of this work demonstrate that a progressive abandonment of agropastoral lands can lead to severe modifications in potential wildfire spread and behavior in Mediterranean areas, thus promoting the likelihood of large and fast-spreading events. Wildfire spread modeling approaches allow us to estimate the potential risks posed by future wildfires to rural communities, ecosystems and anthropic values in the context of land abandonment, and to adopt and optimize smart prevention and planning strategies to mitigate these threats.
This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially ...overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.
Despite the need for preserving the carbon pools in fire-prone southern European landscapes, emission reductions from wildfire risk mitigation are still poorly understood. In this study, we estimated ...expected carbon emissions and carbon credits from fuel management projects ongoing in Catalonia (Spain). The planning areas encompass about 1000 km2 and represent diverse fire regimes and Mediterranean forest ecosystems. We first modeled the burn probability assuming extreme weather conditions and historical fire ignition patterns. Stand-level wildfire exposure was then coupled with fuel consumption estimates to assess expected carbon emissions. Finally, we estimated treatment cost-efficiency and carbon credits for each fuel management plan. Landscape-scale average emissions ranged between 0.003 and 0.070 T CO2 year−1 ha−1. Fuel treatments in high emission hotspots attained reductions beyond 0.06 T CO2 year−1 per treated ha. Thus, implementing carbon credits could potentially finance up to 14% of the treatment implementation costs in high emission areas. We discuss how stand conditions, fire regimes, and treatment costs determine the treatment cost-efficiency and long-term carbon-sink capacity. Our work may serve as a preliminary step for developing a carbon-credit market and subsidizing wildfire risk management programs in low-revenue Mediterranean forest systems prone to extreme wildfires.
We provide 40m resolution wildfire spread, hazard and exposure metric raster grids for the 0.13 million ha fire-prone Bages County in central Catalonia (northeastern Spain) corresponding to node ...influence grid (NIG), crown fraction burned (CFB) and fire transmission to residential houses (TR). Fire spread and behavior data (NIG, CFB and fire perimeters) were generated with fire simulation modeling considering wildfire season extreme fire weather conditions (97th percentile). Moreover, CFB was also generated for prescribed fire (Rx) mild weather conditions. The TR smoothed grid was obtained with a geospatial analysis considering large fire perimeters and individual residential structures located within the study area. We made these raster grids available to assist in the optimization of wildfire risk management plans within the study area and to help mitigate potential losses from catastrophic events.
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