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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.

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 km2 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.

During the 2017 wildfire season in Portugal, unprecedented episodes burned 6% of the country’s area and underscored the need for a long-term comprehensive solution to mitigate future wildfire disasters. In this study, we built and calibrated a national-scale fire simulation system including the underlying fuels and weather data and used the system to quantify wildfire exposure to communities and natural areas. We simulated 10,000 fire season replicates under extreme weather to generate 1.6 million large wildfire perimeters and estimate annual burn probability and fire intensity at 100 m pixel resolution. These outputs were used to estimate wildfire exposure to buildings and natural areas. The results showed a fire exposure of 10,394 structures per year and that 30% of communities accounted for 82% of the total. The predicted burned area in natural sites was 18,257 ha yr−1, of which 9.8% was protected land where fuel management is not permitted. The main burn probability hotspots were in central and northern regions. We highlighted vital priorities to safeguard the most vulnerable communities and promote landscape management programs at the national level. The results can be useful to inform Portugal’s new national plan under implementation, where decision-making is based on a probabilistic methodology. The core strategies include protecting people and infrastructure and wildfire management. Finally, we discuss the next steps necessary to improve and operationalize the framework developed here. The wildfire simulation modeling approach presented in this study is extensible to other fire-prone Mediterranean regions where predicting catastrophic fires can help anticipate future disasters.