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Vegetation fires contribute to 38% to the emission of CO2 into the atmosphere, against 62% caused by the combustion of fossil fuels. Further, it could approach levels of anthropogenic carbon emissions, especially in years of extreme fire activity (e.g. 2003, 2017). According to the equation first proposed by Seiler and Crutzen (1980), fire emission estimation use information on the amount of burned biomass, the emission factors associated with each specific chemical species, the burned area, and the combustion efficiency. Still, simulating emission from forest fires is affected by several errors and uncertainties, due to the different assessment approach to characterize the various parameters involved in the equation. For example, regional assessment relied on fire-activity reports from forest services, with assumptions regarding the type of vegetation burned, the characteristics of burning, and the burned area. Improvements and new advances in remote sensing, experimental measurements of emission factors, fuel consumption models, fuel load evaluation, and spatial and temporal distribution of burning are a valuable help for predicting and quantifying accurately the source and the composition of fire emissions.With the aim to contribute to a better estimation of biomass burning emission, in this work we compared fire emission estimations using two different types of burned area products and combustion efficiency approaches in the framework of the recently developed system for modeling fire emission in Italy (Bacciu et al., 2012). This methodology combines a fire emission model (FOFEM - First Order Fire Effect Model, Reinhardt et al., 1997) with spatial and non-spatial inputs related to fire, vegetation, and weather conditions. The perimeters and burned area of selected large fires that occurred in 2017 in Italy were obtained by the former Corpo Forestale dello Stato (actually Carabinieri C.U.F.A.A.) and by the Copernicus Emergency Management Service (EMS). The vegetation types were derived from CORINE LAND COVER (2012). For each vegetation type, fuel loading was assigned using a combination of field observations and literature data (e.g., Mitsopoulos and Dimitrakopoulos 2007; Ascoli et al., 2019). Fuel moisture conditions, influencing the combustion efficiency, were derived from the daily Canadian Fine Fuels Moisture Code (FFMC), calculated from MARS interpolated weather data (25km x 25km). The daily FFMC was then associated with the two types of fire data with the aim of group fires in function of their relative ease of ignition and flammability of fine fuel (burning conditions, from low to extreme). For the EMS fire, it was also possible to further define fire severity and thus the percentage of combusted crown through the assessed fire damage grade.The results showed differences in the total emissions according to the fire product and the approach to estimate the combustion efficiency. Furthermore, it seems that the difference in the evaluation of severity - and therefore in the degree of combustion of the canopy- affects more than the differences in terms of area burned. Overall, the results pointed out the crucial role of appropriate fuel, fire, and weather data and maps to attain reasonable simulations of fuel consumption and smoke emissions.

Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha-1) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects.

Wildfires represent a major threat to Mediterranean ecosystems and are responsible for relevant impacts to environmental, economic and social values. In the period 2010-2016, the cross-border Interreg Italy-France Maritime territory, which includes Sardinia, Corsica, Tuscany, Liguria and PACA Regions, had about 20,000 wildfire ignitions and a total burned area of about 122,000 ha. In the face of social and environmental conditions and risks of the Maritime Regions, strengthening and developing innovative common guidelines and systems of wildfire management, from the monitoring and forecast to suppression, can provide more effective solutions to the wildfire problem, and can help strengthen cross-border cooperation in case of days with high risk. This work is devoted to introduce the MED-Star project, and to describe his main activities and results, with a focus on the tasks and activities coordinated by the National Research Council of Italy, Institute of BioEconomy (CNR-IBE) of Sassari. MED-Star is a 3-years strategic project supported by the Interreg Italy-France Maritime Program 2014-2020, which is co-financed by the European Regional Development Fund (ERDF). MED-Star is closely linked to 4 joint simple projects (Intermed; Med-Coopfire; Med-Foreste; Med-PSS), which mainly focus on investments in small infrastructures for wildfire risk prevention and support to wildfire suppression operations. The MED-Star project aims to share and discuss fire management policies and the most advanced strategies that can reduce the risk associated with wildfires, also through the combination of joint action plans and pilot / demonstration actions. The partnerships of MED-Star and the related 4 simple projects include the main actors competent at the administrative, technical and scientific level on the wildfire topic in the Maritime area of cooperation, and are able to meet the abovementioned challenges, contributing to 1) the reduction of wildfire risk in the five Regions involved, 2) the definition of strategic and operational solutions, 3) the implementation of operational actions and investments for wildfire prevention, monitoring, forecast and suppression, and 4) the strengthening of joint early warning and risk monitoring systems.

The development of comprehensive fire management and risk assessment strategies is of prominent concern in Southern Europe, due to the expanding scale of wildfire risk. In this work, we applied simulation modeling to analyze fine-scale (100-m resolution) wildfire exposure and risk transmission in the 24,000 km2 island of Sardinia (Italy). Sardinia contains a variety of ecological, cultural, anthropic and touristic resources that each summer are threatened by wildfires, and represents well the Mediterranean Basin environments and conditions. Wildfire simulations based on the minimum travel time algorithm were used to characterize wildfire exposure and risk transmission in terms of annual burn probability, flame length, structures exposed and type and amount of transmission. We focused on the historical conditions associated with large (>50 ha) and very large (>200 ha) wildfires that occurred in Sardinia in the period 1998–2016, and combined outputs from wildfire simulation modeling with land uses, building footprint locations, weather, and historical ignition data. The outputs were summarized for weather zones, main wind scenarios and land uses. Our study characterized spatial variations in wildfire spread, exposure and risk transmission among and within weather zones and the main winds associated with large events. This work provides a novel quantitative approach to inform wildfire risk management and planning in Mediterranean areas. The proposed methodology can serve as reference for wildfire risk assessment and can be replicated elsewhere. Findings can be used to better understand the spatial dynamics and patterns of wildfire risk and evaluate expected wildfire behavior or transmission potential in Sardinia and neighboring regions.

The goal of this study is to analyze fire probability and vulnerability of urban settings in a study area of about 46,000 ha located in a coastal area of North-East Sardinia. The study was carried out in a fire-prone area characterized by predominant and dense shrubland vegetation, by extensive rural-urban interfaces and by a relevant touristic pressure in the summer season. The analysis was performed using both the characterization and mapping of housing units, and the modeling of fire behavior by a probabilistic approach. The first part of the work was based on the analysis of the aerial photographs and field surveys. With this approach, we evaluated the vulnerability to fire of about 13,000 housing units through the evaluation and characterization of five factors: type of vegetation, percentage of the housing units in contact with the vegetation within a buffer area of 10 meters, presence of flammable structures, distance of flammable structures from the houses, and percentage of the flammable structures in contact with the vegetation, within a buffer area of 10 meters. The results highlighted that about 58% of the housing units were located in urban settings, while the remaining 42% were classified as rural-urban interfaces. About the latter, only 25% of units showed a reasonable distance from the surrounding vegetation, and 30% of units presented more than half of their perimeters in contact with the vegetation; in addition, 7% of units were close to highly burnable structures. Afterwards, we used simulation modeling based on the MTT algorithm of FlamMap in order to estimate the spatial variation of the main fire probability and severity indicators. Then we selected 32 different scenarios able to represent the most common and critical conditions for fire occurrence and spread in the study area. In more details, we combined two wind directions (210° and 290°), two wind speeds (32 and 20 km h-1), two fuel moisture conditions (arid and extreme), two ignition patterns (historically based and random), and two active fire spread durations (1 and 3 hours). For each scenario, we simulated 10,000 fires. Overall, the analysis of the fire simulations showed that fuel moisture and ignition patterns were the main factors affecting burn probability. Furthermore, the combination of extreme fuel moisture and strong wind speed represented the most critical scenario. The maps of conditional flame length were strongly affected by vegetation types and topography, particularly in some specific areas and nearby the coastline. Also the fire size maps highlighted hot-spot areas able to originate very large fires, particularly when wind direction and slope effects were compounded. We finally combined the results of the WUI mapping and characterization with the fire simulations, and we identified and quantified the level of exposure and risk of the housing units. As expected, the houses surrounded by shrublands and complex topography, particularly if isolated, showed very high fire exposure profiles, while the lowest exposure was observed in compact villages and towns.

In this work, we analyzed fire exposure and vulnerability of a 46,000 ha study area located in North-East Sardinia. The study area is very prone to fire, and is characterized by predominant and dense shrublands, extensive wildland-urban interfaces and relevant touristic pressure mostlyin summer. We first analyzedthe vulnerability to firesof about 13,000 housing units, and the closest neighboring (10-m buffer) combining aerial photographs and field surveys. About 40% of the housing units of the study area was classified as wildland-urban interface since it was in contact with the vegetation.Afterwards, we used simulation modeling based on the MTT algorithm of FlamMap (Finney 2006) to estimate fine scale fire exposure. For this analysis, we selected 32 different scenarios to represent the most common and critical conditions for fire occurrence and spread in the study area. For each scenario, we fixed 10,000 fire ignition points and we simulated the spread ofwildfires at a resolution of 20 m. We analyzed fire exposure profiles considering burn probability, flame length, fire size and source-sink ratio (SSR). We finally combined the WUI mapping and characterization results with the outputs obtained from the fire simulations, and we identified and quantified the level of risk of the housing units. As expected, the houses surrounded by shrublands and complex topography, particularly when isolated, showed very high fire exposure profiles and overall risk, while the lowest exposure was observed in compact villages and towns and in areas characterized by strategic fuel management.