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.

Rural and forest fires represent one of the most significant sources of emissions in the atmosphere of trace gases and aerosol particles, which significantly impact carbon budget, air quality, and human health. This paper aims to illustrate an integrated modelling approach combining spatial and non-spatial inputs to provide and enhance the estimation of GHG and particulate matter emissions from surface fires using Italy as a case study over the period 2007-2017. Three main improvements characterize the approach proposed in this work: (i) the collection and development of comprehensive and accurate data inputs related to burned area; (ii) the use of the most recent data on fuel type and load; and (iii) the modelling application to estimate fuel moisture, burning efficiency, and fuel consumption considering meteorological factors and combustion phases. On average, Italy's GHG and particulate matter emissions were 2621 Gg yr^-1, ranging from a minimum of 772 Gg yr^-1 in 2013 to a maximum of 7020 Gg yr^-1 in 2007. Emissions from fire disturbances in broadleaf forests, shrublands, and agricultural fuel types account for about 76 % of the total. Results were compared with global and national inventories and showed good agreement, especially considering CO₂ and particulate matter. The approach of this study added confidence in emission estimates, and the results can be utilized in decision support systems to address air quality management and fire impact mitigation policies.

Background: Wildfire frequency, magnitude and impacts in wildland–urban interface (WUI) areas are increasing in the Mediterranean Basin. Aims: We investigated the role played by socio-economic, vegetation, climatic, and zootechnical drivers on WUI wildfire patterns (area burned and wildfire ignitions) in Sardinia, Italy. Methods: We defined WUI as the 100-m buffer area of the anthropic layers. We created a comprehensive and multi-year dataset of explanatory variables and wildfires, and then trained a set of models and evaluated their performances in predicting WUI fires. We used the best models to assess the single variable’s importance and map wildfire patterns. Key results: Random Forest and Support Vector Machine were the best performing models. In broad terms, wildfire patterns at WUI were influenced by socio-economic factors and herbaceous vegetation types. Conclusions: Machine learning models can be useful tools to predict wildfire ignitions and area burned at WUI in Mediterranean areas. Implications: Improved knowledge of the main drivers of wildfires at WUI in fire-prone Mediterranean areas can foster the development or optimisation of wildfire risk reduction and prevention strategies.

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.

In this work, we will present the transboundary wildfire simulation system used to analyze fine-scale (100-m resolution) wildfire hazard and exposure in the regions of the Italy–France Maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria and Provence–Alpes–Côte d’Azur), which covers about 93,000 km² of land. The study area is characterized by a variety of ecological, cultural, anthropic and touristic values that are heavily threatened by wildfires, mostly during the summer season. The wildfire issues in the area are expected to worsen in future years due to a number of contributing factors, including climate change, the abandonment of agrosilvopastoral areas with the resulting increase in fuel load and continuity and the expansion of urban interfaces into fire-prone wildlands. To perform our analysis, among the large set of wildfire spread models available, in the framework of the MED-Star project, we have selected the Minimum Travel Time (MTT) algorithm of Finney (2002) as implemented in FConstMTT. As inputs for our simulations, we produced underlying fuel maps derived from regional land use and forest maps and focused on the historical moisture and weather conditions and patterns associated with the largest wildfires that affected the study area in the last 20 years. The simulation outputs allowed us to characterize spatial variations in wildfire spread and hazard among and within provinces and regions and to identify the most significant hot-spot areas. In addition, we combined the simulated raster data with several explanatory variables and spatial layers (e.g., wildland–anthropic interface maps; provinces; climatic zones; etc.) to characterize exposure levels at different levels. Findings from this work improve regional awareness and knowledge about the spatial dynamics and patterns of wildfire exposure and hazard in the Italy–France Maritime cooperation area.

Wildfire simulators represent a fundamental tool for the planning of wildfire prevention operations, the real-time prediction of fire propagation and thus the adoption of appropriate firefighting strategies. In this paper, we provide an example of the application of the Web-based Wildfire Simulator (WWS) on fires that occurred during the 2021 fire season in Sardinia (Italy), and in particular on the Montiferru fire, a large fire that occurred in the central-western area of the island and mainly driven by the wind. The simulator used high-resolution data provided by the WRF meteorological model initialized with both GFS forecasts and ERA5-ECMWF reanalysis. Vegetation characteristics and associated fuel pattern maps were derived from the analysis of recent Sentinel2 satellite data. Fuel moisture data were estimated using weather data predicted by WRF. The evaluation of the model was carried out using the data collected in field and related to the actual fire propagation observed during the various time steps, and to the related firefighting interventions. In addition, the meteorological data recorded by a few meteorological stations located within the simulation domain and the data on fire severity collected in field were analyzed. The model was able to predict the temporal development of fire events with a good accuracy, considering the mean rate of spread and the rate of spread of the different wildfire sectors. Most of the accuracy can be explained by the goodness of the weather forecasts obtained through the use of GFS forecasts and of the fuel model map. The model was able to accurately predict the propagation of the main fire front, while several deviations from the real perimeter were observed with regard to the fire flanks propagation. In conclusion, wildfire simulators represent a useful tool for all phases of fire prevention, for their operational use in real-time, and therefore to provide information for the firefighting.