Cristina Vega-García

Wildfires are the primary disturbance in Mediterranean forest ecosystems, where climate change is projected to intensify fire hazard by reducing fuel moisture and altering fire regimes. This study evaluates the long-term effectiveness of four forest management archetypes (risk mitigation, adaptation to climate change, carbon stock promotion, and Business-as-Usual) in reducing future wildfire hazard across four SSP climate scenarios. We utilized a robust methodological framework coupling the medfate stand-level dynamics model with the Fuel Characteristics Classification System to project surface fire rate of spread (ROS) in Pinus nigra and Pinus halepensis stands. We evaluated the change in basal area, shrub cover and ROS across all combinations of management and climate scenarios in the period 2020-2100, analyzing the trade-offs between ROS change and the required treatment intensity in two representative study sites in Spain hosting typical Pinus halepensis (Requena) and Pinus nigra (Montmajor) forest communities. Our results suggest that management efficacy is contingent on site-specific characteristics and climate. The risk mitigation strategy, characterized by the most intensive fuel removal, provides consistent ROS reduction but represents the least cost-efficient choice. In contrast, BAU and carbon stock promotion represent effective alternatives (in terms of treatment intensity) in specific contexts, such as high-warming scenarios for Pinus halepensis or Pinus nigra stands, respectively. The adaptation to climate change strategy (designed to promote shading and moisture) paradoxically increases ROS in unburned, arid plots (Requena). This counter-intuitive outcome is attributed to its weaker shrub control, allowing the understory to become a primary surface fuel.

Armed conflicts and other types of violence are key drivers of human-induced landscape change. Since March 2011, Syria has been embroiled in a prolonged and devastating armed conflict causing immense human suffering and extensive destruction. As a result, over five million people have been forced to seek refuge outside the country’s borders, while more than six million have been internally displaced. This study focuses on examining the impact of this conflict on forest cover by identifying the drivers of forest change. To assess this change, Landsat and PALSAR imagery were used to differentiate between forested and non-forested areas. Spectral information was synthetized using the Tasseled Cap transformation and the time series data was simplified and despiked using the LandTrendr algorithm. Our results show that between 2010 and 2019 there was a substantial decrease of 19.3% in forest cover, predominantly concentrated in the northwestern region of Syria. This decline was induced by the armed conflict, with several key drivers contributing to the decline, such as illegal logging activities conducted by both locals and refugees living in nearby forest areas. Drivers such as proximity to refugee camps, roads, and settlements played an important role in producing this change by facilitating access to forests. In addition, the occurrence of explosive events such as bombings and shelling near forests also contributed to this decline by causing forest fires. To mitigate further deforestation and reduce dependence on forests for fuel, it is crucial for local governments in the post-conflict period to offer sustainable alternatives for heating and cooking to both the local populations and refugees. Additionally, governments are recommended to enforce strict laws and regulations to protect forests and combat illegal logging activities. These measures are essential for preserving and restoring forests, promoting environmental sustainability, and ensuring the well-being of both displaced populations and local communities.

Fire ignition probability is an essential component of most fire risk assessment frameworks. This study, framed within the H2020 project FirEUrisk, features a cohesive modelling approach in a set of representative regions (pilot sites; PS) in terms of fire activity across the European territory. These PS encompass different wildfire regimes in contrasting environmental settings: PS-1 Northern Europe, Kalmar Iän (South-East Sweden); PS-2 Central Europe, Southern Brandenburg and Eastern Saxony (Germany), North Bohemia (Czechia), and Lower Silesia (Poland); PS-3 Central Portugal; PS-4 Barcelona province (Spain); and PS-5 Attica region (Greece). Our main goal was to develop a common approach to model human-caused ignitions at a fine-grained spatial resolution (100 meters). For each pilot site we: (i) ascertain which factors influence ignition, hence, addressing potential differences in driving forces and, (ii) provide a spatial-explicit depiction of the patterns of ignition probability. For that propose, we fitted a Random Forest (RF) model in each PS from historical fire records (compiled by local fire agencies) and geospatial layers for land cover, accessibility, and population related factors. All models attained a high predictive accuracy, with AUCs that ranging from 0.69 (Northern Europe) to 0.89 (Attica Region). In turn, the most relevant explanatory variable was the population density that ranked most influential in four out of the five PS, followed by the fuel type, distance to roads, distance to the WUI, and percent cover of forest and wildlands. These findings are a valuable product to upscale future solutions at regional level (beyond NUTS3-type areas), conduct fire behavior modelling simulations, and enrich the science-based decisions which come from the forest and fire management agents at national and European level.

Contemporary fires pose a threat to human assets in southern European regions, and aggressive suppression efforts are ineffective during extreme wildfire episodes. Nevertheless, we currently lack a comprehensive and well-defined strategy focused on creating fire-adapted communities that can effectively withstand the impact of extreme events. This paper aims to define wildfire risk, provide a proof-of-concept risk assessment example conducted in various Mediterranean regions, discuss prevalent misconceptions, and outline future research needs for developing a long-term fire-wise strategy in the European Union. Anticipating surprise catastrophic wildfires becomes crucial in preventing losses within a changing-climate scenario. Moreover, stochastic simulation modeling enables the generation of a plausible set of potential fire footprints that are likely to occur in the coming years. By utilizing a large set of modeled fire footprints and burning fire intensities in the home ignition zone, in this work, we predicted expected losses to buildings. The results showed spatially explicit outcomes and emphasize the need for implementing a risk assessment framework to identify human communities and wildland-urban interface areas at risk. The development and implementation of a probabilistic fire modeling system represent a crucial preliminary step toward developing a long-term adaptation strategy to confront the wildfire crisis in Europe.

As large fires threaten human assets in Mediterranean areas, creating fire-adapted communities became a core long-term goal to cope with extreme wildfires. In addition to early detection and risk-monitoring tools, preemptive efforts are essential to reduce wildfire spread, prevent catastrophic losses, and help minimize casualties. However, despite extensive scientific development to predict potential fire impacts, the poor risk communication with landscape and urban planners is often a barrier to translating primary outcomes into operational projects. Comprehensive solutions for creating fire-adapted communities indeed require not only fuel reduction treatments but also civil protection and fire suppression considerations to facilitate a safe and effective response. In this study, we assembled modeled fire footprints and fire intensity grids with asset locations to generate a set of risk mitigation maps in a large fire-prone Mediterranean area. Previous modeling works showed strong capabilities for predicting future catastrophic fires, as well as containment probabilities, but the integration of landscape-scale management efforts, community protection plans, and strategic fire suppression opportunities is still incipient. First, we used observed ignitions and past fire perimeters to model initial attack success and containment probability. We then calibrated and implemented a fire modeling approach to replicate thousands of years or iterations assuming wildfire season extreme weather conditions. Finally, the results were merged and summarized to provide a set of maps to inform large-scale ongoing risk reduction programs. We present new ideas and geospatial data processing techniques that may facilitate the assimilation of modeled predictions within fire management plans to adapt faster to changing fire regimes. The methods developed in this study could be replicated in southern European Union countries and other wildland–urban interface areas elsewhere.

Lightning-caused fires are comparatively rare in Europe, but they may affect remote forested areas and result in large-scale burnings

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km² of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

Plant diversity is a core value of forests and is rapidly becoming a primary management goal under the threat of global environmental changes. Changing conditions, including forestry interventions, or lack of them, may endanger its preservation. Abandonment of management in forests previously subjected to a multipurpose silviculture and secondary succession is hypothesized to have altered the biodiverse Mediterranean forests in recent years and affected plant diversity. We used data in national forest inventory plots and local landscape ecology metrics from forest cartography, combined with artificial neural networks, to predict richness and Shannon diversity indices for the tree and shrub layers of several Mediterranean forest types. We found that richness and diversity depend on forest structure and on local landscape patterns, and also, though to a lesser degree, on site conditions (mainly soil pH), but not on forest intervention. In order to benefit plant diversity in the forest landscapes analyzed, forest management practices need to promote diameter variety, the presence of large trees, tree cover, variation in the height of trees and shrubs, and a heterogeneous local landscape at the stand level. Aleppo pine forests and Scots pine forests showed more consistent results in their models than cork oak and black pine forests, both of which require further research.

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.