Wildfire Policy in Mediterranean France: How Far is it Efficient and Sustainable?

Role of land-cover and WUI types on spatio-temporal dynamics of fires in the French Mediterranean area

This work aims at assessing, in the French Mediterranean area, the spatio-temporal trends of fires, including their causes, at fine scale (communities), comparing different periods between 1993 and 2017. These trends were compared to those of land-cover and wildland-urban interface (WUI) which were coupled with a spatial analysis of the ignitions in order to highlight the main drivers and preferential areas. Fire density was highly variable among communities, hotspots being located mostly close to big cities but spatially varying in time in contrast to fire occurrence and burned area. A decrease in the unknown cause proportion and a variation of the cause frequency were highlighted among periods, criminal fires being the most frequent and deleterious, especially before 2009, as well as those due to negligence during private activities, mostly after 2009. Land cover classes significantly varied among periods, artificialized and natural areas presenting a reversed trend compared with agricultural areas. Natural areas were the most affected by ignitions (60%), regardless of the period; this trend is slowly decreasing. WUI represented ∼30% of the study area, the different types varying spatially (denser clustered types mostly located in the South-East) and showed an increase over time, especially for both clustered types but with high variability among communities. Half of the ignitions occurred in WUI, with "very dense clustered" and "scattered" types being the most affected, especially in 2009. Better understanding the spatio-temporal evolution of fires and of their causes should allow refining the fire policies in terms of awareness raising, firefighting means, and land management.

Mapping the forest fire risk zones using artificial intelligence with risk factors data

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Modeling wildfire activity is crucial for informing science-based risk management and understanding the spatiotemporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two-component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10-100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

Despite escalating expenditures in firefighting, extreme fire events continue to pose a major threat to ecosystem services and human communities in Mediterranean areas. Developing a safe and effective fire response is paramount to efficiently restrict fire spread, reduce negative effects to natural values, prevent residential housing losses, and avoid causalties. Though current fire policies in most countries demand full suppression, few studies have attempted to identify the strategic locations where firefighting efforts would likely contain catastrophic fire events. The success in containing those fires that escape initial attack is determined by diverse structural factors such as ground accessibility, airborne support, barriers to surface fire spread, and vegetation impedance. In this study, we predicted the success in fire containment across Catalonia (northeastern Spain) using a model generated with random forest from detailed geospatial data and a set of 73 fire perimeters for the period 2008-2016. The model attained a high predictive performance (AUC = 0.88), and the results were provided at fine resolution (25 m) for the entire study area (32,108 km² ). The highest success rates were found in agricultural plains along the nonburnable barriers such as major road corridors and largest rivers. Low levels of containment likelihood were predicted for dense forest lands and steep-relief mountainous areas. The results can assist in suppression resource pre-positioning and extended attack decision making, but also in strategic fuels management oriented at creating defensive locations and fragmenting the landscape in operational firefighting areas. Our modeling workflow and methods may serve as a baseline to generate locally adapted models in fire-prone areas elsewhere.

Development of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in Greece

A solution to the growing problem of catastrophic wildfires in Greece will require a more holistic fuel management strategy that focuses more broadly on landscape fire behavior and risk in relation to suppression tactics and ignition prevention. Current fire protection planning is either non-existent or narrowly focused on reducing fuels in proximity to roads and communities where ignitions are most likely. A more effective strategy would expand the treatment footprint to landscape scales to reduce fire intensity and increase the likelihood of safe and efficient suppression activities. However, expanding fuels treatment programs on Greek landscapes that are highly fragmented in terms of land use and vegetation requires: (1) a better understanding of how diverse land cover types contribute to fire spread and intensity; and (2) case studies, both simulated and empirical, that demonstrate how landscape fuel management strategies can achieve desired outcomes in terms of fire behavior. In this study, we used Lesvos Island, Greece as a study area to characterize how different land cover types and land uses contribute to fire exposure and used wildfire simulation methods to understand how fire spreads among parcels of forests, developed areas, and other land cover types (shrublands, agricultural areas, and grasslands) as a way to identify fire source–sink relationships. We then simulated a spatially coordinated fuel management program that targeted the fire prone conifer forests that generally burn under the highest intensity. The treatment effects were measured in terms of post-treatment fire behavior and transmission. The results demonstrated an optimized method for fuel management planning that accounts for the connectivity of wildfire among different land types. The results also identified the scale of risk and the limitations of relying on small scattered fuel treatment units to manage long-term wildfire risk.

Recent wildfires in Central Chile: Detecting links between burned areas and population exposure in the wildland urban interface

Wildfires are gaining importance in the Mediterranean regions owing to climate change and landscape changes due to the increasing closeness between urban areas and forests prone to wildfires. We analysed the dry season wildfire occurrences in the Mediterranean region of Central Chile (32°S-39°30' S) between 2000 and 2017, using satellite images to detect burned areas, their landscape metrics and the land use and covers (vegetal) pre-wildfire, in order to determine the population living in areas that may be affected by wildfires. The existing regulations in western Mediterranean countries (Portugal, Spain, France, and Italy) were used to identify and define the wildland-urban interface (WUI) areas, quantifying the people inhabiting them and estimating the population affected by burned areas from 2001 to 2017. We used the Google Earth Engine to process MODIS products and extract both burned areas and land covers. We detected that 25% of the urban population inhabits WUI areas (i.e. Biobío, Araucanía and Valparaíso regions) where the urban population exposed to burned areas exceeds 40%. Most of the land use and land covers affected by wildfires are anthropogenic land covers, classified as savannas, croplands, evergreen broadleaf forests and woody savannas, representing >70% of the burned areas. Urban areas show only 0.6% of the burned surface from 2001 to 2017. We estimate that 55,680 people are potentially affected by wildfires, and 50% of them are in just one administrative region. These results show the imperative need for public policies as a regulating force for establishing WUI areas with the purpose of identifying wildfire risk in urban areas, such as establishing prevention methods as firewalls and prescribed fires.

Fire regime dynamics in mainland Spain. Part 2: A near-future prospective of fire activity

The current research belongs to a series of two manuscripts aiming at describing spatial-temporal dynamics of fire regime and its drivers in Spain. In this work, we present the first attempt to produce a spatial-temporal delimitation of homogeneous fire regime zones in Spain providing insights into the near future. The analyses were based on historical fire records; leveraging autoregressive models to project fire features into the near future. We evaluated the spatial extent of homogenous fire regime zones in three different periods: past (1974-1994), current (1995-2015) and future (2016-2036). To do so, we applied Principal Component Analysis and Ward's hierarchical clustering to identify zones of fire regime on the basis of the spatial and temporal arrangement of their main fire features: number of fires, burned area, burnt area from natural-caused fires, incidence of large fires (> 100 ha) and seasonality. Clusters of fire regime were trained in the current period, being later projected into the past and future periods using of k-Nearest Neighbor classification. ARIMA modeling forecasted a shrinkage in all fire features except natural-caused fires that remained stable. Overall, we detected a transition from significant fire incidence in the past towards a situation with moderate impact of fires in the near future. The Mediterranean coast experienced the largest decline in fire activity with few locations maintaining the historical levels of occurrence of large fires. On the other hand, the Northwestern end of Spain depicted a progression towards winter fire activity while still linked to large fires. This pattern persisted in the near future along the northern coast, whereas an intermix of minor fire progression and regression was expected thorough the hinterlands and the Mediterranean.

Fire regime dynamics in mainland Spain. Part 2: A near-future prospective of fire activity

The current research belongs to a series of two manuscripts aiming at describing spatial-temporal dynamics of fire regime and its drivers in Spain. In this work, we present the first attempt to produce a spatial-temporal delimitation of homogeneous fire regime zones in Spain providing insights into the near future. The analyses were based on historical fire records; leveraging autoregressive models to project fire features into the near future. We evaluated the spatial extent of homogenous fire regime zones in three different periods: past (1974-1994), current (1995-2015) and future (2016-2036). To do so, we applied Principal Component Analysis and Ward's hierarchical clustering to identify zones of fire regime on the basis of the spatial and temporal arrangement of their main fire features: number of fires, burned area, burnt area from natural-caused fires, incidence of large fires (> 100 ha) and seasonality. Clusters of fire regime were trained in the current period, being later projected into the past and future periods using of k-Nearest Neighbor classification. ARIMA modeling forecasted a shrinkage in all fire features except natural-caused fires that remained stable. Overall, we detected a transition from significant fire incidence in the past towards a situation with moderate impact of fires in the near future. The Mediterranean coast experienced the largest decline in fire activity with few locations maintaining the historical levels of occurrence of large fires. On the other hand, the Northwestern end of Spain depicted a progression towards winter fire activity while still linked to large fires. This pattern persisted in the near future along the northern coast, whereas an intermix of minor fire progression and regression was expected thorough the hinterlands and the Mediterranean.

The role of short-term weather conditions in temporal dynamics of fire regime features in mainland Spain

In this paper we investigate spatial-temporal associations of fire weather danger and fire regime features from 1979 to 2013. We analyze monthly time series of fire activity (number of fires and burned area) and fire weather danger rating indices (Fire Weather Index, Burning Index and Forest Fire Danger Index) at two spatial scales: (i) regionally, splitting the Spanish mainland into Northwest, Hinterland and Mediterranean regions; and (ii) locally, using the EMCWF grid. All analyses are based on decomposing time series to retrieve differential indicators of seasonal cycles, temporal evolution and anomalies. At regional scale we apply lagged cross-correlation analysis (4 lags or months before fire) to explore seasonal associations; and trend detection tests on the temporal evolution component. At the local scale, we calculate Pearson correlation coefficients between each individual index and the 18 possible fire-activity subsets according to fire size (all sizes, >1 ha and >100 ha) and source of ignition (natural, unintended and arson); this analysis is applied to both cycles, temporal and anomalies series. Results suggest that weather controls seasonal fire activity although it has limited influence on temporal evolution, i.e. trends. Stronger associations are detected in the number of fires in the Northwest and Hinterland regions compared to the Mediterranean, which has desynchronized from weather since 1994. Cross-correlation analysis revealed significant fire-weather associations in the Hinterland and Mediterranean, extending up to two months prior fire ignition. On the other hand, the association between temporal trends and weather is weaker, being negative along the Mediterranean and even significant in the case of burned area. The spatial disaggregation into grid cells reveals different spatial patterns across fire-activity subsets. Again, the connection at seasonal level is noticeable, especially in natural-caused fires. In turn, human-related wildfires are occasionally found independent from weather in some areas along the northern coast or the Ebro basin. In any case, this effect diminishes as the size of the fire increases. Our work suggests that for some regions of mainland Spain, these fire danger indices could provide useful information about upcoming fire activity up to two months ahead of time and this information could be used to better inform wildland fire prevention and suppression activities.

Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain

Southern European countries rely largely on fire suppression and ignition prevention to manage a growing wildfire problem. We explored a more wholistic, long-term approach based on priority maps for the implementation of diverse management options aimed at creating fire resilient landscapes, restoring cultural fire regimes, facilitating safe and efficient fire response, and creating fire-adapted communities. To illustrate this new comprehensive strategy for fire-prone Mediterranean areas, we developed and implemented the framework in Catalonia (northeastern Spain). We first used advanced simulation modeling methods to assess various wildfire exposure metrics across spatially changing fire-regime conditions, and these outputs were then combined with land use maps and historical fire occurrence data to prioritize different fuel and fire management options at the municipality level. Priority sites for fuel management programs concentrated in the central and northeastern high-hazard forestlands. The suitable areas for reintroducing fires in natural ecosystems located in scattered municipalities with ample lightning ignitions and minimal human presence. Priority areas for ignition prevention programs were mapped to populated coastal municipalities and main transportation corridors. Landscapes where fire suppression is the principal long-term strategy concentrated in agricultural plains with a high density of ignitions. Localized programs to build defensible space and improve self-protection on communities could be emphasized in the coastal wildland-urban interface and inner intermix areas from Barcelona and Gerona. We discuss how the results of this study can facilitate collaborative landscape planning and identify the constraints that prevent a longer term and more effective solution to better coexist with fire in southern European regions.

Clearing shrubland and extensive livestock farming: Active prevention to control wildfires in the Mediterranean mountains

Forest fires are one of the main environmental problems in Mediterranean environments and different fire prevention policies have been applied: livestock grazing, prescribed fires and a combination of both. However, none present satisfactory results. In that context, in 1986 the Regional Government of La Rioja started the Plan for Shrub Clearing (PSC), combining shrub clearings and livestock grazing to control fires and improve the land management of abandoned mountain areas. Our study aims to analyse the effects of shrub clearings on forest fires in La Rioja and to compare the main results with those observed in Spain in the last 30 years. We apply an interdisciplinary methodology based on the analysis of the evolution of cleared areas, the evolution of wildfires in La Rioja and Spain, mapping land use and land cover changes, and quantifying the combustibility. Results obtained in La Rioja are very positive compared with the evolution of Spain, both in the reduction of the number of fires and the burned areas. Decreases in the combustible material, fuel load and biomass and in the occurrence of fires (>1 ha) were observed. In addition, clearing shrubland and extensive livestock provided other environmental benefits (i.e. mosaic landscapes, ecosystem services). Finally, this study suggested that they are good and sustainable techniques to prevent and control wildfires and they could be used as a land management strategy in other Mediterranean areas.

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

In crisis situations, systems, organizations, and people must react and deal with events that are inherently unpredictable before they occur: vital societal functions and thus infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Progress concerning its conceptualization has been made but it remains difficult to assess and apply in practice. The results of this article stem from a literature review allowing the analysis of current advances in the development of proposals to improve the management of infrastructure resilience. The article: (i) identifies different dimensions of resilience; (ii) highlights current limits of assessing and controlling resilience; and (iii) proposes several directions for future research that could go beyond the current limits of resilience management, but subject to compliance with a number of constraints. These constraints are taking into account different hazards, cascade effects, and uncertain conditions, dealing with technical, organizational, economical, and human domains, and integrating temporal and spatial aspects.