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

A new fire policy reinforcing aggressive fire suppression was established in Mediterranean France in response to the devastating wildfires of the 1990s, but to what extent this has changed fire activity yet remains poorly understood. For this purpose, we compared the number and location of ignitions and of burned areas between two 20‐year periods (1975–1994 vs. 1995–2014), in parallel to the changes in fuel covering, human activity promoting ignitions, and fire weather. The number of fires decreased almost continuously since 1975, but sharply after 1994, suggesting an effect of better fire prevention due to the new policy. But the major change in fire activity is a considerable reduction in fire size and burned areas after 1994, especially during summer and in the most fire‐prone places, in response to massive efforts put into fire suppression. These reductions have occurred while the covering by fuel biomass, the human pressure on ignition, and the fire weather index increased, thus making the study area more hazardous. Our results suggest that a strategy of aggressive fire suppression has great potential for counterbalancing the effects of climate changes and human activities and for controlling fire activity in the short term. However, we discuss whether such a suppression‐oriented approach is sustainable in the context of global changes, which cast new fire challenges as demonstrated by the devastative fires of 2003 and 2016. We advocate for a more comprehensive fire policy to come.     

Wildfire Risk Transmission in the Colorado Front Range,USA

Wildfires are a global phenomenon that in some circumstances can result in human casualties, economic loss, and ecosystem service degradation. In this article we spatially identify wildfire risk transmission pathways and locate the areas of highest exposure of human populations to wildland fires under severe, but not uncommon, weather events. We quantify varying levels of exposure in terms of population potentially affected and tie the exposure back to the spatial source of the risk for the Front Range of Colorado, USA. We use probabilistic fire simulation modeling to address where fire ignitions are most likely to cause the highest impact to human communities, and to explore the role that various landowners play in that transmission of risk. Our results indicated that, given an ignition and the right fire weather conditions, large areas along the Front Range in Colorado could be exposed to wildfires with high potential to impact human populations, and that overall private ignitions have the potential to impact more people than federal ignitions. These results can be used to identify high‐priority areas for wildfire risk mitigation using various mitigation tools.     

Assessing Transboundary Wildfire Exposure in the Southwestern United States

We assessed transboundary wildfire exposure among federal, state, and private lands and 447 communities in the state of Arizona, southwestern United States. The study quantified the relative magnitude of transboundary (incoming, outgoing) versus nontransboundary (i.e., self‐burning) wildfire exposure based on land tenure or community of the simulated ignition and the resulting fire perimeter. We developed and described several new metrics to quantify and map transboundary exposure. We found that incoming transboundary fire accounted for 37% of the total area burned on large parcels of federal and state lands, whereas 63% of the area burned was burned by ignitions within the parcel. However, substantial parcel to parcel variation was observed for all land tenures for all metrics. We found that incoming transboundary fire accounted for 66% of the total area burned within communities versus 34% of the area burned by self‐burning ignitions. Of the total area burned within communities, private lands contributed the largest proportion (36.7%), followed by national forests (19.5%), and state lands (15.4%). On average seven land tenures contributed wildfire to individual communities. Annual wildfire exposure to structures was highest for wildfires ignited on state and national forest land, followed by tribal, private, and BLM. We mapped community firesheds, that is, the area where ignitions can spawn fires that can burn into communities, and estimated that they covered 7.7 million ha, or 26% of the state of Arizona. Our methods address gaps in existing wildfire risk assessments, and their implementation can help reduce fragmentation in governance systems and inefficiencies in risk planning.     

Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain)

The present study analyzes the effects of different socioeconomic factors on the frequency of fire ignition occurrence, according to different original causes. The data include a set of documented ignition points in the region of Catalonia for the period 1995–2008. The analysis focused on the spatial aggregation patterns of the ignitions for each specific ignition cause. The point‐based data on ignitions were interpolated into municipality‐level information using kernel methods as the basis for defining five ignition density levels. Afterwards, the combination of socioeconomic factors influencing the ignition density levels of the municipalities was analyzed for each documented cause of ignition using a principal component analysis. The obtained results confirmed the idea that both the spatial aggregation patterns of fire ignitions and the factors defining their occurrence were specific for each of the causes of ignition. Intentional fires and those of unknown origin were found to have similar spatial aggregation patterns, and the presence of high ignition density areas was related to high population and high unemployment rates. Additionally, it was found that fires originated from forest work, agricultural activities, pasture burning, and lightning had a very specific behavior on their own, differing from the similarities found on the spatial aggregation of ignitions originated from smokers, electric lines, machinery, campfires, and those of intentional or unknown origin.     

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.     

Provision of a Wildfire Risk Map: Informing Residents in the Wildland Urban Interface

Wildfires in the wildland urban interface (WUI) are an increasing concern throughout the western United States and elsewhere. WUI communities continue to grow and thus increase the wildfire risk to human lives and property. Information such as a wildfire risk map can inform WUI residents of potential risks and may help to efficiently sort mitigation efforts. This study uses the survey-based contingent valuation (CV) method to examine annual household willingness to pay (WTP) for the provision of a wildfire risk map. Data were collected through a mail survey of the East Mountain WUI area in the State of New Mexico (USA). The integrated empirical approach includes a system of equations that involves joint estimation of WTP values, along with measures of a respondent's risk perception and risk mitigation behavior. The median estimated WTP is around U.S. $12 for the annual wildfire risk map, which covers at least the costs of producing and distributing available risk information. Further, providing a wildfire risk map can help address policy goals emphasizing information gathering and sharing among stakeholders to mitigate the effects of wildfires.     

Toward Improved Communication about Wildland Fire: Mental Models Research to Identify Information Needs for Natural Resource Management

The management of natural- and human-induced wildland fires is an intricate process that must balance two considerations: that of fire as a necessary natural disturbance and that of the risks that fire poses. Reconciling tradeoffs between these risks and benefits is contingent upon informed, directed, and two-way communication between wildland fire managers and stakeholders. In an effort to aid with the design of such a communication effort, this study used mental models research to determine the unique wildland fire information and decision-making needs of stakeholders living at the wildland-urban interface of a fire-prone area. While the analysis revealed many similarities in how stakeholders conceptualized the risks and benefits of wildland fire, many misconceptions and important gaps in understanding on the part of both expert and nonexpert respondents were identified.     

Forest Fire Risk Management and Public Participation in Changing Socioenvironmental Conditions: A Case Study in a Mediterranean Region

Under current conditions of accelerated socioenvironmental change in the Mediterranean forested landscapes, fire is one of the most critical and difficult risks to tackle within the region. This article summarizes the lessons learned from a project based on the participatory integration of qualitative local stakeholders' knowledge with expert GIS fire simulations carried out in the County of El Bages, Catalonia, Spain. First, in this article, a theoretical model--the forest fire circle--is presented in order to explain the reasons for the rise in the damage and frequency of forest fires in this Mediterranean area. Second, it describes the methodology developed and the stages followed during the project. Results show that: (1) the advocacy of old forest reactive management paradigm assumptions and practices based on uncontrolled forest succession can put vast wooded areas of the Mediterranean basin at critical risk; and (2) forest fire management approaches that ignore the crucial role of long-term prevention and local capacity building strategies have failed. In the final section, the content and the specific dimensions of the old reactive paradigm that has characterized forest fire risk management in Catalonia are discussed and contrasted with the possibly emerging preventative paradigm.     

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku,Japan Earthquake and Tsunami

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models—generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out‐of‐sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out‐of‐sample predictions of the total number of ignitions in the region within one or two. Prefecture‐level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response‐covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates.     

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981–2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981–2010, 2011–2040, and 2041–2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041–2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.     

Wildfire Exposure Analysis on the National Forests in the Pacific Northwest,USA

We analyzed wildfire exposure for key social and ecological features on the national forests in Oregon and Washington. The forests contain numerous urban interfaces, old growth forests, recreational sites, and habitat for rare and endangered species. Many of these resources are threatened by wildfire, especially in the east Cascade Mountains fire‐prone forests. The study illustrates the application of wildfire simulation for risk assessment where the major threat is from large and rare naturally ignited fires, versus many previous studies that have focused on risk driven by frequent and small fires from anthropogenic ignitions. Wildfire simulation modeling was used to characterize potential wildfire behavior in terms of annual burn probability and flame length. Spatial data on selected social and ecological features were obtained from Forest Service GIS databases and elsewhere. The potential wildfire behavior was then summarized for each spatial location of each resource. The analysis suggested strong spatial variation in both burn probability and conditional flame length for many of the features examined, including biodiversity, urban interfaces, and infrastructure. We propose that the spatial patterns in modeled wildfire behavior could be used to improve existing prioritization of fuel management and wildfire preparedness activities within the Pacific Northwest region.     

More Large Accidents Through Complexity

Many would tend to characterize modern society as steeped in complexity. This complexity is seen by a number of professional people to imply risk-taking--or risk-making--on a high level. "A single mistake may give consequences of quite different proportions from earlier times," as one critic puts it. This hypothesis of increasing risks on a high level--of worst things getting worse--has been tested on fires in Sweden. Fire has always been a powerfully upsetting agent to the social fabric. Today industrial fires dominate the picture economically. The cost of industrial fires is about three times the cost of residential fires in Sweden. It is of interest to note that the damage cost of the worst fires (as measured in insurance payments) does not form an increasing part of overall fire damage costs, as one would expect from the above hypothesis. Whether the hypothesis holds with regard to indirect costs due to production stand-still is more uncertain. We can conclude that the potential of complexity to create large abnormal occurrences may have been somewhat prematurely announced--at least with regard to fires in Sweden.     

Expecting the Unexpected: Predicting Physiological and Psychological Wildfire Preparedness from Perceived Risk,Responsibility, and Obstacles

People who live in wildfire‐prone communities tend to form their own hazard‐related expectations, which may influence their willingness to prepare for a fire. Past research has already identified two important expectancy‐based factors associated with people's intentions to prepare for a natural hazard: Perceived risk (i.e., perceived threat likelihood and severity) and perceived protection responsibility. We expanded this research by differentiating the influence of these factors on different types of wildfire preparedness (e.g., preparations for evacuation vs. for defending the house) and measured actual rather than intended preparedness. In addition, we tested the relation between preparedness and two additional threat‐related expectations: the expectation that one can rely on an official warning and the expectation of encountering obstacles (e.g., the loss of utilities) during a fire. A survey completed by 1,003 residents of wildfire‐prone areas in Perth, Australia, revealed that perceived risk (especially risk severity) and perceived protection responsibility were both positively associated with all types of preparedness, but the latter did not significantly predict preparedness after controlling for other predictors and demographics. Also, the two new expectancy‐based factors were significantly associated with all types of preparedness, and remained significant predictors of some types of preparedness after controlling for other predictors and demographics: the expectation of being able to rely on an official fire warning and expecting to lose electricity both still predicted less preparedness around house resilience, and expecting to lose water still predicted increased planning preparedness. We discuss public policy implications that follow from this research.     

Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.     

Fire Risk Analysis for Nuclear Power Plants: Methodological Developments and Applications

A methodology to quantify the risk from fires in nuclear power plants is described. This methodology combines engineering judgment, statistical evidence, fire phenomenology, and plant system analysis. It can be divided into two major parts: (1) fire scenario identification and quantification, and (2) analysis of the impact on plant safety. This article primarily concentrates on the first part. Statistical analysis of fire occurrence data is used to establish the likelihood of ignition. The temporal behaviors of the two competing phenomena, fire propagation and fire detection and suppression, are studied and their characteristic times are compared. Severity measures are used to further specialize the frequency of the fire scenario. The methodology is applied to a switchgear room of a nuclear power plant.     

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.     

The Sidewalk as a Contested Space: Women’s Negotiation of Socio-Spatial Processes of Exclusion in Public Urban Space in Saudi Arabia; The Case of Al Tahlia Street

Riyadh is one of the most gender-segregated cities in the world. However, as gender segregation is less enforced on sidewalks, it provides an optimal case study for a space where women and men may be co-present. Thus, this paper aims to increase the understanding of the relationship between sociocultural norms and spatial programming regarding spatio-temporal inclusion or exclusion in public urban spaces. The results show that women’s use and access to sidewalks are influenced by gender norms, religious values, gendered regulations, and generic spatial programming. For instance, regulations limit the use of outdoor seating to men only, thus sidewalks adjacent to, e.g., cafes function as mono-gender spaces dominated by men. However, young women negotiate spatially bounded gender norms through their presence, behaviour, and dress. Although sidewalks are conceived as men’s space, women account for nearly half of the users, but their use often goes unnoticed as women self-regulate their spatio-temporal and visible presence. The study presents six types of women’s spatio-temporal behaviours with varying degrees of visible and invisible users. Ultimately, this paper argues that planning for inclusive sidewalks cannot be addressed solely through the ‘universal’ characterization of space; it should also be supplemented by context-specific knowledge regarding the socio-spatial needs.     

Fire Risks in Oil Refineries: Economic Analysis of Camera Monitoring

A probabilistic method is presented to evaluate the economic value of fire monitoring by closed circuit TV camera in petroleum refineries. The proposed model is restricted to the analysis of risk reduction in an area where fires can be caused either by pump failure or by failure of valves and lines. The benefits come from reducing the time during which the fire grows undetected. Fire growth and expected values of losses are analyzed by a Markov model that includes five phases: (1) active undetected growth, (2) detection, (3) fire growth at the beginning of the firemen's intervention, (4) fire control, and (5) fire extinction. The results (e.g., the expected net present value of the investment) show that the proposed monitoring investment is attractive for an illustrative example.     

The Potential Impact of an Anthrax Attack on Real Estate Prices and Foreclosures in Seattle

This article provides a methodology for the economic analysis of the potential consequences of a simulated anthrax terrorism attack on real estate within the Seattle metropolitan area. We estimate spatially disaggregated impacts on median sales price of residential housing within the Seattle metro area following an attack on the central business district (CBD). Using a combination of longitudinal panel regression and GIS analysis, we find that the median sales price in the CBD could decline by as much as $280,000, and by nearly $100,000 in nearby communities. These results indicate that total residential property values could decrease by over $50 billion for Seattle, or a 33% overall decline. We combine these estimates with HUD's 2009 American Housing Survey (AHS) to further predict 70,000 foreclosures in Seattle spatial zones following the terrorism event.     

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

In the United Kingdom, dwelling fires are responsible for the majority of all fire‐related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three‐part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation.