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The current study attempts to assess the effect of community preparedness on property damage costs during wildfires. The focus is primarily on how various aspects of community preparedness, such as early warning systems, early risk assessment, emergency response plans, and fire-resistant landscaping, influence the extent of property damage costs during wildfires. For this purpose, data were collected from 384 Greek residents from different regions of the country using an online questionnaire. In this case, analysis was performed utilizing SPSS version 22.0. According to the findings, survey respondents replied that fire suppression was the most common property cost associated with wildfire. The study contributes to existing knowledge by providing insights into the specific factors that affect property damage expenditure during wildfires, specifically the intricate relationship between the expenses of property loss caused by wildfires and community preparation. The study's findings can be utilized by policymakers and communities to improve preparedness plans and consequently decrease the impact of wildfires on property and people. [ABSTRACT FROM AUTHOR]

Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and a quasi-experimental design. We assess the evolving nature of social vulnerability at a local scale, emphasizing both spatial and temporal dynamics. Initially, we identified social vulnerability trends in relation to varying levels of wildfire risk. The research then employs propensity score matching to contrast areas affected by wildfires in 2012 with similar non-affected regions, thereby quantifying the short-term shifts in social vulnerability post-wildfires. The results indicate that regions with a high wildfire risk may display elevated vulnerability, characterized by an increase in unemployment rates and a reduction in high-income households. These findings tentatively demonstrate the compounded effect of wildfires on already vulnerable populations, highlighting the critical need for targeted interventions. Ultimately, this study underscores the importance of integrating dynamic social vulnerability assessments into wildfire management and planning, aiming to enhance community resilience and equitable resource distribution in the face of escalating wildfire threats. [ABSTRACT FROM AUTHOR]

In recent decades, wildfires have become common disasters that threaten people's lives and assets, particularly in wildland–urban interfaces (WUIs). Developing an effective evacuation strategy for a WUI presents challenges to emergency planners because of the spatial variations in biophysical hazards and social vulnerability. The aim of this study was to map priority WUIs in terms of evacuation. The factors considered were the seriousness of the risk of wildfire exposure, and the population centres whose greatest constraints on the evacuation process stemmed from the nature of the exposed population and the time required to travel to the nearest shelter/refuge. An integrated framework linking wildfire hazard, social vulnerability, and the time taken to travel by foot or by car to the nearest refuge/shelter was applied. The study area includes two municipalities (Lousã and Sertã) in the mountainous areas of central Portugal that are in high-wildfire-risk areas and have very vulnerable and scattered pockets of exposed population. The combination of wildfire risk and travelling time to the nearest shelters made it possible to identify 20% of the WUIs that were priority areas for evacuation in the case of Sertã. In the case of Lousã, 3.4% were identified, because they were highly exposed to wildfire risk and had a travelling time to the nearest shelter of more than 15 min on foot. These results can assist in designing effective pre-fire planning, based on fuel management strategies and/or managing an effective and safe evacuation. [ABSTRACT FROM AUTHOR]

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. [ABSTRACT FROM AUTHOR]

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as 'exposures', which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, 'exposure risk' denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method's efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O   (m × n × p) to O   (m × n) . [ABSTRACT FROM AUTHOR]

This study explores the relationship between wildfire exposure, social vulnerability, and community resilience across the 26 states east of the Mississippi River. This work centers around one research question: are there spatial differences in wildfire exposure that disproportionately impact disadvantaged communities in the Eastern United States over the recent period (2000–2020)? Employing remotely sensed wildfire data and ancillary datasets, we analyze and map the extensive wildfire exposure in the Eastern United States and compare it with spatial metrics of social vulnerability and community resilience to examine the social burdens of wildfire exposure in the Eastern U.S. A discernible wildfire exposure pattern emerges, with the Southeast bearing the highest exposure levels, largely attributed to human-caused and prescribed burning. By establishing a measure of disadvantaged counties using social vulnerability and community resilience, we identify regions where wildfire exposures could have the most adverse impact—areas characterized by highly vulnerable populations and limited community capacity to respond effectively to potential events. In evaluating wildfire risk, we conclude that considering not only exposure levels but also the inclusion of disadvantaged areas (incorporating social vulnerability and community resilience) is essential for understanding the disparate impact of wildfires on individuals and the communities where they live. [ABSTRACT FROM AUTHOR]

We investigated the influence of fire severity, logging of burnt wood, local ecological factors and their interaction on the natural regeneration, survival and growth of maritime pine (Pinus pinaster Ait.), following a fire that took place in 2005. During the period 2006–2020, a sample of 1900 seedlings were monitored, in which three post-fire treatments were applied: (1) Early logging (before seedling emergence); (2) Delayed logging (after emergence); and (3) No management. Multivariate semi-parametric and non-parametric techniques were used to model seedling survival, estimated density and growth of natural pine regeneration. Seedling survival was 31% with a mean density of more than 2000 seedlings/ha at the end of the study period. Logging before seedling emergence was positively related with pine survival and density. Delayed logging resulted in the lowest seedling density and regeneration. Fire severity had a negative influence on regeneration density. The findings indicate that site conditions and fire severity have a stronger influence on natural regeneration of maritime pine than subsequent post-fire management treatments. In order to ensure the presence of maritime pine in pure or mixed stands, silvicultural work is required to control competition from other species and reduce the risk of new wildfires. [ABSTRACT FROM AUTHOR]

In many landscapes worldwide, fire regimes and human–fire interactions were reorganised by colonialism and continue to be shaped by neo-colonial processes. The introduction of fire suppression policies and state-centric property-rights systems across conservation areas and the intentional erasure of Indigenous governance systems and knowledge have served to decouple Indigenous fire-dependent communities from culturally mediated fire regimes and fire-adapted landscapes. This has driven a decline in anthropogenic fires while simultaneously increasing wildfire risk where Indigenous people have been excluded, resulting in widespread social–ecological vulnerabilities. Much contemporary fire research also bears colonial legacies in its epistemological traditions, in the global geographical distribution of research institutions, and the accessibility of research outputs. We report on a two-day workshop titled 'Fire Management Across Contested Landscapes' convened concurrently in Nairobi, Kenya, and London, UK. The workshop formed part of a series of workshops on 'Decolonising Fire Science' held by the Leverhulme Centre for Wildfires, Environment and Society, UK. The workshop in Nairobi invited diverse Kenyan stakeholders to engage in participatory activities that facilitate knowledge sharing, aiming to establish an inclusive working fire network. Activities included rich pictures, world café discussions, participatory art, and the co-development of a declaration to guide fire management in Kenya. Meanwhile, in London, Leverhulme Wildfires researchers explored participatory research methodologies including rich pictures and participatory video, and developed a declaration to guide more equitable research. There were opportunities throughout the workshop for participants in Nairobi and London to engage in dialogue with one another, sharing their experiences and understandings of complex fire challenges in Kenya and globally. [ABSTRACT FROM AUTHOR]

Background: Climate change is a strong contributing factor in the lengthening and intensification of wildfire seasons, with warmer and often drier conditions associated with increasingly severe impacts. Land managers are faced with challenging decisions about how to manage forests, minimize risk of extreme wildfire, and balance competing values at risk, including communities, habitat, air quality, surface drinking water, recreation, and infrastructure. Aims: We propose that land managers use decision analytic frameworks to complement existing decision support systems such as the Interagency Fuel Treatment Decision Support System. Methods: We apply this approach to a fire-prone landscape in eastern Washington State under two proposed landscape treatment alternatives. Through stakeholder engagement, a quantitative wildfire risk assessment, and translating results into probabilistic descriptions of wildfire occurrence (burn probability) and intensity (conditional flame length), we construct a decision tree to explicitly evaluate tradeoffs of treatment alternative outcomes. Key Results: We find that while there are slightly more effective localized benefits for treatments involving thinning and prescribed burning, neither of the UWPP's proposed alternatives are more likely to meaningfully minimize the risk of wildfire impacts at the landscape level. Conclusions: This case study demonstrates that a quantitatively informed decision analytic framework can improve land managers' ability to effectively and explicitly evaluate tradeoffs between treatment alternatives. [ABSTRACT FROM AUTHOR]

As climate change increases the frequency and severity of wildfires across the Western U.S., there is an urgent need for improved wildfire preparedness and responses. Socially marginalized communities are particularly vulnerable to wildfire effects because they disproportionately lack access to the resources necessary to prepare for and recover from wildfire and are frequently underrepresented in the wildfire planning process. As an exemplar of how to understand and improve preparedness in such communities, this research identified communities in Ventura County facing heightened marginalization and risk of wildfire using spatial analysis. Researchers then deployed a county-wide survey and held focus groups in two communities identified in the spatial analysis. Research revealed that non-English speakers, women, people of color, and newer residents in Ventura County are less prepared for wildfire than other groups. Based on these findings, this paper recommends an expansion of traditional risk mitigation programs, strengthened community engagement efforts, and strategies that increase community resources and leadership to decouple marginalization and wildfire vulnerability. [ABSTRACT FROM AUTHOR]

BRIDGE (bridging science and local communities for wildfire risk reduction) is an action–research project developing a community-based disaster risk reduction (CBDRR) process which is being fostered through a collaborative innovation laboratory (InnoLab). BRIDGE integrates different forms of knowledge and action, linking science and local communities to reduce their vulnerability and to enhance strategies for forest fire risk reduction. Applied in Monchique, a forest-fire-prone municipality in Portugal, the InnoLab creates a space for dialogue and knowledge sharing between multiple actors that, directly or indirectly, manage forest territories. BRIDGE attempts to facilitate social learning about forest fire risks, strengthen collaborative networks and enhance adaptive capacities (socially and institutionally) for forest fire prevention. This paper shares results of the InnoLab activities, which include (i) school community activities on wildfire risk perception; (ii) participatory mapping of Monchique's vulnerabilities and risks; (iii) participatory sessions to create the community visioning for Monchique's territory, to define priorities and capacities to be improved, and to identify functions and responsibilities to act upon; and (iv) workshop on CBDRR strategies for action. Lessons are shared on how InnoLab can represent an innovative participatory approach to promote CBDRR processes in forest-fire-prone territories by both contributing to the recognition of local knowledge systems and encouraging the active role of forest communities through strengthened local networks for a more lasting commitment to forest fire risk reduction policy. [ABSTRACT FROM AUTHOR]

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard's likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments. [ABSTRACT FROM AUTHOR]

The increasing incidence of wildfires in the Mediterranean region has raised significant scientific and environmental concerns. This study focuses on a retrospective analysis of wildfire ignition and propagation within the context of the Monte Catillo Natural Reserve in Italy. After conducting a comprehensive review of the current state-of-the-art wildfire susceptibility mapping, propagation modeling, probability assessment, forest vulnerability models, and preventive silvicultural measures, we examine the regulatory framework surrounding wildfires in the national context, with a specific focus on prevention, prediction, and active firefighting measures. A geostatistical model of wildfire occurrence was developed, starting with the characterization of the area vegetation and anthropogenic factors influencing wildfire ignition. After that, wildfire observations from the period between 2010 and 2021 were included. The objective is to generate a wildfire hazard map for two distinct vegetation communities. To accomplish this, a statistical analysis was applied using the Poisson Model, assessing its goodness-of-fit by comparing observed frequencies with experimental data through the chi-square test. In conclusion, this model serves as a valuable tool for characterizing wildfire hazards, including ignition probabilities and propagation scenarios, within the Monte Catillo Natural Reserve. The research significantly contributes to enhancing our understanding of wildfire dynamics and plays a crucial role in the development of effective strategies for wildfire risk management. [ABSTRACT FROM AUTHOR]

Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management. [ABSTRACT FROM AUTHOR]

Anyone who has tried lighting a campfire on a windy day can appreciate how difficult it could be. However, despite real-life experience and despite laboratory experiments which have demonstrated that fire ignition risk dramatically decreases beyond a certain wind threshold, current fire weather indices (FWIs) do not take this effect into account and assume a monotonic relation between wind velocity and ignition risk. In this paper, we perform a global analysis which empirically quantifies the probability of ignition as a function of wind velocity. Using both traditional methods (a logistic regression and a generalized additive model) and machine learning techniques, we find that beyond a threshold of approximately 3–4 m/s, the ignition risk substantially decreases. The effect holds when accounting for additional factors such as temperature and relative humidity. We recommend updating FWIs to account for this issue. [ABSTRACT FROM AUTHOR]

In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic responses to wildfires in the Tangier-Tetouan-Al Hoceima (TTA) region, widely reputed to exhibit the most significant incidences of wildfires in Morocco. We adopted a combined approach, using burned area products (Fire_CCI51: 2002–2020) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and active fires from the Fire Information for Resource Management System (FIRMS: 2001–2022) and processing them with spatiotemporal statistical methods: optimized hotspot analysis (OHA) and emerging hotspot analysis (EHA). The main findings indicate that the TTA region recorded an average of 39.78 km2/year of burned areas, mostly located in forests (74%), mainly cork oak and matorral stands (50%). The OHA detected hotspots covering 2081 km2, with 63% concentrated in the provinces of Chefchaouen and Larache. Meanwhile, clusters of EHA extended over 740 km2 and were composed of the oscillating coldspot (OCS) and oscillating hotspot (OHS) patterns at 50% and 30%, respectively. Additionally, an average of 149 fires/year occurred, located mostly in forests (75%), mainly cork oak and matorral stands (61%). The OHA detected active fire hotspots covering 3904 km2, with 60% located in the provinces of Chefchaouen and Larache. Clusters of EHA over 941 km2 were composed of the oscillating hotspot (OHS) and new hotspot (NHS) patterns at 57% and 25%, respectively. The prevalence of the oscillating and new models mirrors, respectively, the substantial fluctuations in wildfires within the region alternating between periods of high and low wildfire activities and the marked increase in fires in recent times, which has occasioned the emergence of novel hotspots. Additionally, we identified six homogeneous wildfire zones to which we assigned three strategic responses: "maintain" (73% of the territory), "monitor and raise awareness" (14% of the territory), and "reinforce" (13% of the territory). These strategies address current wildfire management measures, which include prevention, risk analysis, preparation, intervention, and rehabilitation. To better allocate firefighting resources, strategic responses were classified into four priorities (very high, high, medium, and low). Last, the wildfire zoning and strategic responses were validated using burned areas from 2021 to 2023, and a global scheme was suggested to assess the effectiveness of future wildfire measures. [ABSTRACT FROM AUTHOR]

One of the most effective methods of preventing large-scale wildfires is creating fuelbreaks, buffer zones whose purpose is to stop or delay the spread of the fire, providing firefighters an opportunity to control the fire. Fuelbreaks are already applied in several countries and have proven their effectiveness. However, creating fuelbreaks involves deforestation, so the length of the fuelbreaks should be minimized as much as possible. In this paper, we propose the implementation of a greedy Dijkstra-based fuelbreak planning algorithm which identifies locations in which fuelbreaks could significantly reduce the risk of large wildfires, at a relatively low deforestation cost. We demonstrate the stages and output of the algorithm both on artificial forests and on actual forests in Israel. We discuss the factors which determine the cost effectiveness of fuelbreaks from a tree-economy perspective and demonstrate how fuelbreaks' effectiveness increases as large wildfires become more frequent. [ABSTRACT FROM AUTHOR]

This paper aims to provide a better understanding of the transition towards a new paradigm of wildfire risk management in Victoria that incorporates Aboriginal fire knowledge. We show the suitability of cultural burning in the transformed landscapes, and the challenges associated with its reintroduction for land management and bushfire risk reduction after the traumatic disruption of invasion and colonization. Methods of Environmental History and Regional Geography were combined with Traditional Ecological Knowledge to unravel the connections between past, present and future fire and land management practices. Our study area consists of Dja Dja Wurrung and Bangarang/Yorta Yorta Country in north-central Victoria. The results show (i) the ongoing socio-political process for building a renewed integrated fire and land management approach including cultural burning, and (ii) the opportunities of Aboriginal fire culture for restoring landscape resilience to wildfires. We conclude that both wildfire risk management and cultural burning need to change together to adapt to the new environmental context and collaborate for mutual and common benefit. [ABSTRACT FROM AUTHOR]

We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in a fire-prone wildland–urban interface (WUI) facing conditions outside the bounds of experience. We describe how five social and ecological system (SES) submodels interact over time and space to generate highly variable alternative futures even within the same scenario as stochastic elements in simulated wildfire, succession, and landowner decisions create large sets of unique, path-dependent futures for analysis. We applied the modeling system to an 815 km2 study area in western Oregon at a sub-taxlot parcel grain and annual timestep, generating hundreds of alternative futures for 2007–2056 (50 years) to explore how WUI communities facing compound risks from increasing wildfire and expanding periurban development can situate and assess alternative risk management approaches in their localized SES context. The ability to link trends and uncertainties across many futures to processes and events that unfold in individual futures is central to the modeling system. By contrasting selected alternative futures, we illustrate how assessing simulated feedbacks between wildfire and other SES processes can identify tradeoffs and leverage points in fire-prone WUI landscapes. Assessments include a detailed "post-mortem" of a rare, extreme wildfire event, and uncovered, unexpected stabilizing feedbacks from treatment costs that reduced the effectiveness of agent responses to signs of increasing risk. [ABSTRACT FROM AUTHOR]

Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources. [ABSTRACT FROM AUTHOR]

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk. [ABSTRACT FROM AUTHOR]

Galicia is the Spanish region most affected by wildfires, and these wildfire patterns are the object of intense research. However, within Galicia, the mountain areas have certain socioeconomic and ecological characteristics that differentiate them from the rest of the region and have thus far not received any specific research attention. This paper proposes an analysis of the spatial wildfire patterns in the core Galician mountain systems in terms of the frequency, ratio of affected area, suppression time, and extension. The contiguity relations of these variables were examined in order to establish neighbour interactions and identify local concentrations of wildfire incidences. Furthermore, a spatial econometric model is proposed for these dependent variables in terms of a set of land cover (coniferous, transitional woodland–shrub) and land use (agricultural, industrial), complemented by population density, ecological protection, and common lands. The relevance of these parameters was studied, and it was found amongst other results, that economic value (agricultural and/or industrial) mitigates wildfire risk and impact, whereas ecological protection does not. In terms of land cover, conifers reduce the frequency and affected area of wildfires, whereas transitional land has a mixed effect, mitigating suppression time and extension but increasing the wildfire frequency. Suggestions for policy improvements are given based on these results, with a particular emphasis on the need for coordination of local policies in order to take into account the neighbour dependencies of wildfire risk and impact. [ABSTRACT FROM AUTHOR]

Wildfire is the primary cause of deforestation in fire-prone environments, disrupting the forest transition process generated by multiple social-ecological drivers of modernization. Given the positive feedback between climate change and wildfire-driven deforestation, it seems necessary to abstract the primary- or micro-characteristics of wildfire event(s) and focus on the general behavior of the phenomenon across time and space. This paper intends to couple wildfire self-organizing criticality theory (SOC) and modernization statistics to propose a verisimilar explanation of the phenomenon's evolution in the past decades and a prediction of its trends in Greece. We use power law distributions of the fire frequency–magnitude relationship to estimate the basic SOC parameters and the Weibull reliability method to calculate large-size wildfires' conditional probability as a time function. We use automatic linear modeling to search for the most accurate relationship between wildfire metrics and the best subset of modernization predictors. The discussion concentrates on reframing the political debate on fire prevention vs. suppression, its flaws and limitations, and the core challenges for adopting more efficient wildfire management policies in Greece. [ABSTRACT FROM AUTHOR]

In many regions, the frequency and extent of wildfires has increased in recent years, a trend which is expected to continue. Hence, there is a need for effective fire management strategies. Such strategies need to be based on accurate and complete data on vegetation condition and post-fire effects, collected in the field as well as by remote sensing approaches. Unfortunately, wildfire databases are often incomplete in terms of their spatial and temporal coverage, as well as the documentation of fire outcomes. The aim of this study was to devise a methodology to construct a comprehensive national wildfire database. We developed and tested this methodology for Israel, as a case study. The methodology combines data on wildfires in Israel from two sources: remote sensing and field data collected by governmental agencies, representing the period between 2015 and 2022. The resulting database contains 2276 wildfires larger than 10 ha with information (where and when available) on their occurrence date, fire duration, ignition cause, and more. The methodology presented here provides a blueprint for developing large-scale and comprehensive fire databases elsewhere, and facilitates future studies on wildfire risk by providing a robust, unified database of the fire history of Israel from 2015 onwards. [ABSTRACT FROM AUTHOR]

Landscape patterns and composition were identified as key drivers of fire risk and fire regimes. However, few studies have focused on effective policymaking aimed at encouraging landowners to diversify the landscape and make it more fire-resilient. We propose a new framework to support the design of wildfire mitigation policies aimed at promoting low-risk fire regimes based on land use/land cover choices by landowners. Using the parishes of a fire-prone region in central Portugal as analysis units, a two-step modelling approach is proposed, coupling an agent-based model that simulates land use/land cover choice and a logistic model that predicts fire regimes from a set of biophysical variables reported as important fire regime drivers in the literature. The cost-effectiveness of different policy options aimed at promoting low-risk fire regimes at the parish level is assessed. Our results are in line with those of previous studies defending the importance of promoting landscape heterogeneity by reducing forest concentration and increasing agricultural or shrubland areas as a measure to reduce the risk of wildfire. Results also suggest the usefulness of the framework as a policy simulation tool, allowing policymakers to investigate how annual payments supporting agricultural or shrubland areas, depending on the policy mix, can be very cost-effective in removing a substantial number of parishes from high-risk fire regimes. [ABSTRACT FROM AUTHOR]

Fire regimes in Mediterranean countries have been shifting in recent decades, including changes in wildfire size and frequency. We sought to describe changes in fire regimes across two periods (1975–1995 and 1996–2018) in a fire-prone region of central Portugal, explore the relationships between these regimes and territorial features, and check whether these associations persisted across periods. Two independent indicators of fire regimes were determined at parish level: fire incidence and burn concentration. Most parishes presented higher values of both indicators in the second period. Higher values of fire incidence were associated with lower population densities, lower proportions of farmland areas and higher proportions of natural vegetation. Higher levels of burn concentration were associated with smaller areas of farmland and natural vegetation. These associations differed across periods, reflecting contrasting climatic and socio-economic contexts. Keeping 40% of a parish territory covered by farmland was effective to buffer the increased wildfire risks associated with different management and climate contexts. The effectiveness of higher population densities in keeping fire incidence low decreased in the last decades. The results can improve the knowledge on the temporal evolution of fire regimes and their conditioning factors, providing contributions for spatial planning and forest/wildfire management policies. [ABSTRACT FROM AUTHOR]

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions. [ABSTRACT FROM AUTHOR]

Although it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A review of the previous studies focusing on each aspect has been done and used with wildfire records from 2011 to 2019 in Queensland, Australia, to compile the required input models to detect risky wildfire regions. Machine learning (ML) methods of Decision Tree (DT) and Support Vector Machine (SVM) were used to perform hazard assessment. The reason was to select the most accurate outcomes for the rest of the analysis. Among accuracy assessment techniques, the Area Under Curvature (AUC) method was used to evaluate the hazard maps. Prediction rates of 89.21% and 83.78% were obtained for DT and SVM, respectively. The DT prediction value showed that the DT-hazard map was more accurate than the SVM-hazard map. Vulnerability analysis was implemented by assigning weights to each factor according to the literature. Lastly, in order to create the wildfire risk map, the hazard and vulnerability indices were combined. The risk map showed that particularly dense urbanization regions are under future wildfire risk. To perform preliminary land use planning, this output can be used by local governmental authorities. [ABSTRACT FROM AUTHOR]

Wildfires are complex phenomena, both in time and space, in ecosystems. The ability to understand wildfire dynamics and to predict the behaviour of the propagating fire is essential and at the same time a challenging practice. A common approach to investigate and predict such phenomena is making the most of power of numerical models and simulators. Improved and more accurate methods for simulating fire dynamics are indispensable to managing suppression plans and controlled burns, decreasing the fuel load and having a better assessment of wildfire risk mitigation methodologies. This paper is focused on the investigation of existing simulator models applicable in predicting wildfire spread and wind fire interaction. The available software packages are outlined with their broad range of applications in fire dynamic modeling. Significance of each work and associated shortcomings are critically reviewed. Finally, advanced simulations and designs, accurate assumptions, and considerations for improving the numerical simulations, existing knowledge gaps in scientific research and suggestions to achieve more efficient developments in this area are revisited. [ABSTRACT FROM AUTHOR]

Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering. [ABSTRACT FROM AUTHOR]

Wildfire risk assessment provides important tools to fire management, by analysing and aggregating information regarding multiple, interactive dimensions. The three main risk dimensions hazard, exposure and vulnerability, the latter considered in its social dimension, were quantified separately at the local scale for 972 civil parishes in central mainland Portugal and integrated into a wildfire risk index. The importance of each component in the level of risk varied, as assessed by a cluster analysis that established five different groups of parishes, each with a specific profile regarding the relative importance of each dimension. The highest values of wildfire risk are concentrated in the centre-south sector of the study area, with high-risk parishes also dispersed in the northeast. Wildfire risk level is dominated by the hazard component in 52% of the parishes, although with contrasting levels of magnitude. Exposure and social vulnerability dominate together in 32% of the parishes, with the latter being the main risk driver in only 17%. The proposed methodology allows for an integrated, multilevel assessment of wildfire risk, facilitating the effective allocation of resources and the adjustment of risk reduction policies to the specific reality in each parish that results from distinct combinations of the wildfire risk dimensions. [ABSTRACT FROM AUTHOR]

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas. [ABSTRACT FROM AUTHOR]

(1) Background: Federal land managers in the US are charged with risk-based decision-making which requires them to know the risk and to direct resources accordingly. Without understanding the specific factors that produce risk, it is difficult to identify strategies to reduce it. (2) Methods: Risk characterized by U.S. land managers during wildfires was evaluated from 2010–2017 to identify factors driving risk perceptions. Annotation from 282 wildfires in two regions with distinctive risk profiles, the Northwest and Southwest Geographic Areas, were qualitatively coded using the risk assessment framework of hazards, values, and probability from the Relative Risk Assessment in the Wildland Fire Decision Support System (WFDSS). (3) Results: The effects of climate on seasonal severity, fuel condition, and fire behavior emerged as the most influential factors driving risk perceptions and characterizations of risk in both regions. Monsoonal precipitation extended the longevity of landscape barriers, especially in the Southwest. The results suggest that a scarcity of values at risk and a mild fire environment produce low risk fires regardless of location, while high risk fires reflect specific local values and geography, under the umbrella of dry climate. (4) Conclusions: the climatic contrasts between the two regions highlight how influential climate change will be on future characterizations of wildfire risk. [ABSTRACT FROM AUTHOR]

The increasing wildfire activity and rapid population growth in the wildland–urban interface (WUI) have made more Americans exposed to wildfire risk. WUI mapping plays a significant role in wildfire management. This study used the Microsoft building footprint (MBF) and the Montana address/structure framework datasets to map the WUI in Montana. A systematic comparison of the following three types of WUI was performed: the WUI maps derived from the Montana address/structure framework dataset (WUI-P), the WUI maps derived from the MBF dataset (WUI-S), and the Radeloff WUI map derived from census data (WUI-Z). The results show that WUI-S and WUI-P are greater than WUI-Z in the WUI area. Moreover, WUI-S has more WUI area than WUI-P due to the inclusion of all structures rather than just address points. Spatial analysis revealed clusters of high percentage WUI area in western Montana and low percentage WUI area in eastern Montana, which is likely related to a combination of factors including topography and population density. A web GIS application was also developed to facilitate the dissemination of the resulting WUI maps and allow visual comparison between the three WUI types. This study demonstrated that the MBF can be a useful resource for mapping the WUI and could be used in place of a national address point dataset. [ABSTRACT FROM AUTHOR]

The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science community and implements computationally efficient and scalable modeling techniques to allow for new high-resolution, CONUS-wide hazard generation. Burn probability, flame length, and ember spread for the years 2022 and 2052 are computed from two ten-year representative Monte Carlo simulations of wildfire behavior, utilizing augmented LANDFIRE fuel estimates updated with all the available disturbance information. FSF-WFM utilizes ELMFIRE, an open-source, Rothermel-based wildfire behavior model, and multiple US Federal Government open data sources to drive the simulations. LANDFIRE non-burnable fuel classes within the wildland–urban interface (WUI) are replaced with fuel estimates from machine-learning models, trained on data from historical fires, to allow the propagation of wildfire through the WUI in the model. Historical wildfire ignition locations and NOAA's hourly time series of surface weather at 2.5 km resolution are used to drive ELMFIRE to produce wildfire hazards representative of the 2022 and 2052 conditions at 30 m resolution, with the future weather conditions scaled to the IPCC CMIP5 RCP4.5 model ensemble predictions. Winds and vegetation were held constant between the 2022 and 2052 simulations, and climate change's impacts on the future fuel conditions are the main contributors to the changes observed in the 2052 results. Non-zero wildfire exposure is estimated for 71.8 million out of 140 million properties across CONUS. Climate change impacts add another 11% properties to this non-zero exposure class over the next 30 years, with much of this change observed in the forested areas east of the Mississippi River. "Major" aggregate wildfire exposure of greater than 6% over the 30-year analysis period from 2022 to 2052 is estimated for 10.2 million properties. The FSF-WFM represents a notable contribution to the ability to produce property-specific, climate-adjusted wildfire risk assessments in the US. [ABSTRACT FROM AUTHOR]

This paper presents the results of a survey that was undertaken to examine homeowners' FireSmart mitigation practices and investigate existing incentives and barriers to uptake of FireSmart Canada's recommended wildfire mitigation activities in the Urban Service Area of Fort McMurray Alberta. Single-family residential property owners, the large majority of whom were affected by the Horse River wildfire, were invited to participate in an online survey. A total of 496 surveys were completed, with a response rate of 38%. We found that most of the participants generally perceive a low to moderate wildfire risk to their properties: they felt there was a low chance of a catastrophic fire happening soon and/or 'enough' had already been done to reduce the immediate risk. Although about half of the participants searched for information about FireSmart, having information or knowledge of FireSmart did not translate into substantial adoption of recommended mitigation actions. Survey participants generally preferred and implemented more of the low-cost, low effort mitigation measures such as cutting grasses and cleaning debris, likely for reasons other than wildfire risk reduction. With regard to structural measures, we found asphalt shingles and vinyl siding were present on the majority of homes; although this was not a choice but was provided by the builder or on the home when it was purchased. Very few respondents were willing to replace their siding or roof––the cost was the single biggest factor. In addition, we identified several other factors as negatively influencing homeowners' mitigation actions, including the tendency to shift responsibility to the municipal government and social pressure such as neighbors not being as proactive in completing FireSmart mitigation measures. Recommendations that may help promote positive wildfire mitigation behaviors are discussed. [ABSTRACT FROM AUTHOR]

Mechanical mastication is a fuel management strategy that modifies vegetation structure to reduce the impact of wildfire. Although past research has quantified immediate changes to fuel post-mastication, few studies consider longer-term fuel trajectories and climatic drivers of this change. Our study sought to quantify changes to fuel loads and structure over time following mastication and as a function of landscape aridity. Measurements were made at 63 sites in Victoria, Australia. All sites had been masticated within the previous 9 years to remove over-abundant shrubs and small trees. We used generalised additive models to explore trends over time and along an aridity gradient. Surface fuel loads were highest immediately post-mastication and in the most arid sites. The surface fine fuel load declined over time, whereas the surface coarse fuel load remained high; these trends occurred irrespective of landscape aridity. Standing fuel (understorey and midstorey vegetation) regenerated consistently, but shrub cover was still substantially low at 9 years post-mastication. Fire managers need to consider the trade-off between a persistently higher surface coarse fuel load and reduced shrub cover to evaluate the efficacy of mastication for fuel management. Coarse fuel may increase soil heating and smoke emissions, but less shrub cover will likely moderate fire behaviour. [ABSTRACT FROM AUTHOR]

Wildfire is a complex problem because of the diverse mix of actors and landowners involved, uncertainty about outcomes and future conditions, and unavoidable trade-offs that require ongoing negotiation. In this perspective, we argue that addressing the complex challenge of wildfire requires governance approaches designed to fit the nature of the wildfire problem. For instance, while wildfire is often described as a cross-boundary problem, understanding wildfire risk as transboundary highlights important political and institutional challenges that complicate collaboration across jurisdictions and shared stewardship. Transboundary risk requires collaborative governance that attends to the distribution of power, authority, and capacity across the range of actors relevant to particular fire-prone landscapes. Wildfire is also changing in unprecedented ways and multiple, interacting uncertainties make predicting future wildfires difficult. Anticipatory governance can build our capacity to integrate uncertainty into wildfire decision-making and manage risk in proactive ways. Finally, competing interests and values mean that trade-offs are inherent to the wildfire problem. Risk governance links science and society through deliberative, participatory processes that explicitly navigate tradeoffs and build legitimacy for actions to address wildfire risk. Governance approaches that better target the nature of the wildfire problem will improve our ability to coexist with fire today and in the future. [ABSTRACT FROM AUTHOR]

A wildfire occurred in Shawnee State Forest located in southern Ohio that consumed 1215 hectares. Based on earlier forest inventories it was known that paulownia (Paulownia tomentosa), a non-native invasive tree species, occurred in the forest. The objective of this study was to determine if paulownia heavily colonized areas two years after the fire where the burn occurred, and if its presence had a negative impact on the regeneration (<137 cm height) of native species--red and white oaks (Quercus sp.), red maple (Acer rubrum), and yellow-poplar (Liriodendron tulipifera). Two years after the fire, paulownia had invaded the burned areas but not at significantly higher densities than occurred in the unburned areas. Fire significantly reduced the number of regenerating stems of white oak and red maple two years after the fire, whereas the number of regenerating stems of red oak increased slightly and that of yellow-poplar increased significantly. In areas where paulownia occurred that experienced wildfire, all species studied displayed a reduction in the number of regenerating stems compared to paulownia's absence in the burn areas. Where paulownia occurred in areas not affected by the wildfire, all the native species studied displayed a reduction in the number of regenerating stems. The average heights of red oak, white oak, and red maple were significantly taller when growing in areas affected by the wildfire due to a more open canopy. However, there was no significant change in the average heights of yellow-poplar. The presence of paulownia in both the burned and unburned areas reduced the number of regenerating stems of the native species studied. [ABSTRACT FROM AUTHOR]

Quantifying livelihood vulnerability to wildland fires in the United States is challenging because of the need to systematically integrate multidimensional variables into its analysis. We aim to measure wildfire threats amongst humans and their physical and social environment by developing a framework to calculate the livelihood vulnerability index (LVI) for the top 14 American states most recently exposed to wildfires. The LVI is computed by assessing each state's contributing factors (exposure, sensitivity, and adaptive capacity) to wildfire events. These contributing factors are determined through a set of indicator variables that are categorized into corresponding groups to produce an LVI framework. The framework is validated by performing a principal component analysis (PCA), ensuring that each selected indicator variable corresponds to the correct contributing factor. Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability. In contrast, California, Florida, and Texas experience the least livelihood vulnerability. While California has one of the highest exposures and sensitivity to wildfires, results indicate that it has a relatively high adaptive capacity, in comparison to the other states, suggesting it has measures in place to withstand these vulnerabilities. These results are critical to wildfire managers, government, policymakers, and research scientists for identifying and providing better resiliency and adaptation measures to support states that are most vulnerable to wildfires. [ABSTRACT FROM AUTHOR]

A key challenge in the United States is how to manage wildfire risk across boundaries and scales, as roles, responsibilities, and ability to act are distributed among actors in ways that do not always incentivize collective action. In this review paper, we provide several conceptual contributions to the understanding of wildfire management through the application of boundary spanning frameworks. This includes: (1) a characterization of four major types of boundaries in managing wildfire risk; (2) a review of major boundary spanning features and frameworks that integrate them; and (3) consideration of current and potential applications of the boundary spanning construct to the domain of wildfire management. Our goal is to advance knowledge of how actors in this arena may overcome "parallel play" to more collectively address wildfire risk. We generate new thinking about wildfire management, and offer potential implications and questions for future research, policy, and management. [ABSTRACT FROM AUTHOR]

In Australia, residents can choose to remain to defend their property against bushfire but, since the 2009 Black Saturday bushfires, considerable emphasis is placed on leaving early, well in advance of a bushfire. However, many householders delay their protective decision. The insights offered by the literature into how and why some people leave early before their personal safety is threatened can inform bushfire safety policy and practice. This systematic review reports the findings of 90 papers selected from 216 identified through a search of papers in Scopus, Science Direct and Google Scholar published between 1995 and May 2021 in English. This review establishes the reasons people leave early; the influence of official and unofficial warnings; gender and other demographics; the influence of self-evacuation archetypes; planning and preparation; the influence of children and other dependents and pets; triggers initiating leaving; factors impeding and facilitating leaving; and policy issues around early leaving. This review also details 12 seminal studies that capture much of the evidence on the decision to leave early. [ABSTRACT FROM AUTHOR]

An increase in the frequency of severe fire events, as well as a growing interest in wildfire mitigation strategies, has created a demand for skilled managers of landscape fire and a better community understanding of fire behaviour. While on-ground experience is essential, there is potential to substantially enhance training and community engagement with explanatory simulations. Through this work, we explore landscape fire behaviour as a complex system where understanding key behaviour characteristics is often more important and achievable than prediction. It is argued that this approach has particular value in Northern Australia, where fires burn across vast and sparsely inhabited landscapes that are largely under Indigenous ownership. Land and fire management in such complex cross-cultural contexts requires combining traditional and local knowledge with science and technology to achieve the best outcomes. We describe the workings of the model, a stochastic cellular automata fire behaviour simulation, developed through a participatory modelling approach for Northern Australia; the outputs generated; and a range of operational applications. We found that simulation assisted training and engagement through the development of an understanding of fire dynamics through visualisation, underpinned by landscape data sets, and engaging a culturally diverse set of land managers in discussions of fire management. We conclude that there is scope for a broader use of explanatory fire simulations to support development of shared understandings of fire management objectives. [ABSTRACT FROM AUTHOR]

With increasing forest and grassland wildfire trends strongly correlated to anthropogenic climate change, assessing wildfire danger is vital to reduce catastrophic human, economic, and environmental loss. From this viewpoint, the authors discuss various approaches deployed to evaluate wildfire danger, from in-situ observations to satellite-based fire prediction systems. Lately, the merit of soil moisture in predicting fuel moisture content and the likelihood of wildfire occurrence has been widely realized. Harmonized soil moisture measurement initiatives via state-of-the-art soil moisture networks have facilitated the use of soil moisture information in developing innovative applications for wildfire prediction and risk management applications. Additionally, the increasing availability of remote-sensing data has enabled the monitoring and modeling of wildfires across various terrestrial ecosystems. When coupled with remotely sensed data, field-based soil moisture measurements have been more valuable predictors of assessing wildfire than alone. However, sensors capable of acquiring higher spectral information and radiometry across large spatiotemporal domains are still lacking. The automation aspect of such extensive data from remote-sensing and field data is needed to rapidly assess wildfire and mitigation of wildfire-related damage at operational scales. [ABSTRACT FROM AUTHOR]

Wildfires continue to destroy houses, but an understanding of the complex mix of risk factors remains elusive. These factors comprise six themes: preparedness actions (including defensible space), response actions (including defence), house construction, landscape fuels, topography and weather. The themes span a range of spatial scales (house to region) and responsible agents (householders through government to entirely natural forces). We conducted a statistical analysis that partitions the contribution of these six themes on wildfire impact to houses, using two fires that destroyed 200 houses in New South Wales (Australia) in October 2013 (the Linksview andMt York fires). We analysed 85 potential predictor variables using Random Forest modelling. The best predictors of impact were whether the house was defended and distance to forest toward the direction of fire spread. However, predictors from all four of the other themes had some influence, including distance to the nearest other burnt house (indicating house-to-house transmission) and vegetation cover up to 40 m from the house. The worst-placed houses (undefended, without adequate defensible space, with burnt houses nearby and with a westerly aspect) were 10 times more likely to be impacted than the best-placed houses in our study. The results indicate that householders are the agents most able to mitigate risk in the conditions experienced in these fires through both preparation and active defence. [ABSTRACT FROM AUTHOR]