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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]

Private landowners in the southern Willamette Valley of Oregon, USA were surveyed. The survey queried probabilities of implementing specific fuels reduction projects in extensive areas of specific forest types on their property. The projects were described in relation to the beginning and target forest types, the actions required, costs, and long-term maintenance. Forest types were first rated for scenic beauty and informed levels of wildfire risk reduction, scarce habitat production, and associated property rights risks. Propensities to perform each fuels reduction project were then obtained. These were adversely affected by disbelief in heightened wildfire risks or climate change, higher project costs, feelings of hopeless vulnerability to wildfire, and low aesthetic affections for target forests. Propensities were enhanced by aesthetic affection for target forests, belief in the efficaciousness of fuels reduction, previous experience with wildfire evacuation, and higher incomes. All landowners favored thinning of young conifer forests, but some were averse to thinning of mature conifer forests. Anthropocentric landowners, mainly farmers, foresters, and some small holders, tended to favor conventional thinnings toward commercially valuable conifer forests and avoided long-term habitat maintenance. Nature-centric landowners, mainly some rural residents and wealthy estate owners, leaned more toward long term habitat goals and oak forests. [ABSTRACT FROM AUTHOR]

Wildfires are increasing in severity, and magnitude in the Mediterranean Basin in recent years, reaching a yearly average of 450 000 ha over the last decade. Drivers include climate change, land-use change, and land abandonment. Wildfire mitigation requires landscape-level action as impact to each parcel is affected by the conditions of the others. We conducted a case study of a regional-level initiative that develops community efforts to mitigate wildfires through silvo-pastoral agroforestry systems, using an integrated landscape management approach. This approach involves collaboration among stakeholders to achieve multiple objectives. In order to derive insights into its potential, we asked participating land managers: (1) What motivates their participation?, (2) How do they perceive initiative outcomes?, and as urban outmigrants with non-traditional goals are increasing in rural areas, (3) Do responses differ between rural and neo-rural participants? Our results show that managers feel highly affected by wildfires and are strongly motivated to reduce wildfire risk. Land abandonment and inappropriate policy were major concerns. The initiative was seen to have positive outcomes for individual participants as well as the region, and to stimulate community connectedness. We conclude that fit to local contexts, integrated landscape management can be a well-received approach to reducing wildfire risk. Agroforestry systems in Extremadura can act as "productive fuelbreaks" that reduce fire risk over extensive areas, while restoring traditional landscapes. We suggest that programs to reduce wildfire risk can also be used as a leverage point for financing rural revival and provision of multiple ecosystem services. [ABSTRACT FROM AUTHOR]

Context: WUI wildfire disasters are increasing, as fires are pushed by strong winds and drier fuels across landscapes and into communities. Possible disasters make maintaining and restoring landscape-scale fire in fire-adapted ecosystems difficult. Rapid action is needed to reduce building loss in WUI wildfire disasters. Objectives: In a Colorado study, I used distance-based empirical modeling to refine potential risk of building loss in WUI wildfire disasters to focus risk-reduction efforts. Methods: New empirical modeling showed 95% of USA building loss in WUI wildfire disasters was within 100 m of wildland vegetation. I used modeling to estimate and map potential relative risk of a WUI wildfire disaster for each of 2,185,953 buildings in Colorado. Results: High-risk buildings were 241,375 or 11% of total buildings. However, the 20–40 m essential defensible space around these buildings covered only 46,767–114,084 ha. Area within 100 m of wildland vegetation, containing these buildings, covered 475,840 ha or 1.8% of Colorado's 27 million ha. About 95% of at-risk land within 100 m of wildland vegetation is not federally owned, and WUI wildfire disasters are mostly from fires started on private land. Conclusions: Treating ≤ 114,084 ha of defensible space could leave the 27 million ha of Colorado with lower WUI wildfire disaster-risk to buildings. High risk of building loss is rarely a federal land-management problem. If the goal is rapid reduction of building loss in WUI wildfire disasters, focus resources on defensible space 20–40 m from WUI buildings within 100 m of wildland vegetation. [ABSTRACT FROM AUTHOR]

Wildfires have caused significant ecological and social losses in terms of forest benefits, private dwellings, and suppression costs. Although great efforts have been made in wildfire policies and wildfire-mitigating strategies on private and public lands, devastating wildfires continue to occur. This implies there is a need for effective incentive-driven policies to encourage forest owners to undertake an increasing level of wildfire-mitigating actions. This study evaluates the effectiveness of alternative incentive-driven policies for the problem of two adjacent forest owners under various scenarios of misinformation about wildfire occurrence and spread using a stochastic dynamic model. The study also investigates how the implementation of these policies encourages wildfire-mitigating actions, yields larger reductions in social losses, and alleviates free-riding behavior. The outcomes of the analysis confirm that the effectiveness of incentive programs in reducing social losses and increasing forest value is influenced by the level of misinformation held by a forest owner when making wildfire prevention decisions. The results also revealed that fuel stock regulation is more effective at mitigating wildfire damages and associated costs than cost-share programs under all misinformation scenarios. It was also found that fuel stock regulation could correct free-riding behavior due to the restrictive nature of this policy. The findings provide additional motivation for educational programs that seek to improve forest owners' knowledge about the private benefits of fuel removal and collaboration efforts between neighboring forest owners. Collaborative efforts could yield substantial savings for the government through eliminating cost-share programs and reducing suppression costs. [ABSTRACT FROM AUTHOR]

This study aims to introduce new methods for assessing risk of wildfires around power transmission line corridors (PTCs) using multiple sourced active monitoring data. The proposed approach starts from fire analysis using interpretative structural modeling (ISM) from historical data of fire-caused transmission line (TL) fault, which emphasizes on the fire regimes and the fire factors. The ISM illustrates the sequence and relationships among fire factors. The main influencing factors are then ranking using analytic hierarchy process (AHP) to distinguish each relative importance. FRP, a high-resolution fire risk index conducting fire risk around PTCs, was constructed considering these factors as a series of assessment criterion. Variable meteorological-type factors such as maximum temperature and minimum relative humidity, less variable basic elements such as surface conditions, and human activity elements are all weighed and considered in FRP. The risk of this line can be obtained by calculating the collected monitoring data from TLC. Moreover, the safety risk level can be analyzed based on this assessment and the risk map of the power corridor can be used to help the power department to improve the maintenance plan of the power corridor. [ABSTRACT FROM AUTHOR]

To assess wildfire risk linked to lightning-caused wildfires, the present study tests the spatial- and temporal-scale distribution variability of lightning fires in Western Canada between 1981 and 2018. We examined clustering, trends, distances between clusters, and the fire season. For this study, the nearest neighbours, K-function, Moran's I, Mann–Kendall, and the Getis-ord Gi* statistics were used. These statistics were visualized by a Space Time Cube model with a hexagon grid. Lightning-ignited wildfires cluster spatially up to 270 km with an observed overall nonsignificant decreasing trend for the number of fires. Overall, northeastern Alberta, central Saskatchewan, and southeastern British Columbia show clustering of lightning fires. In June, there is significant clustering in northwestern and eastern Alberta, while in July fires cluster in northeastern Alberta and in southeastern British Columbia. In August, fire clusters occurred only in southeastern and south-central British Columbia. These results highlight regions that are experiencing persistent lightning fire clustering activity. This provides a focal point to assess wildfire risk to communities and values at risk, while informing local and regional management agencies in preparedness, resource capacity management, and detection. Additionally, it provides a baseline for future research into biophysical modelling of wildfire initiations. [ABSTRACT FROM AUTHOR]

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed. [ABSTRACT FROM AUTHOR]

Wildfires have become increasing threats to residents, built environments, and ecosystems in the USA. Individual responsibility plays an important role in reducing structure-level ignitability, and in turn, overall community vulnerability. When wildfire cannot be completely prevented through risk reduction efforts, homeowners insurance can serve as the second line of defense by allowing homeowners to transfer risk. To understand homeowner decisions on wildfire-related proactive actions and the effects of such decisions on the housing recovery, this study conducted an online survey of homeowners living at high to extreme risk of wildfire in the Western United States and collected data related to two types of proactive actions: individual-level risk reduction actions and homeowners insurance. First, a regression model for each proactive action was estimated to identify key characteristics of homeowners and house/property that had the greatest impact. The results indicated that homeowner age and household income were the two common factors affecting their decisions about home hardening and insurance policies, while the only statistically significant factor in homeowner decisions about defensible space was satisfaction with the surrounding environment (e.g., scenic beauty, privacy). Moreover, the effects of each proactive action on the housing recovery process were evaluated. The results showed that home hardening was a more effective action in reducing wildfire damage to a house than defensible space was, which was consistent with homeowner perception. The survey results also indicated that homeowners with insurance were less likely to experience post-wildfire financial hardship, and subsequently were more likely to repair their damaged houses. [ABSTRACT FROM AUTHOR]

In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on wildfire risk in fire-prone areas, a fire-spread modeling approach to evaluate the variations in wildfire potential induced by different spatial patterns and percentages of land abandonment was applied. The study was carried out in a 1200 km2 agropastoral area located in north-western Sardinia (Italy) mostly covered by herbaceous fuels. We compared nine land abandonment scenarios, which consisted of the control conditions (NA) and eight scenarios obtained by combining four intensity levels (10, 20, 30, 40%) and two spatial patterns of agropastoral land abandonment. The abandonment scenarios hypothesized a variation in dead fuel load and fuel depth within abandoned polygons with respect to the control conditions. For each abandonment scenario, wildfire hazard and likelihood at the landscape scale was assessed by simulating over 17,000 wildfire seasons using the minimum travel time (MTT) fire spread algorithm. Wildfire simulations replicated the weather conditions associated with the largest fires observed in the study area and were run at 40 m resolution, consistent with the input files. Our results highlighted that growing amounts of land abandonment substantially increased burn probability, high flame length probability and fire size at the landscape level. Considering a given percentage of abandonment, the two spatial patterns of abandonment generated spatial variations in wildfire hazard and likelihood, but at the landscape scale the average values were not significantly different. The average annual area burned increased from about 2400 ha of the control conditions to about 3100 ha with 40% land abandonment. The findings of this work demonstrate that a progressive abandonment of agropastoral lands can lead to severe modifications in potential wildfire spread and behavior in Mediterranean areas, thus promoting the likelihood of large and fast-spreading events. Wildfire spread modeling approaches allow us to estimate the potential risks posed by future wildfires to rural communities, ecosystems and anthropic values in the context of land abandonment, and to adopt and optimize smart prevention and planning strategies to mitigate these threats. [ABSTRACT FROM AUTHOR]

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries. A new study develops a machine learning framework to observationally constrain CMIP6-simulated fire carbon emissions, finding a weaker increase in 21st-century global fires but higher increase in their socioeconomic risks than previously thought. [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]

This study was conducted following the fires that took place in Rocallaura (Spain) between 23/06/2016 and 19/07/2017. The aim is to analyse the importance of soil on forest planning and management mechanisms to mitigate the impacts of forest fire re-ignitions on risk and management framework. The main factors found to influence the occurrence of re-ignitions in a forest ecosystem dominated by Pinus halepensis were weather conditions, possible future climatic changes, soil and subsoil biomass amount and the physico-chemical properties of the organic layer. The re-ignition dynamics were included in the study as a new parameter—fire persistence—to be considered in fire regimes. It is proposed that fire persistence should be included in strategic framework for integrating wildfire risk in forest planning. This will entail identifying and including the potential for re-ignition in fire risk maps and fire suppression policies and implementing forest management actions in areas vulnerable to re-ignition. [ABSTRACT FROM AUTHOR]

Building more homes and amenities in the wildland-urban interface (wui) is not a sustainable practice as it is associated with a greater risk of wildfire, social vulnerability, and ecological damage. Yet, the issue of whether or how to regulate the expansion of the wui remains contentious and largely unresolved in understanding sustainable development. There are fewer studies that explore how wildfire risks are compounded by social vulnerability of people who reside in the fire prone wui. Additionally, much of the extant research is focused on the national or regional level management of ecosystems and forest fires, with a clear lack of focus on local level dynamics. To fill these gaps, our analysis outlines the preliminary steps to identify social vulnerability, ecological damage, and wildfire risk in the wui fire hazard zones of the highest severity type. Utilizing gis mapping, wildfire risk, and census data on social vulnerability, our analysis reveals patterns of the wui expansion in the San Francisco Bay Area from 1990 to 2010 and provides policy recommendations from a sustainable development perspective to address social vulnerability, wildfire risk, and ecological concerns over the wui. [ABSTRACT FROM AUTHOR]

The spatial and temporal variability of dryness conditions in the territory of Kazakhstan during the period 1979–2021 was investigated using monthly and hourly ERA5 reanalysis data on air temperature and precipitation as well as various aridity indices. A large part of the territory is characterized by the air temperature increase in summer and spring, as well as precipitation reduction, especially during the summer months. It was shown that the end of the 20th century (1979–2000) and the beginning of the 21st century (2001–2021) are characterized by different trends in air temperature and precipitation. All applied indices, i.e., the Palmer Drought Severity Index (PDSI), the Keetch–Byram Drought Index (KBDI), Standardized Precipitation (SPI) and Standardized Precipitation Evapotranspiration (SPEI), showed increased dryness in most parts of the territory of Kazakhstan. KBDI indicated an increased risk of wildfires, especially in the southwestern and northwestern regions. The hottest and driest areas are situated in the regions that are simultaneously affected by rising temperatures and reduced precipitation in spring and summer. The strongest increase in aridity and fire risk in the southwest and northwest is mainly due to reduced precipitation in the summer. Minimal risks of droughts occur in the northern and central regions, where conditions in the early 21st century became even less favorable for drought formation compared to the late 20th century (increased precipitation in both spring and summer and lower summer temperatures). [ABSTRACT FROM AUTHOR]

Nowadays, wildfires are an increasing challenge for the defence sector. The fire risk of a given area depends only in part on human factors and the number of registered fires. A fire occurs when the moisture content of dead biomass drops to a level, where the fire can already spread between the individual pieces of fuel. Daily fire danger forecast examines the constant and changing components of the fire environment. This determines the flammability of the biomass; the rate of fire spread makes firefighting more difficult. The fire danger forecast identifies the fire hazard periods when fires can occur. Fire Risk Assessment Systems have been developed in many countries around the world. In addition to the daily fire risk, these include parameters describing the vulnerability of the areas affected by the fire. National risk assessments are available in many countries around the world using several methodologies. The Joint Research Centre of the European Commission has developed a community-wide approach to forest fire risk assessment, using scientific results and studying good practices. In this approach, the risk of a forest fire is made up of the effects of daily fire hazards and vulnerabilities. The risk of fire due to weather conditions is associated with ignition and the spread of fire. The authors examine in the paper the basic criteria to assess wildfire risk at the pan-European level. The authors analyse external and internal risk factors in an observation plot and examine how international recommendations can be utilised in Hungary. [ABSTRACT FROM AUTHOR]

Managing wildfire risk across boundaries and scales is critical in fire-prone landscapes around the world, as a variety of actors undertake mitigation and response activities according to jurisdictional, conceptual and administrative boundaries, based on available human, organisational, technical and financial resources. There is a need to catalyse coordination more effectively to collectively manage wildfire risk. We interviewed 102 people across five large landscape case studies in the western United States to categorise how people and organisations were deployed in range and forestlands to collectively address wildfire risk. Across all cases, actors spanned boundaries to perform functions including: (1) convening meetings and agreements; (2) implementing projects; (3) community outreach; (4) funding support; (5) project planning; (6) scientific expertise. These functions fostered conducive boundary settings, concepts and objects to communicate and work across boundaries, navigating challenges to implementing work on the ground. This work highlights context-specific ways to advance cross-boundary wildfire risk reduction efforts and uses a boundary spanning lens to illustrate how collective action in wildfire management evolves in different settings. This research highlights prescribed fire as a gateway for future collective action on wildfire risk, including managing naturally ignited wildfires for resource benefits and improving coordination during wildfire suppression efforts. Actors must collectively manage wildfire risk across administrative, conceptual, organisational and other boundaries in fire-prone landscapes. We interviewed practitioners in five cases across the western United States, identifying how actors were engaging in collective action to address wildfire risk, organised through a typology of actor functions and boundary-spanning features. [ABSTRACT FROM AUTHOR]

Wildfires are catastrophes of natural origin or initiated by human activities with high disruptive potential. "Portugal, located in western Iberia, has recently experienced several large fire events, including megafires, due to a combination of factors such as orography, vegetation, climate, and socio-demographic conditions that contribute to fuel accumulation.". One approach to studying fire danger is to use fire weather indices that are commonly used to quantify meteorological conditions that can lead to fire ignition and spread. This study aims to provide high-resolution (~ 6 km) future projections of the Fire Weather Index (FWI) for Portugal using the Weather Research and Forecasting (WRF) model, forced by the Max Planck Institute (MPI) model from the CMIP6 suite, under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP58.5) for the present period (1995–2014) and two future periods (2046–2065 and 2081–2100). The results show good agreement between FWI and its subcomponents from the WRF and reanalysis. The modelled FWI reproduced the climatological distribution of fire danger Projections indicate an increase in days with very high to extreme fire danger (FWI > 38) across all scenarios and time frames, with the southern and northeastern regions experiencing the most significant changes. The southern and northeastern parts of the territory experienced the largest changes, indicating significant changes between the scenarios and regions. This study suggests that FWI and its subcomponents should be investigated further. Our results highlight the importance of creating new adaptation measures, especially in the areas most at risk, prepared in advance by different players and authorities, so that the increasing risk of wildfires can be mitigated in the future. [ABSTRACT FROM AUTHOR]

The frequency of wildfire disasters has surged fivefold in the past 50 years due to climate change. Preemptive de-energization is a potent strategy to mitigate wildfire risks but substantially impacts customers. We propose a multistage stochastic programming model for proactive de-energization planning, aiming to minimize economic loss while accomplishing a fair load delivery. We model wildfire disruptions as stochastic disruptions with varying timing and intensity, introduce a cutting-plane decomposition algorithm, and test our approach on the RTS-GLMC test case. Our model consistently offers a robust and fair de-energization plan that mitigates wildfire damage costs and minimizes load-shedding losses, particularly when pre-disruption restoration is considered. • Formulate a multistage stochastic mixed-integer programming model to manage wildfire risk in power system operations. • Incorporate de-energization and restoration operations to balance wildfire risk and load-shedding cost. • Implement a decomposition algorithm comprising several cut families to efficiently solve the large-scale mixed-integer program. [ABSTRACT FROM AUTHOR]

The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in the related contexts of optimal transmission switching or grid restoration planning. This paper explores the accuracy of the DC OPS formulation and the ability to recover an AC-feasible power flow solution after de-energization decisions are made. We also extend the OPS problem to include variants with the AC, Second-Order-Cone, and Network-Flow power flow equations, and compare them to the DC approximation with respect to solution quality and time. The results highlight that the DC approximation overestimates the amount of load that can be served, leading to poor de-energization decisions. The AC and SOC-based formulations are better, but prohibitively slow to solve for even modestly sized networks thus demonstrating the need for new solution methods with better trade-offs between computational time and solution quality. • This paper considers a planning problem for optimizating Public Safety Power Shutoffs. • The problem is modeled with 4 power flow formulationsm: AC, DC, SOC, and NF. • These formulations demonstrate a tradeoff between solution quality (accurate estimates of load shed) and solution speed (orders of magnitude difference). • The formulations are made available in the open-source software package PowerModelsWildfire.jl. [ABSTRACT FROM AUTHOR]

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s42408-023-00236-w. [ABSTRACT FROM AUTHOR]

Wildfire activity throughout western North America is increasing which can have important consequences for species persistence. Native species have evolved disturbance-adapted traits that confer resilience to natural disturbance provided disturbances operate within their historical range of variability. This resilience can erode as disturbance regimes change and begin operating outside this range. We assessed wildfire impacts during 1987–2018 on the northern spotted owl, an imperiled species with complex relationships with late and early seral forest in the Pacific Northwest, USA. We analyzed population- and individual-level wildfire impacts across the frequent-fire portion of the owl's geographic range at two spatial scales and uncovered important nuances involving wildfire risk. When comparing survival of owls on burned vs unburned territories, we detected no differences in apparent survival, and owls overwhelmingly remained on burned territories indicating no measurable population-level wildfire impacts. However, when including territory-scale fire characteristics we detected negative individual-level wildfire impacts that indicated apparent survival decreased and territory displacement increased with burn severity and extent within an owl's territory. Northern spotted owls were also more sensitive to fire effects within their core use area indicating that where fire burns is important for spotted owl conservation. These findings indicate nuance is required when discussing wildfire impacts to spotted owls, and that changing fire regimes in this portion of the northern spotted owl's range have not yet translated into negative population-wide impacts. However, dwindling populations and continued fire regime changes could exceed the adaptive capacity of remaining spotted owls, thereby resulting in negative population-wide impacts. [ABSTRACT FROM AUTHOR]

The impacts of wildfires on tourism are growing, with concomitant detrimental consequences for tourists and destinations. However, only a few studies have assessed wildfires in tourism-dependent regions, and none have systematically explored fire risks. This paper summarizes the academic literature on tourism and wildfires that serves as a background for developing a tourism fire exposure index. The index identifies regions in the European Union at the NUTS2 level with high wildfire risks and high tourism dependency. Results show that exposure is exceptionally high in regions of Portugal and Mediterranean islands belonging to Greece and Spain. As vulnerabilities can be strategically reduced through adaptation, findings serve as a basis for the development of an integrated wildfire management framework for exposed destinations. [ABSTRACT FROM AUTHOR]

The growing scale of natural hazards highlights the need for models of governance capable of addressing risk across administrative boundaries. However, risk governance systems are often fragmented, decentralized, and sustained by informal linkages among local-level risk mitigation planning processes. Improving resilience to the effects of environmental change requires a better understanding of factors that contribute to these linkages. Using data on the patterns of participation of 10,199 individual stakeholders in 837 community wildfire protection plans (CWPPs) within the western U.S., we document the emergence of a locally clustered but spatially extensive wildfire risk governance network. Our evaluation of factors that contribute to connectivity within this network indicates that risk interdependence (e.g., joint exposure to the same fires) between planning jurisdictions increases the prospects for linkages between planning processes, and that connectivity is also more likely among planning processes that are more proximate and similar to one another. We discuss how our results advance understanding of how changing hazard conditions prompt risk mitigation policy networks to reorganize, which in turn affects risk outcomes at multiple spatial scales.

Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization ability of fire risk assessment models. Therefore, in this paper, we explored the potential of integration machine learning algorithms to build wildfire risk assessment models. Based on analyzing fire data's spatial and temporal distribution, we selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors. Next, we used the Bayesian optimization (BO) algorithm to perform hyperparametric optimization solutions for various machine learning models: support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Finally, we constructed an integration machine learning algorithm to acquire a fire risk grading map and the importance evaluation corresponding to each triggering factor. For validation purposes, we selected Liangshan Prefecture in Sichuan Province as the specific study area and obtained MCD64A1 burned area product to extract the extent of burned areas in Liangshan Prefecture from 2011 to 2020. The accuracy, kappa coefficient, and area under curve (AUC) were then applied to assess the predictive power and consistency of the fire risk classification maps. The experimental analysis showed that among the three models, FR-BO-XGBoost had the best performance in wildfire risk assessment in the Liangshan region (AUC = 0.887), followed by FR-BO-RF (AUC = 0.876) and FR-BO-SVM (AUC = 0.820). The feature importance result indicated that the study area's most significant effects on wildfires were precipitation, NDVI, land cover, and maximum temperature. The proposed method avoided the subjective weighting and model linearization problems. Compared with the previous methods, it automatically acquired the importance of the triggering factors to the wildfire, which had certain advantages in wildfire risk assessment, and was worthy of further promotion. [ABSTRACT FROM AUTHOR]

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R 2   = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method. [ABSTRACT FROM AUTHOR]

This study aimed at delineating the wildfire risk zones in a fire-prone region located in a rarely addressed area of western Iran (Paveh city) by assessing the potential of factors such as NDVI, topographic factors (elevation, slope, and aspect), land cover, and evaporation in explaining the fire occurrence probability. Analytic hierarchy process (AHP) and geographical information system (GIS) methods were used synergistically to integrate the mentioned factors into analysis, following an informed categorization of each factor based on the information on previous fire occurrence. In the AHP process, elevation and evaporation data were considered to be the most critical factors. It was found that the predicted wildfire risk areas were in agreement with past fire events by the use of the methodology proposed by this study. Accordingly, the study's final wildfire risk map indicated that approximately 64.7% of the study area is located in the high- and very high-risk zones. Land-use planners and decision-makers may use the developed map to setup and implement fire prevention strategies and enhance or develop the fire-surveillance logistics and infrastructure, including but not limited to the positions of fire watchtowers, fire lines, and fire sensors, with the aim to minimize potential fire impacts. [ABSTRACT FROM AUTHOR]

Background: Prescribed burns have been increasingly utilised in forest management in the past few decades. However, their effectiveness in reducing the risk of destructive wildfires has been debated. The sandpile model of self-organised criticality, first proposed to model natural hazards, has been recently applied to wildfire research for describing a negative linear relationship between the logarithm of fire size, in area burned, and the logarithm of fire incidence number of that size. Aims: We demonstrate the applicability of the sandpile model to an understanding of wildfire incidence and its trend with interested factors, such as prescribed burns. Methods: We leverage the sandpile model to perform a series of simulations, along with comparisons to historical wildfire data in three American states: Florida, California, and Georgia. Key results: Both simulated and historical data indicate that increased prescribed burning is associated with lowered incidence of large wildfires. Conclusions: Our study justifies the application of the sandpile model to wildfire research and establishes a novel method for facilitating the investigation of potential risk factors of wildfires. Implications: The sandpile model may be utilised for the development of optimal strategies for prescribed burning. An R-script for sandpile model simulation is available for further wildfire investigation. The sandpile model uses accumulating sand grains to approximate net fuel deposition. When a collapse occurs, it spreads, simulating the spread of fire. Data from both a sandpile model simulation and an analysis of historical forest fire data suggest that prescribed burning reduces the risk of destructive wildfires. [ABSTRACT FROM AUTHOR]

Forest fire is one of the serious threats to the population and infrastructure of Irkutsk Oblast because its territory is heavily forested. This paper discusses the main stages of solving the problem of forecasting the risk of forest fires via a case-based approach, including data preprocessing, formation of a case model, and creation of a prototype of a case-based expert system. The main contributions of the paper are the following: a case model that provides a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire; a case-base containing information about wildfires in Irkutsk Oblast for the period from 2017 to 2020; and a methodology for creating prototypes of case bases providing the transformation of decision tables of a special type. The approbation of the approach was carried out for separate forest districts, namely Bodaibinsk and Kazachinsk-Lena. The accuracy score was used for the evaluation of the results of forecasting the risk of wildfires. The average score value reached 0.51. The evaluation results revealed that application of the case-based approach can be considered as the initial stage for deeper investigations with the use of different methods (data mining, neural networks) for more accurate forecasting. [ABSTRACT FROM AUTHOR]

Understanding the patterns and underlying drivers of forest structure is critical for managing landscape processes and multiple resource management. Merging several landscape-scale datasets, including long-term fire histories, airborne LiDAR, and downscaled topo-climatic data, we assessed complex ecological questions regarding the interactions of forest structure, climate, and fire in the Yosemite National Park, a protected area historically dominated by frequent fire and largely free of the impacts of commercial industrial logging. We found that forest structure broadly corresponded with forest types arranged across elevation-driven climatic gradients and that repeated burning shifts forest structure towards conditions that are consistent with increased resilience, biodiversity, and ecosystem health and function. Specifically, across all forest types, tree density and mid-canopy strata cover was significantly reduced compared to overstory canopy and the indices of forest health improved after two fires, but no additional change occurred with subsequent burns. This study provides valuable information for managers who seek to refine prescriptions based on an enhanced understanding of fire-mediated changes in ladder fuels and tree density and those seeking to define the number of treatments needed to mitigate severe fire risk and enhance resiliency to repeated fires. In addition, our study highlights the utility of large-landscape LiDAR acquisitions for supporting fire, forest, and wildlife management prioritization and wildfire risk assessments for numerous valued resources. [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]

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction. [ABSTRACT FROM AUTHOR]

Pinus halepensis forests, as Mediterranean-type ecosystems, are subject to high levels of wildfire risk in times of drought, with meteorological conditions of water stress and very high temperatures, mainly in summer. Considering the difficulty of knowing the phenological state of this species, the objective of this research was to evaluate the possibility of implementing the electrical responses (voltage and short-circuit current) as a variable in fire risk management models, compared to live fuel moisture. On the one hand, the obtained results demonstrate non-significant differences between the moisture content of the different fractions of the living branches (base and half of the branch and live fuel), even in times of drought with hydric stress and very high temperatures. Live fuel moisture of Pinus halepensis does not show significant seasonal variations under the influence of extreme fire risk factors. For this reason, it should be complemented with other variables for fire risk management models. On the other hand, the differences registered in the electrical signal show oscillations with significant variations, which are strongly correlated with the periods of extremely favourable meteorological conditions for wildfires. So, the voltages measured show ranges that correspond with great accuracy to the FWI. Voltage variation is dependent on the hydraulic dynamic plant behaviour and a result of the physiological response of pine trees to abiotic stress of drought. It is an easy-to-measure electrical parameter as well as a very reliable indicator with a high correlation with wildfire risk. Thus, electrical responses could add more knowledge about the phenological state of the trees in dependence on stress climatic conditions, allowing integration of these variables in the preventive wildfire modelling and management. [ABSTRACT FROM AUTHOR]

There is an imperative for fire agencies to quantify the potential for prescribed burning to mitigate risk to life, property and environmental values while facing changing climates. The 2019–2020 Black Summer fires in eastern Australia raised questions about the effectiveness of prescribed burning in mitigating risk under unprecedented fire conditions. We performed a simulation experiment to test the effects of different rates of prescribed burning treatment on risks posed by wildfire to life, property and infrastructure. In four forested case study landscapes, we found that the risks posed by wildfire were substantially higher under the fire weather conditions of the 2019–2020 season, compared to the full range of long-term historic weather conditions. For area burnt and house loss, the 2019–2020 conditions resulted in more than a doubling of residual risk across the four landscapes, regardless of treatment rate (mean increase of 230%, range 164–360%). Fire managers must prepare for a higher level of residual risk as climate change increases the likelihood of similar or even more dangerous fire seasons. [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

The Mediterranean region is a densely populated and economically relevant area with complex orography including mountain ranges, islands, and straits. In combination with pressure gradients, this creates many mesoscale wind systems that cause, e.g., wind gusts and wildfire risk in the Mediterranean. This article reviews the recent state of the science of several mesoscale winds in the Mediterranean and associated processes. Previous work, including case studies on several time ranges and resolutions, as well as studies on these winds under future climate conditions, is discussed. Simulations with grid spacings of 25 to 50 km can reproduce winds driven by large-scale pressure patterns such as Mistral, Tramontane, and Etesians. However, these simulations struggle with the correct representation of winds channeled in straits and mountain gaps and around islands. Grid spacings of 1–3 km are certainly necessary to resolve these small-scale features. The smaller grid spacings are widely used in case studies, but not yet in simulations over large areas and long periods, which also could help to understand the interaction between small-scale phenomena in separate locations. Furthermore, by far not all Mediterranean straits, islands, and mountain gaps were studied in-depth and many interesting Mediterranean small-scale winds still need to be studied. [ABSTRACT FROM AUTHOR]

The increasing frequency of destructive wildfire incidents in the United States, particularly in the West, is well-documented, and the key causal variables are increasingly well understood. Among stakeholders with heightened concerns about risks from destructive wildfire are insurance companies and the homeowners they insure. The cancellation and nonrenewal of insurance due to wildfire risk has received media attention in the wake of major wildfire seasons, particularly in California. However, less attention has been directed to wildfire-related risks borne by lower-income policy holders, specifically. For example, the probability of maintaining or replacing an at-risk policy increases when a homeowner invests in fire protection measures. However, these investments are comparatively costly for lower-income homeowners. The present research aims to identify regions in the lower 48 states where moderate and high wildfire risk, lower income, and insurability are coterminous risks. The concentration of at-risk homes in counties with comparatively high wildfire hazard potential and comparatively higher poverty rates are considered. This paper also considers how the concentrated market share of insurance underwriting may pose a risk to lower income homeowners, considering the overlap between highly concentrated insurance markets and states with high wildfire risk and higher poverty rates. [ABSTRACT FROM AUTHOR]

Cytisus spp. is present throughout the Portuguese territory. Although most of these species are considered native in Portugal, at least one species has already been reported as invasive in other parts of the world. Different measures of control have been investigated, and the application of herbicides is the most commonly used. This review gathers information about the biochemical profile and analytical methods used for the evaluation of the potential bioactivities of three species of the genus Cytisus, better known as brooms, which were used in traditional medicine through the production of infusions and decoctions for the treatment of several health problems, mainly due to their high value of phenolic compounds. However, little research has been conducted on its biological activities as a potential antioxidant, anti-inflammatory, and antimicrobial agent. Furthermore, one species (Cytisus striatus) has not been subjected to extensive research in identifying chemical compounds and evaluating their potential bioactivities. This species (known as the Portuguese broom) has a great expression in one of the forest typologies with a considerable percentage in Portugal. This research work is essential to encourage a scientific and sustainable valorisation of Cytisus spp. (namely C. striatus), which will consequently contribute to forest cleaning and management to reduce the risk of wildfires. [ABSTRACT FROM AUTHOR]