Wildfire Risk Research Articles

Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making

AbstractPeople frequently make decisions based on uncertain information. Prior research has shown that visualizations of uncertainty can help to support better decision making. However, research has also shown that different representations of the same information can lead to different patterns of decision making. It is crucial for researchers to develop a better scientific understanding of when, why and how different representations of uncertainty lead viewers to make different decisions. This paper seeks to address this need by comparing geospatial visualizations of wildfire risk to verbal descriptions of the same risk. In three experiments, we manipulated the specificity of the uncertain information as well as the visual cues used to encode risk in the visualizations. All three experiments found that participants were more likely to evacuate in response to a hypothetical wildfire if the risk information was presented verbally. When the risk was presented visually, participants were less likely to evacuate, particularly when transparency was used to encode the risk information. Experiment 1 showed that evacuation rates were lower for transparency maps than for other types of visualizations. Experiments 2 and 3 sought to replicate this effect and to test how it related to other factors. Experiment 2 varied the hue used for the transparency maps and Experiment 3 manipulated the salience of the borders between the different risk levels. These experiments showed lower evacuation rates in response to transparency maps regardless of hue. The effect was partially, but not entirely, mitigated by adding salient borders to the transparency maps. Taken together, these experiments show that using transparency to encode information about risk can lead to very different patterns of decision making than other encodings of the same information.

Mortality attributable to PM 2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Smart City Wildfire Risk Analysis with Fuzzy Multi-Criteria Decision-Making

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It’s crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city’s resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.

Uplifting local ecological knowledge as part of adaptation pathways to wildfire risk reduction: A case study in Montseny, Catalonia (Spain).

Living with wildfires in an era of climate change requires adaptation and weaving together many forms of knowledge. Empirical evidence of knowledge co-production in wildfire management is lacking in Mediterranean European areas. We explored how local ecological knowledge can be leveraged to reduce wildfire risk through an adaptation pathways process in the Montseny massif and wider Tordera River watershed of Catalonia, Spain: an area stewarded through forestry and agriculture, tourism, nature conservation, and fire management. We combined different methods (e.g., a timeline and Three Horizons framework) throughout three workshops with agents of change to co-create adaptation pathways to reduce wildfire risk, integrating a historical perspective of the landscape while envisioning desirable futures. Our results showed that local ecological knowledge and other soft adaptation strategies contribute to innovative sustainable development initiatives that can also mitigate wildfire risk. The adaptation pathways approach holds much potential to inform local policies and support wildfire-based community initiatives in diverse contexts.

Remote sensing applications for prescribed burn research

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity.

Using Optimization Modeling to Determine Locations for Aerial Firefighting Resources Based on Wildfire Risk Data

The increase of the global mean temperature likely leads to more frequent and prolonged fire weather periods, with hot and dry conditions. This would also lead to increased frequency and magnitude of wildfires, and increased risk of spatial and temporal co-occurrence. A key to control and suppress wildfires during fire weather is a strong initial response, for which aerial firefighting resources can be used. This paper investigates the potential of optimization modeling to support pre-wildfire location planning of aerial firefighting resources, by developing a binary integer programing model and test it on Swedish case data. The model produces useful results given considerably less amount of available input data to determine the locations, compared with the manual planning. With relatively short solution times, it shows potential to support fast relocation of resources during sudden changes of circumstances, and to facilitate implementation of a standard operating procedure, with less expert knowledge dependence.

WUI-PEM: Wildfire Phased Zone Evacuation Methodology

Wildfires are a growing threat to wildland-urban interface (WUI) communities. Globally over the last decade hundreds of wildfire related deaths have occurred along with hundreds of billions in economic losses. If the encroachment of fire into a community cannot be stopped, evacuation becomes necessary to save lives. In response to the increasing threat, WUI communities must properly prepare and implement an evacuation plan, as orchestrating an evacuation within a limited time frame is a complex challenge. While several fire and evacuation simulation tools have been developed to aid planning, a comprehensive methodology for constructing and assessing evacuation plans is still evolving. This article fills some of the current gaps with a simple six-step process to evaluate wildfire risk, individual evacuation zone traffic demand, and formulate a staged zone-based full evacuation plan. This methodology can be used for wildfire evacuation planning, evacuation plan management, and training.

Wildfire Evacuation Challenges in Multi-ethnic Communities: An Information Communication Perspective

The areas with high wildfire risk of wildfire are inhabited by multiple ethnic groups in China. In the event of a wildfire, there are many challenges for the government to release evacuation information to residents in multi-ethnic communities. Based on the Shannon-Weaver model of communication, a whole-process communication model of evacuation information is constructed to reflect the challenges during the whole process, including the dynamic variability, regional difference and cross-language expression of evacuation information at the stage of information generation, the inadequate communication capability of information carriers and instability of communication infrastructures at the stage of information transmission, and comprehension impairment and irrational perception at the stage of information perception. In response to these challenges, the authors also make some suggestions from the intelligent information technology, crisis response system and offline communication channels.

Fire regime attributes shape pre-fire vegetation characteristics controlling extreme fire behavior under different bioregions in Spain

BackgroundDesigning effective land management actions addressed to increase ecosystem resilience requires us to understand how shifting fire regimes are shaping landscapes. In this study, we aim to assess the link between fire regime and pre-fire vegetation biophysical characteristics (type, amount, and structure) in controlling extreme fire behavior across Atlantic-Transition-Mediterranean bioregions in Spain marked by different summer drought conditions and dominant plant regenerative traits. We used remote sensing metrics to estimate fire severity and pre-fire vegetation characteristics in eight study areas recently affected by large and highly severe wildfires under different environmental contexts. Furthermore, to account for fire regime attributes, we retrieved, for each target wildfire, the perimeter of the past wildfires that occurred between 1985 and 2022 and calculated fire recurrence, the time the since last fire (TSLF), and fire severity of previous wildfires (FSPW). The effect of fire regime attributes on pre-fire vegetation was examined using generalized linear mixed models (GLMMs).ResultsDuring the study period, fire recurrence decreased significantly in all bioregions analyzed. Fire severity increased under Atlantic conditions and decreased under Mediterranean environmental context, where the time since the last fire was the highest. Pre-fire fuel type and amount were identified as primary drivers of fire severity, being both strongly modulated by fire regime but following distinct mechanisms depending on the environmental context of each bioregion. In Atlantic sites, more frequent past wildfires of low to moderate fire severity were associated with a greater dominance of fire-prone shrublands with moderate fuel amounts, which increases the risk of severe wildfires. Similar trends occurred in Transition and Mediterranean sites but under the previous occurrence of highly severe wildfires. Specifically, long times after highly severe wildfires (> 30 years) increased fuel amount in conifer-dominated ecosystems in all bioregions analyzed, heightening susceptibility to extreme fire behavior.ConclusionsOur findings highlight that fire-prone ecosystems need adaptative management strategies to mitigate the effects of fire regime changes, but these actions should be specific to the climatic and ecological context.

Fractional cover mapping of wildland-urban interface fuels using Landsat, Sentinel 1 and PALSAR imagery

Fuels within the immediate vicinity of a house (e.g., within 30–60 m), referred to as the ‘home-ignition zone’, are important determinants of structure damage during wildfires. Methods for mapping home-ignition zone fuels using earth observing satellites are lacking, limiting the capacity to quantify the spatial and temporal dynamics of urban fuel hazard and wildfire risk. Here, we (i) develop a methodology to map the fractional cover of five common fuel types (i.e., bare, buildings, herbaceous, woody and water cover) at the scale of the home-ignition zone (45 m radius area) using Landsat, Sentinel 1, and PALSAR imagery, and (ii) demonstrate that these maps improve prediction of house loss in wildfires in western Canada. A fractional cover database was created across thirteen cities via manual digitisation of aerial photos and used to train and validate random forest models consisting of combinations of Landsat, Sentinel 1 and PALSAR indices. The best models for the fractional cover of each fuel type explained most of the variation in the data (R2 = 0.66–0.96) and had high accuracy (Mean absolute error = 0.02–0.11) with low bias (Mean error = −0.005–0.008). The combination of optical (Landsat) and synthetic aperture radar (Sentinel 1 and/or PALSAR) improved model accuracy relative to models containing only multi-spectral indices. A house loss database was then derived for five major fires in western Canada and used to examine the effect of mapped fractional cover on house loss rates for 227 neighbourhood blocks. House loss rates were greater in blocks with a low amount of defensible space (i.e., areas of low fuel hazard; bare, herbaceous, or water cover) surrounding houses compared to those with extensive defensible space. This finding is consistent with house loss research globally and exposure assessment guidelines for communities in North America. Our fuel cover mapping methodology will facilitate improvements to models of house loss through the inclusion of important home-ignition zone fuel properties, allowing for spatially and temporally dynamic (i.e., annual) estimates of house loss likelihood and wildfire risk to communities.

Downed wood removal effects on survival and site fidelity of woodrats in a California oak woodland

AbstractOwing to the increasing intensity and frequency of wildfires in the western United States, the removal of woody debris (downed dead wood and snags) from fire‐prone landscapes is being evaluated for wildfire mitigation. Consequently, the study of the ecological value of coarse woody debris to sustain dependent species has become of foremost importance. From 2004–2009, we used a before‐after control‐impact (BACI) study design to assess the effects of downed wood removal on a population of big‐eared woodrats (Neotoma macrotis) in an oak woodland (Quercus spp.) in coastal‐central California, USA. Using Pollock's robust design mark‐recapture analyses (with 12 primary capture occasions represented by trapping each spring and fall, and secondary occasions composed of 3 trap nights), we estimated survival and emigration rates. Further, applying mixed‐effects models, we evaluated the effects of 7 habitat attributes on woodrat abundance and reproduction. Following the experimental removal of downed wood from the 11 randomly selected treatment plots, woodrat survival was higher (P = 0.013), and emigration was lower (P = 0.007) among 11 control plots on which downed wood was retained. Woodrat abundance within plots was best predicted by stem density, demonstrating positive demographic associations with habitat complexity. Plot‐level reproductive success (i.e., the presence of juveniles within a plot during spring) was more likely with increasing snag density. These findings indicate that snags and downed dead wood are key components of high‐quality habitat for big‐eared woodrats. Management of woody debris to mitigate wildfire risk should consider the ecological value of this habitat attribute for big‐eared woodrats and other dependent species.

Near-term fire weather forecasting in the Pacific Northwest using 500-hPa map types

Background Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting. Aims In this study, we assess the impact of synoptic-scale upper-air patterns on the occurrence of large wildfires and widespread fire outbreaks in the US Pacific Northwest. Additionally, we examine how discrete upper-air map types can augment subregional models of wildfire risk. Methods We assess the statistical relationship between synoptic map types, surface weather and wildfire occurrence. Additionally, we compare subregional fire danger models to identify the predictive value contributed by upper-air map types. Key results We find that these map types explain variation in wildfire occurrence not captured by fire danger indices based on surface weather alone, with specific map types associated with significantly higher expected daily ignition counts in half of the subregions. Conclusions We observe that incorporating upper-air map types enhances the explanatory power of subregional fire danger models. Implications Our approach provides value to operational wildfire management and provides a template for how these methods may be implemented in other regions.

A strategy to enhance soil quality and soil organic carbon stock in abandoned lands: Pasture regeneration through shrub clearing

AbstractFarmland abandonment in Mediterranean mid‐mountain areas can lead to the degradation or recovery of soil properties. Typically, after abandonment, fields are covered with shrubs, which hinders livestock grazing and constitutes high wildfire risk. To avoid these negative effects, shrubs are cleared in selected areas. The main objective of this study is to evaluate whether shrub clearing can improve soil quality and enhance carbon sequestration. A case study was conducted in the Leza Valley (La Rioja, Spain), examining five different land uses/management practices in both acid and alkaline soil environments. These included shrubland (control), three shrub clearing sites (managed <5, 15, and 25 years ago), and control pasture (reference). Soil replicates at different depths (0–40 cm) were collected, and physico‐chemical properties were analysed. A theoretical map of potential shrub clearing areas was created, and the potential to accumulate carbon was evaluated. Results showed that shrub clearing increases soil organic carbon and nitrogen, specially in 25‐year clearings. The increases were greater and significant on alkaline environments. The cartographical analysis reveals that 3388 ha can be still cleared in the Leza Valley, which could increase soil organic carbon accumulation by up to 31.6% in 25‐year period. We propose shrub‐clearing as a strategy for enhancing carbon sequestration in Mediterranean mountain areas.

Wildfire risk management in the era of climate change.

The August 8, 2023R Lahaina fire refocused attention on wildfires, public alerts, and emergency management. Wildfire risk is on the rise, precipitated through a combination of climate change, increased development in the wildland-urban interface (WUI), decades of unmitigated biomass accumulation in forests, and a long history of emphasis on fire suppression over hazard mitigation. Stemming the tide of wildfire death and destruction will involve bringing together diverse scientific disciplines into policy. Renewed emphasis is needed on emergency alerts and community evacuations. Land management strategies need to account for the impact of climate change and hazard mitigation on forest ecosystems. Here, we propose a long-term strategy consisting of integrating wildfire risk management in wider-scope forest land management policies and strategies, and we discuss new technologies and possible scientific breakthroughs.

Modelling wildfire risk using GIS and Analytical Hierarchy Process (AHP) in Aberdare afromontane forest ranges, Kenya

The knowledge of wildfire risk is crucial to sensitize and create awareness on fire prevention strategies and mobilization of resources to counter the spread after early detection. This study was undertaken to determine the most important environmental and anthropogenic factors associated with wildfire risk in Aberdares ranges. An integrated participatory decisions and geospatial analysis was used. A pair wise comparative analysis of seven factors namely: proximity to roads, proximity to farming areas within the forest, mean precipitation, elevation, slope, land cover and NDVI was undertaken to attribute weight of each factor in relative to the other though Analytical Hierarchy Process (AHP). A wildfire risk equation was developed using the criteria weights of respective factors and risk map developed using QGIS version 3.16. Results indicated that land cover (0.39) and NDVI (0.23) were the most important factors in developing wildfire risk maps while proximity to roads (0.04) was the least. Wildfire risk maps shows that Aberdare ranges is within low (43%) and moderate (30%) risk zone and the area occupied by high and very high zones is 13% and 4 % respectively. The study recommends testing the applicability of developed method in other areas with different climatic and land cover characteristics.

Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina.

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran's tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.

Wildfire risk and insurance: research directions for policy scientists

Wildfire risk and insurance: research directions for policy scientists

Mapping Wildland-urban interfaces to support wildfire management over fire-prone forest outskirts of the Zhytomyr region

Recent wildfire events in Ukraine caused considerable economic and human losses, drawing the attention of public opinion in Ukraine to further research the issue related to the management of the risks of forest fires, specially, in the current context of climate change and due to the growing frequency of critical fire weather conditions. Current approaches to fighting wildfires in Ukraine are focused on fire extinction, currently omitting the management of vegetation fuels and their effect on wildfire behaviour to facilitate its mitigation. Due to current wildfire risks to the population and forests, and insufficient research on this issue in Ukraine, it is needed to further develop and test new approaches to reduce wildfire risk. For that purpose, it is required a deep understanding of the fire resilience of vegetation as well as the factors that make the communities vulnerable. In this manuscript, a method for assessing and mapping the Wildland-urban interface with a focus on fire risks for part of the Ukrainian Polissya is suggested. Wildland-urban interface zones were delineated for settlements in the study area and used to identify areas for wildfire risk remediation and silvicultural practices to increase forest resilience to fire. A biodiversity analysis of the main tree species, undergrowth, and understory of the study region, produced a list of local deciduous species that could be used to reduce fire intensity by increasing their proportion in pure pine forests. The volume of silvicultural efforts to increase forest resilience to fire and reduce wildfire risks to human settlements was assessed for one of the most forested regions of Ukraine. Moreover, the first comprehensive assessment of wildlands, which can potentially contribute to wildfire impacts on communities, was provided, making recommendations to reduce wildfire risks for the settlements. In this study, feasible and effective methods to assess Wildland-Urban-Interfaces, and reduce fire risks are suggested, suggesting a methodology concerning wildfire risks for a heavily forested region of Ukraine. Moreover, the suggested approaches that could be used in Ukrainian Forest Management to mitigate wildfire risks in the context of climate change, urbanization, and low resistance of pure pine stands to fires as well as pests and diseases

An efficient, multi-scale neighbourhood index to quantify wildfire likelihood

Background To effectively reduce future wildfire risk, several management strategies must be evaluated under plausible future scenarios, requiring models that provide estimates of how likely wildfires are to spread to community assets (wildfire likelihood) in a computationally efficient manner. Approaches to quantifying wildfire likelihood using fire simulation models cannot practically achieve this because they are too computationally expensive. Aim This study aimed to develop an approach for quantifying wildfire likelihood that is both computationally efficient and able to consider contagious and directionally specific fire behaviour properties across multiple spatial ‘neighbourhood’ scales. Methods A novel, computationally efficient index for quantifying wildfire likelihood is proposed. This index is evaluated against historical and simulated data on a case study in South Australia. Key results The neighbourhood index explains historical burnt areas and closely replicates patterns in burn probability calculated using landscape fire simulation (ρ = 0.83), while requiring 99.7% less computational time than the simulation-based model. Conclusions The neighbourhood index represents patterns in wildfire likelihood similar to those represented in burn probability, with a much-reduced computational time. Implications By using the index alongside existing approaches, managers can better explore problems involving many evaluations of wildfire likelihood, thereby improving planning processes and reducing future wildfire risks.

Changing Conditions: Global Warming-Related Hazards and Vulnerable Rural Populations in Mediterranean Europe

Human-induced climate change has profound effects on extreme events, particularly those linked to global warming, such as heatwaves, droughts, and wildfires. These events disrupt ecosystems, emphasizing the imperative to understand the interactions among them to gauge the risks faced by vulnerable communities. Vulnerability levels vary primarily based on a community’s resources. Rural areas, especially in the Mediterranean region of Europe, are experiencing acute depopulation, creating a complex situation affecting various aspects of society, from economic declines to cultural heritage loss. Population decline in rural regions weakens resources, leading to the abandonment of built environments, fostering desertification, and elevating the risk of wildfires. Communities undergoing this deterioration process become exceptionally vulnerable, especially when dealing with and recovering from extreme natural phenomena. This review offers insights into the dynamics of these hazards and the predominant challenges in rural areas. By focusing on a topic that has received limited attention, the aim is to inform future research initiatives, ultimately improving risk assessment and mitigation strategies for these vulnerable communities.