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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]

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. [ABSTRACT FROM AUTHOR]

Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans. [ABSTRACT FROM AUTHOR]

Wildfires are among the most common natural disasters in many world regions and actively impact life quality. These events have become frequent due to climate change, other local policies, and human behavior. Fire spots are areas where the temperature is significantly higher than in the surrounding areas and are often used to identify wildfires. This study considers the historical data with the geographical locations of all the "fire spots" detected by the reference satellites covering the Brazilian territory between January 2011 and December 2022, comprising more than 2.2 million fire spots. This data was modeled with a spatio-temporal generalized linear mixed model for areal unit data, whose inferences about its parameters are made in a Bayesian framework and use meteorological variables (precipitation, air temperature, humidity, and wind speed) and a human variable (land-use transition and occupation) as covariates. The meteorological variables humidity and air temperature showed the most significant impact on the number of fire spots for each of the six Brazilian biomes. [ABSTRACT FROM AUTHOR]

Despite increasing wildfire severity and range, rapid development in the fire-prone Wildland–Urban Interface (WUI) has continued, and many neighborhoods are at risk of a constrained wildfire evacuation due to a high ratio of houses to community road-network exits. In Texas, Hays County is prone to fire, and rapid population growth has created a substantial WUI. Despite this, there is not sufficient research addressing neighborhood-level evacuation risks. The goal of this research, then, is to search Hays County for neighborhoods that face the highest combined risk of wildfire and potential evacuation difficulty. This research provides a limited use case wherein local decision-makers can quantify the combined risk of wildfire and constrained evacuation at the neighborhood scale by making use of standard spatial analysis techniques and publicly available datasets. The results show an alarming trend of low-egress neighborhoods in fire-prone areas within Hays County which carry the risk of a very difficult evacuation in cases when wildfire warning time is short. By using publicly available datasets and standard techniques, this research provides methods for local decision-makers across the state to identify these at-risk neighborhoods within their own jurisdictions which may aid in emergency planning and mitigation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks. [ABSTRACT FROM AUTHOR]

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development. [ABSTRACT FROM AUTHOR]

Over the last few years, new land use planning instruments to reduce the negative consequences of recent land use/cover changes (farmland abandonment, wildfires) have been proposed in Galicia (northwest Spain). Understanding the complex relationship between biophysical constraints, socioeconomic drivers and land use/cover changes is paramount for their successful implementation. In this work, we present an analysis of recent (2005–2017) land use/cover changes in the region, along with a classification of municipalities in homogeneous groups with different patterns of land use and land use change. We then characterize those groups regarding the demographic and employment structure, the economic performance, the characteristics of the primary sector, the land ownership structure and the relative importance of recent wildfire events and the biophysical suitability for the main productions of the primary sector in the region. The results allowed us to identify four different groups of municipalities which are clearly separated by specific patterns of land use (an area where most of the population lives, an area devoted to forest production, another for farming production and a final one dominated by semi-natural covers). These four areas followed a gradient of decreasing levels of population density and economic activity. While land use patterns in different areas could be explained largely by biophysical suitability, the fragmentation of land ownership emerged as a relevant factor, which can explain the greater presence of farmland abandonment—and, therefore, higher wildfire risk—in certain areas. These results offer relevant guidelines for the successful implementation of the new land use planning instruments in the region. [ABSTRACT FROM AUTHOR]

AbstractEnvironmental governance outcomes hinge on the design and implementation of management decisions. Yet, available research methodologies can be limited in their ability to capture the complexity of decision-making and implementation processes and in turn predict and explain environmental governance outcomes. We present participatory process mapping as a method for examining the pathways that emerge and evolve over time that result in natural resource management decisions and on-the-ground outcomes, as perceived by participants in collaborative processes. The approach leverages a large-N comparative design, participatory diagraming within semi-structured interviews, and mental modeling toward a method that can fit into both quantitative and qualitative research designs and analysis. The method is highly flexible, offering a pathway toward capturing causal relationships while also enabling incorporation of diverse elements and relationships. Natural resource management is nonlinear and is shaped by diverse social-ecological elements, and process mapping diagrams elicited through this method reflect that reality. [ABSTRACT FROM AUTHOR]

Due to extreme weather conditions and complex geographical features, the environments around power lines in forest areas have a high risk of wildfires. Once a wildfire occurs, it causes severe damage to the forest ecosystem. Monitoring wildfires around power lines in forested regions through deep learning can reduce the harm of wildfires to natural environments. To address the challenges of wildfire detection around power lines in forested areas, such as interference from complex environments, difficulty detecting small target objects, and high model complexity, a lightweight wildfire detection model based on the improved YOLOv8 is proposed. Firstly, we enhanced the image-feature-extraction capability using a novel feature-extraction network, GS-HGNetV2, and replaced the conventional convolutions with a Ghost Convolution (GhostConv) to reduce the model parameters. Secondly, the use of the RepViTBlock to replace the original Bottleneck in C2f enhanced the model's feature-fusion capability, thereby improving the recognition accuracy for small target objects. Lastly, we designed a Resource-friendly Convolutional Detection Head (RCD), which reduces the model complexity while maintaining accuracy by sharing the parameters. The model's performance was validated using a dataset of 11,280 images created by merging a custom dataset with the D-Fire data for monitoring wildfires near power lines. In comparison to YOLOv8, our model saw an improvement of 3.1% in the recall rate and 1.1% in the average precision. Simultaneously, the number of parameters and computational complexity decreased by 54.86% and 39.16%, respectively. The model is more appropriate for deployment on edge devices with limited computational power. [ABSTRACT FROM AUTHOR]

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

Accurately estimating the volume of woody vegetation is critical for assessing fuel characteristics and associated wildfire risks in shrublands. However, few studies have investigated the branch volume of multi-stemmed shrubs, a dominant life form in wildfire-prone drylands. This study predicts branch volume using the inflection point of branch diameter. This inflection point, identified using the "Segmented" package in R, marks the transition from a gradual decrease to a significant reduction in diameter along the stem. The volume of branch segment above this point is calculated as a cone, and below it, a cylinder. We validated this method on various species such as Caragana korshinskii, Salix psammophila, and Vitex negundo. Good estimations were achieved with an average 19.2% bias relative to reference branch volumes, outperforming conventional methods that subjectively treated the whole branch as either a cylinder (96.9% bias) or a cone (−34.4% bias). We tallied branches by basal diameter and provided inventories for easily locating the inflection point, as well as using two-way branch volume tables for rapid volume predictions in shrubland. In general, we developed an effective method for estimating branch volumes of multi-stemmed shrubs, enabling its application to larger-scale shrubland volumetric prediction. This advancement supports wildfire hazard assessment and informs decision-making in fuel treatments. [ABSTRACT FROM AUTHOR]

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

Wildfire risk is growing in the forested portion of the Wildland-Urban Interface, especially in the Western United States. Some commentators have suggested that the time has come for measures that would reduce the human presence in this area. In certain circumstances, however, family forestland owners have a salutary effect on forest resiliency. The attempt to exclude family forestland owners would create more problems than it would solve. A better strategy would be to assist family forestland owners in fireproofing their residences and improving the health of their forests. [ABSTRACT FROM AUTHOR]

Environmental crises, such as wildfires, can cause major losses of human life, infrastructure, biodiversity, and cultural values. In many such situations, incident controllers must make fateful choices about what to protect—and hence what to abandon. With an online representative survey of >2000 adult Australians, we investigated social attitudes to this dilemma. We used best–worst scaling to assess preferences across a set of 11 assets representing human life, infrastructure, biodiversity, and cultural values. Survey respondents overwhelmingly prioritized a single human life (best–worst score of 6647 out of possible score ranging from −10695 to 10695), even if that choice resulted in extinction of other species. Inanimate (replaceable) objects were accorded lowest priority (best–worst scores of −4655 for a shed and −3242 for a house). Among biodiversity assets, respondents prioritized protecting a population of the iconic koala (Phascolarctos cinereus) (best–worst score of 1913) ahead of preventing the extinction of a snail (score −329) and a plant species (−226). These results variably support current policy in that they emphasize the importance the community places on protection of human life, but results diverged from conventional practice in rating some biodiversity assets ahead of infrastructure. The preference for protecting a population of koalas ahead of action taken to prevent the extinction of an invertebrate and plant species corroborates previous research reporting biases in the way people value nature. If noncharismatic species are not to be treated as expendable, then the case for preventing their extinction needs to be better made to the community. Given the increasing global incidence of high‐severity wildfires, further sampling of societal preferences among diverse asset types is needed to inform planning, policy, and practice relating to wildfire. Other preemptive targeted management actions (such as translocations) are needed to conserve biodiversity, especially noniconic species, likely to be imperiled by catastrophic events. [ABSTRACT FROM AUTHOR]

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

People 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. [ABSTRACT FROM AUTHOR]

Wildfire agencies use fire danger rating systems (FDRSs) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian fire weather index (FWI) system, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly fire radiative power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI's ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system's value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior. [ABSTRACT FROM AUTHOR]

Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used in Australia. Studying the joint tail dependence structure of high-resolution spatial FFDI data is thus crucial for estimating current and future extreme wildfire risk. However, existing likelihood-based inference approaches are computationally prohibitive in high dimensions due to the need to censor observations in the bulk of the distribution. To address this, we construct models for spatial FFDI extremes by leveraging the sparse conditional independence structure of Hüsler–Reiss-type generalized Pareto processes defined on trees. These models allow for a simplified likelihood function that is computationally efficient. Our framework involves a mixture of tree-based multivariate generalized Pareto distributions with randomly generated tree structures, resulting in a flexible model that can capture nonstationary spatial dependence structures. We fit the model to summer FFDI data from different spatial clusters in Mainland Australia and 14 decadal windows between 1999 and 2022 to study local spatiotemporal variability with respect to the magnitude and extent of extreme wildfires. Our proposed method fits the margins and spatial tail dependence structure adequately and is helpful in providing extreme wildfire risk estimates. Our results identify a significant increase in spatially aggregated fire risk across a substantially large portion of Mainland Australia, which raises serious climatic concerns. Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

Accounting for ecosystem service values in wildfire risk mitigation remains a challenge. In this study we utilize public participatory GIS methods to measure ecosystem service values and overlay those values with maps of wildfire hazard. Our first objective is to understand sociocultural demand for ecosystem services, and our second objective is to show how non-monetary ecosystem service valuation methods can be used to inform wildfire risk mitigation decisions. Regulating ecosystem services, such as water quality, biodiversity/habitat, and air quality, along with recreation and aesthetics, were the most highly valued ecosystem services in our study. These ecosystem service values were clustered around roads, towns, and water features and correlated with accessibility, education, and income. These values also had significant overlap with wildfire hazard, suggesting that this non-monetary mapping approach could provide a more participatory method of incorporating people's preferences into decisions about where to target wildfire mitigation efforts. [ABSTRACT FROM AUTHOR]

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters. [ABSTRACT FROM AUTHOR]

Wildfire frequency and the total area burned in Brazil have steadily increased over time. However, in the Federal District, wildfire hazards have risen due to wildfires reaching several of its protected areas. Wildfire occurrence/incidence risk refers to the likelihood or probability of a wildfire event occurring in a particular geographic area. The risk is typically determined by factors such as weather conditions, topography, vegetation, and human activities and often measured through analyses of historical wildfire data and predictive models that estimate the likelihood of future wildfire events. High wildfire occurrence/incidence risk areas are those more likely to experience a wildfire incident, whereas low-risk ones are less susceptible to it. This study compares 8 machine learning models that predict wildfire occurrence risk worldwide so that they can be adopted in Brazil. They considered correlations among climate conditions, spatial location, topographic features, anthropogenic explanatory features, and fire occurrence and a dataset enriched with Brazilian governmental open data was comprised of observations on 16 climate features of 5 monitoring stations and satellite data on fires occurred over the past two decades and topographic, hydrographic, and anthropogenic features, such as Normalized Difference Vegetation Index (NDVI), urbanization index, and distance to rivers/roads. According to the results, fire risk can be predicted with 0.99 accuracy and the models showed more sensitive to NDVI, atmospheric pressure, and relative humidity, as demonstrated by a study on the impact of features. [ABSTRACT FROM AUTHOR]

Global environmental and social change are pushing wildfire activity and impact beyond known trajectories. Here, we conducted a targeted review to distill five wildfire challenges that we argue form opportunities for their governance (research aim 1). We exemplified our arguments by drawing from the case of Cyprus (research aim 2), a small island country in the south-east European Mediterranean Basin at risk of extreme wildfire impact. Findings indicate that burning for social and ecological resource benefits, innovative management paradigms and anticipatory governance systems offer actionable solutions to the wildfire paradox and the limits of suppression. Local adaptive institutions and a reconceptualisation of wildfire as a risk and process beyond technocratic interpretations are necessary to account for broader social conditions shaping wildfire regimes and community impact. Governance systems that accommodate collective action have proven suitable to address multiple wildfire complexities linked with different socio-economic systems and values. A systematic literature review, policy review, and qualitative data collection on wildfire management in Cyprus track back to the initial framing. Our case study offers insights for tackling wildfires with actionable steps through overarching governance systems, and illustrates the potential for change in thinking of and acting on wildfire in flammable landscapes globally. Global change is pushing fire activity and impact beyond known trajectories. We reviewed the literature to bring forward five themes of attention for governance systems. The initial framing was then applied to the case of Cyprus, to exemplify the potential and urgency for change in wildfire thinking and action globally. [ABSTRACT FROM AUTHOR]

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. Remote sensing is frequently used in fire ecology and management, but its applications for prescribed burning are not well established. We review studies that use remote sensing for prescribed burn research, finding that wildfire remote sensing approaches are not optimised for small, short-duration and low-severity burns typical of prescribed fire. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spatially explicit and climate sensitive information on the responses of ecosystems to global change processes is required for strategic planning and prioritization of conservation and restoration. Indicators of ecological resilience and invasion resistance (R&R) are used increasingly to understand the influence of climate on ecosystem responses to these change processes and determine effective management strategies. We developed landscape-scale indicators of R&R based on climate regimes for southwest U.S. drylands that accounted for precipitation seasonality and climate change using 1 km Daymet climate data (1980–2019). We used temperature, precipitation, and monsoon index data to construct climate sensitive and ecologically meaningful climate regimes mirroring the soil temperature and moisture regimes in the U.S. National Cooperative Soil Survey data and Ecological Site Descriptions that are widely used by managers in the western U.S. We evaluated relationships of the climate regimes and R&R indicators to the dominant ecological types, plant species, including the widespread exotic annual, cheatgrass (Bromus tectorum), plant functional groups, and annual burn probabilities. The climate regimes were associated with the dominant ecological types and plant functional groups. Particularly important was identification of a monsoon index value (0.30) to delineate areas with a summer moist (ustic) precipitation regime as indicated by the frequency of C4 vs. C3 grass species. Cover of cheatgrass, a C3 species, was highest in warm (mesic) and very warm (hypermesic) areas with a winter moist (xeric) or dry (aridic) precipitation regime and corresponded well to the resistance indicator. Annual burn probabilities were highest in areas with a winter moist precipitation regime and high precipitation (productivity) and did not correspond directly to cheatgrass cover. Changes in climate regimes from 1980 to 1999 to 2000–2019 resulted in decreases in both R&R by about 10 %. Climate-sensitive R&R indicators can be used in conservation and restoration planning to determine effective strategies and locate management activities to maximize ecological benefits and returns on investment. Recent changes in climate indicate the need to assess the direction and rate of climate change within the focal area and consider the likely effects on R&R when developing management strategies.

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. This study assesses the predictive value of synoptic-scale upper-air map types in near-term fire weather forecasting in the US Pacific Northwest. A set of 13 patterns of 500-hPa geopotential heights are associated with spatially heterogeneous variation in wildfire activity within the region. [ABSTRACT FROM AUTHOR]

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. Wildfire likelihood is quantified in a computationally efficient manner using a multi-scale 'neighbourhood index'. This is achieved by aggregating fire behaviour properties across multiple neighbourhood scales. The index is much more computationally efficient than alternative approaches and considers contagious and directionally specific fire behaviour properties across multiple spatial 'neighbourhood' scales. This article belongs to the Collection Fire & Climate. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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

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

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

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

Increased wildfire activity in the western United States can lead to detrimental cascading effects to water quality. After fires, burned areas may experience significant runoff‐induced erosion and sediment transport into rivers and reservoirs, which could rapidly overwhelm existing drinking water treatment plants. This paper couples an assessment of wildfire risk with an evaluation of water utility preparedness to understand where key fire‐related drinking water vulnerabilities exist. Wildfire risk assessments were constructed and expanded from a commonly used methodology co‐developed between researchers and water managers (Edel et al., 2002), to understand drinking water impacts on water quality after wildfires. A water utility preparedness index was created for this study using publicly available information to contextualize how well utilities may be able to respond to water quality degradation after fires. Results indicate that 22% of utilities studied (10% of the population served) were underprepared for fire and 11% of watersheds used were at greater risk of wildfire (9% of the population served). However, nearly three‐quarters of utilities (76% of the population served) showed a moderate risk of fire and some need for improved fire preparedness. Information developed here could provide a useful framework from which utility managers can better assess their likely wildfire risk and preparation plans. [ABSTRACT FROM AUTHOR]

With the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures. [ABSTRACT FROM AUTHOR]

Wildfires are a common phenomenon in Mediterranean environments. This study seeks to synthesize the main results of existing studies from the last decade on this topic and to highlight the need for managing soil impacted by wildfires in the Mediterranean environment. Researchers have found that the impact of fire during a forest fire on the soil, and the subsequent consequences on soil ecosystem services and functions, is great and produces negative consequences for the soil. The physical, chemical, and biological properties of soil have been extensively analyzed, and a very high number of studies during the last ten years have been performed on different study areas with a common component: the Mediterranean ecosystem. However, the effects of these fires on the multifunctionality of the soil itself, ecosystem services, and soil functionality, which they provide to humans, have not. It is therefore essential to know the impact of fires in a fire-prone ecosystem such as the Mediterranean one on the soil and how these services and functions are affected. In this way, the decision can be taken to carry out restoration measures, especially after very severe forest fires and if the recurrence is high. This conclusion is even more important in the context of global change in which more severe and recurrent fires are expected, and therefore actions to be considered are expected to be more necessary to avoid land degradation, as many of the studies compiled here have shown. [ABSTRACT FROM AUTHOR]

Background: Wildfires have caused significant damage in Chile, with critical infrastructure being vulnerable to extreme wildfires. Aim: This work describes a methodology for estimating wildfire risk that was applied to an electrical substation in the wildland–urban interface (WUI) of Valparaíso, Chile. Methods: Wildfire risk is defined as the product between the probability of a wildfire reaching infrastructure at the WUI and its consequences or impacts. The former is determined with event trees combined with modelled burn probability. Wildfire consequence is considered as the ignition probability of a proxy fuel within the substation, as a function of the incident heat flux using a probit expression derived from experimental data. The heat flux is estimated using modelled fire intensity and geometry and a corresponding view factor from an assumed solid flame. Key results: The probability of normal and extreme fires reaching the WUI is of the order of 10−4 and 10−6 events/year, respectively. Total wildfire risk is of the order of 10−5 to 10−4 events/year Conclusions: This methodology offers a comprehensive interpretation of wildfire risk that considers both wildfire likelihood and consequences. Implications: The methodology is an interesting tool for quantitatively assessing wildfire risk of critical infrastructure and risk mitigation measures. A methodology offering a comprehensive interpretation of wildfire risk considering both wildfire likelihood and asset vulnerability is proposed. The former is estimated with an event tree, while the latter is considered as the ignition probability of a polymeric fuel within a critical infrastructure. Wildfire risk is ~10−4 events/year. This article belongs to the Collection ICFFR. [ABSTRACT FROM AUTHOR]

Managing private forests for wildfire resilience is challenging due to conflicting social, economic, and ecological decisions that may result in an increase of surface fuel loads leading to greater fire risk. Due to fire suppression and a changing climate, land managers in fire-prone regions face an increasing threat of high severity fires. Thus, land managers need fuel treatment options that match their forest types and management objectives. One potential option for producers that graze livestock is silvopasture management, where livestock, forages, and overstory vegetation are carefully managed for co-benefits on the same unit of land. This study compared forest composition and structure, fuel types, and vegetative biomass between silvopasture and non-grazed managed forests in Washington, U.S. We show that silvopasture management results in reductions in grass biomass, litter, and duff depth when compared to non-grazed managed forest. These findings point to the integrated nature of silvopasture, where management of overstory composition and structure, understory vegetation, and grazing can reduce fuel loads and potential wildfire risk. [ABSTRACT FROM AUTHOR]

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

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

The development of monitoring and early warning tools for environmental and agricultural applications is highly restricted in scarce climate data regions. In particular, precipitation data is a key input for several environmental monitoring tools on which decision-makers rely. However, precipitation records are collected by rain gauge stations, but these are frequently inhomogeneous and scarce in some regions of the world, especially in South America and Africa. In such cases, the use of alternative precipitation data sources is necessary to correctly assess its spatial and temporal variations. Therefore, we evaluate the possibility of using the ERA5 data with different automatic enhancement methods. Three adjustment approaches were evaluated in Northern Patagonia, which is an example of a scarce data area: (1) modifying the ERA5 daily data with three different regression models, one depending on lag and lead days, a distributed lag model, and a simple linear regression model, (2) detecting the lower time window of precipitation accumulation that can represent the observed precipitation variations, and (3) determining a window size and cut-off frequency of a low-pass filter to have data that represent well the low-frequency variation. The lag-distributed models improved the ERA5 data precipitation. A combination of approaches 1 and 2 showed the best performance for enhancing the ERA5 precipitation data, with a minimum of 6-day time window accumulation. However, this enhanced performance is not spatially homogeneous and it is poor in the northeastern region. This tool allows the use of data from ERA5 in sites where daily precipitation input data is scarce or inaccurate for different environmental and agricultural applications aimed at offering permanent and updated information, such as monitoring drought, flood, wildfire risk, or pest outbreaks. These applications are key to reducing ecosystem, production, and infrastructure loss in regions where climate data is a strong restriction. [ABSTRACT FROM AUTHOR]

As wildfires are increasingly causing negative impacts to communities and their livelihoods, many communities are demanding more proactive and locally driven approaches to address wildfire risk. This marks a shift away from centralized governance models where decision-making is concentrated in government agencies that prioritize reactive wildfire suppression. In British Columbia (BC), Canada, Community Forests—a long-term, area-based tenure granted to Indigenous and/or local communities—are emerging as local leaders facilitating proactive wildfire management. To explore the factors that are enabling local governance approaches to managing wildfire risk, we conducted semi-structured interviews with 26 Community Forest managers across BC. Managers highlighted financial and social capacity, especially trust and relationships with both community members and government agencies, as crucial factors influencing their ability to undertake proactive management. These factors enable Community Forests to address wildfire risk not only within their own tenure area, but also at household, community, and landscape scales, while balancing diverse community values, objectives for forest management, and legal and policy obligations. Despite ongoing challenges, Community Forests emphasized the importance of scaling up their efforts to address wildfire risk and are a critical form of local wildfire governance that can help advance proactive wildfire management across BC. [ABSTRACT FROM AUTHOR]

From 2017 to 2023, British Columbians experienced four record-breaking wildfire seasons, resulting in reduced air quality, mass evacuations and the destruction of homes, properties and livelihoods. Wildfire risk reduction is vital to breaking the sequence of disaster that has befallen such communities as Kelowna, BC in 2003, Ft. McMurray, AB in 2016, and Lytton, BC in 2021. As the City of Penticton ('the City') is located in a wildfire-prone environment, its Fire Department, FireSmart Team and Emergency Program have worked closely together to facilitate a proactive and comprehensive approach towards reducing the impacts of wildfire on Penticton's neighbourhoods, businesses and residents through a variety of wildfire mitigation initiatives. This paper discusses the City's efforts to achieve a holistic wildfire risk management plan through alignment with the seven disciplines of FireSmart and the four pillars of emergency management, namely: the use of education; land use planning and development considerations; vegetation management; emergency planning; and cross training and interagency cooperation. The paper describes the challenges the City has faced, as well its successes, and provides recommendations to help other local authorities reduce the risk of wildfire in their communities. [ABSTRACT FROM AUTHOR]