Effects Of Wildfire Research Articles

Comparing the Effects of Wildfire and Hazard Reduction Burning Area on Air Quality in Sydney

Landscape fires emit smoke that contains particulate matter (PM) that can be harmful to human health. Prescribed fires or hazard reduction burns (HRBs) and wildfires can substantially reduce air quality in populated areas. While HRBs reduce the size and PM output of future wildfires, they also produce PM. There is a critical question of whether conducting HRBs adds to or reduces the total PM (prescribed + wildfire PM) exposure of populations, which has important community health implications. We modelled mean 24 h PM2.5 in Sydney from fire type, 24 h active regional fire area and weather predictors. We compared HRB and wildfire area effects via an interaction between fire area and fire type, and we found that a non-linear effect of fire area was most appropriate. We conducted a trade-off analysis by using the model to predict increased HRB area scenarios and calculated the number of HRB and wildfire days with ‘Fair’ or worse air quality under each scenario. Regional HRBs and wildfire areas had similar effects on PM2.5 in Sydney. Increasing regional HRB area produced substantial increases in HRB exceedance days (>12.5 µgm−3) in Sydney but only a small reduction in wildfire exceedance days. Our results indicate that small fires in the region have a higher per-hectare impact on PM levels in Sydney, and consequently, increasing regional HRB area would result in more poor air quality days overall (HRB + wildfire days) in Sydney.

Wildfire risk weighting and behaviour prediction using open geospatial data and ontologies

This article presents a novel approach to wildfire risk assessment and behaviour prediction by leveraging open geospatial data and ontologies. The proposed methodology includes a spatially weighted index model and multicriteria analysis to represent the risk of forest fires in the affected area. It bridges gaps in theory and practice, offering a comprehensive solution for evaluating potential forest fire risk in near real time, predicting fire behaviour and elucidating the semantics of fire management. During dry and hot conditions, forest fires tend to escalate. Hence, we propose an algorithm that combines experts’ empirical criteria and open-source data to identify dangerous fires in near real time, aiding authorities in directing attention to the riskiest areas. The objective is to predict forest fire behaviour, a complex and nonlinear system influenced by dynamic factors such as weather conditions, topography and land use. Our methodology enables real-time assessment of potential forest fire risks, complemented by predictive fire behaviour scenarios and a descriptive ontology of fire management semantics. We examine existing fire-related ontologies and propose a comprehensive one encompassing incident descriptions, firefighting resources, actor interrelations and knowledge for effective action. By classifying fire sources, our algorithm enables strategic decision-making to prevent uncontrolled fires. This solution significantly enhances data using semantic and spatial relationships among wildfire resources. Furthermore, we demonstrate how ontologies improve data integration and interoperability among diverse systems and organisations involved in forest fire risk management, fostering better coordination and faster responses to critical situations. To facilitate decision-making, we create decision-making scenarios linked to analysed hot spots, drawing from open hot spot data such as National Aeronautics and Space Administration (NASA) Fire Information for Resource Management System (FIRMS), OpenStreetMap (OSM), OpenWeatherMap (OWM) and OpenTopoData (OTD). We propose an ordinal and linguistic classification system (F1–F5) denoting risk levels as low, moderate, high, very high and extreme. These values are obtained through factor aggregation and fuzzy logic. A publicly accessible, interactive web map displays the results derived from this model. Overall, our contributions to wildfire risk management provide authorities with a valuable tool to make informed decisions and mitigate the damaging effects of wildfires.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

A global synthesis and conceptualization of the magnitude and duration of soil carbon losses in response to forest disturbances.

Forest disturbances are increasing around the globe due to changes in climate and management, deteriorating forests' carbon sink strength. Estimates of global forest carbon budgets account for losses of plant biomass but often neglect the effects of disturbances on soil organic carbon (SOC). Here, we aimed to quantify and conceptualize SOC losses in response to different disturbance agents on a global scale. Global. 1983-2022. Forest soils. We conducted a comprehensive global analysis of the effects of harvesting, wildfires, windstorms and insect infestations on forest SOC stocks in the surface organic layer and top mineral soil, synthesizing 927 paired observations from 151 existing field studies worldwide. We further used global mapping to assess potential SOC losses upon disturbance. We found that both natural and anthropogenic forest disturbances can cause large SOC losses up to 60 Mg ha-1. On average, the largest SOC losses were found after wildfires, followed by disturbances from windstorms, harvests and insects. However, initial carbon stock size, rather than disturbance agent, had the strongest influence on the magnitude of SOC losses. SOC losses were greatest in cold-climate forests (boreal and mountainous regions) with large accumulations of organic matter on or near the soil surface. Negative effects are present for at least four decades post-disturbance. In contrast, forests with small initial SOC stocks experienced quantitatively lower carbon losses, and their stocks returned to pre-disturbance levels more quickly. Our results indicate that the more carbon is stored in the forest's organic layers and top mineral soils, the more carbon will be lost after disturbance. Robust estimates of forest carbon budgets must therefore consider disturbance-induced SOC losses, which strongly depend on site-specific stocks. Particularly in cold-climate forests, these disturbance-related losses may challenge forest management efforts to sequester CO2.

Effects of Wildfire Events on California Radiation Oncology Clinics and Patients

PurposeThe impact of climate-driven events, such as wildfires, on healthcare delivery and cancer care is a growing concern. Patients with cancer undergoing radiotherapy are particularly vulnerable to treatment interruptions, which have a direct impact on survival. We report the results of a study characterizing the impact of wildfires on radiation oncology clinics and their patients. Methods and MaterialsA survey of California radiation oncologists was used to evaluate emergency preparedness and the impact of wildfires on the delivery of radiotherapy services between 2017-2022. Descriptive statistics and Pearson's chi-squared tests were performed to investigate potential relationships between provider characteristics, practice settings, and perceptions of the impact of wildfire events. California Department of Forestry and Fire Protection (CAL FIRE) data were employed to map the geographic distribution of wildfires to clinic locations. ResultsResponse rate was 12.3% (51/415 radiation oncologists), representing 25% of clinics (43/176) in 41% (24/58) of California counties. 61% (31/51) of respondents reported being impacted by a wildfire, two of which are rural clinics (100%, 2/2) and 29 are (59%, 29/49) metropolitan practices. Of these, 18% (9/51) reported a clinic closure, and 29% (15/51) reported staffing shortages. Respondents reported impacts on patients, including having to evacuate (55%, 28/51), cancel/reschedule treatments (53%, 27/51), and experiencing physical, mental, or financial hardship due to wildfires (45%, 23/51). Respondents described impacts on clinical operations, including being forced to transfer patients (24%, 12/51), transportation interruptions (37%, 19/51), regional/community evacuations (35%, 18/51), and physical/mental health impacts (27%, 14/51) on clinic personnel. Less than half of the respondents (47%, 24/51) reported their workplace had a wildfire emergency preparedness plan. Additionally, geographic analysis revealed that 100% (176/176) of clinics were located within 25 miles of a wildfire. ConclusionsThis study highlights the impact of wildfires on radiation oncology clinics and patients, and underscores the need for emergency preparedness planning to minimize the consequences of such disasters.

Wildfire-Residential Risk Analysis Using Building Characteristics and Simulations to Enhance Structural Fire Resistance in Greece

Urban areas adjacent to wildlands are very dangerous zones for residents and their properties during a wildfire event. We attempted to connect wildfire simulations with field inventories and surveys to create a framework that can be used to enhance the fire resistance of residential structures located in the wildland-urban interface (WUI). Legal restrictions and the lack of economic incentives for WUI residents greatly limit the potential to appropriately intervene to enhance their property’s fire resistance. By studying in situ the resilience of building materials and combining them with exposure metrics produced from wildfire simulations, we created an index that helps to assess fire risk at the property level. The proposed index can support property owners to optimally manage the vegetation near or inside their property. State agencies can use our proposed index to estimate with a consistent methodology which properties are more exposed and with higher risk from fire damage so that specific fuel and vegetation management practices on and around them can be suggested or enforced.

What data are needed to detect wildfire effects on coastal ecosystems? A case study during the Thomas Fire

Wildfires are growing in frequency and severity worldwide, with anthropogenic climate change predicted to worsen the effects of wildfires in the future. While most wildfire impacts occur on land, coastal fires can also affect the ocean via smoke production and ash deposition. The impacts of wildfires on marine ecology and biogeochemistry have been studied infrequently, as it is difficult to conduct fieldwork rapidly and safely during unpredictable natural disasters. Increasingly, remote sensing measurements are used to study the impacts of wildfires on marine ecosystems through optical proxies. Given the optical impacts of smoke and in-water ash, these measurements may be limited in their scope and accuracy. Here, we evaluate the potential and limitations of remote sensing data collected from MODIS-Aqua to describe the effects of wildfires on optics and phytoplankton observations. Using samples collected in the Santa Barbara Channel (California, USA) during the Thomas Fire in December 2017, we found that MODIS-Aqua data were unsuited for interpreting ecosystem effects during a wildfire. Our results identified a persistent overestimation of chlorophyll-a concentration from MODIS-Aqua compared to in situ measurements. Optical models applied to in situ radiometry data overestimated the absorption by colored dissolved organic matter (CDOM) during the wildfire. Satellites will remain an important tool to measure the impacts of wildfires on marine ecosystems, but this analysis demonstrates the importance of in situ sampling to quantify the impacts of wildfires on ocean ecology and biogeochemistry due to the difficulty of interpreting remote sensing data during these events.

Hydrology, rather than wildfire burn extent, determines post‐fire organic and black carbon export from mountain rivers in central coastal California

AbstractCoastal mountain rivers export disproportionately high quantities of terrestrial organic carbon (OC) directly to the ocean, feeding microbial communities and altering coastal ecology. To better predict and mitigate the effects of wildfires on aquatic ecosystems and resources, we must evaluate the relationships between fire, hydrology, and carbon export, particularly in the fire‐prone western United States. This study examined the spatiotemporal export of particulate and dissolved OC (POC and DOC, respectively) and particulate and dissolved black carbon (PBC and DBC, respectively) from five coastal mountain watersheds following the 2020 CZU Lightning Complex Fires (California, USA). Despite high variability in watershed burn extent (20–98%), annual POC, DOC, PBC, and DBC concentrations remained relatively stable among the different watersheds. Instead, they correlated significantly with watershed discharge. Our findings indicate that hydrology, rather than burn extent, is a primary driver of post‐fire carbon export in coastal mountain watersheds.

Operational fuel model map for Atlantic landscapes using ALS and Sentinel-2 images

BackgroundIn the new era of large, high-intensity wildfire events, new fire prevention and extinction strategies are emerging. Software that simulates fire behavior can play a leading role. In order for these simulators to provide reliable results, updated fuel model maps are required. Previous studies have shown that remote sensing is a useful tool for obtaining information about vegetation structures and types. However, remote sensing technologies have not been evaluated for operational purposes in Atlantic environments. In this study, we describe a methodology based on remote sensing data (Sentinel-2 images and aerial point clouds) to obtain updated fuel model maps of an Atlantic area. These maps could be used directly in wildfire simulation software.ResultsAn automated methodology has been developed that allows for the efficient identification and mapping of fuel models in an Atlantic environment. It mainly consists of processing remote sensing data using supervised classifications to obtain a map with the geographical distribution of the species in the study area and maps with the geographical distribution of the structural characteristics of the forest covers. The relationships between the vegetation species and structures in the study area and the Rothermel fuel models were identified. These relationships enabled the generation of the final fuel model map by combining the different previously obtained maps. The resulting map provides essential information about the geographical distribution of fuels; 32.92% of the study area corresponds to models 4 and 7, which are the two models that tend to develop more dangerous behaviors. The accuracy of the final map is evaluated through validation of the maps that are used to obtain it. The user and producer accuracy ranged between 70 and 100%.ConclusionThis paper describes an automated methodology for obtaining updated fuel model maps in Atlantic landscapes using remote sensing data. These maps are crucial in wildfire simulation, which supports the modern wildfire suppression and prevention strategies. Sentinel-2 is a global open access source, and LiDAR is an extensively used technology, meaning that the approach proposed in this study represents a step forward in the efficient transformation of remote sensing data into operational tools for wildfire prevention.

How Wildfires and Weather Affect Portugal’s Public Health

Researchers dug into data to examine the effects of wildfires, pollutants, and meteorological factors on mortality and cardiovascular health in the Iberian country.

Segmentation of polarimetric radar imagery using statistical texture

Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.

Autumn resource selection by female elk in a recently burned landscape in western Montana

AbstractWildfire activity across the western United States has increased in recent decades, with wildfires burning at a higher severity and larger scale. The effect of wildfires on forest structure and wildlife habitat is largely influenced by wildfire severity; however, few studies have evaluated the effects of wildfire severity on resource selection of ungulates, particularly during hunting seasons, when knowledge of resource selection is essential for making informed management decisions. To fill this knowledge gap, we fit resource selection probability functions for female elk (Cervus canadensis) in years 2 and 3 post‐wildfire to evaluate the effects of wildfire severity and other environmental and anthropogenic factors on elk resource selection during 4 autumn periods with varying levels of hunter pressure (prehunt, archery‐only, backcountry rifle, and rifle). The probability of female elk selecting low‐severity burned forests during the prehunt, archery‐only, backcountry rifle, and rifle periods was 0.99 (95% credible interval [CrI] = 0.98–1.00), 0.99 (CrI = 0.97–1.00), 0.99 (CrI = 0.99–1.00), and 0.0010 (CrI = 0.00067–0.0015]), respectively, and did not strongly differ from the probability of selecting high‐severity burned forests. During the prehunt period, elk also selected areas with greater forage quality and areas farther from open roads. Elk selected similar resources during the archery period, and selected areas with higher hunter pressure. Elk started leaving hunting districts that had higher snowpack (i.e., snow water equivalent; β = −0.84, CrI = −0.96–−0.72) and allowed rifle hunting (β = −5.39, CrI = −5.80–−4.97) but still selected areas with higher hunter pressure (β = 0.92, CrI = 0.78–1.07) during the backcountry rifle period. During the rifle period, elk continued avoiding areas with high snowpack (β = −3.96, CrI = −4.22–−3.71) and started selecting areas with lower hunter pressure (β = −1.71, CrI = −1.79–−1.64) and lower canopy cover. Overall, wildfire affected elk distributions in early autumn 2 and 3 years after fire in our study area, with limited differences in resource selection between wildfire severity categories. By late autumn, hunter pressure and snowpack were the primary factors influencing elk distribution, and wildfire had little influence on selection. When estimating wildfire effects on elk movements during autumn and establishing appropriate hunting regulations, managers should consider the hunting season, hunter pressure, timing and amount of snowpack, location of traditional winter range, and the seasonal elk range burned, as all these factors may contribute to how elk use the landscape in autumn.

Extreme weather—Wildfires & pregnancy

As described in the previous chapter, Chapter 4: Air pollution and pregnancy, there is robust literature on the adverse health impacts of ambient air pollution on perinatal outcomes. With climate change contributing to more extreme weather patterns, wildfire events are becoming more intense and frequent. Wildfire smoke is a major contributor to poor air quality and data are beginning to emerge with respect to the negative impact on perinatal outcomes. The aim of this chapter is to provide an overview of the current literature on wildfire smoke exposure in pregnancy and associated adverse outcomes.

Examining the temporal effects of wildfires on forest birds: Should I stay or should I go?

In flammable ecosystems, forest dynamics are affected by fire suppression, climate change and changes in land use that cause the frequency and intensity of fires to diverge from natural fire regimes. Although afforestation provides new habitats for forest species, fire can reverse its benefits by penalizing forest species. Shorter fire recurrence may also affect the capacity of original habitats to recover with post-fire salvage logging adding to the picture and having both direct and indirect effects on birds. To identify the underlying factors driving temporal variations in forest bird populations after wildfires, we performed counts in 685 transects located in 68 burnt areas in Catalonia (NE Iberian Peninsula), ranging from one to 11 years post-fire. Among the 20 studied forest species, 18 related significantly to time since fire and 14 to water deficit. Post-fire bird responses mainly fitted to non-lineal responses, such as irruptive and inverse-irruptive trends; whereas water deficit matched with the inclined and bell-shaped trends. Individual site fidelity seems behind the decision of forest birds to stay or leave after a fire, leading to increase frequency of occurrence immediately after fire but this effect tends to diminish over time. In terms of the spatial characteristics of burnt areas, the majority of species showed a positive relationship with fire severity heterogeneity and the density of unburnt patches. On the other hand, salvage logging negatively affected forest bird diversity. Bird diversity also decreased with the time since fire and in the driest transects but fire heterogeneity was positive for alpha and beta bird forest diversity. The relationships between forest birds and the mosaic of burnt and unburnt areas created by wildfires plays a critical role in maintaining biodiversity in fire-prone ecosystems. Thus, limiting post-fire disturbance (e.g. salvage logging) and promoting natural regeneration could be an effective strategy that will benefit forest organisms such as birds. Maintaining unburnt patches and creating a mosaic of burn severity and vegetation will promote habitat heterogeneity and benefit a diverse range of bird species.

Evaluating driving behavior patterns during wildfire evacuations in wildland-urban interface zones using connected vehicles data

Wildfire risk is increasing all over the world, particularly in the western United States and the communities in wildland-urban interface (WUI) areas are at the greatest risk of fire. Understanding the driving behavior of individuals to evacuate fire-affected WUI areas is important as the evacuees may encounter low visibility and difficult driving conditions due to burning material and steep topography. This study investigates the driving behavior patterns of individuals during historical wildfire events in rural and urban areas with mandatory evacuation orders using a connected vehicle dataset. This dataset provides the geolocation and timestamp of vehicles’ hard-braking (HB) and hard-acceleration (HA) events. The comparative analysis of the data demonstrated that the reported HA & HB event patterns were consistent in representing the varying driving behavior in response to the changing driving conditions and the depiction of the temporal and spatial impact of the fire in all studied areas. Moreover, the HB event dataset located critical traffic congestion points and the HA event dataset revealed the hurried response of evacuees on the exiting routes as a result of short-notice evacuation. In addition, significant differences in driving behavior patterns were noticed between rural and urban areas.

Wildfire smoke reduces Little Eagle (Hieraaetus morphnoides) flight activity

ABSTRACT There have been few empirical studies of the sensitivity of birds to the effect of air pollutants. In late 2019 and early 2020 the Australian Capital Territory (ACT) and much of south-eastern Australia were affected by extreme wildfire events and smoke extended to surrounding areas. Prior to this event, GPS transmitters had been fitted to a sample of Little Eagles Hieraaetus morphnoides in the ACT as part of a study of their movement behaviour. Three of these birds carried transmitters in the breeding season during the fires and in the previous breeding season. This offered opportunistic analysis of data from both periods to test for effects of smoke on the birds’ flight behaviour. The effects of particulate matter in the air of ≤2.5 microns in diameter (PM2.5) and covariates on flight status were investigated with a binomial generalised linear model with logistic link. The birds were more likely to fly when there were low levels of PM2.5 and the odds of flying decreased as density of PM2.5 increased at a rate of 0.202% per ug/m3. None of the sample birds died during or after smoke exposure, although their respiratory system might have been affected.

Probabilistic Risk Assessment of the Economy‐Wide Impacts From a Changing Wildfire Climate on a Regional Rural Landscape

AbstractA warmer and drier future combined with rising population trends will result in increased wildfire risk. The design of robust mitigation/adaptation strategies requires the assessment of the economy‐wide risks from potentially more frequent large wildfire events under different futures. This study uses a novel interdisciplinary approach by integrating wildfire climate and land‐use forecasts into probabilistic and simulation models of wildfires to estimate direct impacts using damage curves and indirect impacts as lost Gross Domestic Product (GDP) using a computable general equilibrium model. Based on the financial concept of Value at Risk, a probabilistic measure of extreme economy‐wide impacts was developed using GDP as the representative metric, namely “GDP at Risk (G@R),” under various climatic and socio‐economic scenarios. Using the new metric in the Waikato, New Zealand as a case study, due its primary industries' national relevance, it was identified that there is a 5% probability that the region will experience GDP losses greater or equal to NZ$0.1–1.2 billion (similar to the regional GDP growth in 2021) over a 48‐year period from future potential large wildfires affecting vulnerable primary industries. The regional impacts result in larger national GDP losses by a factor of 5 due to the high dependence of downstream sectors on the regional primary industries. While “G@R” estimates are similar across socio‐economic scenarios, there are no discernible patterns when compared across midcentury climate scenarios. The approach developed could be used to assess the consequences from afforestation projects, driven by mitigation policies, and adaptation strategies to reduce wildfire risk.

Using high-resolution land cover data to assess structure loss in the 2018 Woolsey Fire in Southern California

There are growing concerns about increases in the size, frequency, and destructiveness of wildfire events. One commonly used mitigation strategy is the creation and maintenance of defensible space, a zone around buildings where vegetation is managed to increase potential for structures to survive during wildfires. Despite widespread acceptance and advocacy of defensible space, few studies provide empirical evidence documenting the efficacy of different fuel modification practices under real wildfire conditions. The 2018 Woolsey Fire in Los Angeles County, California, occurred a short time after high-resolution (0.07 m2) land cover data were created, providing a unique opportunity to quantify vegetation before the fire. We integrated measurements from this high-resolution land cover data with parcel data, building attributes, and environmental context. We then used Random Forests models to analyze the extent to which these factors predicted structure loss in the wildfire. Variable importance scores showed vegetation around buildings was not a strong predictor of building-level damage outcomes compared to building materials and landscape features such as paved land cover per parcel, elevation, building density, and distance to road networks. Among building materials, multi-paned windows and enclosed eaves were most highly associated with building survival. These results are consistent with other studies that conclude building materials and environmental context are more related to survivorship than defensible space.

Water utility engagement in wildfire mitigation in watersheds in the western United States

Scaling up climate-adaptation in wildfire-prone watersheds requires innovative partnerships and funding. Water utilities are one stakeholder group that could play a role in these efforts. The overarching purpose of this study was to understand water utility engagement in wildfire mitigation efforts in the western United States. We conducted an online survey of water utilities in nine states and received 173 useable responses. While most (68%) respondents were concerned or very concerned about future wildfire events and the impact of wildfire on their operations, only 39% perceived their organization as responsible for mitigating wildfire risk. Federal land ownership decreased feeling responsible for wildfire mitigation, while concern for and information on wildfire increased feeling responsible for mitigation. The perception of response efficacy of mitigation actions for the 68 water utilities engaged in wildfire risk mitigation activities was very high, with most agreeing that mitigation actions are effective. Self-efficacy to implement mitigation actions, however, was mixed, with most utilities wanting more information on wildfire risk and impacts to watershed services. The most reported wildfire mitigation actions were forest thinning and stream restoration. Water utilities engaging in these actions typically partnered with government agencies or other water utilities to complete the work and funded these activities through water user fees and grants. Our findings suggest that water utility engagement in wildfire mitigation for water security could be increased through providing more assessments of wildfire risk to water utilities and through more outreach and engagement with water utilities operating on federal lands.

Impacts of Wildfire Events on California Radiation Oncology Clinics and Patients

The impact of climate-related disasters such as wildfires on healthcare delivery and cancer care is a growing concern. Patients undergoing radiotherapy are particularly vulnerable to treatment interruptions which are known to have a direct impact on survival outcomes. We report results of the first pilot study characterizing the impact of wildfires on radiation oncology clinics and their patients. A survey was sent to 415 California radiation oncologists representing 144 clinics identified using the ASTRO member directory to gather information about clinic and radiation oncologist demographics, wildfires' impacts on the clinic (physical/operational), physicians, staff, and patients between 2017 and 2022, as well as clinics' disaster preparedness efforts. A total of 51 radiation oncologists completed the survey, representing 43 clinics (30% of clinics) in 24 (41%) of California counties. 35 (69%) of respondents self-identified as male, 27 (53%) worked at hospital-affiliated centers, 19 (37%) worked in academic centers, with 49 (96%) practicing in metropolitan areas. A total of 31 clinics were impacted by a wildfire between 2017 and 2020. The two rural clinics (100%) and 29 (59%) of metro practices reported being impacted by wildfires in the last 5 years, with 9 (18%) reporting a clinic closure and 15 (29%) reporting staffing shortages. 28 (55%) of respondents reported impacts on patients including having to evacuate, 27 (53%) having to cancel or reschedule treatments, and 23 (45%) experiencing physical, mental, or financial hardship due to the wildfires. Among the 25 clinics impacted by wildfires, 12 (24%) reported physical/operational impacts including being forced to evacuate patients to another treatment center, transportation interruptions (19, 37%), community and regional evacuations (18, 35%), school closures (18, 35%), and physical/mental health impacts (14, 27%) on staff due to the wildfires. Small clinics (25 staff or less) that experienced a wildfire were twice as likely to experience closures (6 of 16 clinics, 38%) but equally likely to experience staffing shortages (8 of 16, 50 %) as compared to larger practices (7 of 15, 47%). Nearly half of respondents 25 (47%) reported their workplace had a wildfire emergency preparedness plan. The results of this study demonstrate the significant impact wildfires have on patient care in both rural and metropolitan areas. The findings emphasize the importance of emergency preparedness planning to minimize the consequences of such disasters and underscores the need for further research to explore risk factors associated with patient and community vulnerability to climate-related crises. Such research will be essential to informing and developing future emergency preparedness plans.