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Background: Climate change continues to increase the frequency, intensity, and duration of heat events and wildfires, both of which are associated with adverse pregnancy outcomes. Few studies simultaneously evaluated exposures to these increasingly common exposures., Objectives: We investigated the relationship between exposure to heat and wildfire smoke and preterm birth (PTB)., Methods: In this time-stratified case-crossover study, participants consisted of 85,806 California singleton PTBs (20-36 gestational weeks) from May through October of 2015-2019. Birthing parent ZIP codes were linked to high-resolution daily weather, PM 2.5 from wildfire smoke, and ambient air pollution data. Heat day was defined as a day with apparent temperature >98th percentile within each ZIP code and heat wave was defined as ≥2 consecutive heat days. Wildfire-smoke day was defined as a day with any exposure to wildfire-smoke PM 2.5 . Conditional logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) comparing exposures during a hazard period (lags 0-6) compared to control periods. Analyses were adjusted for relative humidity, fine particles, and ozone., Results: Wildfire-smoke days were associated with 3.0% increased odds of PTB (OR lag0 : 1.03, CI: 1.00-1.05). Compared with white participants, associations appeared stronger among Black, Hispanic, Asian, and American Indians/Alaskan Native participants. Heatwave days (OR lag2 : 1.07, CI: 1.02-1.13) were positively associated with PTB, with stronger associations among those simultaneously exposed to wildfire smoke days (OR lag2 : 1.19, CI: 1.11-1.27). Similar findings were observed for heat days and when other temperature metrics (e.g., maximum, minimum) were used., Discussion: Heat and wildfire increased PTB risk with evidence of synergism. As the occurrence and co-occurrence of these events increase, exposure reduction among pregnant people is critical, especially among racial/ethnic minorities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Recently, the United States has experienced, on average, costs of USD 20 billion due to natural and climate disasters, such as hurricanes and wildfires. In this study, we focus on wildfires, which have occurred more frequently in the past few years. This paper examines how various factors, such as the PM10 levels, elevation, precipitation, SOX, population, and temperature, can influence the intensity of wildfires differently across counties in California. More specifically, we use Bayesian analysis to classify all counties of California into two groups: those with more wildfires and those with fewer wildfires. The Bayesian model incorporates prior knowledge and uncertainty for a more robust understanding of how these environmental factors impact wildfires differently among county groups. The findings show a similar effect of the SOX, population, and temperature, while the PM10, elevation, and precipitation have different implications for wildfires across various groups. [ABSTRACT FROM AUTHOR]

Background: Wildfires in the Western United States are a growing and significant source of air pollution that is eroding decades of progress in air pollution reduction. The effects on preterm birth during critical periods of pregnancy are unknown., Methods: We assessed associations between prenatal exposure to wildland fire smoke and risk of preterm birth (gestational age < 37 weeks). We assigned smoke exposure to geocoded residence at birth for all live singleton births in California conceived 2007-2018, using weekly average concentrations of particulate matter ≤ 2.5 µm (PM 2.5 ) attributable to wildland fires from United States Environmental Protection Agency's Community Multiscale Air Quality Model. Logistic regression yielded odds ratio (OR) for preterm birth in relation to increases in average exposure across the whole pregnancy, each trimester, and each week of pregnancy. Models adjusted for season, age, education, race/ethnicity, medical insurance, and smoking of the birthing parent., Results: For the 5,155,026 births, higher wildland fire PM 2.5 exposure averaged across pregnancy, or any trimester, was associated with higher odds of preterm birth. The OR for an increase of 1 µg/m 3 of average wildland fire PM 2.5 during pregnancy was 1.013 (95 % CI:1.008,1.017). Wildland fire PM 2.5 during most weeks of pregnancy was associated with higher odds. Strongest estimates were observed in weeks in the second and third trimesters. A 10 µg/m 3 increase in average wildland fire PM 2·5 in gestational week 23 was associated with OR = 1.034; 95 % CI: 1.019, 1.049 for preterm birth., Conclusions: Preterm birth is sensitive to wildland fire PM 2.5 ; therefore, we must reduce exposure during pregnancy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June-December 2016-2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM 2.5 ) concentrations were aggregated to air basins. A "smoke event" was defined as an air basin-day with a wildfire-specific PM 2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m 3 ). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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 m 2 ) 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

The number of wildfire incidents affecting communities in Wildland–Urban Interface (WUI) areas has been rapidly increasing. Understanding the fire spread between structures and evaluation of the response of the communities to the possible wildfire scenarios are crucial for proper risk management in the existing and future communities. This paper discusses a stochastic methodology to evaluate the community's response to potential wildfire scenarios. The methodology has three primary features: (1) it is based on stochastic modeling of fire spread; (2) it breaks the wildfire incident into two consecutive segments: spread inside the wildland and spread inside the community; (3) it integrates the two spread models in the form of a conditional probability. The paper focuses on fire spread inside the community and applies the proposed methodology to two case studies in California, US. The two case studies demonstrate variations in fire spread within the communities for the given fire scenarios approaching from the wildland. The performance of communities is characterized using cumulative distribution functions of the number of ignited buildings over time. [ABSTRACT FROM AUTHOR]

Large and severe wildfires, exacerbated by climate change and human behavior, are occurring more frequently in many forests across the western United States. While wildfire is a natural part of most terrestrial ecosystems, rapidly changing fire regimes have the potential to alter habitat beyond the adaptive capabilities of species. Spatial assessments of wildfire risks to species habitat may allow managers to pinpoint locations for management activities. To illustrate this, we spatially assessed wildfire risk within habitat that supports the nesting activity of the federally threatened northern spotted owl (Strix occidentalis caurina) in the California redwood coast ecoregion. To accomplish this, we built a scale-optimized ensemble nesting habitat suitability model and identified habitat with the highest wildfire hazard potential. Percent canopy cover at 100-m scale, slope at 400-m scale, and January precipitation at 800-m scale were the most influential environmental covariates for predicting northern spotted owl nesting habitat. Nearly 60% of nesting habitat was predicted to be at high or very high (>1986 index value) wildfire risks. We identified three areas in the Maple Creek Area of Humboldt County, Jackson State Demonstration Forest in Mendocino County, and Point Reyes National Seashore in Marin County, California with a high concentration of nesting habitat that are at a very high risk of experiencing high severity wildfires. We recommend these areas be targeted for future research to understand the impact of wildfire on northern spotted owl as well as management attention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Few studies investigated the impact of particulate matter (PM 2.5 ) on some symptom exacerbations that are not perceived as severe enough to search for medical assistance. We aimed to study the association of short-term daily total PM 2.5 exposure with work loss due to sickness among adults living in California., Methods: We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM 2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM 2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM 2.5 exposure on work loss using logistic regression models., Results: About 1.69% (weighted percentage) of adult respondents reported work loss in the week before the survey interview. The odds ratio of work loss was 1.45 (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.03, 2.03) when a 2-week average of daily total PM 2.5 exposure was higher than 12 µg/m 3 . The OR for work loss was 1.05 (95% CI: 0.98, 1.13) for each 2.56ug/m 3 increase in the 2-week average of daily total PM 2.5 exposure, and became stronger among those who were highly exposed to wildfire smoke (OR = 1.06, 95% CI: 1.00, 1.13), compared to those with lower wildfire smoke exposure (OR = 1.04, 95% CI: 0.79, 1.39)., Conclusions: Our findings suggest that short-term ambient PM 2.5 exposure is positively associated with work loss due to sickness and the association was stronger among those with higher wildfire smoke exposure. It also indicated that the current federal and state PM 2.5 standards (annual average of 12 µg/m 3 ) could be further strengthened to protect the health of the citizens of California., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior. [ABSTRACT FROM AUTHOR]

Understanding whether and how wildfires exacerbate COVID-19 outcomes is important for assessing the efficacy and design of public sector responses in an age of more frequent and simultaneous natural disasters and extreme events. Drawing on environmental and emergency management literatures, we investigate how wildfire smoke (PM 2.5 ) impacted COVID-19 infections and deaths during California's 2020 wildfire season and how public housing resources and hospital capacity moderated wildfires' effects on COVID-19 outcomes. We also hypothesize and empirically assess the differential impact of wildfire smoke on COVID-19 infections and deaths in counties exhibiting high and low social vulnerability. To test our hypotheses concerning wildfire severity and its disproportionate impact on COVID-19 outcomes in socially vulnerable communities, we construct a county-by-day panel dataset for the period April 1 to November 30, 2020, in California, drawing on publicly available state and federal data sources. This study's empirical results, based on panel fixed effects models, show that wildfire smoke is significantly associated with increases in COVID-19 infections and deaths. Moreover, wildfires exacerbated COVID-19 outcomes by depleting the already scarce hospital and public housing resources in local communities. Conversely, when wildfire smoke doubled, a one percent increase in the availability of hospital and public housing resources was associated with a 2 to 7 percent decline in COVID-19 infections and deaths. For California communities exhibiting high social vulnerability, the occurrence of wildfires worsened COVID-19 outcomes. Sensitivity analyses based on an alternative sample size and different measures of social vulnerability validate this study's main findings. An implication of this study for policymakers is that communities exhibiting high social vulnerability will greatly benefit from local government policies that promote social equity in housing and healthcare before, during, and after disasters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The increasing number and severity of wildfires is negatively impacting air quality for millions of California residents each year. Community exposure to PM 2.5 in two main population centers (San Francisco Bay area and Los Angeles County area) was assessed using the low-cost PurpleAir sensor network for the record-setting 2020 California wildfire season. Estimated PM 2.5 concentrations in each study area were compared to census tract-level environmental justice vulnerability indicators, including environmental, health, and demographic data. Higher PM 2.5 concentrations were positively correlated with poverty, cardiovascular emergency department visits, and housing inequities. Sensors within 30 km of actively burning wildfires showed statistically significant increases in indoor (~800 %) and outdoor (~540 %) PM 2.5 during the fires. Results indicate that wildfire emissions may exacerbate existing health disparities as well as the burden of pollution in disadvantaged communities, suggesting a need to improve monitoring and adaptive capacity among vulnerable populations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced or appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Though fine particulate matter (PM 2.5 ) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM 2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Wildfires are important natural disturbances of ecosystems; however, they threaten the sustainability of ecosystems, climate and humans worldwide. It is vital to quantify and map the controlling drivers of wildfires for effective wildfire prediction and risk management. However, high-resolution mapping of wildfire drivers remains challenging. Here we established machine-learning (Random Forests) models using 23 climate and land surface variables as model inputs to reconstruct the spatial variability and seasonality of wildfire occurrence and extent in California. The importance of individual drivers was then quantified based on the Shapley value method. Thus, we provided spatially resolved maps of wildfire drivers at high resolutions up to 0.004° × 0.004°. The results indicated that precipitation and soil moisture are the major drivers dominating 37% of the total burnt area for large and extreme wildfires in summer and 63% in autumn, while elevation plays a major role for 15-58% of burnt areas in small wildfires in all seasons. Winds are also an important contributor to summer wildfires, accounting for 41% of large and extreme burnt areas. This study enhanced our knowledge of spatial variability of wildfire drivers across diverse landscapes in a fine-scale mapping, providing valuable perspectives and case studies for other regions of the world with frequently occurred wildfire., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Understanding how wildfires and modification in plant assemblages interact to influence soil bacteria assemblages is a crucial step in understanding how these disturbances may influence ecosystem structure and function. Here, we resampled soil from three study sites previously surveyed in spring 2016 and 2017 and compared soil bacterial assemblages prior to and six months after (spring 2019) the 2018 Woolsey Fire in the Santa Monica Mountain National Recreation Area using Illumina sequencing of the 16S rRNA gene. All sites harbored both native California sage scrub and a non-native (grassland or forbland) habitat, allowing us to examine how fire influenced bacterial assemblages in common southern California habitats. Most results contrasted with our a-priori hypotheses: (1) richness and diversity increased following the fire, (2) heat/drought resistant and sensitive bacteria did not show consistent and differing patterns by increasing and decreasing, respectively, in relative abundance after the fire, and (3) bacterial assemblage structure was only minimally impacted by fire, with no differences being found between 2017 (pre-fire) and 2019 (post-fire) in three of the six habitats sampled. As sage scrub and non-native grasslands consistently harbored unique bacterial assemblages both before and following the fire, modifications in plant compositions will likely have legacy effects on these soils that persist even after a fire. Combined, our results demonstrate that bacterial assemblages in southern California habitats are minimally affected by fire. Because direct impacts of fire are limited, but indirect impacts, e.g., modifications in plant compositions, are significant, plant restoration efforts following a fire should strive to revegetate sage scrub areas to prevent legacy changes in bacterial composition., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Wildfires in California have been progressively increasing in duration, intensity and frequency. In response, a number of best practices have been developed for mitigating and responding to fires within the state. This paper describes the lessons learned within core capabilities and describes current practices. Reflections are offered as a consideration and should not be interpreted as standards of practice.

Wildfires have become a regular seasonal disaster across the Western region of the United States. Wildfires require a multifaceted disaster response. In addition to fire suppression, there are public health and medical needs for responders and the general population in the path of the fire, as well as a much larger population impacted by smoke. This paper describes key aspects of the health and medical response to wildfires in California, including facility evacuation and shelter medical support, with emphasis on the organization, coordination, and management of medical teams deployed to fire incident base camps. This provides 1 model of medical support and references resources to help other jurisdictions that must respond to the rising incidence of large wildland fires.

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions. [ABSTRACT FROM AUTHOR]

The 2018 Camp Fire caused significant damages to the education and healthcare systems in the town of Paradise, CA. This paper presents the findings of a qualitative case study about disaster impacts and disparities, interdependencies, and recovery strategies of schools and hospitals in Paradise. Four major themes of findings emerged from the qualitative analysis of interviews with teachers, counselors, and administrators in Paradise education and healthcare systems and extensive archival research. First, complex and long-standing mental health challenges are the dominant impact on the educational system. Second, educational and healthcare impacts are shaped by social vulnerability. Third, educational and healthcare systems play a critical role for recovery of socially vulnerable groups due to the interconnectedness of community components. Fourth, adapting to new communication norms and technologies is effective for supporting educational and community recovery. Several specific recommendations are provided based on the findings for building back more resilient and equitable education and healthcare services. [ABSTRACT FROM AUTHOR]

In 2017 and 2018, wildfires in California burned millions of hectares and caused billions of dollars in structure damages. This paper puts these recent fires in a long-term historical context by assembling four decades of data on wildfires in California. We combine administrative data of structure loss due to wildfire with economic data on replacement costs and spatial data on fire locations and sizes. We find that over the period 1979–2018, wildfires in California have been getting larger and that the trend is accelerating. This same trend is seen in the wildland–urban interface. As well, total structure damage from wildfires has grown steadily during the past four decades. Our conclusion is that the recent California fires are not an anomaly, but rather part of a trend towards larger and increasingly destructive wildfires. The area burned in California wildfires has increased steadily in California since the late 1970s, and the trend appears to be accelerating. The same accelerating trend is seen with area burned in the wildland–urban interface and total structure damages. Recent catastrophic wildfires in California are part of a long-term trend towards increasingly large and destructive wildfires. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration. [ABSTRACT FROM AUTHOR]

Wildfires in the southwestern United States, particularly in northern California (nCA), have grown in size and severity in the past decade. As they have grown larger, they have been associated with large emissions of absorbing aerosols and heat into the troposphere. Utilizing satellite observations from MODIS, CERES, and AIRS as well as reanalysis from MERRA-2, the meteorology associated with fires during the wildfire season (June–October) was discerned over the nCA-NV (northern California and Nevada) region during the period 2003–2022. Wildfires in the region have a higher probability of occurring on days of positive temperature (T) anomalies and negative relative humidity (RH) anomalies, making it difficult to discern the radiative effects of aerosols that are concurrent with fires. To attempt to better isolate the effects of large fire emissions on meteorological variables, such as clouds and precipitation, variable anomalies on high fire emission days (90th percentile) were compared with low fire emission days (10th percentile) and were further stratified based on whether surface relative humidity (RH s) was anomalously high (75th percentile) or low (25th percentile) compared with typical fire season conditions. Comparing the simultaneously high fire emission and high RH s data with the simultaneously low fire emission and high RH s data, positive tropospheric T anomalies were found to be concurrent with positive AOD anomalies. Further investigation found that due to shortwave absorption, the aerosols heat the atmosphere at a rate of 0.041 ± 0.016 to 0.093 ± 0.019 K d -1 , depending on whether RH conditions are anomalously positive or negative. The positive T anomalies were associated with significant negative 850–300 hPa RH anomalies during both 75th percentile RH s conditions. Furthermore, high fire emission days under high RH s conditions are associated with negative CF anomalies that are concurrent with the negative RH anomalies. This negative CF anomaly is associated with a significantly negative regional precipitation anomaly and a positive net top-of-atmosphere radiative flux anomaly (a warming effect) in certain areas. The T , RH, and CF anomalies under the simultaneously high fire emission and high RH s conditions compared with the simultaneously low fire emission and high RH s conditions have a significant spatial correlation with AOD anomalies. Additionally, the vertical profile of these variables under the same stratification is consistent with positive black carbon mass mixing ratio anomalies from MERRA-2. However, causality is difficult to discern, and further study is warranted to determine to what extent the aerosols are contributing to these anomalies. [ABSTRACT FROM AUTHOR]

In 2020, wildfires scarred over 4,000,000 hectares in the western United States, devastating urban populations and ecosystems alike. The significant impact that wildfires have on plants, animals, and human environments makes wildfire adaptation, management, and mitigation strategies a critical task. This study uses satellite imagery from Landsat to calculate burn severity and map the fire progression for the Dolan Fire of central Coastal California which occurred in August 2020. Several environmental factors, such as temperature, humidity, fuel type, topography, surface conditions, and wind velocity, are known to affect wildfire spread and burn severity. The aim of this study is the investigation of the relationship between these environmental factors, estimates of burn severity, and fire spread patterns. Burn severity is calculated and classified using the Difference in Normalized Burn Ratio (dNBR) before being displayed as a time series of maps. The Dolan Fire had a moderate severity burn with an average dNBR of 0.292. The ignition site location, when paired with the patterns of fire spread, is consistent with wind speed and direction data, suggesting fire movement to the southeast of the fire ignition site. Patterns of increased burn severity are compared with both topography (slope and aspect) and fuel type. Locations that were found to be more susceptible to high burn severity featured Long Needle Timber Litter and Mature Timber fuels, intermediate slope angles between 15 and 35°, and north- and east-facing slopes. This study has implications for the future predictive modeling of wildfires that may serve to develop wildfire mitigation strategies, manage climate change impacts, and protect human lives. [ABSTRACT FROM AUTHOR]

Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences. [ABSTRACT FROM AUTHOR]

Prescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk. [ABSTRACT FROM AUTHOR]

Background: Wildfires in Mediterranean-type climate regions have numerous impacts on the ecosystem services provided by native shrublands, however, quantifying these impacts is challenging. Aims: We developed a reproducible method to quantify fire impacts on ecosystem services and created a tool for resource managers in southern California. Methods: The SoCal EcoServe tool consists of two components: a desktop tool and an online mapping tool. We used the Alisal Fire of 2021 as a case study and quantified: aboveground live carbon storage using pre- and post-fire biomass data; water runoff, groundwater recharge and sediment erosion retention by integrating data on burn severity into hydrological and sediment erosion models; and estimated recreation services and biodiversity using pre-fire data. Key results: We estimated the Alisal Fire resulted in an immediate post-fire reduction in carbon storage of 25%, of which 20% was estimated to be permanently lost. Water runoff increased by 21%, groundwater recharge 7-fold, and sediment erosion increased 24-fold. Conclusions: The EcoServe tool provides an initial approximation of wildfire impacts that can support damage assessments post-fire, track carbon storage and help identify priorities for post-fire restoration. Implications: We intend the tool to be used by USDA Forest Service resource managers of shurblands in southern California. However, it can provide the framework for future work in shrublands throughout the western USA. We describe an online mapping tool, SoCal EcoServe (https://manzanita.forestry.oregonstate.edu/EcoServeHome/) to estimate wildfire impacts on ecosystem services in shrub-dominated southern California. We describe the methodology and results using the Alisal Fire. The tool is designed to support post-fire damage assessments and restoration decision-making by resource managers. This article belongs to the Collection Fire and Climate. [ABSTRACT FROM AUTHOR]

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 > 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (= 2 consecutive days with > 15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups. [ABSTRACT FROM AUTHOR]

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CAand intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event. [ABSTRACT FROM AUTHOR]

Wildfires in the southwestern United States, particularly in northern California (nCA), have grown in size and severity in the past decade. As they have grown larger, they have been associated with large emissions of absorbing aerosols in to the troposphere. Utilizing satellite observations from MODIS, CERES, AIRS, and CALIPSO, the meteorological effects of aerosols associated with fires during the wildfire season (June–October) were discerned over the nCA-NV (northern California and Nevada) region in the 2003–2022 time frame. As higher temperatures and low relative humidity RH dominate during high surface pressure ps atmospheric conditions, the effects of the aerosols on high (90th percentile) fire days compared to low fire (10th percentile) days were stratified based on whether ps was anomalously high or anomalously low (10th percentile). An increase in tropospheric temperatures was found to be concurrent with more absorbing aerosol aloft, which is associated with significant reductions in tropospheric RH during both 90th and 10th percentile ps conditions. Furthermore, high fire days under low ps conditions are associated with reduced cloud fraction CF , which is consistent with the traditionally-defined aerosol- cloud semi-direct effect. The reduced CF , in turn, is associated with reduced T OA SW radiative flux, a warmer surface, and less precipitation. These changes could create a positive feedback that could intensify fire weather, and therefore extend fire lifetime and impacts. [ABSTRACT FROM AUTHOR]

Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time-consuming to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire (relatively accurate particulate information derived from inputs retrieved easily) R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach, random forest (RF) regression, is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24 h average particulate matter for several large wildfire smoke events in California from 2017–2021. These estimates show excellent agreement with independent measures from filter-based networks. [ABSTRACT FROM AUTHOR]

One of the primary tools used for determining the origin of a wildfire is analyzing burn patterns formed during the fire progression. These patterns, called fire pattern indicators, are interpreted and used to document the direction of fire movement at specific points, creating a directional map back to the specific area of origin. This concept was first set forth in 1978 by a U.S. governmental organization, the National Wildfire Coordinating Group (NWCG). Their recommendations are currently (2016) in the third edition, and in our study, we examine these indicators. Specifically, the objective was to perform a validation exercise where controlled burns were conducted of natural vegetation plots but augmented with 32 identical sets of staged artifacts which would provide additional opportunities for fire movement to create observable directional fire pattern indicators. Three adjacent plots were burned, each using a single point ignition, all located on level, scrubland terrain. The burns were conducted in the fall season, under low to moderate burning conditions. The research was structured as a preliminary study, since only mild terrain and weather conditions were encompassed. The actual fire movements were documented by drone videos, additional ground-based videos, and still photography. Within the three burn plots, a total of 12 data sites out of 32 data sites were selected: each one containing 7 to 12 individual artifacts. Each artifact was photographically documented post-fire, and the actual fire movement direction at that location was established. Assessment entailed the use of four experienced wildland fire investigators, with each one independently assessing the direction and type of fire spread at each artifact using the photographic site evidence. An analysis was then conducted to make a statistical comparison between the actual fire movement direction and the direction estimates provided by the experts analyzing the photographic evidence and the limited information on conditions provided. The results indicate an average error of 103°. These results indicate that additional efforts are needed to study the scientific basis of the indicators and to evolve improvements in both the indicators and in the accompanying guidance to investigators. [ABSTRACT FROM AUTHOR]

The article explores the environmental consequences of climate change-induced wildfires in the Wildland Urban Interface (WUI) of unincorporated areas of California. It investigates how the overlap between unincorporated areas and the WUI creates gaps in law and regulation, affecting land use, lodging, and livelihoods. It examines existing legal frameworks and proposing solutions to address the challenges posed by limited regulation in the face of climate change.

Over the past two decades, wildfire activity in the western USA has increased, especially in California. Wildfires not only affect air quality but also the environment at large, including chemical and physical properties of fire-affected soils, which are of great interest for prediction and mitigation of hydrological consequences. Hyperspectral reflectance can be used to remotely assess the effects of fires on soil and here we use it to characterize soils before and after three 2021 California wildfires (Dixie, Beckwourth Complex, and Caldor fire). We acquired reflectance spectra and compared changes in these spectra with changes in the chemistry of analyzed soils. For all three fires, the results show that 700 nm wavelength reflectance of ash samples collected 1 and 1.5 years after fire decreased between 36% and 76% compared to that of samples collected right after the fires. Additionally, significantly higher visible reflectance has been found for unburned compared to burned soil samples in each region that was studied. Infrared transmission measurements were used to characterize the carbonate content of soil and ash samples demonstrating a mostly positive relationship between carbonate content and visible reflectance, indicating a possible cause and effect between the two. [ABSTRACT FROM AUTHOR]

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. In this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed. [ABSTRACT FROM AUTHOR]

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM 2.5 ). We propose a data-fusion method to study wildfire contribution to PM 2.5 using satellite-derived smoke plume indicators and PM 2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM 2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM 2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy. [ABSTRACT FROM AUTHOR]

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought‐prone, wildfire‐adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub‐watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre‐and‐post wildfire hydrology with a rainfall‐runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub‐watershed (1%–30% at moderate/high severity), we found little evidence of wildfire‐related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post‐wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions. Plain Language Summary: Western United States water resources are vulnerable to changes caused by wildfires. While many studies indicate that streamflow may increase during post‐wildfire years, the compounding factors of drought and wildfire‐adapted landscapes challenge our ability to predict and prepare for coming changes in streamflow. Our current knowledge cannot answer the question "How much wildfire is required for river water flows to change?" Our study results show that drought can buffer the hydrological response to wildfire. While the drought can mitigate post‐wildfire flood potential, effects of the wildfire could emerge years later when high intensity rain events return. Key Points: Little evidence of wildfire‐related shifts in hydrology in drought‐prone Northern California coastal region having a Mediterranean climateWhen the percent of burned area increased beyond 30% of the watershed, the magnitude of the runoff response asymptotesPost‐wildfire hydrological variability did not extend outside of pre‐wildfire streamflow conditions [ABSTRACT FROM AUTHOR]

Background. Pre-fire fuels, topography, and weather influence wildfire behaviour and firedriven ecosystem carbon loss. However, the pre-fire characteristics that contribute to fire behaviour and effects are often understudied for wildfires because measurements are difficult to obtain. Aims. This study aimed to investigate the relative contribution of pre-fire conditions to fire energy and the role of fire advancement direction in fuel consumption. Methods. Over 15 years, we measured vegetation and fuels in California mixed-conifer forests within days before and after wildfires, with co-located measurements of active fire behaviour. Key results. Pre-fire litter and duff fuels were the most important factors in explaining fire energy and contributed similarly across severity categories. Consumption was greatest for the forest floor (litter and duff; 56.8 Mg ha-1) and 1000-h fuels (36.0 Mg ha-1). Heading fires consumed 13.2 Mg ha-1 more litter (232%) and 24.3 Mg ha-1 more duff (202%) than backing fires. Remotely sensed fire severity was weakly correlated (R2 = 0.14) with fuel consumption. Conclusions. 1000-h fuels, litter, and duff were primary drivers of fire energy, and heading fires consumed more fuel than backing fires. Implications. Knowledge of how consumption and fire energy differ among contrasting types of fire behaviours may inform wildfire management and fuels treatments. [ABSTRACT FROM AUTHOR]

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment. [ABSTRACT FROM AUTHOR]

Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially-resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time consuming and expensive to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach (random forests regression) is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24-hour average particulate matter for several large wildfire smoke events in California from 2017–2021. These estimates show excellent agreement with independent measures from filter-based networks. [ABSTRACT FROM AUTHOR]

The Camp Fire was one of California's deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant site in Berkeley, highly time-resolved total carbon (TC), black carbon (BC), and organic carbon (OC) were measured using the Carbonaceous Aerosol Speciation System (CASS, Aerosol Magee Scientific), comprising two instruments, a Total Carbon Analyzer TCA08 in tandem with an Aethalometer AE33. During the period when the air quality was affected by wildfire smoke, the BC concentrations increased four times above the typical air pollution level presented in Berkeley before and after the event, and the OC increased approximately ten times. High-time-resolution measurements allow us to study the aging of OC and investigate how the characteristics of carbonaceous aerosols evolve over the course of the fire event. A higher fraction of secondary carbonaceous aerosols was observed in the later phase of the fire. At the same time, the amount of light-absorbing organic aerosol (brown carbon) declined with time. [ABSTRACT FROM AUTHOR]

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive. [ABSTRACT FROM AUTHOR]

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression. Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports. Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset. Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R 2 = 0.95) and VIIRS (R 2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available. Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring. We developed a fire mapping method that uses active fire data from the MODIS and VIIRS instruments on board satellites to develop a high-resolution wildfire growth database. The satellite-derived fire events show excellent agreement with agency records (R 2 ≥ 0.95). This method can enable many fire-weather modelling and real-time fire applications. [ABSTRACT FROM AUTHOR]

Background: Satellite fire radiative power (FRP) products are a potential source of wildland fire growth measurements that could be used with gridded or observed weather data to study meteorological influences on wildfire behaviour. There is little research to date examining how FRP relates to weather. Aims: The goal of this study is to explore the relationship between satellite wildland fire FRP measurements and wind speed. Methods: We examine GOES-16 FRP data from the summer of 2018 in California, comparing it with winds from the CANSAC reanalysis to understand the relationship between wind and FRP. Examination focuses on statistical distribution of the data and results of two different approaches to relating FRP and wind speed. Key results: FRP is log-normally distributed, and there are significant consequences to using conventional statistical techniques that assume normality. We propose an alternative framing of the wind speed–FRP relationship to preserve more information about the FRP distribution. Conclusions: Use of FRP data for meaningful understanding of fire–weather interactions requires attention to the nature of the FRP empirical distribution. Implications: Applying our alternative approach to relating wildland fire FRP to weather has the potential to retain much more information than conventional approaches, but requires careful examination of the FRP data in each study. Fire radiative power (FRP) is non-Gaussian, yet is often treated as such. We look at the actual distribution and how it relates to wind speed using a novel approach that preserves the distribution of FRP, rather than reducing it to a single (mean) value for a given wind speed. [ABSTRACT FROM AUTHOR]

Property damage from wildfires occurs from spread into built‐up areas, the wildland–urban interface. Fire spread occurs as embers from one burning structure ignite neighboring ones—but mitigation reduces the chances that fire spreads. In this study, we use a simulation model with realistic parameters for a neighborhood in California to illustrate patterns of marginal benefit from mitigation. We extend existing models of fire spread in two novel ways. We show how to describe the no‐regulation equilibrium and social optimal levels of mitigation by incorporating data on a key factor, the distribution of house values in the community. We incorporate insurance in the model and show that it improves homeowner decision‐making and insurance premium regulation. The fire spread simulations show that under plausible parameter values, there is a pattern in which mitigation's marginal benefit is low at low levels of community mitigation, rises to a maximum, and then falls quickly to a low level. We argue that the maximum marginal benefit is a guide to achieving optimal mitigation in a community. Owner mitigation decisions will depend on the distribution of house values in the neighborhood and other factors. In an illustration, we use the distribution of house values in a California community to illustrate the mitigation owners will choose under independent (Nash) investment decisions, and the efficiency‐improving actions involving regulations or insurance premium subsidies that can lead to the social optimum. [ABSTRACT FROM AUTHOR]

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Research on sense of place suggests that people's understandings of themselves and others is closely tied to the feelings they have about the place where they reside. Solastalgia expands on this sociological concept by considering the impacts on the various benefits derived from place when a landscape is changed through acute or chronic environmental disruptions. As such, climate-related disasters affect both tangible and intangible goods. Using 24 qualitative interviews with residents of Paradise, California several months after a wildfire destroyed their town, this exploratory study examines three ways in which the solastalgia experience is socially constructed. This occurs through disruptions to coping resources found in the natural world, embeddedness of history in place, and the experience of "homesickness" for a changed landscape. [ABSTRACT FROM AUTHOR]