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The frequency of wildfires increases yearly and poses a constant threat to the environment and human beings. Different factors, for example surrounding infrastructure to an area (e.g., campfire sites or power lines) contribute to the occurrence of wildfires. In this paper, we propose using a Spatio-Temporal Knowledge Graph (STKG) based on OpenStreetMap (OSM) data for modeling such infrastructure. Based on that knowledge graph, we use the RDF2vec approach to create embeddings for predicting wildfires, and we align different vector spaces generated at each temporal step by partial rotation. In an experimental study, we determine the effect of the surrounding infrastructure by comparing different data composition strategies, which involve a prediction based on tabular data, a combination of tabular data and embeddings, and solely embeddings. We show that the incorporation of the STKG increases the prediction quality of wildfires. [ABSTRACT FROM AUTHOR]

The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy. [ABSTRACT FROM AUTHOR]

In California, wildfires caused by electrical infrastructure have left the state's investor-owned power utilities with major and growing liabilities. But even in such an incendiary environment, the financial industry has demonstrated that it can profit from disaster. This paper uses the 2019-2020 bankruptcy of Pacific Gas & Electric to explain how. In it, I show how "risk" in California's electricity industry is legally constituted, mediated, and allocated. First, I explain how financial perceptions of wildfire risk in California's electricity industry are shaped by the state's legal and regulatory environment, and how the law is used to manage this risk. I then turn to PG&E's bankruptcy to show how litigation functions as a financial strategy. In court, risk is endogenous to legal-financial practice. I develop the concepts of "legal arbitrage" and "leverage" to explain how law mediates the relationship between risk and finance. I adapt the concept of legal arbitrage to show how financial assets (like those in a utility with unprecedented wildfire liabilities) can possess both legal and market value, which can and often do diverge in circumstances of distress. I use the term leverage to refer to a particular kind of legal-financial power which enables actors to transfer risk away from themselves and onto others. In working through these concepts, I argue that that mainstream perceptions of risk in the financial industry are inadequate, especially in an era of increasing climate insecurity. [ABSTRACT FROM AUTHOR]

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]

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]

This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [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]

Debris flows pose a serious threat to communities in mountainous areas, particularly in the years following wildfire. These events have been widely studied in regions where post-wildfire debris flows have been historically frequent, such as southern California. However, the threat of post-wildfire debris flows is increasing in many regions where detailed data on debris-flow physical properties, volume, and runout potential are sparse, such as the Southwest United States (Arizona and New Mexico). As the Southwest becomes more vulnerable to these hazards, there is an increasing need to better characterize the properties of post-wildfire debris flows in this region and to identify similarities and differences with nearby areas, particularly southern California, where there is a greater abundance of data. In this paper, we study the characteristics and downstream impacts of two post-wildfire debris flows that initiated following the 2021 Flag Fire in northern Arizona, United States. We gathered data regarding soil hydraulic properties, rainfall characteristics, watershed response, and debris-flow initiation, runout, volume, grain size, and downstream impacts during the first two monsoon seasons following the containment of the Flag Fire. We also applied established debris-flow runout and volume models that were developed in southern California to our study watershed and compared the output with observations. In the first monsoon season following the fire, there were two post-wildfire debris flows, one of which resulted in damage to downstream infrastructure, and one major flood event. We found that, while more intense rainfall is required to generate debris flows at our study site compared to southern California, burned watersheds in northern Arizona are still susceptible to debris flows during storms with low recurrence intervals in the first year following fire. During the second monsoon season, there were no major runoff events, despite more intense storms. This indicates that the temporal window for heightened debris-flow susceptibility at our study area was less than one year, due to the recovery of soil hydraulic properties and vegetation regrowth. We also found that the debris-flow properties at our study site, such as volume, mobility, and grain size distribution, may differ from those in other regions in the western United States, including southern California, potentially due to regional differences in rainfall characteristics and sediment supply. Differences in rainfall characteristics and sediment supply may have also influenced the performance of the debris-flow runout and volume models, which overpredicted the observed runout distance by 400 m and predicted a volume more than 17 times greater than what was observed. [ABSTRACT FROM AUTHOR]

Post-wildfire investigations of structures provide engineers with data to understand how to harden building infrastructure for future wildfire hazards. This paper presents a preliminary framework where field-collected LiDAR and drone data is integrated with numerical models for post-wildfire assessment of buildings with the goal of learning from wildfire-damaged buildings. The developed framework provides a new and novel approach for post-wildfire instigations of buildings using field-collected data, particularly LiDAR and drone data. This framework is demonstrated on a metal-framed school building in Paradise, California that was heavily damaged in the 2018 Camp Fire. The authors collected drone aerial images and light detection and ranging (LiDAR) scans of Achieve Charter High School after the 2018 Camp Fire. All data were used to create a system-level finite element model to simulate the fire behavior of a portal frame with non-prismatic members. A parametric study was performed with three typical compartment fires to examine the influence of building characteristics on the fire's maximum temperature and duration. Several time–temperature (T–t) curves, representing different fire scenarios, were generated between the two extreme cases produced by the parametric study. Finally, the finite element model was subjected to the generated fire curves, and the results were compared with the measured deformations from the LiDAR data collected in the field. The finite element model, subjected to each fire Time–temperature curve scenario, simulated global deformations within 2% of the true deformations. [ABSTRACT FROM AUTHOR]

Background: Buildings in communities near wildlands, in the wildland–urban interface (WUI), can experience wildfire damage. Aims: To quantitatively assess the relationship between building features and damage, a building wildfire resistance index is developed and validated with the 2013–2017 CAL FIRE (DINS) database from California, USA, and the 2017 Pedrógão Grande Fire Complex post-fire investigation from Portugal. Methods: Three statistical dependence tests are compared to evaluate the relationship between selected building features and damage. The Wildfire Resistance Index (WRI), range: [–1, 1], is proposed and validated as a rating for building wildfire susceptibility. Key results: The most correlated features to wildfire damage are the presence of vent screens and deck materials in California, and exterior walls material and deck materials in Portugal. For Portugal, as WRI increases by 50%, linear regression estimates a 48% decrease in proportion of highly damaged buildings, and a 42% increase in proportion of low damage buildings (R 2 of 0.93 and 0.90, respectively). A total of 65% of California buildings with WRI = 1 were destroyed, compared to average 85% for WRI ≥−0.33. Conclusions: The WRI quantifies the wildfire damage experienced by buildings in two diverse WUI regions. Implications: The WRI could be used as an estimator of wildfire damage but it needs further development. This paper presents a statistical analysis of two post-fire building inspection databases of wildland–urban interface (WUI) damage in California and Portugal. Results compare considered building features' relative correlation to damage, and propose the Wildfire Resistance Index: an index applied to the vulnerability of WUI buildings to wildfire ignition and damage. [ABSTRACT FROM AUTHOR]

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

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

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]

Wildfires have caused devastating consequences to property and human and animal lives, which has become a global problem. Consequently, advanced wildfire prediction models are required to treat complex features and climate conditions. As a result, Machine Learning and Deep Learning models are becoming popular. However, creating a balanced true and false labeled dataset in the wild-fire domain is often challenging. Hence, One-class classification models are a promising approach to overcome this concern. In this paper, several One-class classification models are investigated; linear models (Principal Component Analysis and One-Class Support Vector Machines), outlier ensemble models (Lightweight On-line Detector of Anomalies and Locally Selective Combination of Parallel Outlier Ensembles), proximity-based models (Histogram-based Outlier Score and Rotation-based Outlier Detection), probabilistic models (Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions and Copula-Based Outlier Detection), neural network-based models (Deep One-class Classification and Adversarially Learned Anomaly Detection) is used in two case studies for California and Western Australian states. In conclusion, it was found that Deep learning-based One-class classification models outperform other models in terms of performance and feature importance of showcasing the effectiveness of deep neural network models in the wildfire prediction domain. [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]

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]

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires. [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]

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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]

Wildfires are a prevalent natural disaster that can significantly impact human populations and result in considerable losses. With a changing climate, wildfires in many countries have increased in intensity and frequency, making effective restoration efforts in affected areas crucial. This paper aims to evaluate the efficacy of ordered weighted averaging (OWA), a GIS-based multi-criteria decision analysis technique, in identifying priority areas for wildfire restoration. A case study using the 2009 Station Fire in California is presented, using the restoration criteria of slope, erodibility, proximity to forest cover, and proximity to surface water. By applying both importance and order weights, multiple OWA decision strategies with varying risk levels were examined. Different strategies greatly influence the spatial distribution of land considered high and low priority for wildfire restoration, each with varying levels of trade off. In the OWA decision space, placing full emphasis on the highest (best) values (using the risk-taking OR operator) or the lowest values (using the riskaverse AND operator) resulted in composite priority maps that cannot be recommended for practical use. More nuanced scenarios are achieved with the OWA operators representing a range of compromise decision strategies between these extremes. The OWA technique in GIS can thus help to explore the impact of decision-makers' risk attitudes in a wildfire restoration setting. [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]

In response to increasing wildfire risks, California plans to expand the use of prescribed fire. We characterized the anticipated change in health impacts from exposure to smoke under a future fire‐management scenario relative to a historical period (2008–2016). Using dispersion models, we estimated daily fine particulate matter (PM2.5) emissions from hypothetical future prescribed fires on 500,000‐acres classified as high priority. To evaluate health impacts, we calculated excess daily cardiorespiratory emergency department visit rates attributed to all‐source PM2.5, distinguishing the portion of the burden attributed to prescribed fire. The total burden was differentiated by fire type and by smoke strata‐specific days to calculate strata‐specific burden rates, which were then applied to estimate the burden in the future scenario. This analysis suggests that the exposure to prescribed fire smoke, measured as the number of persons exposed per year, would be 15 times greater in the future. However, these exposures were associated with lower concentrations compared to the historical period. The increased number of exposure days led to an overall increase in the future health burden. Specifically, the northern, central, and southern regions experienced the largest burden increase. This study introduces an approach that integrates spatiotemporal exposure differences, baseline morbidity, and population size to assess the impacts of prescribed fire under a future scenario. The findings highlight the need to consider both the level and frequency of exposure to guide strategies to safeguard public health as well as aid forest management agencies in making informed decisions to protect communities while mitigating wildfire risks. Plain Language Summary: Prescribed fire is a forest management strategy for reducing the risks of wildfires. While some fires are ecologically beneficial, smoke from fires is a major source of airborne particle pollution, which is harmful to human health. This study examined the change in health impacts resulting from an expected increase in the use of prescribed fire within California's high‐priority wildfire risk areas. We used daily counts of cardiorespiratory emergency department visits attributed to air quality combined with model‐generated measures of smoke pollution to estimate health impacts. We compared exposures and the associated health burden on days impacted by wildfire or prescribed fire smoke in the past to the impacts in the hypothetical future scenario with increased prescribed fire. Projections of future prescribed burning in high priority areas suggest that more people would experience smoke more often, although exposures would occur at lower concentrations. With more frequent lower‐level exposure days near populated areas, the health burden would increase relative to past prescribed fire. Understanding the potential impact of prescribed fire may simultaneously help protect public health and increase safety from wildfires. Key Points: A California‐based model of future prescribed burning in high‐priority wildfire risk areas suggested more people will experience smokeAn increased number of exposure days in the future scenario led to an overall increase in the future health burdenThe excess future health burden was due to the cumulative impact of lower exposure days and high population density in high‐priority areas [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]

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]

apidly changing wildfire regimes across the Western United States have driven more frequent and severe wildfires, resulting in wide-ranging societal threats from wildfires and wildfire-generated smoke. However, common measures of fire severity focus on what is burned, disregarding the societal impacts of smoke generated from each fire. We combine satellite-derived fire scars, air parcel trajectories from individual fires, and predicted smoke PM2.5 to link source fires to resulting smoke PM2.5 and health impacts experienced by populations in the contiguous United States from April 2006 to 2020. We quantify fire-specific accumulated smoke exposure based on the cumulative population exposed to smoke PM2.5 over the duration of a fire and estimate excess asthma-related emergency department (ED) visits as a result of this exposure. We find that excess asthma visits attributable to each fire are only moderately correlated with common measures of wildfire severity, including burned area, structures destroyed, and suppression cost. Additionally, while recent California fires contributed nearly half of the country’s smoke-related excess asthma ED visits during our study period, the most severe individual fire was the 2007 Bugaboo fire in the Southeast. We estimate that a majority of smoke PM2.5 comes from sources outside the local jurisdictions where the smoke is experienced, with 87% coming from fires in other counties and 60% from fires in other states. Our approach could enable broad-scale assessment of whether specific fire characteristics affect smoke toxicity or impact, inform cost-effectiveness assessments for allocation of suppression resources, and help clarify the growing transboundary nature of local air quality. [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]

Despite increasing magnitude and frequency of wildfire, understanding hydrological processes contributing to changes in streamflow is not well examined for the entire western contiguous United States (WCONUS). This study provides insight into whether considering spatially varying watershed characteristics, including burn severity patterns, can better explain streamflow trends at broad spatial and temporal scales. Standard geographically weighted regression (GWR) and multi‐scalar (MS) GWR were benchmarked against ordinary least squares (OLS) regression to determine if spatially varying coefficients could better explain spatially varying patterns and processes of peak flow, low flow, center timing of flow, and flashiness trends in burned watersheds. In most cases, GWR reduced spatial autocorrelation and provided insight into regional factors explaining streamflow trends. Both OLS and GWR models did not find burn severity‐based metrics significant in explaining streamflow trends, except the peak flow trends model that included the spatial arrangement of high severity patches quantified using landscape shape index (LSI). Positive peak flow trends coincided with increasing LSI values in Northern California and negative peak flow trends with increasing LSI in the southern WCONUS. This research could guide regionally specific post‐fire response and inform the development of new spatial metrics that could better represent post‐fire hydrological processes. [ABSTRACT FROM AUTHOR]

Wildfire alters the hydrologic cycle, with important implications for water supply and hazards including flooding and debris flows. In this study we use a combination of electrical resistivity and stable water isotope analyses to investigate the hydrologic response during storms in three catchments: one unburned and two burned during the 2020 Bobcat Fire in the San Gabriel Mountains, California, USA. Electrical resistivity imaging shows that in the burned catchments, rainfall infiltrated into the weathered bedrock and persisted. Stormflow isotope data indicate that the amount of mixing of surface and subsurface water during storms was similar in all catchments, despite higher streamflow post-fire. Therefore, both surface runoff and infiltration likely increased in tandem. These results suggest that the hydrologic response to storms in post-fire environments is dynamic and involves more surface-subsurface exchange than previously conceptualized, which has important implications for vegetation regrowth and post-fire landslide hazards for years following wildfire. This study tracks changes in post-fire hydrology in the San Gabriel Mountains, California, USA, and finds that rapid infiltration and storage of subsurface water in burned catchments contributes to increased streamflow during storms. [ABSTRACT FROM AUTHOR]

Increased wildfire activity in the western United States has exposed regional gaps in our understanding of postfire debris‐flow generation. To address this problem, we characterized flows in an unstudied area to test the rainfall intensity‐duration control of the hazard. Our rainfall measurements and field observations from the northern Sierra Nevada (California, USA) show that debris flows resulted from a short burst rainfall during a low‐accumulation storm. In contrast, a much higher accumulation storm (∼10 times more rainfall) with lower short‐duration rainfall rates only produced low‐hazard flooding. We conclude that total storm rainfall is not an ideal metric for identifying the rainfall conditions that initiate runoff‐generated debris flows in the first year after wildfire. Rather, a focus on short‐duration (<1 hr), high‐intensity rainfall that can occur during localized thunderstorms, or bands of intense rainfall during prolonged rainstorms, is more beneficial for the purposes of hazard assessment and warning. Plain Language Summary: The rainfall conditions that trigger fast‐moving slurries of water and debris (or "debris flows") after wildfire have been studied extensively in southern California. However, the transferability of these concepts to areas further north along the western margin of the United States in regions that are being affected by increased fire activity is unclear. We present rainfall measurements from contrasting storm types that affected an area burned by the 2021 Dixie Fire in northern California and describe the resultant postfire flows, which ranged from nuisance water‐rich flows to large and dangerous debris flows. Short bursts of rainfall, as opposed to prolonged (multiday) periods of rainfall, initiated the most hazardous flows. We conclude that rainfall intensity measured over short durations, not storm rainfall total, is the most useful measure of rainfall for identifying runoff‐induced debris‐flow hazard potential in the first year after fire. Key Points: Postfire debris flows described in region where they were previously undocumentedFlow type and sediment yield controlled by differences in short‐duration rainfall ratesDebris‐flow initiation not sensitive to storm rainfall totals in first year after fire [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]

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]

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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The rise in wildfire frequency and severity across the globe has increased interest in secondary succession. However, despite the role of soil microbial communities in controlling biogeochemical cycling and their role in the regeneration of post‐fire vegetation, the lack of measurements immediately post‐fire and at high temporal resolution has limited understanding of microbial secondary succession. To fill this knowledge gap, we sampled soils at 17, 25, 34, 67, 95, 131, 187, 286, and 376 days after a southern California wildfire in fire‐adapted chaparral shrublands. We assessed bacterial and fungal biomass with qPCR of 16S and 18S and richness and composition with Illumina MiSeq sequencing of 16S and ITS2 amplicons. Fire severely reduced bacterial biomass by 47%, bacterial richness by 46%, fungal biomass by 86%, and fungal richness by 68%. The burned bacterial and fungal communities experienced rapid succession, with 5–6 compositional turnover periods. Analogous to plants, turnover was driven by "fire‐loving" pyrophilous microbes, many of which have been previously found in forests worldwide and changed markedly in abundance over time. Fungal secondary succession was initiated by the Basidiomycete yeast Geminibasidium, which traded off against the filamentous Ascomycetes Pyronema, Aspergillus, and Penicillium. For bacteria, the Proteobacteria Massilia dominated all year, but the Firmicute Bacillus and Proteobacteria Noviherbaspirillum increased in abundance over time. Our high‐resolution temporal sampling allowed us to capture post‐fire microbial secondary successional dynamics and suggest that putative tradeoffs in thermotolerance, colonization, and competition among dominant pyrophilous microbes control microbial succession with possible implications for ecosystem function. [ABSTRACT FROM AUTHOR]

Microbial communities are integral for ecosystem processes and their taxonomic composition and function may be altered by a disturbance such as fire. Biocrusts are composed of macroscopic and microscopic organisms and are important for a variety of ecosystem functions, such as nutrient cycling and erosion control. We sought to understand if biocrust community composition and function were altered 1 year after a prescribed fire and 6 years after a wildfire in a coastal California grassland on San Clemente Island. We used shotgun metagenomic sequencing and measurements of chlorophyll content, exopolysaccharide production related to soil stability, and nitrogen fixation. There were no differences in the community composition between unburned samples and the samples burned in the prescribed fire and wildfire. Chlorophyll content differed between the prescribed fire and the controls; however, there were no measured differences in exopolysaccharide production, and nitrogen fixation. However, the wildfire and their respective unburned samples had different functions based on the gene annotations. We compiled one Actinobacteria metagenome-assembled genome from the shotgun sequences which had genes for oxidative and heat stress tolerance. These results suggest that the biocrust community can reach a community composition and function similar to the unburned biocrusts within a year after a prescribed burn and 6 years after a wildfire. However, legacy effects of the wildfire may present themselves in the differences between functional gene sequences. Due to their ability to match the undisturbed community composition and function within years and without intervention, future restoration work should consider the biocrusts in their restoration plans as they may provide valuable ecosystem functions after a disturbance. [ABSTRACT FROM AUTHOR]

Wildfire records demonstrate worsening patterns coupled with the spread to higher altitudes in several regions, raising the risk of post‐wildfire ground failures. This study investigates the post‐wildfire stability of unsaturated hillslopes against rainfall‐triggered shallow landslides. We developed a new physics‐based analytical framework incorporating wildfire‐induced changes in soil properties and near‐surface processes affecting the hillslope stability. A coupled hydromechanical infiltration model is integrated into an infinite slope stability analysis to simulate temporal changes in the depth profiles of soil water content, pressure head, and the resulting factor of safety (F.S.) of a vegetated slope. We consider the antecedent conditions of soil and vegetation cover, including the recovery phase after the fire, wildfire‐induced alterations in transpiration, and time‐varying infiltration rates. The model is verified against numerical simulations and employed in parametric studies evaluating the effects of wildfire severity and rainfall intensity‐duration. For the cases examined, it was shown that wildfire could reduce the F.S. of slopes by 25%. As a case study, the model successfully captured shallow rainfall‐triggered landslides that occurred in the Las Lomas watershed in California, USA, in 2019, 3 years after the Fish Fire burned the area. The proposed model uses measurable hillslope and wildfire characteristics and can be employed to evaluate the risk of shallow landslides in wildfire‐prone areas. Plain Language Summary: Several regions (e.g., Western U.S., Australia) globally experience higher wildfire activities and increased wildfire elevations. The worsening patterns of wildfires elevate the risk of post‐wildfire ground instabilities, which can threaten the integrity of communities and infrastructures in the affected areas. However, the literature is still scarce on a physics‐based yet practical slope stability framework capable of capturing wildfire‐induced alterations in soil and vegetation properties. Here we present a new physics‐based framework to evaluate the post‐wildfire stability of unsaturated hillslopes against rainfall‐triggered shallow landslides. The framework accounts for antecedent conditions of soil and vegetation cover, wildfire‐induced alterations in transpiration and root reinforcement, and time‐varying infiltration rates. For the cases examined, it was shown that wildfire could reduce the factor of safety by 25%. The efficacy of the proposed framework is illustrated through modeling a case study in the Las Lomas watershed in California, USA, which experienced several landslides during a heavy rainstorm in January 2019, 3 years after the Fish Fire burned the area. The proposed model uses measurable hillslope and wildfire characteristics and can be employed to evaluate the risk of shallow landslides in wildfire‐prone areas. Key Points: Increased activity and altitudes of wildfires have elevated the likelihood of post‐wildfire ground failuresA new physics‐based analytical framework is formulated to model the temporal evolution of hillslope stability following wildfiresWildfires can significantly lower the hillslope stability to rainfall‐triggered shallow landslides [ABSTRACT FROM AUTHOR]

Mature forests provide important wildlife habitat and support critical ecosystem functions globally. Within the dry conifer forests of the western United States, past management and fire exclusion have contributed to forest conditions that are susceptible to increasingly severe wildfire and drought. We evaluated declines in conifer forest cover in the southern Sierra Nevada of California during a decade of record disturbance by using spatially comprehensive forest structure estimates, wildfire perimeter data, and the eDaRT forest disturbance tracking algorithm. Primarily due to the combination of wildfires, drought, and drought‐associated beetle epidemics, 30% of the region's conifer forest extent transitioned to nonforest vegetation during 2011–2020. In total, 50% of mature forest habitat and 85% of high density mature forests either transitioned to lower density forest or nonforest vegetation types. California spotted owl protected activity centers (PAC) experienced greater canopy cover decline (49% of 2011 cover) than non‐PAC areas (42% decline). Areas with high initial canopy cover and without tall trees were most vulnerable to canopy cover declines, likely explaining the disproportionate declines of mature forest habitat and within PACs. Drought and beetle attack caused greater cumulative declines than areas where drought and wildfire mortality overlapped, and both types of natural disturbance far outpaced declines attributable to mechanical activities. Drought mortality that disproportionately affects large conifers is particularly problematic to mature forest specialist species reliant on large trees. However, patches of degraded forests within wildfire perimeters were larger with greater core area than those outside burned areas, and remnant forest habitats were more fragmented within burned perimeters than those affected by drought and beetle mortality alone. The percentage of mature forest that survived and potentially benefited from lower severity wildfire increased over time as the total extent of mature forest declined. These areas provide some opportunity for improved resilience to future disturbances, but strategic management interventions are likely also necessary to mitigate worsening mega‐disturbances. Remaining dry mature forest habitat in California may be susceptible to complete loss in the coming decades without a rapid transition from a conservation paradigm that attempts to maintain static conditions to one that manages for sustainable disturbance dynamics. [ABSTRACT FROM AUTHOR]

As wildfires in the United States are becoming more frequent and severe, mitigating the risk of wildfire ignitions from power line faults is an increasingly crucial effort. Long-term ignition prevention strategies, especially converting overhead lines to underground cables, are expensive. Thus, it is important to prioritize upgrades on lines that will reduce wildfire ignition risk the most. However, since so many factors contribute to ignition risk, it is difficult to quantify the wildfire risk associated with power lines. This paper examines how various risk definitions based on historical wildfire risk maps can be used to inform transmission upgrade planning. These risk metrics are evaluated using an optimization model that determines which overhead lines should be undergrounded such that the total wildfire risk in the network is minimized. The risk assignment and upgrade selection are tested on both a synthetic network and the actual transmission lines in California. • Addresses risk of electric power line faults leading to wildfire ignitions. • Proposes a methodology to quantify wildfire risk of electric power lines. • Leverages current wildfire risk maps and estimates ignition probability from historical trends. • Identifies optimal set of lines to underground to reduce wildfire risk. [ABSTRACT FROM AUTHOR]

Although fire is a fundamental ecological process in western North American forests, climate warming and accumulating forest fuels due to fire suppression have led to wildfires that burn at high severity across larger fractions of their footprint than were historically typical. These trends have spiked upwards in recent years and are particularly pronounced in the Sierra Nevada–Southern Cascades ecoregion of California, USA, and neighboring states. We assessed annual area burned (AAB) and percentage of area burned at high and low‐to‐moderate severity for seven major forest types in this region from 1984 to 2020. We compared values for this period against estimates for the pre‐Euro‐American settlement (EAS) period prior to 1850 and against a previous study of trends from 1984 to 2009. Our results show that the total average AAB remained below pre‐EAS levels, but that gap is decreasing (i.e., ~14% of pre‐EAS for 1984–2009, but 39% for 2010–2020 [including ~150% in 2020]). Although the average AAB has remained low compared with pre‐EAS, both the average annual area burned at high severity (AAHS) and the percentage of wildfire area burned at high severity have increased rapidly. The percentage of area burned at high severity, which was already above pre‐EAS average for the 1984–2009 period, has continued to rise for five of seven forest types. Notably, between 2010 and 2020, the average AAHS exceeded the pre‐EAS average for the first time on record. By contrast, the percentage of area that burned at low‐to‐moderate severity decreased, particularly in the lower elevation oak and mixed conifer forest types. These findings underline how forests historically adapted to frequent low‐to‐moderate severity fire are being reshaped by novel proportions and extents of high‐severity burning. The shift toward a high‐severity‐dominated fire regime is associated with ecological disruptions, including changes in forest structure, species composition, carbon storage, wildlife habitat, ecosystem services, and resilience. Our results underscore the importance of finding a better balance between the current management focus on fire suppression and one that puts greater emphasis on proactive fuel reduction and increased forest resilience to climate change and ecological disturbance. [ABSTRACT FROM AUTHOR]

Past studies reported a drastic growth in the wildland–urban interface (WUI), the location where man‐made structures meet or overlap wildland vegetation. Fighting fire is difficult in the WUI due to the combination of wildland and structural fuels, and therefore, WUI areas are characterized by frequent damage and loss of structures from wildfires. Recent wildland fire policy has targeted fire prevention, evacuation planning, fuel treatment, and home hardening in WUI areas. Therefore, it is important to understand the occurrence of wildfire events relative to the location of the WUI. In this work, we have reported the occurrences of wildfires with respect to the WUI and quantified how much of the WUI is on complex topography in California, which intensifies fire behavior and complicates fire suppression. We have additionally analyzed the relative importance of WUI‐related parameters, such as housing density, vegetation density, and distance to wildfires, as well as topographic factors, such as slope, elevation, aspect, and surface roughness, on the occurrence of large and small wildfires and the burned area of large wildfires near the WUI. We found that a very small percentage of wildfire ignition points and large wildfire‐burned areas (>400 ha or 1000 acres) were located in the WUI areas. A small percentage of large wildfires were encountered in WUI (3%), and the WUI area accounted for only 4% of the area burned, which increased to 5% and 56%, respectively, outside WUI (5‐km buffer from WUI). Similarly, 66% of fires ignited outside WUI, whereas only 3.6% ignited within WUI. Results from this study have implications for fuel management and infrastructure hardening, as well as for fire suppression and community response. [ABSTRACT FROM AUTHOR]

Clear Lake (Lake County, CA, USA) is hypereutrophic and used for drinking water, tribal use, and supports a significant fishing economy. The Mendocino Complex (2018), one of the largest wildfires in California's post‐settlement history, burned 40% of the Clear Lake watershed, providing a timely opportunity to study the impacts of historical and current wildfires on this valuable aquatic resource. Using long‐term monthly monitoring data from 1968 to 2019, paired with historical watershed fire data, we found that for the three largest fire years in the watershed's history, 3‐year postfire median July–October epilimnetic total phosphorus (TP) concentrations were below or equal to 3‐year prefire TP concentrations. However, both median TP epilimnetic concentrations and deepwater temperature across the lake have increased since the late 1960s. Long‐term TP increases were more strongly correlated with monthly maximum air temperatures than precipitation, suggesting a potential role of warming‐induced water column stratification, dissolved oxygen (DO) depletion, and high potential for internal phosphorus loading. Hypoxic occurrences were correlated with higher hypolimnetic soluble reactive phosphorus and TP concentrations, but additional high‐frequency monitoring of DO will help determine the duration of anoxia and its contribution to internal phosphorus loading. These long‐term data suggest that for this large, hypereutrophic lake, wildfires did not significantly alter in‐lake TP concentrations based on long‐term, monthly monitoring and that other internal or external sources of TP may mask any wildfire effects. Nonetheless, our study underscores the value of synthesizing decades of water quality, watershed wildfire, and climate data to build a more comprehensive, nuanced picture of multiple long‐term threats to aquatic ecosystems under global change. Moreover, monitoring and studying fire effects across a wide range of lake types beyond this study will help promote more effective lake management during changing climates and increasingly frequent large wildfires. [ABSTRACT FROM AUTHOR]

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas. [ABSTRACT FROM AUTHOR]

As human impacts from wildfires mount, there is a pressing need to understand why structures are lost in destructive fires. Despite growing research on factors contributing to structure loss, fewer studies have focused on why some fires are destructive and others are not. We characterized overall differences between fires that resulted in structure loss ("destructive fires") and those that did not ("non-destructive wildfires") across three California regions. Then, we performed statistical analyses on large fires only (≥100 ha) to distinguish the primary differences between large destructive large fires and large non-destructive fires. Overall, destructive fires were at least an order of magnitude larger than non-destructive fires, with the largest area burned varying by season in different regions. Fire severity was also significantly higher in destructive than non-destructive fires. The statistical analysis showed that, in the San Francisco Bay Area and the northern Sierra Nevada foothills, proximity to the Wildland Urban Interface (WUI) was by far the most important factor differentiating destructive and non-destructive wildfires, followed by different combinations of short-term weather, seasonal climate, topography, and vegetation productivity. In Southern California, wind velocity on the day of the fire ignition was the top factor, which is consistent with previous assumptions that wind-driven fires tend to be most destructive and most of the destruction occurs within the first 24 h. Additionally, Southern California's high population density increases the odds that a human-caused wildfire may occur during a severe fire-weather event. The geographical differences among regions and the variation of factors explaining the differences between large destructive and large non-destructive fires reflects the complexity inherent in decision-making for reducing wildfire risk. Land use planning to reduce future exposure of housing development to fire and increased focus on wildfire ignition prevention emerge as two approaches with substantial potential. [ABSTRACT FROM AUTHOR]

The increasing prevalence of high‐severity wildfire in forests in the US state of California is connected to past forest management, but uncertainty remains regarding the differential effects of land ownership on these trends. To determine whether differing forest management regimes, inferred from land ownership, influence high‐severity fire incidence, we assembled and analyzed a large dataset of 154 wildfires that burned a combined area of more than 971,000 ha in California. We found that where fires occurred, the odds of high‐severity fire on "private industrial" lands were 1.8 times greater than on "public" lands and 1.9 times greater than on "other" lands (that is, remaining lands classified as neither private industrial nor public). Moreover, high‐severity fire incidence was greater in areas adjacent to private industrial land, indicating this trend extends across ownership boundaries. Overall, these results indicate that prevailing forest management practices on private industrial timberland may increase high‐severity fire occurrence, underscoring the need for cross‐boundary cooperation to protect ecological and social systems. [ABSTRACT FROM AUTHOR]

Climate change impacts and rapid development in the wildland-urban interface are increasing population exposure and vulnerability to the harmful effects of wildfire and wildfire smoke. The direct and indirect effects of these hazards may impact future mobility decisions among populations at risk. To better understand how perceptions and personal experience inform wildfire- and smoke-associated migration intentions, we surveyed a representative sample of 1108 California residents following the 2020 wildfire season. We assessed the associations between threat appraisal, coping appraisal, personal experience, migration intentions, the impact of wildfire and smoke on migration intentions and place satisfaction, and the potential likelihood of future migration. Results indicate that roughly a third of our sample intended to move in the next 5 years, nearly a quarter of whom reported that wildfire and smoke impacted their migration decision at least a moderate amount. Prior negative outcomes (e.g., evacuating, losing property) were associated with intentions to migrate. Perceived susceptibility and prior negative outcomes were associated with a greater impact of wildfire and smoke on migration intentions. For those intending to remain in place, prior negative outcomes were associated with a greater impact of wildfire and smoke on place satisfaction, which was in turn associated with a greater reported likelihood of future migration. Our findings suggest that perceptions of and experiences with wildfire and smoke may impact individual mobility decisions. These insights may be leveraged to inform risk communications and outreach campaigns to encourage wildfire and smoke risk mitigation behaviors and to improve climate migration modeling. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent and destructive in California, and it is essential to quantify their potential impacts on drinking water utilities. This study aims to measure the severity of wildfires in each California water utility based on the exposure frequency and the extent of area burned by wildfires in each service area. Our quantitative models show an association between water utility characteristics and their vulnerability to wildfires. Findings indicate that wildfire vulnerability is higher in government-owned utilities than private ones, utilities primarily relying on surface water than groundwater, and utilities using local-sourced water than purchased water. Also, we find a stronger association between wildfire vulnerability and large utilities in terms of population served than small or medium ones. Regarding geography, we find wildfire vulnerability is higher in southern and coastal California utilities than in Northern and inland California. These results help water utilities and land managers identify vulnerable locations and develop wildfire management and disaster preparedness strategies. [ABSTRACT FROM AUTHOR]

The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships. [ABSTRACT FROM AUTHOR]

Past research has shown that households living in manufactured housing have outsized exposure and vulnerability to multiple climate-related hazards, including floods, tornadoes, and hurricanes. Yet, few studies have specifically analyzed the risk of hot and dry climate change-induced hazards, such as extreme heat and wildfires, on households living in manufactured housing. In this study, we first use national American Housing Survey data to provide background context on the adaptive capacity to these hazards among manufactured home occupants nationally. We then focus our primary analysis on California, where the prevalence of such hazards is growing substantially due to climate change. We compile a unique, census tract-level data set of occupied housing unit characteristics by tenure status, along with extreme heat and wildfire hazard measures. We also incorporate address-level locations of mobile home parks in the state. We use descriptive statistics, t-tests, and bivariate correlations to compare the exposure for households in manufactured housing with renter and owner households residing in other housing structure types. We find that households in manufactured housing face consistently higher exposure to heat and fire hazards across all exposure measures. Census tracts with higher proportions of households in manufactured housing are much more likely to be located in areas with greater historical and projected extreme heat hazards than households living in other lower-cost housing types or households in general. Households in manufactured housing are also the most likely of all housing types to be located in tracts affected by past wildfires and in tracts with high future wildfire risk. We conclude by providing recommendations for future research and policy consideration. [ABSTRACT FROM AUTHOR]