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An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies. [ABSTRACT FROM AUTHOR]

Wildfire in California is an increasing threat to life and property. The expansion of urban and suburban development into wildlands limits risk-reduction options like prescribed burning, whereas large-scale mechanical and herbicide treatments can be cost prohibitive and unpalatable to the public. Cattle grazing is a low risk, affordable treatment not frequently considered for use explicitly for fuels reduction in California. To examine the impact of cattle grazing on fire in Napa and Sonoma Counties, California, we quantified its effects as change in average annual burn probability. Probabilities were calculated for 2001-2017 using mixed-effect regression models in combination with a range of grazing intensities and extents. These grazing scenarios were designed to represent current grazing conditions, ungrazed conditions, adding grazing to high priority landscapes, and grazing the full study area. We estimated that under current grazing conditions, cattle grazing reduces average annual burn probability 45% (from 9.9% to 5.4%) compared to ungrazed conditions. Adding grazing to high priority landscapes as identified by the California Department of Forestry and Fire Protection (CAL FIRE) decreased their average annual burn probability by 82% (from 7.6% to 1.4%) compared to under current grazing conditions. Of the scenarios assessed, grazing high priority landscapes heavily while maintaining the current extent and intensity of grazing on other rangelands provided the best return in terms of decreased burn probability per additional area grazed. Finally, we demonstrated how our methodologies can be utilized by fuel managers and planners to identify key areas for treatment with cattle grazing. Our findings suggest cattle grazing provides benefits to the study area by reducing overall burn probability, and that extending its use to treat fuels in priority areas in and around the wildland urban interface could provide further fire-risk reduction on communityadjacent lands. Land managers may find cattle grazing a valuable long term fuel-management tool at the landscape scale. [ABSTRACT FROM AUTHOR]

Evaluation metrics for prediction error, model selection and model averaging on space–time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space–time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation. [ABSTRACT FROM AUTHOR]

Background: Autumn and winter Santa Ana Winds (SAW) are responsible for the largest and most destructive wildfires in southern California. Aims: (1) To contrast fires ignited on SAW days vs non-SAW days, (2) evaluate the predictive ability of the Canadian Fire Weather Index (CFWI) for these two fire types, and (3) determine climate and weather factors responsible for the largest wildfires. Methods: CAL FIRE (California Department of Forestry and Fire Protection) FRAP (Fire and Resource Assessment Program) fire data were coupled with hourly climate data from four stations, and with regional indices of SAW wind speed, and with seasonal drought data from the Palmer Drought Severity Index. Key results: Fires on non-SAW days were more numerous and burned more area, and were substantial from May to October. CFWI indices were tied to fire occurrence and size for both non-SAW and SAW days, and in the days following ignition. Multiple regression models for months with the greatest area burned explained up to a quarter of variation in area burned. Conclusions: The drivers of fire size differ between non-SAW and SAW fires. The best predictor of fire size for non-SAW fires was drought during the prior 5 years, followed by a current year vapour pressure deficit. For SAW fires, wind speed followed by drought were most important. Fires ignited on Santa Ana Wind days are the largest and most destructive fires but other fires are more numerous and account for greater area burned. Fire indices on days after ignition are important predictors of fire size, but drought and wind speed are most closely tied to fire size. [ABSTRACT FROM AUTHOR]

In December 2017, one of the largest wildfires in California history, the Thomas Fire, created a large smoke and ash plume that extended over the northeastern Pacific Ocean. Here, we explore the impact of Thomas Fire ash deposition on seawater chemistry and the growth and composition of natural microbial communities. Experiments conducted in coastal California waters during the Thomas Fire revealed that leaching of ash in seawater resulted in significant additions of dissolved nutrients including inorganic nitrogen (nitrate, nitrite and ammonium), silicic acid, metals (iron, nickel, cobalt and copper), organic nitrogen and organic carbon. After exposure to ash leachate at high (0.25 g ash l-1) and low (0.08 g ash l-1) concentrations for 4 days, natural microbial communities had 59-154% higher particulate organic carbon concentrations than communities without ash leachate additions. Additionally, a diverse assemblage of eukaryotic microbes (protists) responded to the ash leachate with taxa from 11 different taxonomic divisions increasing in relative abundance compared with control treatments. Our results suggest that large fire events can be important atmospheric sources of nutrients (particularly nitrogen) to coastal marine systems, where, through leaching of various nutrients, ash may act as a food for all in protist communities.

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.

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05). [ABSTRACT FROM AUTHOR]

In the summer and fall of 2018 and 2020, major wildfires in Northern California (USA) impacted the San Francisco Bay Area. The remote 2018 and 2020 wildfires produced the highest PM2.5 concentrations experienced in the Tri-Valley of the East Bay Area during those two years. The Tri-Valley is composed of the San Ramon, Amador, and Livermore Valleys, surrounded by local terrain that creates a small airshed. In 1967, the California Air Resources Board created 15 Air Basins defined by regional geography, topographic and meteorological conditions. Airshed is sometimes synonymous with an urban-scale component of an Air Basin. We use airshed as a Tri-Valley component of the Bay Area Air Basin. This airshed spans across two counties (Contra Costa and Alameda) and encompasses four cities: San Ramon, Dublin, Pleasanton, and Livermore. PurpleAir (PA) sensors provided good geographic coverage of variation in PM2.5 in the Tri-Valley airshed. Several studies have established significant health effects from wildfire plumes by associating daily hospital visits with PM2.5 air quality data at local and regional scales. We hypothesize that during the wildfire smoke periods of 2018 and 2020 in the Tri-Valley area, there was an increase in hospitalizations and ED visits for respiratory (asthma and COPD-related) health effects, as compared to the same time periods during years with less fire activity. The primary goal of this study was to confirm health effects from wildfire plumes on a community scale using 5 years of publicly-available health data. However, with only monthly hospitalization data available, directly linking respiratory hospital and emergency department (ED) visits with PM2.5 concentrations was unsuccessful. Also, because COVID-19 masked all other causes of hospital visits in 2020, that year was ultimately eliminated from this study. However, visits during November 2018 being much higher than any other November in 2016, 2017, and 2019 implied a potential cause and effect. Daily hospitalization and air quality data are required to quantify any relationship by regression analysis. These findings help inform future studies on the health effects of air quality at community scales. [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]

Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire. Using Zillow Transaction and Assessment Database (ZTRAX), hereafter "Zillow", real estate data, we explored whether lower-priced structures were more likely to be damaged during the most destructive, recent wildfires in Southern California. Within fire perimeters occurring from 2000–2019, we matched property price data to burned and unburned structures. To be included in the final dataset, fire perimeters had to surround at least 25 burned and 25 unburned structures and have been sold at most seven years before the fire; five fires fit these criteria. We found evidence to support our hypothesis that lower-priced properties were more likely to be damaged, however, the likelihood of damage and the influence of property value significantly varied across individual fire perimeters. When considering fires individually, properties within two 2003 fires–the Cedar and Grand Prix-Old Fires–had statistically significantly decreasing burn damage with increasing property value. Occurring in 2007 and later, the other three fires (Witch-Poomacha, Thomas, and Woolsey) showed no significant relationship between price and damage. Consistent with other studies, topographic position, slope, elevation, and vegetation were also significantly associated with the likelihood of a structure being damaged during the wildfire. Driving time to the nearest fire station and previously identified fire hazard were also significant. Our results suggest that further studies on the extent and reason for disproportionate impacts of wildfire are needed. In the meantime, decision makers should consider allocating wildfire risk mitigation resources–such as fire-fighting and wildfire structural preparedness resources–to more socioeconomically vulnerable neighborhoods. [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 the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation without delays. [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]

In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 kmh-1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (10.5281/zenodo.8327264, Liu et al., 2023). [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]

As wildfire becomes increasingly frequent, many serotinous plant populations risk local extirpation if fire recurs prior to sufficient seed accumulation in the canopy (i.e., "immaturity risk"). Following two 2018 wildfires in northwestern California, we studied seed viability, cone production, and postfire regeneration of a serotinous conifer, knobcone pine (Pinus attenuata), with stand ages (time since fire) ranging from 6 to 79 years. Cone density per tree was more strongly associated with tree diameter than age, and cone density was positively related to postfire seedling regeneration. Most of the postfire knobcone pine regeneration established during the first year with high survivorship in the following first postfire year. Adjusting for survivorship, the estimated minimum age for knobcone pine to promote self‐replacement (one recruit per tree) was 9.5 years (or 4.6‐cm dbh) and the probability of reburning at the modern fire rotation of 43 years was 19.8%. Based on our results, we found that immaturity risk was currently low for knobcone pine. Our approach provides a quantitative method to assess immaturity risk in knobcone pine and other serotinous conifer species that can be used to evaluate future risk under rapidly changing climate and fire conditions. [ABSTRACT FROM AUTHOR]

Damages from wildfires have increased dramatically in recent years. This study uses a boundary discontinuity design to estimate the effect of wildfire-hazard disclosure on house prices. Using the universe of single-family sales transactions from Zillow's Transaction and Assessment Database in California from 2015 through 2022, we find that, on average, homes facing disclosure requirements sold for approximately 4.3% less than nearby homes that did not. Price impacts are higher in recent years, after several damaging wildfires. Our findings highlight the use of disclosure regulations to ensure that disaster risks are reflected in housing markets. [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]

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

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

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]

Innovative solutions are necessary for efficient monitoring and mitigation techniques as climate change increases the frequency and severity of natural catastrophes. In this study, we investigate how Cloud Internet of Things (IoT) might help mitigate climate-related calamities. Through the use of cloud computing and sensor networks, Cloud IoT facilitates the capture, processing, and distribution of data in near-real time. This strategy promotes catastrophe planning, response, and recovery by offering early warnings, predictive insights, and simplified communication. The article emphasises the cumulative influence of Cloud IoT components such as sensor networks, data analytics, decision support systems, and remote control in the context of disaster management. Cloud IoT is useful in real-world scenarios, such as the tracking of floods in Bangladesh and the identification of wildfires in California. These cases show how the technology may prevent injuries and preserve property through early warnings and well-coordinated responses. Despite its potential, it faces obstacles including ensuring the safety of data and dealing with issues related to the necessary infrastructure. In conclusion, including Cloud IoT in disaster management provides a cost-effective, scalable, and efficient solution, greatly contributing to constructing resilient communities and creating a sustainable future in the face of climate-induced natural catastrophes. [ABSTRACT FROM AUTHOR]

Novel wildfire regimes are rapidly changing global ecosystems and pose significant challenges for biodiversity conservation and ecosystem management. In this study, we used DNA metabarcoding to assess the response of arthropod pollinator communities to large‐scale wildfires across diverse habitat types in California. We sampled six reserves within the University of California Natural Reserve System, each of which was partially burned in the 2020 Lightning Complex wildfires in California. Using yellow pan traps to target pollinators, we collected arthropods from burned and unburned sites across multiple habitat types including oak woodland, redwood, scrub, chamise, grassland, forest, and serpentine habitats. We found no significant difference in alpha diversity values between burned and unburned sites; instead, seasonal variations played a significant role in arthropod community dynamics, with the emergence of plant species in Spring promoting increased pollinator richness at all sites. When comparing all sites, we found that burn status was not a significant grouping factor. Instead, compositional differences were largely explained by geographic differences, with distinct communities within each reserve. Within a geographic area, the response of arthropods to fire was dependent on habitat type. While communities in grasslands and oak woodlands exhibited recovery following burn, scrublands experienced substantial changes in community composition. Our study highlights the importance of examining community responses to wildfires across broad spatial scales and diverse habitat types. By understanding the nuanced dynamics of arthropod communities in response to fire disturbances, we can develop effective conservation strategies that promote resilience and maintain biodiversity in the face of increasing wildfire frequency and severity driven by climate change. [ABSTRACT FROM AUTHOR]

Fire is a dominant force shaping patterns of plant diversity in Mediterranean‐type ecosystems. In these biodiversity hotspots, including California's endangered coastal scrub, many species remain hidden belowground as seeds and bulbs, only to emerge and flower when sufficient rainfall occurs after wildfire. The unique adaptations possessed by these species enable survival during prolonged periods of unfavorable conditions, but their continued persistence could be threatened by nonnative plant invasion and environmental change. Furthermore, their fleeting presence aboveground makes evaluating these threats in situ a challenge. For example, nitrogen (N) deposition resulting from air pollution is a well‐recognized threat to plant diversity worldwide but impacts on fire‐following species are not well understood. We experimentally evaluated the impact of N deposition on post‐fire vegetation cover and richness for three years in stands of coastal sage scrub that had recently burned in a large wildfire in southern California. We installed plots receiving four levels of N addition that corresponded to the range of N deposition rates in the region. We assessed the impact of pre‐fire invasion status on vegetation dynamics by including plots in areas that had previously been invaded by nonnative grasses, as well as adjacent uninvaded areas. We found that N addition reduced native forb cover in the second year post‐fire while increasing the abundance of nonnative forbs. As is typical in fire‐prone ecosystems, species richness declined over the three years of the study. However, N addition hastened this process, and native forb richness was severely reduced under high N availability, especially in previously invaded shrublands. An indicator species analysis also revealed that six functionally and taxonomically diverse forb species were especially sensitive to N addition. Our results highlight a new potential mechanism for the depletion of native species through the suppression of ephemeral post‐fire bloom events. [ABSTRACT FROM AUTHOR]

Wildfires have become larger and more frequent because of climate change, increasing their impact on air pollution. Air quality forecasts and climate models do not currently account for changes in the composition of wildfire emissions during the commonly observed progression from more flaming to smoldering combustion. Laboratory measurements have consistently shown decreased nitrogen dioxide (NO2) relative to carbon monoxide (CO) over time, as they transitioned from more flaming to smoldering combustion, while formaldehyde (HCHO) relative to CO remained constant. Here, we show how daily ratios between column densities of NO2 versus those of CO and HCHO versus CO from the Tropospheric Monitoring Instrument (TROPOMI) changed for large wildfires in the Western United States. TROPOMI‐derived emission ratios were lower than those from the laboratory. We discuss reasons for the discrepancies, including how representative laboratory burns are of wildfires, the effect of aerosols on trace gas retrievals, and atmospheric chemistry in smoke plumes. Plain Language Summary: Climate change has led to an increase in the frequency and size of wildfires in the Western United States. The gases and particles released from wildfires impact air quality and climate, so it is important to understand the chemical composition of these emissions. In current air quality forecasts and climate models, the composition of wildfire emissions is based on the dominant vegetation burned and is assumed to be constant over time. In contrast, measurements from laboratory burns indicate that the composition of emissions from fires changes over time, as fires progress from more flaming combustion to flameless burning dominated by smoke (smoldering). It is challenging to have daily field measurements of the emissions from long‐lived wildfires, but there are instruments in space that can make daily observations of wildfires globally. In this study, we show how the composition of emissions from wildfires in California, Oregon, and Washington changed over time, as they progressed from more flaming to more smoldering combustion, using observations from a satellite instrument called TROPOMI. The analysis of the composition of wildfire emissions and their evolution over time using TROPOMI could improve air quality forecasting and climate modeling globally. Key Points: Space‐based remote sensing instruments can be used to observe changes in the composition of wildfire emissions over timeChanges in wildfire emissions composition observed with TROPOMI were caused by evolving combustion conditions rather than aerosol shieldingTROPOMI observations can be used to help parametrize how modeled wildfire emissions should change with evolving combustion conditions [ABSTRACT FROM AUTHOR]

Accentuated by climate change, catastrophic wildfires are a growing, distributed global public health risk from inhalation of smoke and dust. Underrecognized, however, are the health threats arising from fire-altered toxic metals natural to soils and plants. Here, we demonstrate that high temperatures during California wildfires catalyzed widespread transformation of chromium to its carcinogenic form in soil and ash, as hexavalent chromium, particularly in areas with metal-rich geologies (e.g., serpentinite). In wildfire ash, we observed dangerous levels (327-13,100 µg kg−1) of reactive hexavalent chromium in wind-dispersible particulates. Relatively dry post-fire weather contributed to the persistence of elevated hexavalent chromium in surficial soil layers for up to ten months post-fire. The geographic distribution of metal-rich soils and fire incidents illustrate the broad global threat of wildfire smoke- and dust-born metals to populations. Our findings provide new insights into why wildfire smoke exposure appears to be more hazardous to humans than pollution from other sources. Carcinogenic heavy metals are an underappreciated public health concern from wildfire. Fire severity, geology, and ecosystem type influence landscape-scale production of hexavalent chromium, concentrated in wind-dispersible particles. [ABSTRACT FROM AUTHOR]

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]

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual's location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0's performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters. [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.

Massive wildfires and extreme fire behavior are becoming more frequent across the western United Statescreating a need to better understand how megafire behavior will evolve in our warming world. Here, the fire spread model Prometheus is used to simulate the initial explosive growth of the 2020 August Complex, which occurred in northern California (CA) mixed conifer forests. High temperatures, low relative humidity, and daytime southerly winds were all highly correlated with extreme rates of modeled spread. Fine fuels reached very dry levels, which accelerated simulation growth and heightened fire heat release (HR). Model sensitivity tests indicate that fire growth and HR are most sensitive to aridity and fuel moisture content. Despite the impressive early observed growth of the fire, shifting the simulation ignition to a very dry September 2020 heatwave predicted a >50% increase in growth and HR, as well as increased nighttime fire activity. Detailed model analyses of how extreme fire behavior develops can help fire personnel prepare for problematic ignitions.

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.

Government agencies must make rapid and informed decisions in wildfires to safely evacuate people. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, we employ online survey data from evacuees of the 2017 Northern California Wildfires (n=37), the 2017 Southern California Wildfires (n=175), and the 2018 Carr Wildfire (n=254) to inform a policy-oriented traffic evacuation simulation model. We test our simulation for a hypothetical wildfire evacuation in the wildland urban interface (WUI) of Berkeley, California. We focus on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger). We found that reducing household vehicles (i.e., to 1 vehicle per household) and increasing GPS-enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced a slightly longer ETE. Both contraflow (on limited links due to resource constraints) and slowing fire speed were effective in lowering exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. We recommend agencies develop a communication and parking plan to reduce evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.

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This study explored the feasibility of using fish skin bandages as a therapeutic option for third-degree skin burns. Following the California wildfires, clinical observations of animals with third-degree skin burns demonstrated increased comfort levels and reduced pain when treated with tilapia fish skin. Despite the promises of this therapy, there are few studies explaining the healing mechanisms behind the application of tilapia fish skin. In this study, mice with third-degree burns were treated with either a hydrocolloid adhesive bandage (control) (n = 16) or fish skin (n = 16) 7 days post-burn. Mice were subjected to histologic, hematologic, molecular, and gross evaluation at days 7, 16, and 28 post-burn. The fish skin offered no benefit to overall wound closure compared to hydrocolloids. Additionally, we detected no difference between fish skin and control treatments in regard to hypermetabolism or hematologic values. However, the fish skin groups exhibited 2 times more vascularization and 2 times higher expression of antimicrobial defensin peptide in comparison to controls. Proteomic analysis of the fish skin revealed the presence of antimicrobial peptides. Collectively, these data suggest that fish skin can serve as an innovative and cost-effective therapeutic alternative for burn victims to facilitate vascularization and reduce bacterial infection. [ABSTRACT FROM AUTHOR]

Wildfires have become an increasingly important source of organic gases and particulate matter in the western USA. A large fraction of organic particulate matter emitted in wildfires is semivolatile, and the oxidation of organic gases in smoke can form lower-volatility products that then condense on smoke particulates. In this research, we measured the gas- and particle-phase concentrations of semivolatile organic compounds (SVOCs) during the 2017 northern California wildfires in a downwind urban area, using semivolatile thermal desorption aerosol gas chromatography (SV-TAG), and measured SVOCs in a rural site affected by biomass burning using cTAG (comprehensive thermal desorption aerosol gas chromatography mass spectrometry) in Idaho in 2018. Commonly used biomass burning markers such as levoglucosan, mannosan, and nitrocatechols were found to stay predominantly in the particle phase, even when the ambient organic aerosol (OA) was relatively low. The phase partitioning of SVOCs is observed to be dependent on their saturation vapor pressure, while the equilibrium absorption model underpredicts the particle-phase fraction of most of the compounds measured. Wildfire organic aerosol enhanced the condensation of polar compounds into the particle phase but not some nonpolar compounds, such as polycyclic aromatic hydrocarbons. [ABSTRACT FROM AUTHOR]

In the western United States, prolonged drought, warming climate, and historical fuel build-up have contributed to larger and more intense wildfires, as well as longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heatwaves, windstorms, droughts, and varying levels of active fire suppression, influence fire spread. Here we develop the GOES-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a dataset of hourly fire perimeters, active fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019-2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters with using 12-hourly data from the VIIRS-derived Fire Event Data Suite (FEDS) and final fire perimeters from California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has coarser resolution (2 km at the equator), the final fire perimeters from GOFER correspond reasonably well with those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP. GOFER fills a key temporal gap present in other fire tracking products that rely on low-earth- orbit imagery, where perimeters are available at 12-hour intervals or longer, or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km/h. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in rates of fire spread on diurnal time scales. The resulting GOFER dataset has a broad set of applications, including the development of predictive models for fire spread and improvement of atmospheric transport models for surface smoke estimates. [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]

Interactions between humans and wildfires have increased in many regions over the last decades driven by climate and land-use changes. A shift towards more adaptive fire management and policies is urgently needed but remains difficult to achieve. Better understanding of public interest in wildfire can facilitate this transition, as the public is a key driver for policy decisions. We used Google Trends to assess temporal patterns (2004–2020) in public interest on wildfires worldwide and in five case study countries (Australia, Canada, Indonesia, Portugal, USA). Public interest consistently shows a cyclic pattern with low background and short-lasting spikes during fire seasons and catastrophic events. Wildfires that receive the most interest worldwide are located in Western countries, especially the USA. There is usually high demand for news on wildfires when spikes in interest happen. Overall global interest in wildfire has risen twice: first for a short period in 2007–2008, concomitant to catastrophic wildfires in California, and again since 2017, probably triggered by a series of catastrophic fire events around the globe. Nevertheless, public interest in wildfire is low when compared with socioeconomically more costly earthquakes or hurricanes. The short and seasonal interest in wildfire may present an important obstacle to the implementation of wildfire mitigation policies that require year-round approaches. However, the fact that the public uses the internet to obtain basic knowledge about wildfire functioning and impacts, especially during the interest spikes, can facilitate targeting awareness campaigns. These could be not only about wildfires but also about broader related environmental issues. • Worldwide public interest in wildfires is on the rise (measured using Google Trends). • Interest shows a cyclic pattern with spikes during fire seasons and catastrophic events. • Global public interest strongly linked to wildfires in the West (especially USA). • Public interest in wildfire much lower than for earthquakes, hurricanes and storms. • Very strong correlation between public interest and demand for news on the topic. [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]

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]

Over the past four decades, annual area burned has increased significantly in California and across the western USA. This trend reflects a confluence of intersecting factors that affect wildfire regimes. It is correlated with increasing temperatures and atmospheric vapour pressure deficit. Anthropogenic climate change is the driver behind much of this change, in addition to influencing other climate-related factors, such as compression of the winter wet season. These climatic trends and associated increases in fire activity are projected to continue into the future. Additionally, factors related to the suppression of the Indigenous use of fire, aggressive fire suppression and, in some cases, changes in logging practices or fuel management intensity, collectively have produced large build-ups of vegetative fuels in some ecosystems. Human activities provide the most common ignition source for California's wildfires. Despite its human toll, fire provides a range of ecological benefits to many California ecosystems. Given the diversity of vegetation types and fire regimes found in the state, addressing California's wildfire challenges will require multi-faceted and locally targeted responses in terms of fuel management, human-caused ignitions, building regulations and restrictions, integrative urban and ecosystem planning, and collaboration with Tribes to support the reinvigoration of traditional burning regimes. [ABSTRACT FROM AUTHOR]

The article analyzes the labor market impacts of destructive California wildfires utilizing panel data structure of multiple wildfire events across time in multiple counties in the state and using a generalized difference-in-difference modeling approach. Data shows wildfire trends, seasons and geographic differences. It discusses the impact of wildfires on local employment changes and overall labor market across industries including construction, professional and business services.