Southern California Wildfires Research Articles

Evidence of increasing wildfire damage with decreasing property price in Southern California fires.

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.

A Quantitative Analysis of Fuel Break Effectiveness Drivers in Southern California National Forests

Fuel and wildfire management decisions related to fuel break construction, maintenance, and use in fire suppression suffer from limited information on fuel break success rates and drivers of effectiveness. We built a dataset of fuel break encounters with recent large wildfires in Southern California and their associated biophysical, suppression, weather, and fire behavior characteristics to develop statistical models of fuel break effectiveness with boosted regression. Our results suggest that the dominant influences on fuel break effectiveness are suppression, weather, and fire behavior. Variables related to fuel break placement, design, and maintenance were less important but aligned with manager expectations for higher success with wider and better maintained fuel breaks, and prior research findings that fuel break success increases with accessibility. Fuel breaks also held more often if burned by a wildfire during the previous decade, supporting the idea that fuel breaks may be most effective if combined with broader fuel reduction efforts.

Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data

ABSTRACT Wildfires have significant impacts on human lives, critical infrastructures, and Earth’s ecosystems. Accurate and timely information on burned area (BA) affected by wildfires is vital to better understand the drivers of wildfire events, as well as its relevance for biogeochemical cycles, climate, and air quality, and to aid wildfire management. Single satellite data have been used to detect the characteristics of wildfires, retrospectively mapping BAs at a variety of spatial resolutions in previous studies. However, due to the trade-off between spatial and temporal resolutions, single-source satellite data are not sufficient to characterize the explicit dynamics of BAs at high resolutions in both space and time. Thus, a two-stage near real-time BA mapping method was developed in this study to take advantage of the high temporal frequency of coarse resolution sensors and the fine spatial resolution of medium resolution sensors in BA mapping by synergizing freely available coarse and medium spatial resolution (MSR) sensors. First, high temporal frequency sensors such as MODIS and VIIRS were used to identify wildfires and potential BAs. Then, multiple MSR sensors such as Sentinel-2A/2B, Landsat OLI, and Resourcesat AWiFS were synthesized for extracting the BAs with more spatial details in near real-time. We applied the method in California, USA, where wildfires occurred in northern and southern parts in 2017. The results showed that the proposed method is promising for BA mapping with an overall accuracy of 0.84 and 0.85 for wildfires in northern and southern California, respectively. Additionally, the proposed method greatly improved the frequency and reduced the latency, with an average interval of 3.5 days (3 days) and latency of 4 days (sub-daily) for wildfires in southern (northern) California. The extracted BAs illustrated accurate spatial details with MSR sensors. Our method can significantly take advantage of multi-source remote-sensing observations to accurately map the BAs of active wildfires in near real-time. More importantly, the method can be applied to other geographic regions where wildfires risk humans and ecosystems.

ECOSTRESS reveals pre‐fire vegetation controls on burn severity for Southern California wildfires of 2020

AbstractAimTopography, climate and plant water stress influence the spatial patterns of burn severity (damage to soils and vegetation associated with wildfires); however, the relative importance of these predictors remains contested. We hypothesized that high‐resolution pre‐fire vegetation water stress, measured using evapotranspiration (ET), evaporative stress index (ESI) and water use efficiency (WUE), are strong predictors of burn severity.LocationCalifornia, USA.Time period2019–2020.Major taxa studiedForests, chaparral, oaks and grasslands.MethodsWe used random forest regression modelling with remotely sensed ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) plant water stress from the spring–summer and year before the fires, predictors to represent spatial autocorrelation, and topography to predict the spatial patterns of burn severity for six fires in the Sierra Nevada and the Southern California Mountains.ResultsPlant water stress was the dominant predictor for the Southern California Mountains, and spatial autocorrelation in Sierra Nevada. Elevation was a leading predictor for burn severity for both regions, with greater predictability in higher‐elevation and steeper terrain. Model results varied by vegetation type: for the Southern California Mountains, grasslands had the highest predictability [R2 = .50 (high elevation); R2 = .58 (steep)], followed by savanna and open shrublands, which are typified by chaparral‐type vegetation [R2 = .43 (savanna, high elevation); R2 = .40 (open shrubland, high elevation)]. For grasslands, annual ET was the leading predictor (R2 = .58), consistent with the idea that growing season grassland enhancement increases fuel availability. The predictability of burn severity was also sensitive to the time of day of the ECOSTRESS acquisition and varied by vegetation type. Afternoon ET was the leading predictor for high‐elevation grasslands (R2 = .50), and morning predictors were important for steep‐sloped woody savanna (R2 = .18).Main conclusionsOur findings provide insights on how antecedent plant water stress predicts burn severity and can inform pre‐fire season monitoring and fuel management.

Understanding California wildfire evacuee behavior and joint choice making

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.

Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California

Government agencies must make rapid and informed decisions in wildfires to evacuate people safely. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, this study employs 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. The simulation is tested for a hypothetical wildfire evacuation in the wildland-urban interface of Berkeley, California. The study focuses 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). It was found that reducing evacuating household vehicles (i.e., to one 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 slightly longer ETEs. Both contraflow (on limited links because of resource constraints) and slowing fire speed were effective in lowering number of exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. It is recommended that agencies develop a communication and parking plan to reduce the number of evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.

Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California

Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953–2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation.

Trust and compassion in willingness to share mobility and sheltering resources in evacuations: A case study of the 2017 and 2018 California Wildfires

Advances in the sharing economy – such as transportation network companies (e.g., Lyft, Uber) and home sharing (e.g., Airbnb) – have coincided with the increasing need for evacuation resources. While peer-to-peer sharing under normal circumstances often suffers from trust barriers, disaster literature indicates that trust and compassion often increase following disasters, improving recovery efforts. We hypothesize that trust and compassion could trigger willingness to share transportation and sheltering resources during an evacuation.To test this hypothesis, we distributed a survey to individuals impacted by the 2017 Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). We estimate binary logit choice models, finding that high trust in neighbors and strangers and high compassion levels significantly increase willingness to share across four sharing scenarios. Assuming a high trust/compassion population versus a low trust/compassion population results in a change of likelihood to share between 30% and 55%, depending on scenario. Variables related to departure timing and routing – which capture evacuation urgency – increase transportation sharing willingness. Volunteers in past disasters and members of community organizations are usually more likely to share, while families and previous evacuees are typically less likely. Significance of other demographic variables is highly dependent on the scenario. Spare seatbelts and bed capacity, while increasing willingness, are largely insignificant. These results suggest that future sharing economy strategies should cultivate trust and compassion before disasters via preparedness within neighborhoods, community-based organizations, and volunteer networks, during disasters through communication from officials, and after disasters using resilience-oriented and community-building information campaigns.

Spatial heterogeneity of winds during Santa Ana and non-Santa Ana wildfires in Southern California with implications for fire risk modeling

In Southern California, the Santa Ana winds are famous for their role in spreading large wildfires during the fall/winter season. Combined with Southern California's complex topography, Santa Anas create challenges for modeling wind-fire relationships in this region. Here, we assess heterogeneity of winds during Santa Ana and non-Santa Ana days, on days with and without large-fire ignitions, across a modern high-density observational network of 30 meteorological stations. Wind speeds on Santa Ana days with a large fire ignition (mean windspeed = 5.19 m/s) are significantly higher than on Santa Ana days without large fire ignitions (3.96 m/s), while on non-Santa Ana days winds are generally weaker, during both fire (2.30 m/s) and non-fire (2.38 m/s) days. Hierarchical clustering of meteorological stations during both Santa Ana and non-Santa Ana days reveals groups of stations with consistently similar wind speed and directions. All stations clearly exhibit high wind speeds on Santa Ana days, and most record contrasting wind characteristics during Santa Ana versus non-Santa Ana ignitions. Additionally, our analysis revealed that key geographic siting traits are not represented in the network, including few stations with northwest aspect and upper slope in the southern mountains.

A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n = 226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.

IMPACT OF INFORMAL AND FORMAL SOCIAL NETWORKS DURING A WILDFIRE ON PSYCHOLOGICAL RECOVERY

Having the support of a social network can play an important role towards psychological well-being for those impacted by a disaster. The purpose of this study was to investigate how type (emotional, tangible, informational) and source (family, friends, others) of social support during three Southern California wildfires impacted the psychological well-being of three victim-groups (based on severity of exposure) of community-residing adults (N= 203; meanage = 63.4 years; range 50-94 years) who responded to surveys about their experiences during the 2007, 2014, and 2017 northern San Diego County, CA wildfires. Analyses revealed that emotional social support from friends and neighbors was the most frequently received type and source of support, especially for the primary victim-group, those with residential loss. All victim-groups exhibited healthy psychological well-being. Discussion will focus on how informal groups differed and were similar to formal groups in providing social support during a disaster to encourage psychological recovery.

Machine learning models accurately predict ozone exposure during wildfire events

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

Modeling Study of the Air Quality Impact of Record-Breaking Southern California Wildfires in December 2017.

We investigate the air quality impact of record-breaking wildfires in Southern California during 5-18 December 2017 using the Weather Research and Forecasting model with Chemistry in combination with satellite and surface observations. This wildfire event was driven by dry and strong offshore Santa Ana winds, which played a critical role in fire formation and air pollutant transport. By utilizing fire emissions derived from the high-resolution (375 × 375 m2) Visible Infrared Imaging Radiometer Suite active fire detections, the simulated magnitude and temporal evolution of fine particulate matter (PM2.5) concentrations agree reasonably well with surface observations (normalized mean bias = 4.0%). Meanwhile, the model could generally capture the spatial pattern of aerosol optical depth from satellite observations. Sensitivity tests reveal that using a high spatial resolution for fire emissions and a reasonable treatment of plume rise (a fair split between emissions injected at surface and those lifted to upper levels) is important for achieving decent PM2.5 simulation results. Biases in PM2.5 simulation are relatively large (about 50%) during the period with the strongest Santa Ana wind, due to a possible underestimation of burning area and uncertainty in wind field variation. The 2017 December fire event increases the 14-day averaged PM2.5 concentrations by up to 231.2 μg/m3 over the downwind regions, which substantially exceeds the U.S. air quality standards, potentially leading to adverse health impacts. The human exposure to fire-induced PM2.5 accounts for 14-42% of the annual total PM2.5 exposure in areas impacted by the fire plumes.

Assessing Quality through Measurement of Unmet Needs of Disaster Populations after the 2007 Southern California Wildfires

Assessing Quality through Measurement of Unmet Needs of Disaster Populations after the 2007 Southern California Wildfires

Visualizing Risk: Drawing Together and Pushing Apart with Sociotechnical Practices

This paper examines the relationship between collaborative disaster mapping and conceptions of risk. It looks at the practice of mapmaking during the 2007 wildfires in Southern California to explore sociotechnological issues in creating a shared understanding. By comparing and contrasting how two different, yet intertwined, maps were made this paper focuses on how the sociotechnical acts of alignment necessary for the production of the maps change how risk, threat and uncertainty are approached. One mapmaking practice pulled the actors into a more centralized alignment producing risks related to managing authority and security. The other provided a more distributed collaboration and produced risks related to public trust and consistency. This paper argues that mapmaking is characterized as a messy, distributed network of knowledge production in which the meaning of risk emerges through the collaborations that evolve in making sense of the wildfires, not as an a priori definition.

An individual-based model of chaparral vegetation response to frequent wildfires

The Santa Monica Mountains are home to many species of chaparral shrubs that provide vegetative cover and whose deep roots contribute to the stability of the steep slopes. Recently, native chaparral have been threatened by an unprecedented drought and frequent wildfires in Southern California. Besides the damage from the wildfires themselves, there is the potential for subsequent structural losses due to erosion and landslides. In this paper, we develop a mathematical model that predicts the impact of drought and frequent wildfires on chaparral plant community structure. We begin by classifying chaparral into two life history types based on their response to wildfires. Nonsprouters are completely killed by a fire, but their seeds germinate in response to fire cues. Facultative sprouters survive by resprouting but also rely on seed germination for post-fire recovery. The individual-based model presented here simulates the growth, seed dispersal, and resprouting behavior of individual shrubs across two life history types as they compete for space and resources in a rectangular domain. The model also incorporates varying annual rainfall and fire frequency as well as the competition between plants for scarce resources. The parameters were fit using seedling and resprout survivorship data as well as point quarter sampling data from 1986 to 2014 at a biological preserve within the natural landscape of the Malibu campus of Pepperdine University. The simulations from our model reproduce the change in plant community structure at our study site which includes the local extinction of the nonsprouter Ceanothus megacarpus due to shortened fire return intervals. Our simulations predict that a combination of extreme drought and frequent wildfires will drastically reduce the overall density of chaparral, increasing the likelihood of invasion by highly flammable exotic grasses. The simulations further predict that the majority of surviving shrubs will be facultative sprouting species such as Malosma laurina.

The importance of building construction materials relative to other factors affecting structure survival during wildfire

Structure loss to wildfire is a serious problem in wildland-urban interface areas across the world. Laboratory experiments suggest that fire-resistant building construction and design could be important for reducing structure destruction, but these need to be evaluated under real wildfire conditions, especially relative to other factors. Using empirical data from destroyed and surviving structures from large wildfires in southern California, we evaluated the relative importance of building construction and structure age compared to other local and landscape-scale variables associated with structure survival. The local-scale analysis showed that window preparation was especially important but, in general, creating defensible space adjacent to the home was as important as building construction. At the landscape scale, structure density and structure age were the two most important factors affecting structure survival, but there was a significant interaction between them. That is, young structure age was most important in higher-density areas where structure survival overall was more likely. On the other hand, newer-construction structures were less likely to survive wildfires at lower density. Here, appropriate defensible space near the structure and accessibility to major roads were important factors. In conclusion, community safety is a multivariate problem that will require a comprehensive solution involving land use planning, fire-safe construction, and property maintenance.

Valuing morbidity effects of wildfire smoke exposure from the 2007 Southern California wildfires

This study estimated the economic costs associated with morbidity from the wildfires that occurred in 2007 in southern California. We used the excess number of hospital admissions and emergency department visits to quantify the morbidity effects and used medical costs to estimate the economic impact. With data from 187 hospital facilities and 140 emergency departments located in five counties in southern California, we found evidence of significant acute adverse health reactions to wildfire-smoke exposure. Specifically, we found approximately 80 excess respiratory-related hospital admissions, 26 excess acute cardiovascular-related hospital admissions, nearly 760 excess respiratory-related emergency department visits, and 38 excess acute cardiovascular-related emergency department visits. We estimated that the associated medical costs were over $3.4 million. Since these cost estimates do not consider costs related to other adverse health effects, such as mortality, symptoms that were not severe enough to warrant going to the emergency room or hospital, or the costs of avoiding exposure to wildfire smoke, our estimates do not reflect the full health-related costs of wildfire smoke exposure.

Effects of wildfire on stream algal abundance, community structure, and nutrient limitation

AbstractWe studied the effects of wildfires in southern California on stream algal communities and their responses to nutrient enrichment. The intensity of scouring floods, concentrations of nutrients during ensuing rains (4–13× increases depending on nutrient), and postflood sediment deposition were greater at sites in burned than in unburned basins (UN sites). Postfire storms reduced algal biomass >10× more at sites in burned than in unburned basins. After the fire and subsequent floods, algal biomass, particularly of Cladophora, was up to 5 (chlorophyll a) to 20 (macroalgal mass) times higher at sites in burned basins with burned riparian vegetation (BRB sites) than at sites in unburned basins (UN sites), but algal abundance at sites in burned basins with intact riparian vegetation (BRI sites) was as low as 10 to 30% of that recorded at UN sites. Two to 3 mo after the wet season, nutrient and algal levels were similar among sites in UN, BRI, and BRB basins. We used summer nutrient diffusing substrata (...

Identification of two distinct fire regimes in Southern California: implications for economic impact and future change

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California’s wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990–2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.