In seasonally dry environments, the amount of water held in living plant tissue—live fuel moisture (LFM)—is central to vegetation flammability. LFM-driven changes in wildfire size and frequency are particularly important throughout southern California shrublands, which typically produce intense, rapidly spreading wildfires. However, the relationship between spatiotemporal variation in LFM and resulting long-term regional patterns in wildfire size and frequency within these shrublands is less understood. In this study, we demonstrated a novel method for forecasting the LFM of a critical fuel component throughout southern California chaparral, Adenostema fasciculatum (chamise) using gridded climate data. We then leveraged these forecasts to evaluate the historical relationships of LFM to wildfire size and frequency across chamise-dominant California shrublands. We determined that chamise LFM is strongly associated with fire extent, size, and frequency throughout southern California shrublands, and that LFM–wildfire relationships exhibit different thresholds across three distinct LFM domains. Additionally, the cumulative burned area and number of fires increased dramatically when LFM fell below 62%. These results demonstrate that LFM mediates multiple aspects of regional wildfire dynamics, and can be predicted with sufficient accuracy to capture these dynamics. Furthermore, we identified three distinct LFM ‘domains’ that were characterized by different frequencies of ignition and spread. These domains are broadly consistent with the management thresholds currently used in identifying periods of fire danger. Methods

All live fuel moisture data used in the model calibration were drawn from the National Fuel Moisture Database (NFMD, https://www.wfas.net/nfmd/public/index.php (accessed on 9/1/2020)), and these consisted of 19,639 individual observations of chamise fuel moisture across 61 sites throughout California, spanning the years 1977 through 2017. Climate data used in this study were drawn from the California Basin Characterization Model v8 [28], and consisted of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) measurements through the years 1951–2016. This dataset represents a 270 m grid-based model of water balance calculations that incorporates not only climate inputs (through PRISM climate data [29]) but also solar radiation, topographic shading, and cloudiness, along with soil properties to estimate evapotranspiration [30]. Using these monthly values, we calculated the mean maximum temperature (TMX), mean actual evapotranspiration (AET), mean climatic water deficit (CWD), mean precipitation (PPT), and mean soil moisture storage (STR) at 1, 6, and 12 month periods with lags of 1, 2, 3, 4, 5, and 6 months. Fire history data were drawn from FRAP fire perimeter data [31], which incorporate the perimeters of all known fires from 1878 through 2017. Vegetation data used to identify chamise vegetation in this study were drawn from both CALFIRE FRAP FVEG data [32] and the LANDFIRE 2016 Existing Vegetation Type (EVT) dataset [33]. Data Preparation Observations of LFM were merged with data recording the latitude and longitude of each site and then filtered to exclude those observations not pertaining to chamise. As an LFM below 50 can represent dead material on the sampled shrubs, observed in situ estimates of LFM below 50% (which were exceedingly rare) were also excluded. Because LFM within each site was often recorded at inconsistent intervals that did not align with the monthly climate data used in this study, and many sites incorporated observations from multiple individual plants (the number of which also varied over time), we then calculated a single mean LFM within each month and site in which observations were present. In order to reduce the computational load to a manageable scale, all climate data were rescaled to 1 km pixels through spatial averaging, conducted using Rasterio in Python v3.7 [34]. Six-month and twelve-month mean TMX and total PPT, AET, CWD, and STR were then extracted at monthly timesteps using python v3.7. Predicting Live Fuel Moisture across California The most relevant climate parameters, lags, and window durations were identified by regressing each LFM observation against the corresponding monthly climate parameters (including TMX, PPT, AET, CWD, and STR) with lags of 1 to 6 months, as well as against the six-month means of each parameter over the six months preceding each observation. Overall relationships between chamise LFM and local climate at monthly timescales were then modeled using a generalized additive model (GAM) framework. To minimize computational time while allowing for nonlinear relationships between local climate and LFM, a maximum of five smoothing terms was allowed for each climate parameter. In order to determine the ability of this modeling technique to predict LFM in both (a) novel locations and (b) months not present in the training data, model performance was assessed using multidimensional k-fold cross-validation. All data were divided by month and year into to one of five randomly assigned temporal groups of equal size, and all were similarly divided into five randomly assigned spatial groups. GAM models were then constructed iteratively, while holding out one temporal and one spatial group as a testing data set within each iteration. The ability of these models to successfully predict LFM at monthly timescales was evaluated by calculating the mean Pearson correlation coefficient between the predicted LFM at training sites and months not used in model development, and the observed mean monthly LFM recorded at those sites and months across all model iterations. In order to avoid unnecessary complexity within these models and to limit the computational requirements, only parameters of which the inclusion increased the mean Pearson correlation coefficient by 0.02 or more were excluded from the selected model. In order to incorporate as long a wildfire series as possible, LFM was predicted monthly from 1952 through 2017. Identifying Fires of Interest First, we identified those fires in which chamise was likely to represent a major component of the overall fuel by eliminating those fires in which <50% of the burned area was predicted to consist of either Southern California coastal scrub or dry mesic chaparral according to the FVEG land cover dataset produced by CALFIRE-FRAP. Similarly, we eliminated all fires in which <50% of the burned area was predicted to consist of either mixed chaparral, chamise-redshanks chaparral, or coastal scrub according to EVT vegetation maps. Because of concerns surrounding mismatches among vegetation types between FVEG land cover data and EVT vegetation maps, only those fire scars which met both of these sets of criteria were selected for further analysis. It should be noted that these vegetation maps were static over time and did not attempt to incorporate variation in vegetation cover that may have occurrred across the study period or immediately after disturbance events. However, annual assessments of vegetation cover throughout the study period were not available. Thus, although land cover may have fluctuated somewhat throughout the study period and immediately after fires or other disturbance events, these data nevertheless represented the best available data pertaining to the spatial distribution of chamise-dominated vegetation across California. To evaluate the relationship of chamise LFM to the mean fire size, frequency, and cumulative area burned across southern Californian forests, it was first necessary to measure the predicted (and observed) LFM within the area burned during each fire. In order to summarize the predicted LFM within each fire at the time of ignition based on the gridded LFM estimates produced in this study, the mean predicted LFM in the month and year in which the initial ignition occurred was calculated across the entirety of each fire scar. The resulting data included 1818 individual fires from the year 1952 through 2017. Identifying Critical Thresholds in LFM and Relationship to Burned Area To evaluate the relationship between LFM and fire, and to identify critical LFM thresholds associated with shifts in fire behavior, we first calculated the cumulative area burned with decreasing (simulated) LFM for all selected fire scars. As previous studies have shown that observed thresholds in LFM–wildfire relationships may be biased due to differences in the freequency with which different values of LFM occur over space and time [27], we converted these LFM values into percentile ranks based on the distribution of simulated LFM across the duration and spatial extent of this study. By carrying out this step, we corrected for any differences in the spatial or temporal frequency of LFM across the study area, which might otherwise bias the apparent relationships to cumulative area burnt. Using these percentile LFM values, we then conducted piecewise or ‘broken stick’ regression [35] in order to identify transition points in LFM that were associated with an increasing burned area. After identifying thresholds in LFM–wildfire relationships using LFM percentiles, these percentile ranks could then be converted back into actual LFM values in order to identify the transition points in LFM–cumulative burned area relationships. Identifying Critical Thresholds in LFM and Relationship to Mean Fire Size In order to determine whether the mean size of wildfires varied significantly with LFM, we similarly conducted piecewise analyses of the relationship between LFM and the mean size of all wildfires in which the predicted LFM (based on the mean LFM value across the burned area of each wildfire event) fell within a 5 percentile span (e.g., all fires in which LFM fell within the 5th to the 9.99th percentile). By evaluating mean fire size within set percentile ranges of

Chaparral and coastal sage scrub mapped near Santa Ana, California, by the Weislander Vegetation Type Map survey in 1940 matches a similar distribution of this shrubland vegetation mapped in 1887.

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires. Methods This dataset provides estimates of 30 m resolution burn severity, and emissions of CO2, CO, CH4, non-methane organic compounds (NMOC), SO2, NH3, NO, NO2, nitrogen oxides (NOx = NO + NO2), PM2.5, OC, and BC. WBSE was implemented for California large wildfires on a per-fire event scale since 1984 and also a daily scale since 2002. The inventory implementation steps, input datasets, and output data are summarized in figure 1 in Xu et al, 2022. Burn severity calculation Fire records for California from 1984 to 2019 were retrieved from MTBS (https://mtbs.gov/viewer/index.html) via interactive viewer on 8 May 2021, resulting in a dataset with a total of 1623 wildfires. We also acquired fire perimeters for 74 large wildfires in 2020 from CAL FIRE (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) and calculated dNBR for each 2020 fire using the dNBR calculation tool with Google Earth Engine (GEE). This process first selects either initial assessment or extended assessment for each fire. The initial assessment utilizes Landsat images acquired immediately after a fire to capture first-order fire effects. The extended assessment uses images obtained during the growing season following the fire to identify delayed first-order effects and dominant second-order effects (Eidenshink et al 2007). We utilized LANDFIRE Biophysical Settings (BPS) to determine which assessment type to apply for each fire burned in 2020. After Picotte et al (2021), we used extended assessment if the majority of general vegetation groups within the fire perimeter are forests, while initial assessment is used when the majority of general vegetation groups are grassland/shrubland. By contrast, MTBS uses extended assessment for forest and shrubland types. We did not delineate grasslands into burn severity categories. Instead, we classified them as burned ('grass burn') because of difficulties in assessing vegetation change. Post-fire images for extended assessment were selected during the next peak of the green season (June–September) using the mean compositing approach suggested by Parks et al (2018). Composite post-fire images acquired immediately within two months after the fire containment dates were used for the initial assessment. Composite pre-fire images for extended and initial assessments were acquired with the matching periods from the preceding year. The dNBR images were produced by quantifying the spectral difference between composite pre-fire and post-fire Landsat scenes. We calculated the unitless, continuous CBI variable from dNBR/NBR values using the linear and Sigmoid B regression models developed for the CONUS by Picotte et al (2021). CBI values were then classified following thresholds modified based on Crotteau et al(2014) into six severity classes: unburned, low severity, moderate severity, high severity, grass burn, and non-processing area. Emissions calculation Emissions of all species are calculated as a function of area burned, fuel loading, the fraction of vegetation burned based on burn severity, and an emissions factor specific to each vegetation type using the following equation modified from the FINN model (Wiedinmyer et al 2011). Fuel categories were assigned from LANDFIRE EVT products. For emissions calculations, EVT data were then categorized into five general vegetation categories: grass, shrub, forest under 5500 feet (1676 m), forest between 5500–7500 feet (1676–2286 m), and forest above 7500 feet (2286 m), updated for California ecosystems. Fuel consumption was determined following Hurteau et el 2014 assigning fuel loading and consumption values for each severity class for the five general vegetation categories based on the First Order Fire Effects Model v5 (Reinhardt et al 1997). Emission factors for greenhouse gases, particulate matter, and reactive trace gases were updated with recent data for each general vegetation class using results from recent field campaigns and studies specific for California ecosystems and Western U.S. ecosystems. Day of burning and daily emissions To assign the day of burning for individual pixels, NASA fire information for resource management system (FIRMS) active fire products from MODIS (Collection 6) within 750 m of the fire perimeter shapefiles supplied by MTBS or CAL FIRE were selected for interpolation to account for detections that might be outside the boundary due to detection radius. VIIRS 375 m data, when available since 2012, was added to complement MODIS data with improved performance to assign burn dates using the fire progression raster tool (figure 4). We filtered the MODIS/VIIRS detection points to the date range of interest and created a 500 m buffer around each point. Points were then converted to circle polygons to represent each point's detection extent properly. The average date was selected as the proper date in regions of overlapping buffers. We then calculated daily emissions and assigned them to the centroids of the aggregated daily progression polygons.

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The California desert occupies the southeastern 27% of California (11,028,300 ha, 110,283 km2 or 27,251,610 ac). It includes two ecoregional provinces comprised of five desert regions (“ecological sections”; Miles and Goudy 1997). The American Semi-Desert and Desert Province (warm deserts) includes the Mojave Desert, Sonoran Desert, and Colorado Desert sections in the southern 83% of the California desert. The Intermountain Semi-Desert Province (cold deserts) includes the Southeastern Great Basin and Mono sections in the northern 17% of the region. Previous analyses of fire patterns across the California desert have used point occurrence data. Point occurrence data can have limitations because they can: (1) represent the containment area rather than actual fire area; (2) extend to include unburned areas as contiguous within the fire boundary; (3) be incomplete and estimated before the end of burning; and (4) be reported only in public agency boundaries. Point data also often contain errors associated with the initial recording, or subsequent transcription from paper to electronic records, of the point of origin of a fire. Point datasets also can contain redundancies, such as the same fire being reported by multiple responding agencies that can affect derived statistics such as fire area. Additionally, because points are one dimensional, the area they conceptually represent cannot be readily parsed using other spatial data (e.g. by desert regions and/or ecological zones). More accurate, detailed, and spatially-explicit fire data are available using Landsat satellite imagery from the Monitoring Trends in Burn Severity (MTBS) program. We used these data to precisely document fire area (area within fire perimeters) for fires ≥405 ha (1,000 ac) between 1984 and 2013 in the California desert (www.mtbs.gov; accessed 6/30/2015). Previous fire analyses have also stratified analyses by ecological zones derived from 4 Küchler potential vegetation types (barren, desert shrub, juniper-pinyon, sagebrush). That approach does not distinguish how the relative proportions of vegetation types comprising each ecological zone varies among California desert regions, or explain how the ecotones between the zones shift upslope with decreasing latitude moving from the cold deserts in the north to the warm deserts in the south. These limitations hinder their application to specific areas within the desert bioregion. We derived ecological zones derived from 43 LANDFIRE vegetation biophysical setting types, plus various non-wildland (e.g. developed urban/agriculture/roads) and non-burnable (e.g. open water/barren) areas (Rollins 2009). We also omitted from analyses non-wildland and non-burnable areas (2,003,148 ha [4,949,887 ac]), and focused instead on the remaining burnable wildland areas (9,025,152 ha [22,301,636 ac]). The 43 biophysical setting types were grouped into 13 general vegetation types, which were further grouped into four elevation-based ecological zones plus one riparian zone according to their constituent plant associations. The resulting 5 ecological zones were then intersected with the boundaries of the 5 desert regions of the California to create a map and associated burnable wildland area statistics. A diagram was also created illustrating the relative elevational positions of each ecological zone and vegetation type along a latitudinal gradient from cold deserts to warm deserts. These data were developed to assess the distribution of wildfire regimes across California deserts for the chapter "Southeast Deserts Bioregion" in the book "Fire in California's Ecosystems, Second Edition" published by University of California Press. Miles, S. R. and C. B. Goudy. 1997. Ecological subregions of California: section and subsection descriptions. USDA Forest Service, Pacific Southwest Region, R5-EM-TP-005, San Francisco, CA. Rollins Matthew G. (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18, 235-249.

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Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2023 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. “Estimating Wildfire Impacts on the Biomass of Southern California’s Chaparral Shrublands.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California's population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

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This map shows the percentage of energy produced by three types of natural gas power plants in each county - combined cycle, peaker, and aging plants. Data is for 2020. The table shows the raw data for the amount of energy produced and the fuel used by each plant type, as well as the number of each type of plant per county. The heat rates are also given.

This project was initiated to provide land managers with spatial information on the potential for recreation destinations to be closed or disrupted due to wildfire. Wildfires are a common occurrence in fire prone landscapes including much of southern California. Wildfires of any size can close national forests to the public for short durations due to safety concerns for forest visitors while the fire is active. However, larger, more destructive wildfires can lead to long-term recreation displacement by removing trail and campsite infrastructure, killing surrounding vegetation leading to safety concerns from falling trees, and increase the time to restore recreation opportunities. In this project, we create spatial data to show where the greatest risk of long-term recreation displacement due to wildfire is likely. We developed these recreation displacement likelihood datasets using two weather scenarios "dry" and "moderate". Each recreation displacement likelihood map was created using the following five spatial data inputs: canopy height, flame length probability, potential heat per unit area, burn probability, and potential fire severity. Canopy height was used as a measurement of vegetation type most likely to cause long-term disruption to recreation, that is fire-killed tall trees are more likely to disrupt recreation than shrubs or grass fuel types. Flame length probability and potential heat per unit area were used to measure fire intensity and amount of energy released from a fire. Burn probability conveys the likelihood of a fire occurring at a given location across the landscape. Potenial Fire severity indicates how damaging a fire would be if an ignition occurred. This data publication includes a separate geodatabase for dry and moderate weather conditions. Both of these geodatabases include 5 rasters: potential for fire to impact recreation, potential fire severity, burn probability, potential heat per unit area, and flame length probability. A geodatabase containing priority and non-priority trail, road, and place of interest vector data, which show where highly frequented locations overlap with the above-mentioned datasets, is also provided.The main goals of this project were to determine where wildfires are most likely to occur within the Angeles National Forest. Then, if a wildfire occurs what are the potential long-term impacts of burning to places of interest that are important to recreation.The recreation displacement likelihood datasets were developed using two weather scenarios "dry" and "moderate" following Scott and Burgan (2005).