- Metric Name: Structure Exposure Score - Data Vintage: The data are current through 2022. - Unit Of Measure: Relative index, 10 classes This metric combines two data layers; one is the Wildland Urban Interface (WUI) as defined by Carlson et al. 2022 (see WUI definition section for more information), and a second data layer, Structure Exposure Score (SES), developed by Pyrologix LLC. The WUI includes the intermix and interface zones which collectively identify areas where structures occur and/or where structures are within a 1.5 miles wildland vegetation (see definition above) . The distance selected for the interface definition is based on research from the California Fire Alliance suggesting that this is the average distance firebrands can travel from an active wildfire front. Structure Exposure Score is an integrated rating of wildfire hazard that includes the likelihood of a wildfire reaching a given location along with the potential intensity and ember load when that occurs. SES varies considerably across the landscape. Pyrologix uses a standard geometric-interval classification to define the ten classes of SES, where each class break is 1.5 times larger than the previous break. So, homes located within Class X are 1.5 times more exposed than those in Class IX, and so on. This metric represents SES for WUI areas only. 1. 1 (SES I): 0 2. 2 (SES II): 0.01 to 50 3. 3 (SES III): 50 to 75 4. 4 (SES IV): 75 to 113 5. 5 (SES V): 113 to 169 6. 6 (SES VI): 169 to 253 7. 7 (SES VII): 253 to 380 8. 8 (SES VIII): 380 to 570 9. 9 (SES IX): 570 to 854 10. 10 (SES X): 854+ Creation Method: WUI: The current delineation of the WUI (Carlson et al. 2022) uses a mapping algorithm with definitions of the WUI; two classes of WUI were identified: - the intermix, where there is at least 50% vegetation cover surrounding buildings - the interface, where buildings are within 2.4 km (1.5 miles) of a patch of vegetation at least 5 km2 in size that contains at least 75% vegetation. Both classes required a minimum building density of 6.17 buildings per km2 (using a range of circular neighborhood sizes). Structure Exposure Score (SES): is a proprietary index representing the level of wildfire exposure for a structure (e.g., a home) if one were to exist on a given pixel. It is an integrated measure that includes three components: the likelihood of a wildfire of any intensity occurring in a given year (annual burn probability), potential wildfire intensity for a given pixel, and ember load to that pixel from surrounding vegetation. SES data was produced by Pyrologix LLC, a wildfire threat assessment research firm, as part of a spatial wildfire hazard assessment across all land ownerships for the state of California. The ongoing work generally follows the framework outlined in Scott and Thompson (2013), with custom methods and significant improvements developed by Pyrologix. The project generally consists of three components: fuelscape calibration and updates, wildfire hazard assessment, and risk assessment. It utilizes a combination of wildfire models and custom tools, including the FSim large wildfire simulator (Finney et al., 2011), and WildEST, a custom modeling tool developed by Pyrologix (Scott, 2020). To date, this work has resulted in a wide variety of spatial data layers related to wildfire hazard and risk, including Structure Exposure Score (SES), representing conditions prior to the 2020, 2021 and 2022 fire seasons. Work to date has been funded by the USDA Forest Service Region 5, the California Energy Commission, and the USDI Bureau of Land Management with data contributions from CAL FIRE. For this project, the FSim large-fire simulator is used to quantify annual wildfire likelihood across the analysis area. FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to make a spatially resolved estimate of the contemporary likelihood and intensity of wildfire across the landscape. WildEST (Wildfire Exposure Simulation Tool) is used to quantify wildfire intensity and ember loads across the analysis area. WildEST is a deterministic wildfire modeling tool developed by Pyrologix that integrates spatially continuous weather input variales, weighted based on how they will likely be realized on the landscape. This makes the deterministic intensity values developed with WildEST more robust for use in effects analysis than the stochastic intensity values developed with FSim. This is especially true in low wildfire occurrence areas where predicted intensity values from FSim are reliant on a very small sample size of potential weather variables. It also allows for more appropriate weighting of high-spread conditions into fire behavior calculations. WildEST also produces indices of conditional and expected ember production from vegetated areas (pixels) and load to other pixels in the analysis area. Please reference the Pyrologix 2021 project report (Volger et al., 2021) for more information on WildEST analysis. FSim was run for the CAL 2022 fuelscape at 120m resolution. WildEST was run for the CAL 2022 fuelscape at 30-m resolution. Both models utilized gridded hourly historical California weather data provided by CALFIRE. Results for annual burn probability (FSim), fire intensity (WildEST) and ember load (WildEST) were used to create Structure Exposure Score. - Credits: Pyrologix, LLC Primary data contact: James Newman (California State BLM Office) jnewman@blm.gov. This 2022 dataset is an update produced by Pyrologix (pyrologix.com) for the Bureau of Land Management (BLM) California State Office. The original 2020 dataset was developed by Pyrologix for the USFS Pacific Southwest Region. Scott, Joe H.; Thompson, Matthew P.; Calkin, David E. 2013. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p: Data disclaimer: The user must be aware of data conditions and must ultimately bear responsibility for the appropriate use of the information with respect to possible errors, possible omissions, map scale, data collection methodology, data currency, and other conditions specific to certain data. WUI, Carlson et al, 2022 Carlson, Amanda R., David P. Helmers, Todd J. Hawbaker, Miranda H. Mockrin, and Volker C. Radeloff. 2022. “The Wildland–Urban Interface in the United States Based on 125 Million Building Locations.” Ecological Applications e2597. https://doi. org/10.1002/eap.259: