20250804 17205100 1.0 FALSE usfs_fireandaviation.usfs.USFS_EDW_RMRS_WRC_WildfireHazardPotential Server=iipp-arcgis-d-3.cmwoth4tqfhc.us-east-1.rds.amazonaws.com; Service=sde:postgresql:iipp-arcgis-d-3.cmwoth4tqfhc.us-east-1.rds.amazonaws.com; Database=usfs_fireandaviation; User=usfs; Version=sde.DEFAULT ArcSDE Connection -20037507.067200 20037507.842788 2136104.661300 11596994.661300 1 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 16 FALSE None 1 AMD TRUE continuous unsigned integer 100000 100000 1000 20 2048 20 75 0.01 10 600 300 Basic 0 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC Basic None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D -1 -1 0.8 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC None,Basic,Full NONE <_docversion_ Sync="TRUE">10 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) 307200 30 30720 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape) -1 1 1 0 0 0 0 0 0 20250812 20140500 20250819 12162800 150000000 5000 ISO19115_3 USDA Forest Service, Fire Modeling Institute (FMI) 406-329-4836 Missoula Fire Sciences Laboratory 5775 US Hwy 10 W Missoula MT 59808 US eva.c.karau@usda.gov This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS. 20250819 ArcGIS Metadata 1.0 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None TIFF 2020 Files zipped using the zipfile module in Python 2.7.16 (.ZIP file format version 6.3.6). (We recommend putting all *.zip files into a folder named Data before unzipping.) Georeferenced TIFF file. RPS, cRPS, Exposure Type, BP, CFL, FLEP4, FLEP8: 32 Bit floating point, WHP: 16 Bit unsigned integer; LZW compression; pyramids: levels 5, Nearest Neighbor resampling https://doi.org/10.2737/RDS-2020-0016-2 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None PDF see Format Specification Portable Document Format file https://doi.org/10.2737/RDS-2020-0016-2 https://doi.org/10.2737/RDS-2020-0016-2 USFS_EDW_RMRS_WRC_WildfireHazardPotential 2024 2nd Scott, Joe H. Dillon, Gregory K. Jaffe, Melissa R. Vogler, Kevin C. Olszewski, Julia H. Callahan, Michael N. Karau, Eva C. Lazarz, Mitchell T. Short, Karen C. Riley, Karin L. Finney, Mark A. Grenfell, Isaac C. Forest Service Research Data Archive Fort Collins, CO raster digital data <div style='text-align:Left;'><div><div><p><span>The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape. </span></p><p><span>National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download. </span></p><p><span>The specific raster datasets in this publication include: </span></p><p><span>Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application. </span></p><p><span>Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application. </span></p><p><span>Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources. </span></p><p><span>Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application. </span></p><p><span>Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity. </span></p><p><span>Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity. </span></p><p><span>Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity. </span></p><p><span>Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.</span></p><p><span>Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2)</span></p><p><span>Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.</span></p></div></div></div> The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products. Funding for this project was provided by USDA Forest Service, Fire and Aviation Management; USDA Forest Service, Fire Modeling Institute, which is part of the Rocky Mountain Research Station, Fire, Fuel and Smoke Science Program; and USDA Forest Service through an ORISE agreement under the U.S. Department of Energy (DE-SC0014664). Work on dataset development was primarily completed by Pyrologix, LLC under contract with the USDA Forest Service, Fire Modeling Institute. Author Information: Joe H. Scott Pyrologix, LLC https://orcid.org/0009-0008-3246-1190 Gregory K. Dillon USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0009-0006-6304-650X Melissa R. Jaffe Pyrologix, LLC https://orcid.org/0009-0002-8623-407X Kevin C. Vogler Pyrologix, LLC https://orcid.org/0000-0002-7080-2557 Julia H. Olszewski USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0003-3205-7100 Michael N. Callahan Pyrologix, LLC https://orcid.org/0009-0009-4937-5405 Eva C. Karau USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0009-0009-6776-9387 Mitchell T. Lazarz USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0002-4558-4949 Karen C. Short USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0002-3383-0460 Karin L. Riley USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0001-6593-5657 Mark A. Finney USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0002-6584-1754 Isaac C. Grenfell USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0002-3779-1681 USDA Forest Service, Fire Modeling Institute (FMI) 406-329-4836 Missoula Fire Sciences Laboratory 5775 US Hwy 10 W Missoula MT 59808 US eva.c.karau@usda.gov This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS. United States conterminous United States CONUS Alaska Hawaii ISO 19115 Topic Category environment geoscientificInformation society structure National Research & Development Taxonomy Ecology, Ecosystems, & Environment Fire Fire detection Fire ecology Fire effects on environment Fire suppression, pre-suppression Prescribed fire Environment and People Forest management Landscape management burn probability hazard fuels management fire suppression fire likelihood fire planning risk assessment wildfire hazard potential environment geoscientificInformation society structure Ecology Ecosystems & Environment Fire Fire detection Fire ecology Fire effects on environment Fire suppression pre-suppression Prescribed fire Environment and People Forest management Landscape management burn probability hazard fuels management fire suppression fire likelihood fire planning risk assessment wildfire hazard potential United States conterminous United States CONUS Alaska Hawaii <div style='text-align:Left;'><div><div><p><span>These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation: </span></p><p><span>Scott, Joe H.; Dillon, Gregory K.; Jaffe, Melissa R.; Vogler, Kevin C.; Olszewski, Julia H.; Callahan, Michael N.; Karau, Eva C.; Lazarz, Mitchell T.; Short, Karen C.; Riley, Karin L.; Finney, Mark A.; Grenfell, Isaac C. 2024. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0016-2</span></p><p><span>The datasets presented here are the product of modeling, and as such carry an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. No warranty is made by the Originator as to the accuracy, reliability, or completeness of these data for individual use or aggregate use with other data, or for purposes not intended by the Originator. These datasets are intended to provide nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. Data included here are not intended to replace locally-calibrated state, regional, or local risk assessments where they exist. It is the responsibility of the user to be familiar with the value, assumptions, and limitations of these national data publications. Managers and planners must evaluate these data according to the scale and requirements specific to their needs. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification.</span></p><p><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. </span></p><p><span>Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/). </span></p><p><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. </span></p><p><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English. </span></p><p><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. </span></p><p><span>USDA is an equal opportunity provider, employer, and lender. </span></p></div></div></div> Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials. The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States 2020 1st Scott, Joe H. Gilbertson-Day, Julie W. Moran, Christopher Dillon, Gregory K. Short, Karen C. Vogler, Kevin C. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0016 Spatial datasets of probabilistic wildfire risk components for the United States (270m) 2023 3rd Dillon, Gregory K. Scott, Joe H. Jaffe, Melissa R. Olszewski, Julia H. Vogler, Kevin C. Finney, Mark A. Short, Karen C. Riley, Karin L. Grenfell, Isaac C. Jolly, W. Matthew Brittain, Stuart Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2016-0034-3 Wildfire Risk to Communities: Community Wildfire Risk Reduction Zones for the United States 2024 Dillon, Gregory K. Lazarz, Mitchell T. Karau, Eva C. Story, Scott J. Pohl, Kelly A. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2024-0030 Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States 2024 2nd Jaffe, Melissa R. Scott, Joe H. Callahan, Michael N. Dillon, Gregory K. Karau, Eva C. Lazarz, Mitchell T. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0060-2 A deterministic method for generating flame-length probabilities 2020 Scott, Joe H. Department of Agriculture, Forest Service, Rocky Mountain Research Station Fort Collins, CO conference proceedings p. 195-205 Proceedings of the fire continuum-preparing for the future of wildland fire https://www.fs.usda.gov/research/treesearch/62336 Wildfire Hazard Potential for the United States (270-m), version 2023 2023 4th Dillon, Gregory K Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2015-0047-4 Wildland fire potential: A tool for assessing wildfire risk and fuels management needs 2015 Dillon, Gregory K. Menakis, James Fay, Frank U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Fort Collins, CO conference proceedings p. 60-76 https://www.fs.usda.gov/treesearch/pubs/49429 true -180.00000 -67.93318 63.90442 18.85415 Ground condition 2020-12-31 2022-12-31 See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S. This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. There are two companion data publications that are part of the WRC 2.0 data update: one that includes datasets of wildfire hazard and risk for populated areas of the nation, where housing units are currently present (Jaffe et al. 2024, https://doi.org/10.2737/RDS-2020-0060-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030). Microsoft Windows Server 2016 Technical Preview Version 10.0 (Build 17763) ; Esri ArcGIS 13.5.2.57366 1 -179.999989 179.999996 71.561341 18.839976 The datasets described here are derived from wildfire simulation modeling, and their exact accuracy cannot be measured. They are intended to be relative measures of wildfire risk for planning purposes. The FSim burn probability dataset was objectively evaluated and calibrated against historical wildfire occurrence statistics within 136 distinct regions of contemporary wildfire activity (pyromes) across the United States (Short et al. 2020). See Dillon et al. (2023) for a more detailed description of FSim calibration. Some LANDFIRE fuels and vegetation data used as inputs have also been evaluated for efficacy and calibrated to meet the objectives of LANDFIRE. More information can be found at: https://www.landfire.gov/lf_evaluation.php. No explicit evaluation or calibration is possible for the WildEST (FlamMap-based) intensity datasets, however the LANDFIRE FBFM is an important input which has been evaluated, and the WildEST modeling system is rooted in established fire behavior models and methods (see Finney 2006, and Scott 2020). As such, modeled intensity is considered a robust characterization as input to the flame length probability products. Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3 Finney, Mark A. 2006. An overview of FlamMap fire modeling capabilities. In: Andrews, Patricia L.; Butler, Bret W., comps. 2006. Fuels management-how to measure success: conference proceedings. 28-30 March 2006 in Portland, OR. Proceedings. RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 213-220. https://www.fs.usda.gov/research/treesearch/25948 Scott, Joe H. 2020. A deterministic method for generating flame-length probabilities. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, eds. 2020. Proceedings of the fire continuum-preparing for the future of wildland fire. 2018 May 21-24 in Missoula, MT. Proceedings. RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 195-205. https://www.fs.usda.gov/research/treesearch/62336 Short, Karen C.; Grenfell, Isaac C.; Riley, Karin L.; Vogler, Kevin C. 2020. Pyromes of the conterminous United States. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0020 Quantitative accuracy cannot be evaluated. Unknown Pixel values in these Wildfire Risk to Communities datasets should be within the following ranges: Risk to Potential Structures (RPS): Floating point values between 0 and 12.3. Conditional Risk to Potential Structures (cRPS): Floating point values between 0 and 100. Exposure Type: Floating point values between 0 and 1. Burn Probability (BP): Floating point values between 0 and 0.13. Conditional Flame Length (CFL): Floating point values between 0 and 408.2. Flame Length Exceedance Probability – 4 ft (FLEP4): Floating point values between 0 and 1. Flame Length Exceedance Probability – 8 ft (FLEP8): Floating point values between 0 and 1. Wildfire Hazard Potential (WHP): Integer values between 0 and 99853. All pixels that are part of the land and water of the United States have valid non-negative values. The Wildfire Risk to Communities (WRC) datasets are based on wildfire simulation modeling. Given the relatively short time available for analysis and production of the WRC datasets, the methods for this project were designed to leverage the existing national wildfire simulation data from Dillon et al. (2023) without further local calibration or modeling work, although one minor edit to the off-the-shelf fuel data was required to calibrate fire occurrence in northern Minnesota. To make the WRC data most useful to communities, we implemented a process to downscale the national burn probability data from their native 270-m cell size to the native 30-m resolution of the nationally available LANDFIRE fuels and vegetation data. Through this process, we also used geospatial smoothing techniques to account for wildfire hazard in parts of communities adjacent to wildland vegetation that may have indirect exposure to wildland fire. The overall process is described in the process steps below. NOTE: Dataset-specific FGDC-CSDGM metadata are provided with each TIFF file. Additional details regarding the process steps can be found in \Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf. 2023 FSim BP (Dillon et al. 2023) 1. Downscale the nationally-available FSim Burn Probability (BP) data to 30-m resolution using a raster upsampling process. The first step in this process was to fill in the 270-m cells that resulted in a BP of zero (regardless of whether or not the pixels were burnable) with the mean of the non-zero cells immediately surrounding them. This was done by setting zero-BP cells to NoData, then running two low-pass filters over the 270-m raster. The remaining NoData values were then reverted back to zero, and the resulting raster was resampled to 30-m using cubic convolution, which does some interpolation among the 30-m cells within each 270-m cell. A 30-m processing mask was used after the resampling so that the BP was set to NoData for open water and snow/ice land covers (based on the LANDFIRE 2.2.0 FBFM40 dataset) and any BP values less than zero produced in the cubic convolution set back to zero. The second step in creating the 30-m BP was to identify and set aside nonzero BP values from isolated blocks of burnable fuel less than 500 ha in size. This was accomplished by identifying contiguous patches of burnable fuel using the ArcGIS Region Group tool on the 30-m, burnable fuel grid (LANDFIRE 2.2.0 FBFM40). After isolating the small islands of burnable fuel surrounded by nonburnable, the nonzero BP values within the islands were temporarily set to NoData to prevent burn probability from being spread well into nonburnable areas from these small islands. 2023 FSim BP; LANDFIRE FBFM40; FPA FOD 2. Spatially smooth BP and allow for non-zero values in otherwise nonburnable areas to mimic the effects of wildfire penetration into developed housing areas. To be consistent with existing definitions of Wildland Urban Interface (and with the distance of observed spread during urban conflagrations), the 30-m resampled BP results were expanded into adjacent nonburnable areas by setting NoData back to zero (except in open water, snow and ice, and small burnable islands), then performing three iterative 510-m moving-window means. BP was not allowed to spread into open water or snow and ice land covers, but it was allowed into bare ground and agriculture land covers as well as developed urban areas. This method results in BP values that rapidly diminish with increasing distance into nonburnable areas. The total distance BP values are spread into nonburnable areas is 1530 m (approximately 1 mile) from the three iterative focal mean operations. 2023 FSim BP; LANDFIRE FBFM40; FPA FOD 3. Simulate wildfire intensity characteristics using WildEST, a custom implementation of USDA Forest Service FlamMap modeling system. This process produces landscape-scale spatial data representing flame-front characteristics at 30-m spatial resolution. It incorporates the USDA Forest Service WindNinja model to adapt the general wind speed and wind direction to reflect the influence of topography, makes use of the dead fuel moisture conditioning feature of FlamMap, and calculates intensity for a fire burning in the direction of maximum spread (headfire) for the full range of 216 weather conditions. Finally, the relative frequency of each weather type gives weight to each FlamMap run. This process is outlined in Scott (2020). Scott, Joe H. 2020. A deterministic method for generating flame-length probabilities. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, eds. 2020. Proceedings of the fire continuum-preparing for the future of wildland fire. 2018 May 21-24 in Missoula, MT. Proceedings. RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 195-205. https://www.fs.usda.gov/research/treesearch/62336 2023 LANDFIRE FBFM40; WildEST FLPs (Finney 2006) 4. Calculate Conditional Flame Length by executing FlamMap 216 times, using the WildEST utility. This process produced flame-length rasters reflecting a range of weather types – combinations of wind speed, wind direction and moisture content scenario. These 216 flame-length rasters were combined into a weighted mean as the sum-product of flame-length and weather-type probability across all weather types. Conditional Flame Length was not oozed into developed areas. 2023 WildEST FLPs 5. Calculate the Conditional Risk to Potential Structures (cRPS) raster at 30-m by applying one of three response functions representing the relative effect of wildfire on structures (i.e., relative degree of damage or loss) at different intensities to the 30-m flame-length probability (FLP) rasters produced by the WildEST utility. cRPS is essentially conditional Net Value Change (Scott and Thompson 2015; Scott et al. 2013) if each pixel were to have a house as the only high-valued resource or asset, and a consistent response function for the effect of wildfire on a structure at each of six intensity classes is applied at each pixel. Response functions were developed for three lifeforms separately: grass/herbaceous, shrub, and tree and reflect the assumption that consequence is greatest in tree fuels, lower in shrubs, and lowest in grass fuels, across all intensity levels. A value of 0 means no damage to a structure, and a value of -100 means complete loss. The response function values used were: 25, 40, 55, 70, 85, 100 for FLP1 - FLP6 respectively, for tree life form; 20, 35, 50, 65, 80, 95 for FLP1 – FLP6 respectively for shrub life form; and 10, 25, 40, 55, 70, 85 for FLP1 - FLP6 respectively, for grass/herbaceous life form. The response functions were applied to all pixels across the landscape, even if no structures were present, as follows: FLP1 * 25, FLP2 * 40, etc. Then cRPS was calculated by adding the resulting products across all FLPs. Finally, the cRPS was generated using a modified version of the oozing approach described for BP, using three iterative 510-m moving window means, but without decaying the cRPS values. Scott, Joe H.; Thompson, Matthew P. 2015. Emerging concepts in wildfire risk assessment and management (Publ.). In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 196-206. https://www.fs.usda.gov/treesearch/pubs/49444 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. https://doi.org/10.2737/RMRS-GTR-315 2023 LANDFIRE FBFM40; LANDFIRE EVT; WildEST FLPs 6. Calculate Flame Length Exceedance Probability for 4-foot flames (FLEP4) at 30-m resolution from the 30-m flame-length probability (FLP) rasters produced by WildEST. FLEP4 is found by summing the flame length probabilities that represent flame lengths above 4 feet after accounting for spread and intensity in non-heading directions. FLEP4 was not oozed into developed areas. 2023 LANDFIRE FBFM40; WildEST FLPs 7. Calculate Flame Length Exceedance Probability for 8-foot flames (FLEP8) at 30-m resolution from the 30-m flame-length probability (FLP) rasters produced by WildEST. FLEP8 is found by summing the flame length probabilities that represent flame lengths above 8 feet after accounting for spread and intensity in non-heading directions. FLEP8 was not oozed into developed areas. 2023 LANDFIRE FBFM40; WildEST FLPs 9. Calculate Risk to Potential Structures (RPS) by multiplying the 30-m cRPS raster by the 30-m BP raster. 2023 FSim BP; LANDFIRE FBFM40; FPA FOD; WildEST FLPs 10. Generate the Exposure Type raster by applying the smoothing process described above for burn probability using the LANDFIRE 2.2.0 fuels data as the primary input. Assign a value of one to all burnable pixels and a value of 0 in all nonburnable pixels in the original 30-m resolution LANDFIRE data. Then apply the spatial smoothing used for BP (three iterative 510-m focal means) to spread values into otherwise non-burnable areas, using the same steps described above to handle small patches of burnable vegetation and other land cover types. if the underlying land cover is considered burnable in the LANDFIRE fuel, the Exposure Type is “direct” (pixel value of 1). The exposure type is “indirect” (pixel value between 0 and 1) if two conditions are met: 1) the land cover is nonburnable urban, agricultural, or bare ground, and 2) the smoothed BP > 0. Finally, the exposure type is “nonexposed” (pixel value of 0) if the underlying land cover is nonburnable and the upsampled BP = 0. 2023 FSim BP; LANDFIRE FBFM40; FPA FOD 11. Calculate Wildfire Hazard Potential (WHP) at 30-m resolution using methods established by Dillon et al. (2015) and Dillon (2023). a) Using the upsampled BP and WildEST FLPs (both described in other metadata documents in this volume), multiply BP by each FLP to get the actual probabilities of fire occurrence in each flame length class. b) Weight the probabilities in each flame length class by the potential hazard they represent and sum them to derive a measure of large wildfire potential. Weights used were: FLP1 and FLP2 - 1; FLP3 and FLP4 - 8; FLP5 - 25; FLP6 - 75. c) Create a separate surface of small wildfire potential based on ignition locations for fires smaller than 300 acres (generally not accounted for in FSim). d) Integrate the large wildfire potential created in process steps 1-2 with the small wildfire potential created in process step 3. This was done by weighting each according to its relative contribution to total wildfire potential, then adding the weighted values. e) Apply a set of resistance to control weights based on fireline construction rates in different fuel types. f) Convert WHP values to integers by multiplying by 10,000 and rounding to the nearest whole number (preserves four decimal places of precision). The final WHP raster is at 30-m resolution, but it was not "oozed" into developed areas. Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429 Dillon, Gregory K. 2023. Wildfire Hazard Potential for the United States (270-m), version 2023. 4th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2015-0047-4 FSim BP; FPA FOD; WildEST FLPs; LANDFIRE FBFM40; LANDFIRE EVT Burn probability (BP) was one of the primary spatial inputs to datasets presented here. BP was modeled with FSim and provided information about the overall probability of any 270-meter pixel experiencing a large fire of any intensity. The LANDFIRE fuels data used as input to FSim reflects fuel disturbances occurring through the end of 2020. Spatial dataset of probabilistic wildfire risk components for the United States (270m) FSim BP 2023 3rd Dillon, Gregory K. Scott, Joe H. Jaffe, Melissa R. Olszewski, Julia H. Vogler, Kevin C. Finney, Mark A. Short, Karen C. Riley, Karin L. Grenfell, Isaac C. Jolly, Matthew W. Brittain, Stuart Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2016-0034-3 Ground Condition 2020-12-31 The LANDFIRE Fire Behavior Fuel Models layer was a primary input to the FSim BP and FlamMap-based FLP datasets. It was also used as an input to the WHP mapping process. LANDFIRE 2.2.0 40 Scott and Burgan Fire Behavior Fuel Models layer LANDFIRE FBFM40 2022 2.2.0 U.S. Department of Agriculture, Forest Service U.S. Department of the Interior raster digital data Scott, Joe H.; Burgan, Robert E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 p. https://doi.org/10.2737/RMRS-GTR-153 https://www.landfire.gov/fuel.php Ground Condition 2020 The FPA point fire occurrence database (FPA FOD) was used in the process of creating the burn probability (BP). It was also used when producing Wildfire Hazard Potential to create a surface of small wildland fire potential. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014] FPA FOD 2022 6th Short, Karen C. Forest Service Research Data Archive Fort Collins, CO vector digital data Spatial wildfire occurrence additional information is available in: Short, Karen C. 2014. A spatial database of wildfires in the United States, 1992-2011. Earth Systems Science Data 6:1-27. https://doi.org/10.5194/essd-6-1-2014 and https://www.fs.usda.gov/research/treesearch/45689 https://doi.org/10.2737/RDS-2013-0009.6 Observed 1992 2020 The LANDFIRE EVT layer was used to spatially apply resistance to control weights to create the final WHP and to define covariates in the response function applied to RPS and cRPS. LANDFIRE 2020 Existing Vegetation Type layer LANDFIRE EVT 2022 2.2.0 U.S. Department of Agriculture, Forest Service U.S. Department of the Interior raster digital data Rollins, Matthew G. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18: 235-249. https://doi.org/10.1071/wf08088 https://landfire.gov/evt.php Ground Condition 2020 Flame Length Probabilities (FLPs) were generated with WildEST, a FlamMap-based fire characteristics modeling process (Finney 2006, Scott 2020). FLPs provided information about the conditional probability of particular fire intensity levels (i.e., likelihood of a particular intensity level, given a fire) for every 30-meter pixel. The fuels used for modeling intensity reflect conditions as of the end of 2022. An overview of FlamMap modeling capabilities WildEST FLPs 2006 Finney, Mark A. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Fort Collins, CO conference proceedings p. 213-220 Fuels management-how to measure success: conference proceedings https://www.fs.usda.gov/research/treesearch/25948 Ground Condition 2022-12-31 Below is a description of the files included in this data publication. DATA FILES (8 × 51) Georeferenced TIFF files are provided for the each of the following spatial extents: continental U.S., the District of Columbia, and each U.S. state ([EXTENT] = CONUS, District of Columbia, Alabama, Alaska, Arizona, Arkansas, California, ..., Wisconsin, and Wyoming). For each extent there are eight different raster datasets (each of which is available as a separate downloadable zip file): 1. \Data\BP_[EXTENT].tif: Continuous values of annual burn probability with a 30 m pixel size. Referred to in the Wildfire Risk to Communities web application as Wildfire Likelihood. Values for the United States are between 0 and 0.14. 2. \Data\CFL_[EXTENT].tif: Continuous values of Conditional Flame Length (CFL) in feet with a 30 m pixel size. This is the mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity. Values for the United States are between 0 and 861.7. 3. \Data\cRPS_[EXTENT].tif: Continuous values of Conditional Risk to Potential Structures with a 30 m pixel size. Referred to in the Wildfire Risk to Communities web application as Wildfire Consequence. This is essentially the conditional Net Value Change if each pixel were to have a house as the only highly-valued resource or asset, and a consistent response function for the effect of wildfire on a structure at each of six intensity classes is applied at each pixel. Values for the United States are between 0 and 100. 4. \Data\Exposure_[EXTENT].tif: Continuous values of exposure type that depict the type of wildfire exposure a housing unit would experience with a 30 m pixel size. Referred to in the Wildfire Risk to Communities web application as Exposure Type. A value of 1 is "direct" exposure. Values between 0 and 1 represent "indirect" exposure, with higher values representing closer proximity to directly exposed areas (i.e., areas of burnable wildland vegetation). A value of 0 represents "nonexposed" areas that have nonburnable land cover and are more than 1530 m (approx. 1 mile) from burnable wildland vegetation. 5. \Data\FLEP4_[EXTENT].tif: Continuous values of Flame Length Exceedance Probability for flames greater than 4 feet with a 30 m pixel size. Values represent the probability of having flame lengths greater than 4 feet if a fire occurs. Probability values between 0 and 1. 6. \Data\FLEP8_[EXTENT].tif: Continuous values of Flame Length Exceedance Probability for flames greater than 8 feet with a 30 m pixel size. Values represent the probability of having flame lengths greater than 8 feet if a fire occurs. Probability values between 0 and 1. 7. \Data\RPS_[EXTENT].tif: Continuous values of Risk to Potential Structures with a 30 m pixel size. Referred to in the Wildfire Risk to Communities web application as Risk to Homes. This is essentially the expected Net Value Change if each pixel were to have a house as the only highly-valued resource or asset, and a consistent response function for the effect of wildfire on a structure at each of six intensity classes is applied at each pixel. Values for the United States are between 0 and 13.2. 8. \Data\WHP_[EXTENT].tif: Continuous integer values of the Wildfire Hazard Potential index with a 30 m pixel size. Values for the United States are between 0 and 99,853. (Associated OVR files are included and contain pyramids that allow the raster datasets to draw more quickly in GIS software. Associated XML files contain dataset-specific FGDC-CSDGM metadata containing a description of the content, quality, and other characteristics of the data.) SUPPLEMENTAL FILES (3) 1. \Supplements\WRC_V2_DataPercentiles.xlsx: Microsoft Excel Open XML spreadsheet (XLSX) file containing percentile values for the RPS, CRPS, and WHP raster datasets. Values are provided for each of these three datasets in one percentile increments for all of CONUS and for each state individually. Percentile thresholds are useful and appropriate for displaying these rasters by classes in GIS software. In the Wildfire Risk to Communities web application, the RPS layer (Risk to Homes) is displayed using class breakpoints at 40th, 70th, 90th, and 95th percentile. Standard class breaks for the WHP dataset are 44th, 67th, 84th, and 95th percentiles. 2. \Supplements\WRC_V2_Landscape-wideRisk_GISDataSymbology.pdf: Portable Document Format (PDF) file with suggested class definitions and colors for displaying the landscape-wide risk raster datasets in GIS software. 3. \Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf: PDF file containing detailed descriptions of the data products included in this publication and the methods used to create them. Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429 Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3 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. https://doi.org/10.2737/RMRS-GTR-315 EPSG 6.18.3(9.3.1.2) 2 1 0 -20037507.067200 2136104.661300 -20037507.067200 11596994.661300 20037507.842788 11596994.661300 20037507.842788 2136104.661300 0.387794 6866549.661300 1335834 29.999996 315363 30.000000 Band_1 65535.000000 1.000000 16 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