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Corporate data contained within this raster is intended for spatial display and analysis purposes. The purpose of the Pacific Northwest's (PNW) Quantitative Wildfire Risk Assessment (QWRA) is to provide foundational information about wildfire hazard across the Pacific Northwest region (which encompasses the states of Oregon and Washington). Such information supports both wildfire and vegetation management planning and provides a defensible, robust suite of analytics that are used by numerous partners across Oregon and Washington. The modeling provides spatially resolved information about wildfire likelihood and intensity across the region. The fire hazard data used captures disturbance, fuel treatments, and fire history through calendar year 2024. HVRAs, relative importance, and response function are unchanged from the 2023 QWRA. Data finalized and released 10/31/2025.
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| summary:
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Corporate data contained within this raster is intended for spatial display and analysis purposes. The purpose of the Pacific Northwest's (PNW) Quantitative Wildfire Risk Assessment (QWRA) is to provide foundational information about wildfire hazard across the Pacific Northwest region (which encompasses the states of Oregon and Washington). Such information supports both wildfire and vegetation management planning and provides a defensible, robust suite of analytics that are used by numerous partners across Oregon and Washington. The modeling provides spatially resolved information about wildfire likelihood and intensity across the region. The fire hazard data used captures disturbance, fuel treatments, and fire history through calendar year 2024. HVRAs, relative importance, and response function are unchanged from the 2023 QWRA. Data finalized and released 10/31/2025.
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| accessInformation:
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This assessment was completed by Oregon State University in collaboration with the Oregon Department of Forestry, Washington State Department of Natural Resources, the U.S. Bureau of Land Management and the U.S. Forest Service.
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| description:
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<div style='text-align:Left;'><div><div><div style='font-size:12pt'><p><span>A QWRA considers several different components, each resolved spatially across the region, including:</span></p><ul><li><p><span>likelihood of a fire burning, </span></p></li><li><p><span>the intensity of a fire if one should occur,</span></p></li><li><p><span>the exposure of assets and resources based on their locations, and </span></p></li><li><p><span>the susceptibility of those assets and resources</span></p></li></ul><p><span /></p><p><span>Data users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: </span><a href='https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdf' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdf</span></span></a></p><p><span /></p><p><span>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 generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events (Finney et al., 2011). FSim generates stochastic simulation data based on many thousands of iterations, then integrates those iterations into a probabilistic result. An FSim iteration spans one entire year.</span></p><p><span>These FSim model results were completed on the 2024 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2024 and includes all historical fuel disturbances through 2024. This simulation is calibrated to the 2024 trend in wildfire occurrence.</span></p><p><span>This dataset is a 30-m cell size raster representing annual burn probability (BP) across the analysis area. BP is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified time period (1 year). Estimates of BP were generated with the large-wildfire simulation system, FSim. FSim’s stochastic simulation approach can be computationally intensive and therefore, time constraining on large landscapes. Simulations were modeled at 120-m resolution and upsampled to 30m using iterative spatial smoothing. Please reference the PNW QWRA 2023 report (linked above) for more detailed information regarding the smoothing methodology.</span></p><p><span>BP could be used in a wide range of planning applications where understanding the likelihood of wildfire occurrence is important. For example, the BP raster could be used to prioritize fuel treatments in areas where they would most likely be impacted by wildfire or in allocating protection resources to fire districts most likely to have large fire occurrence.</span></p><p><span /></p><p><span style='font-weight:bold;'>Primary Data Contact:</span><span> Ian Rickert, Regional Fire Planner, Forest Service R6/R10, </span><a href='mailto:ian.rickert@usda.gov' style='text-decoration:underline;'><span>ian.rickert@usda.gov</span></a></p><p><span /></p><p><span>Additional information on FSim can be found in the following references:</span></p><p><span>Finney, Mark A.; McHugh, Charles W.; Grenfell, Isaac C.; Riley, Karin L.; Short, Karen C. 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stochastic Environmental Research and Risk Assessment. 25: 973-1000.</span></p><p><span>Short, Karen C.; Finney, Mark A.; Vogler, Kevin C.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2020. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. </span><a href='https://doi.org/10.2737/RDS-2016-0034-2' style='text-decoration:underline;'><span>https://doi.org/10.2737/RDS-2016-0034-2</span></a><span> Please reference the PNW QWRA report (linked above) for more detailed information. Raster resolution is 30m. Data finalized 11/17/2022.</span></p><p><span>Ketchum, D., Jencso, K., Maneta, M.P., Melton, F., Jones, M.O., Huntington, J., 2020. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing 12, 2328. </span><a href='https://doi.org/10.3390/rs12142328' style='text-decoration:underline;'><span>https://doi.org/10.3390/rs12142328</span></a></p></div></div></div></div>
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<div style='text-align:Left;'><div><div><p style='margin:0 0 14 0;'><span><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 user is responsible to verify the limitations of the geospatial data and to use the data accordingly. As a work of the United States Government, these data are within the public domain of the United States.</span></span></p><p style='margin:0 0 14 0;'><span><span>Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at </span></span><a href='https://creativecommons.org/public-domain/cc0/' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>https://creativecommons.org/public-domain/cc0/</span></span></a><span><span>).</span></span></p><p style='font-weight:bold;margin:0 0 14 0;'><span><span>Non-Discrimination Statement</span></span></p><p style='margin:0 0 14 0;'><span><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></span></p><p style='margin:0 0 14 0;'><span><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></span></p><p style='margin:0 0 14 0;'><span><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at </span></span><a href='https://www.usda.gov/oascr/how-to-file-a-program-discrimination-complaint' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>How to File a Program Discrimination Complaint</span></span></a><span><span> 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: </span></span><a href='mailto:program.intake@usda.gov' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>program.intake@usda.gov</span></span></a><span><span>.</span></span></p><p style='margin:0 0 14 0;'><span><span>USDA is an equal opportunity provider, employer, and lender.</span></span></p><div style='font-size:12pt'><p><span>Oregon State University 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></div></div></div></div>
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USFS_NWCC_PNW_30m_2025_BurnProbability
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Image Service
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["Image Service","Region 6","R6","United States Wildland Fire Service","USWFS","U.S. Forest Service","USFS","Bureau of Land Management","BLM","Bureau of Indian Affairs","BIA","Tribal","Tribes","US Fish and Wildlife Service","USFWS","Washington Department of Natural Resources","WADNR","Oregon Department of Forestry","ODF","Oregon State Fire Marshall","OSFM","private","Oregon","OR","National Interagency Fire Center","NIFC","Washington","WA","wildfire risk","quantitative wildfire risk assessment","QWRA","highly valued resources and assets","HVRA","Pacific Northwest","PNW"]
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en-US
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USFS_NWCC_PNW_30m_2025_BurnProbability
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