A QWRA considers several different components, each resolved spatially across the region, including:

• likelihood of a fire burning,

• the intensity of a fire if one should occur,

• the exposure of assets and resources based on their locations, and

• the susceptibility of those assets and resources

Data users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdf

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.

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.

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.

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.

Primary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

Additional information on FSim can be found in the following references:

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.

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. https://doi.org/10.2737/RDS-2016-0034-2 Please reference the PNW QWRA report (linked above) for more detailed information. Raster resolution is 30m. Data finalized 11/17/2022.

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. https://doi.org/10.3390/rs12142328