Layer: CC_2022 - Burn probability (ID: 0)

Name: CC_2022 - Burn probability

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Description: 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.The FSim model was calibrated so that the annual number of large fires and annual large-fire area burned were within 10% of the predicted value for the year 2022 (or 2012 for one of the runs). The estimated values were determined using ordinary least-squares regression on the 1992-2020 FOD. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 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 PNRA_2022 report for more detailed information regarding the smoothing methodology.The smoothed BP values were modified to account for irrigated agriculture before calculating wildfire risk in the QWRA. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”), and data collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels as well as pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. All pixels in the irrigated agriculture mask were assigned a BP of 0.0001. 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.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 PNRA_2022 report 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

Copyright Text: Primary data contact: Ian Rickert (Regional Fire Management Planning Specialist) ian.rickert@usda.gov This dataset was developed for The USFS Pacific Northwest Region (R6) by Pyrologix LLC (www.pyrologix.com), and modified for use in the QWRA by Oregon State University, College of Forestry (andy.mcevoy@oregonstate.edu).

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