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).
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This dataset represents the mean conditional flame length. For each pixel, it was calculated as the sum product of all FIL rasters and the midpoint flame length of each FIL class. For FIL6 we used a midpoint flame length of 100 feet to represent torching trees.Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability gt 12 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of greater than 12 foot flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability 8-12 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of 8 - 12 feet flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability 6-8 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of 6 - 8 feet flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability 4-6 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of 4 - 6 feet flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability 2-4 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of 2 - 4 feet flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Name: CC_2022-Fire-Effects Flame Length Probability 0-2 ft
Display Field:
Type: Raster Layer
Geometry Type: null
Description: WildEST – Wildfire Exposure Simulation ToolWildEST is a scripted geospatial process used for simulating potential fire behavior characteristics that addresses two shortcomings of current methods. WildEST performs multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations.Most WildEST results apply to the head of the fire. For fire-effects calculations, WildEST also generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire. These “NVC” FLPs are analogous to FLP rasters produced by FSim. These WildEST 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. WildEST results were modified for risk calculations in the QWRA using an irrigated agriculture mask to assign FLPs to pixels which are likely to be irrigated during fire season. 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. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. This fire-effects flame-length probability (FLP) dataset represents the probability of 0 - 2 ft. flame lengths for a given pixel in the fuelscape, incorporating both heading and non-heading spread directions. Fire-effects flame-length probability rasters are used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Probability is calculated using non-heading fire behavior and is weighted according to WTP, incorporating both temporal frequencies and the influence of high-spread conditions. 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).
Description: The 2022-capable fuelscape reflects current fuelscape conditions for the year 2022 and includes historical fuel disturbances from 2012-2021, inclusively. The fuelscape consists of geospatial data layers representing surface fuel model, canopy cover, canopy height, canopy base height, and canopy bulk density developed from LANDFIRE Remap 2016 30-m raster data and edited based on feedback from interagency fuels and fire staff across Oregon and Washington.Topography characteristics were captured using topography data (slope, aspect, and elevation) from the LANDFIRE 2020 (LF2.2.0). Note: A ‘greeness’ mask was used to adjsut fuels marked as burnable by LANDFIRE to a nonburnable fuel model. These areas were shown to be irrigated throughout the fire season. Please reference the PNRA 2022 report for more detaild information.This Fire Behavior Fuel Model 40 dataset may be used as an input for fire modeling purposes. Raster resolution is 30m. Data finalized 8/29/2022.
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).