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Service Description: This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales.
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
Pyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), 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 PNW QWRA 2023 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. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315.
Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). 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. FSim generates stochastic simulation data based on many thousands of iterations, then integrates those iterations into a probabilistic result. 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. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified time period (1 year).
The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Integrated Expected Net Value Change (ieNVC) represents risk integrated across eight HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing risk to multiple HVRAs.
Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values.
Citations:
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
Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315
Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-z
Integrated_Expected_Wildfire_Risk
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Description: This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. 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 resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), 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 PNW QWRA 2023 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. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). 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. FSim generates stochastic simulation data based on many thousands of iterations, then integrates those iterations into a probabilistic result. 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. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified time period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Integrated Expected Net Value Change (ieNVC) represents risk integrated across eight HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing risk to multiple HVRAs.Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations: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/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-z
Service Item Id: c3b0af7890a9453fa015ab5b623b46e7
Copyright Text: 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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Document Info:
Title: Integrated Expected Wildfire Risk (2023)
Author:
Comments: This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. 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 resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), 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 PNW QWRA 2023 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. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). 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. FSim generates stochastic simulation data based on many thousands of iterations, then integrates those iterations into a probabilistic result. 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. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified time period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Integrated Expected Net Value Change (ieNVC) represents risk integrated across eight HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing risk to multiple HVRAs.Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations: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/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-z
Subject: This layer depicts expected Net Value Change (eNVC) integrated across all eight HVRAs included in the PNW QWRA 2023. Expected Net Value Change represents expected fire impacts at any location based on which HVRAs are present, the expected fire behavior, and the likelihood of a wildfire occurring. Data finalized and released 12/20/23.
Category:
Keywords: wildfire risk,quantitative wildfire risk assessment,QWRA,Pacific Northwest,PNW,oe_core_layer,oe_viewer_layer
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