Description: OverviewSenate Bill 762 (SB 762), enacted in 2021, was an omnibus bill which advanced a suite of wildfire programs collectively aimed at helping Oregon’s communities and landscapes adapt to a changing fire environment. Under SB762, OSU was responsible for developing three specific data products that would help state agencies develop and implement those wildfire programs in a strategic manner. Senate Bill 762 was amended by Senate Bill 80 in 2023, but OSU is still responsible for three maps to support state agencies:A comprehensive statewide map of wildfire hazard (“statewide hazard map”). Senate Bill 80, Section 1. The map will represent hazard at all locations in Oregon by integrating estimates of annual wildfire likelihood and wildfire intensity, and summarizing the results with each tax lot in the state. Each tax lot will be classified as either “low,” “moderate,” or “high” hazard. The statewide hazard map represents the environmental hazard based on climate, weather, topography and vegetation. A statewide map of the wildland-urban interface (“WUI”). Senate Bill 762, Section 7 (7)(c); and Senate Bill 80, Section 2. The WUI is defined by two general criteria: the density of structures, and the proximity and amount of flammable vegetation. The WUI map represents locations in Oregon where there are enough structures and sufficient flammable vegetation to support a potential future wildfire disaster. A map of locations of socially and economically vulnerable communities. Senate Bill 762, Section 7(7)(d). The map of social vulnerability in Oregon uses 15 indicators collected from the 2020 American Community Survey data summarized at the Census Block Scale. Social vulnerability of each Census block group is represented relative to all other Census block groups. State agencies are not required to use the social vulnerability map and data pertaining to that map are not included in this geodatabase. More information about social vulnerability can be found at: https://ir.library.oregonstate.edu/concern/datasets/z890s265n. The statewide hazard map and the WUI map are intended to be used in conjunction with one another by the Oregon State Fire Marshal and the Department of Consumer and Business Services, Building Codes Division during implementation of specific wildfire programs. Namely:Senate Bill 80, Section 3 directs the Oregon State Fire Marshal to develop minimum defensible space requirements which will apply to all lands that are both within the WUI and classified as high hazard in the statewide hazard map.Senate Bill 80, Section 11 directs the Department of Consumer and Business Services, Building Codes Division to adopt wildfire hazard mitigation building code standards that apply to new dwellings located both in the WUI and on a property classified as high hazard in the statewide hazard map. Wildland-Urban InterfaceCreating a statewide map of the WUI involved two general steps. First, we determined which parts of Oregon met the minimum building density requirements to be classified as WUI. Second, for those areas that met the minimum building density threshold, we evaluated the amount and proximity of wildland or vegetative fuels. Following is a summary of geospatial tasks used to create the WUI. Detailed geospatial processing steps are described in the technical guide here: https://oe.oregonexplorer.info/externalcontent/wildfire/data/SB80_Public_Data.zipDevelop a potential WUI map of all areas that meet the minimum density of structures and other human development - According to OAR 629-044-1011, the boundary of Oregon’s WUI is defined in part as areas with a minimum building density of one building per 40 acres, the same threshold defined in the federal register (Executive Order 13728, 2016), and any area within an Urban Growth Boundary (UGB) regardless of the building density. Step One characterizes all the locations in Oregon that could be considered for inclusion in the WUI on building density and UGB extent alone. The result of Step One was a map of potential WUI which was then further refined into final WUI map based on fuels density and proximity in Step Two. Compile statewide tax lots. Map all eligible structures and other human development. Simplify structure dataset to no more than one structure per tax lotCalculate structure density and identify all areas with greater than one structure per 40 acresAdd urban growth boundaries to all the areas that meet the density requirements from the previous step.Classify WUI based on amount and proximity of fuel. The WUI is also defined by the density and proximity of wildland and vegetative fuels (“fuels”). By including density and proximity of fuels in the definition of the WUI, the urban core is excluded, and the focus is placed on those areas with sufficient building density and sufficient fuels to facilitate a WUI conflagration. Consistent with national standards, we further classified the WUI into three general classes to inform effective risk management strategies. The following describes how we refined the potential WUI output from step one into the final WUI map.Intermix WUI: Areas that met the minimum building density threshold in step one and which had at least 50% vegetative or wildland fuel cover were classified as Intermix WUIInterface WUI: Interface WUI includes areas that met the minimum building density threshold in step one, and which had less than 50% vegetative and/or wildland fuel cover but were within 1.5 miles of a large patch (≥ 2 sq. miles) of at least 75% vegetation and/or wildland fuelsOccluded WUI includes areas that met the minimum building density threshold in step one, and which had less than 50% vegetative and/or wildland fuel cover but were within 1.5 miles of a moderate patch (1 – 2 sq. miles) of at least 75% vegetation and/or wildland fuels.Detailed geospatial processing steps are described in the technical guide available at https://oe.oregonexplorer.info/externalcontent/wildfire/data/SB80_Public_Data.zip
Service Item Id: 72cb33ee5b14499395b086afa737c599
Copyright Text: Data developed by Chris Dunn and Andy McEvoy at Oregon State University (andy.mcevoy@oregonstate.edu).
Description: Wildfire hazard (hazard) in these data represent the potential for damage to structures and other human development as a result of four criteria: climate, weather, topography and vegetation. We used simulation models to quantify and map burn probability and fire intensity for all locations, and then combined burn probability and fire intensity to calculate hazard. Burn probability is an estimate of the average annual likelihood that a fire will impact a given location. Pyrologix LLC. used the large fire simulator, FSim, to estimate burn probability based on 2022 landscape conditions. The modeling landscape used LANDFIRE 2.0.0 fuel data which had been updated to reflect disturbances through the end of 2021, and which was adjusted based on input from regional fire and fuels professionals. FSim uses spatial fire ignition probabilities based on historical fire records (1992 – 2021) and daily weather inputs sampled from observed weather (2007 – 2021) to simulate 10,000 plausible fire seasons across 14 modeling landscapes in Oregon. In each modeling landscape, Pyrologix calibrated the model using observed patterns in historical climate-fire occurrence linkages. All modeling was performed at 120-meter resolution and resampled to 30-meters. Fire intensity was modeled by Pyrologix LLC using the WildEST simulation tool and the same modeling landscape described above. Pyrologix developed 216 weather scenarios based on unique combinations of wind speed, wind direction and fuel moisture sampled from observed weather records. Wildfire behavior was simulated under each weather scenario, producing fire intensity level probabilities (represented as flame lengths) based on the average flame length from simulations and the relative likelihood of each weather scenario. All modeling was performed at a 30-meter resolution. The fire intensity levels include:FIL1: 0-2 ft. flame lengthFIL2: 2-4 ft. flame lengthsFIL3: 4-6 ft. flame lengthsFIL4: 6-8 ft. flame lengthsFIL5: 8-12 ft. flame lengthsFIL6: > 12 ft. flame lengthsOAR 629-44-1026 requires that hazard be adjusted in all agricultural areas identified as irrigated in at least one of five representative years. We used IrrMapper (Ketchum et al., 2020) binary data layers from 2017 – 2021 and identified all pixels in Oregon that were mapped as irrigated in at least one of those layers. We clipped the IrrMapper data to the extent of mapped agricultural fields using mapped field boundaries produced by the Oregon Water Resources Department (OWRD; Bromley et al., 2024). During review of draft data in spring and summer 2024, county planners from Hood River and Baker County identified data gaps in IrrMapper. OSU worked with county planners to verify the data gaps and develop corrections that were applied statewide. In Hood River County, planners observed that while the majority of orchards were mapped as irrigated in IrrMapper, there were persistent holes in the data which indicated the portion of a field had not been irrigated when in fact it had. The data gaps did not appear to align with any features on the ground (e.g., recently cleared crops, change in farming practices, etc.). To address this data gap, we selected all fields shown to be growing irrigated orchard crops from the OWRD field boundaries data and added the extent of those fields to the irrigated extent identified using IrrMapper. By adding field boundary extents, we filled in data holes among orchards in IrrMapper. In Baker County, planners observed that IrrMapper data frequently did not characterize wetlands as irrigated, even when the wetlands are identified as irrigated hay fields in the OWRD data. We selected freshwater forested/shrub wetlands and freshwater emergent wetlands from the National Wetland Inventory (NWI; U.S. Fish and Wildlife Service, 2023), intersected the selected wetlands with the OWRD department, and added extent to the irrigated agriculture mask. The result was a raster mask representing:All areas within mapped agricultural fields identified as irrigated in at least one of five years (2017 – 2021)Irrigated orchardsCropped wetlandsUsing that irrigated agricultural mask, we adjusted burn probability and fire intensity modifier values at the corresponding locations. We decreased burn probability to 0.0001 and set the fire intensity modifier value to 10. All other burn probability and fire intensity modifier values outside the mask were not adjusted. Before calculating hazard, we applied a fire intensity modifier to the fire intensity dataset. The purpose of the fire intensity modifiers is to place fire intensity on a simplified scale and to capture variation in hazard across dominant fuel types (i.e. grass, shrub, and timber). For each pixel in the fire intensity raster, based on the dominant fuel type and on which FIL the weighted average flame length was in, we substituted the following fire intensity modifiers:Grass: (FIL1) 10; (FIL2) 20; (FIL3) 30; (FIL4) 50; (FIL5) 60; (FIL6) 70Shrub: (FIL1) 15; (FIL2) 25; (FIL3) 40; (FIL4) 60; (FIL5) 80; (FIL6) 95 Timber: (FIL1) 20; (FIL2) 30; (FIL3) 50; (FIL4) 70; (FIL5) 80; (FIL6) 95We multiplied the fire intensity modifier and burn probability datasets together at 30-meter resolution to calculate a pixel-level hazard value. Then, we averaged the pixel level hazard values within each tax lot to determine the property-level hazard value. In some cases, a tax lot did not include a pixel center and so the ArcGIS tool ‘zonal statistics’ did not report a property-level average hazard value. In those cases, we converted the tax lot to a point geometry, and extracted the underlying pixel-level hazard value. Based on average property-level pixel value, each property was assigned to one of three classes:Low Hazard: 0 - < 0.001911Moderate Hazard: >= 0.001911 - < 0.137872High Hazard: >= 0.137872Detailed geospatial processing methods are available in the technical guide here: https://oe.oregonexplorer.info/externalcontent/wildfire/data/SB80_Public_Data.zip.REFERENCES:Bromley, Matt; Minor, Blake; Beamer, Jordan (2024). Oregon hydrologic area agricultural field boundaries and field level and hydrologic unit water use data [Dataset]. Dryad. https://doi.org/10.5061/dryad.2v6wwpzvw 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/rs12142328U. S. Fish and Wildlife Service. 2023. National Wetlands Inventory. https://www.fws.gov/program/national-wetlands-inventory. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C.
Service Item Id: 72cb33ee5b14499395b086afa737c599
Copyright Text: Data developed by Chris Dunn and Andy McEvoy at Oregon State University (andy.mcevoy@oregonstate.edu).