This dataset is a product of the PNW QWRA 2025. The purpose of the PNW QWRA 2025 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

WildEST results were modified for risk calculations in the PNW QWRA 2025 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.

The PNW QWRA 2025 evaluated risk to eight HVRAs: People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Agriculture eNVC represents risk integrated across all Agriculture sub-HVRAs. The Agriculture HVRA is intended to evaluate wildfire risk to cropland and associated infrastructure. We mapped the extent of croplands using the last five years of Cropscape data from the U.S. Department of Agriculture (USDA-NASS, 2022). All pixels that were considered cultivated were included in the HVRA extent and the most common crop type associated with each cultivated pixel was used to classify the sub-HVRA as either perennial or annual. Sub-HVRAs included:

1. Perennial crops

2. Annual crops

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. 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.

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

Citations:

USDA-NASS, 2022. USDA National Agricultural Statistics Service Cropland Layer (2022). https://nassgeodata.gmu.edu/CropScape/

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