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<div style='text-align:Left;'><div><div><p><span>The Northern Rockies Wildfire Risk Assessment estimates the potential effects of wildfire to a limited set of Highly Valued Resources and Assets (HVRAs) within the Northern Rockies Geographic Area. Potential effects are expressed as the expected net value change (eNVC) to HVRAs. The eNVC results account for the likelihood of a wildfire occurring and exposing HVRAs, the range of intensity under which a wildfire is expected to burn, and the susceptibility of the HVRAs exposed, as described in 'A Wildfire Risk Assessment Framework for Land and Resource Management' (Scott et al. 2013).Wildfire hazard data of annual burn probability (i.e., likelihood of a wildfire occurring) and conditional flame-length probability (i.e., likelihood of burning in different flame-length classes should a wildfire occur) were acquired from the Strategic Analytics Branch (SAB) of the USDA Forest Service. The SAB hazard data is a hybrid of data sources for the western and eastern United States. This results in a small edge effect in the Dakota Prairie Grasslands (DPG) where the two datasets are adjacent. For the western states, annual burn probability was modeled, using custom circa 2025 fuels data (WildWest; Gannon et al. 2026), with the USDA Forest Service FSim Model (Finney et al. 2011) at 120-m resolution. For the rest of the DPG, annual burn probability comes from the National FSim results (Dillon et al. 2023), modeled using LANDFIRE 2020 (v2.2.0) fuels data at 270-m resolution. The 270-m data were upsampled to 120-m resolution and mosaicked with the burn probability results from WildWest. The combined 120-m resolution burn probability results were then upsampled to 30-m resolution to preserver the resolution of the HVRA characterization, fuels mapping and intensity modeling. For more detailed information on data sources and post-processing, see Gannon et al. (2026).The conditional flame-length probability (FLP) data were also acquired from the SAB. FLP data were modeled with the Pyrologix LLC's Wildfire Exposure Simulation Tool (WildEST). WildEST uses a deterministic framework based on the USDA Forest Service FlamMap Software (Finney et al. 2021) to estimate the conditional probability of burning at different fire intensity levels accounting for terrain, fuel conditions, and historical weather. Details on WildEST methods can be found in the companion report “WildEST methods and outputs for the 2024 National Wildfire Risk Assessment” (Scott et al. 2024).Housing unit density data were acquired from the USDA Forest Service Wildfire Risk to Communities Project, which estimates housing unit density with 2020 census housing unit data and a comprehensive building footprint dataset (Jaffe et al. 2024). Response functions and subHVRA relative importance values also come from the Wildfire Risk to Communities project. The response functions reflect an expectation for higher losses with increasing fire intensity and with increasing fire residence times, resistance to control, and spotting potential across the grass-shrub-tree fuel type gradient, consistent with the observed loss rates across fuel types conditional on wildfire exposure (Radeloff et al. 2023). SubHVRA relative importance increases across seven classes of housing unit density to reflect the greater quantity of human assets exposed to fire in areas with higher housing unit density. The relative importance is based on density alone and does not account for home value.References:Dillon GK, Scott JH, Jaffe MR, Olszewski JH, Vogler KC, Finney MA, Short KC, Riley KL, Grenfell IC, Jolly WM, Brittain S (2023) Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. U.S. Department of Agriculture, Forest Service Research Data Archive. (Fort Collins, CO) DOI:10.2737/RDS-2016-0034-3Finney MA, McHugh CW, Grenfell IC, Riley KL, Short KC (2011) A simulation of probabilistic wildfire risk components for the continental United States. Stochastic Environmental Research and Risk Assessment 25, 973-1000. DOI:10.1007/s00477-011-0462-zGannon B, Stratton R, Arkin J, Jaffe M, Moran C, Wilson K, Gilbertson-Day J, Scott J (2026) National All-lands Wildfire Risk Assessment (NaWRA) 2025 Methods. U.S. Department of Agriculture, Forest Service, Strategic Analytics Branch, ReportJaffe MR, Scott JH, Callahan MN, Dillon GK, Karau EC, Lazarz MT (2024) Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States. 2nd Edition. U.S. Department of Agriculture, Forest Service Research Data Archive. (Fort Collins, CO) 15p. DOI:10.2737/RDS-2020-0060-2Radeloff VC, Mockrin MH, Helmers D, Carlson A, Hawbaker TJ, Martinuzzi S, Schug F, Alexandre PM, Kramer HA, Pidgeon AM (2023) Rising wildfire risk to houses in the United States, especially in grasslands and shrublands. Science 382, 702-707. DOI:10.1126/science.ade9223Scott JH, Thompson MP, Calkin DE (2013) A wildfire risk assessment framework for land and resource management. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. General Technical Report RMRS-GTR-315. (Fort Collins, CO) 83 p.Scott JH, Moran CJ, Callahan MN, Brough AM (2024) WildEST methods and outputs for the 2024 National Wildfire Risk Assessment. Pyrologix report to U.S. Department of Agriculture, Forest Service, Fire and Aviation Management. (Missoula, MT) 25 p.</span></p></div></div></div>
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<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</span></p></div></div></div>
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