National burn probability (BP) and conditional fire intensity level (FIL) data were generated for the conterminous United States (US) using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

National burn probability (BP) and conditional fire intensity level (FIL) data were generated for the conterminous United States (US) using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

The data shared are spatially explicit projections of wildfire burn probability across Canada’s forested ecozones under multiple future climate scenarios at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Four future climate scenarios were used to examine the spatiotemporal distribution of burn probability in the 21st century based on climate, vegetation, and topographic conditions ( Mulverhill et al. 2024). Projected burn probability is provided for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods, including 2021-2040, 2041-2060, 2061-2080, and 2081-2100, along with a baseline period representing average climate conditions and burn probability between 1991 and 2020. Outputs represent the probability that the conditions (climate, vegetation, topography) of a given pixel resemble those of historically burned areas. All non-climate variables were held static; therefore, projections represent burn probability under future climate scenarios given contemporary (2020) forest conditions. When using this dataset, please cite Mulverhill et al. (2025), as below. Mulverhill, C., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & Bater, C. W. (2025). Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios. Canadian Journal of Remote Sensing, 51(1). https://doi.org/10.1080/07038992.2025.2560347(Mulverhill et al. 2025). For a detailed description of the source data and methods applied to the baseline period to enable the Mulverhill et al. (2025) projections, see: Mulverhill, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., and Bater, C.W. 2024. “Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 209 pp. 279–295. https://doi.org/10.1016/j.isprsjprs.2024.02.006(Mulverhill et al. 2024).

This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.

This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.

Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.

Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.

Spatial data layers and analytical code used for the analyses and summaries presented by Bradstock et al. 2021. The base data layers comprise: Raster data in GeoTIFF format (.tif file extension) for long-term fire history, 2019/20 fire occurrence and severity, logging history, native vegetation (SVTM forest formations), tenure, topography. Raster layers for predicted habitat suitability of selected fauna species. Vector data (mostly polygon features) for study area and tenure and forest management boundaries, together with fire history, logging history and vegetation layers used to derive the corresponding raster data layers used for analyses. All code used for analyses is provided as Rmarkdown documents. Each document contains code in the R statistical language, together with explanatory text. Documents can be opened in the freely available RStudio software (https://www.rstudio.com) to run the code, or in any text editor to read code and text. See file 00_README.txt for further description of files and folders.

This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts.