These data were compiled so that annual wildfire could be modelled across the sagebrush region in the western United States. Our goal was to understand how wildfire probability relates to climate and fuel conditions across the entire sagebrush region. To do this we developed a statistical model that represents the relationship between annual wildfire probability and a small number of climate and fuel variables. Specifically, created predictions of wildfire probability using a biologically plausible logistic regression model that related wildfire probability to mean temperature, annual precipitation, the proportion summer precipitation (PSP), and aboveground biomass of annual herbaceous plants and perennial herbaceous plants. The biomass variables were used as proxies for fine fuel availability. These data represent annual fire occurrence in 1 km pixels (i.e. did a given pixel burn that year), predicted wildfire probability, as well as the three year running average (i.e. average across the current and previous two years) of climate and vegetation variables. These data were collected across the sagebrush region (the extent of the study area is provided by the cell_number_ids.tif file). The climate and vegetation data were compiled using a existing gridded dataset (Daymet) of daily precipitation and temperature, and vegetation data were summaries of annual estimates of aboveground biomass of annual and perennial herbaceous plants from the Rangeland Analysis Platform (https://rangelands.app/). These data can be used to understand spatial and temporal variability in wildfire occurrence and modelled wildfire probability between 1988 and 2019 and how that variability relates to spatial and temporal variability in climate and vegetation.

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.

Data includes field observed cover of several vegetation functional groups, e.g. annual herbaceous, perennial herbaceous, exotic annual grasses, perennial bunch grasses, shrubs, sagebrush shrubs, non-sagebrush shrubs, bare mineral soil, rocks, and litter. Additionally, if tree canopy was present within field plots, tree canopy cover was estimated, forest type recorded, and the number of individual trees rooted within the plot counted. Standard fire behavior fuel models were classified for each plot. Available data from remotely sensed models estimating soil properties, topographic features, solar radiation indices, and vegetation cover by functional group are also included.

This table summarizes areas of burn severity, sagebrush biophysical types, and soil temperature/moisture regimes within large wildfires from 1984 to 2013 occuring within greater sage-grouse population areas. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

This table summarizes areas of burn severity, sagebrush biophysical types, and soil temperature/moisture regimes within large wildfires from 1984 to 2013 occuring within greater sage-grouse population areas. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

In the southwestern US, the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989-2011 and between 25-42.5ºN, to annual metrics of the Normalized Difference Vegetation Index (NDVI; an indicator of vegetation productivity) and daily-resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern US. We mapped correlations between winter AR precipitation during landfalling ARs and 1) annual maximum NDVI and 2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. The data produced by this study include four sets of eight raster grids (total = 32 grids) representing Spearman Rank correlation coefficients for four types of comparisons across eight different latitudinal bands. Each dataset is named according to the comparison type and latitude of AR landfall. The four types of comparisons (with corresponding filenames indicated in parentheses) include: 1) annual winter atmospheric river precipitation vs. total annual winter precipitation (AR_WinterPrecip), 2) annual winter atmospheric river precipitation vs. annual maximum NDVI (AR_NDVI), 3) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire during the same year by Level IV ecoregion (AR_Fire_SameYear), and 4) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire with a 1-year lag by Level IV ecoregion (AR_Fire_OneYearLag). The eight landfall latitudes are indicated in filenames as follows: 25N, 27_5N, 30N, 32_5N, 35N, 37_5_N, 40N, 42_5N.

This table summarizes land area occuring within unique combinations of sage-grouse management zones, sagebrush community types, soil temperature/moisture regimes, and times burned from 1984 to 2013. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

This table summarizes land area occuring within unique combinations of sage-grouse management zones, sagebrush community types, soil temperature/moisture regimes, and times burned from 1984 to 2013. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

Data includes functional group cover of annual grasses, perennial grasses and shrubs, and model predicted fire behavior for the years of 2018-2021.

Data includes functional group cover of annual grasses, perennial grasses and shrubs, and model predicted fire behavior for the years of 2018-2021.