This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the temperate-boreal ecotone Great Xing’an Mountains of northeastern China, and simulation results that underlie figures and analysis in the accompanying publication. The study compared the impacts of small and large fires on vegetation dynamics. The data release includes input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area for LANDIS PRO; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in ten-year timesteps. Output for four climate and fire scenarios are included for a 115-year simulation period (i.e., 1985 – 2100). A baseline scenario that applied observed climate and the historical fire regime from (1967 – 2006) was used for model calibration and evaluation. Three climate-change scenarios evaluated interactions between fire size and projected future climate under the GFDL-CM3 model with the RCP8.5 emissions scenario: (1) climate change and no fire, (2) climate change and small, frequent fires, and (3) climate change and large, infrequent fires.

This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the area burned by the Black Dragon Fire in northeast China in 1987, and simulation results that underlie figures and analysis in the accompanying publication. The data release includes the fire perimeter of Great Dragon Fire; input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area before the Great Dragon Fire; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in two-year timesteps.

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. The data are provided in comma separated values (CSV) format.

This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We provide initial conditions in 1987 as rasters and tables (i.e., species-age cohorts, aboveground biomass, soil carbon and nitrogen in surface litter 3 soil layers, soil percent sand, soil percent clay, soil wilting point, soil field capacity, soil drainage, soil storm flow and base flow fractions, and soil depth), historical fire data for model calibration, climate-inferred lognormal fire size distributions for each simulation year, and LANDIS-II control files including parameters for species and functional groups. Outputs from 10 replicates for each of 5 GCMs and the control scenario are provided as rasters and tables. Tables include spatially-weighted mean annual temperature and precipitation of the GYE for each GCM and the control scenario, summarize annual area burned by scenario, summarize biomass pools, and summarize changes in mean stand age. Rasters include annual simulated fire severity for 2015-2100, simulated total aboveground biomass in 4-year timesteps, aboveground biomass of all species in 4-year timesteps, stand age in 4-year timesteps, maximum and minimum cohort age for three dominant species (Pinus contorta, Picea engelmannii, and Pseudotsuga menziesii) in 4-year timesteps, forest type in 1988 and 2100, net ecosystem exchange in 2040 and 2100, and total ecosystem carbon in 4-year timesteps.

This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We provide initial conditions in 1987 as rasters and tables (i.e., species-age cohorts, aboveground biomass, soil carbon and nitrogen in surface litter 3 soil layers, soil percent sand, soil percent clay, soil wilting point, soil field capacity, soil drainage, soil storm flow and base flow fractions, and soil depth), historical fire data for model calibration, climate-inferred lognormal fire size distributions for each simulation year, and LANDIS-II control files including parameters for species and functional groups. Outputs from 10 replicates for each of 5 GCMs and the control scenario are provided as rasters and tables. Tables include spatially-weighted mean annual temperature and precipitation of the GYE for each GCM and the control scenario, summarize annual area burned by scenario, summarize biomass pools, and summarize changes in mean stand age. Rasters include annual simulated fire severity for 2015-2100, simulated total aboveground biomass in 4-year timesteps, aboveground biomass of all species in 4-year timesteps, stand age in 4-year timesteps, maximum and minimum cohort age for three dominant species (Pinus contorta, Picea engelmannii, and Pseudotsuga menziesii) in 4-year timesteps, forest type in 1988 and 2100, net ecosystem exchange in 2040 and 2100, and total ecosystem carbon in 4-year timesteps.

This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 – 2020 and output from global climate models (GCMs) from 1980 – 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. For each ecoregion, we produced 1000 replicate simulations of annual area burned (ha).

This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 – 2020 and output from global climate models (GCMs) from 1980 – 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. For each ecoregion, we produced 1000 replicate simulations of annual area burned (ha).

LANDFIRE's (LF) 2022 Canopy Bulk Density (CBD) describes the mass of available canopy fuel per unit canopy volume that would burn in a crown fire. A spatially explicit map of CBD supplies information used in fire behavior models to determine the spread characteristics of active crown fires across the landscape. CBD for disturbed and non-disturbed areas is determined via a general linear model (GLM) relating Canopy Height (CH) and Canopy Cover (CC) to CBD (Reeves et al 2009). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years. To designate disturbed areas where CBD is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

LANDFIRE's (LF) 2022 Canopy Bulk Density (CBD) describes the mass of available canopy fuel per unit canopy volume that would burn in a crown fire. A spatially explicit map of CBD supplies information used in fire behavior models to determine the spread characteristics of active crown fires across the landscape. CBD for disturbed and non-disturbed areas is determined via a general linear model (GLM) relating Canopy Height (CH) and Canopy Cover (CC) to CBD (Reeves et al 2009). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years. To designate disturbed areas where CBD is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

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.