Wildfire activity in the United States saw a significant increase in 2024, with approximately *** million acres burned. This marks a more than ********* increase from the previous year. Such development boosts the concerning upward trend in wildfire damage across the country that has developed in the past half a century. Humans or lightning? A wildfire can start by natural causes. In 2024, Oregon and Arizona were the states most affected, each with more than *** cases recorded. Nevertheless, human-caused wildfires continue to play a significant role in the overall landscape. In 2024, over ****** wildfires in the U.S. were attributed to human activity, resulting in more than *** million acres burned. Wildfire suppression The financial burden of wildfire suppression remains substantial. The estimated costs of wildfire suppression in the U.S. stood at almost *** million U.S. dollars in 2023, a 13-fold increase in comparison to 1985. As climate change continues to alter weather patterns and create more favorable conditions for wildfires, the need for effective prevention, management, and suppression strategies is becoming increasingly critical.

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density. Methods Initial Communities Data The initial communities layer is the base vegetation layer that sets the starting conditions for the exchange of carbon, water, energy, species interactions, disturbance effects, and other landscape processes. The initial treatment optimization study in this landscape (Krofcheck et al., 2019) used 68 Forest Inventory and Analysis (FIA) plots from within the Santa Fe National Forest that had been inventoried in 2010 or later and had not burned since 2005. Forest types represented by the FIA plots were piñon-juniper, ponderosa pine, Douglas-fir (Pseudotsuga menziesii), Engelman spruce (Picea engelmannii) and limber pine (Pinus flexilis). The latter three were grouped into a general mixed-conifer forest type. The authors then used elevation, transformed aspect using Topographic Radiation Aspect Index, TRASP (Roberts & Cooper, 1989), and a tasseled cap transformation of spectral data from Landsat 8 (available at https://www.usgs.gov/landsat-missions/landsat-8) as predictors for Random Forest models and used the rfUtilities library (Evans & Murphy, 2018) to select the most parsimonious model for each forest category separately. Existing vegetation classification from the Southwest Regional Gap Analysis (SWReGap, https://swregap.org/) using the ‘yaImpute’ library (Crookston & Finley, 2008) was used to stratify the measured plots for the imputation. We determined plot sampling intensity by calculating the relative area of land each plot represents within its forest type. To evaluate the influence of additional plot data on the initial communities layer and its effects on model behavior, we used data collected as part of the planning process by the US Forest Service. These data were located entirely within the study area and included 1072 plots from 111 stands inventoried in 2011, where each stand included between 3 and 31 plots. Plot data were collected using a common stand exam protocol using variable radius plots. The specific Basal Area Factor (BAF) prism chosen for each stand was a function of stand density and they ranged from 10 to 30 BAF. We had coordinates for the centroid of each stand, but not for each individual plot, which effects the imputation process. We used the tree data from each plot to determine a specific forest type and generalized category (e.g. piñon-juniper, ponderosa pine, mixed-conifer), corresponding to the FIA classification, and added an aspen forest type, resulting in a total of four generalized forest types. We also defined non-forested areas and included two generalized species parameterizations to represent shrubs that resprout and shrubs that do not resprout following fire. We used all FIA and common stand exam (CSE) plot data (n=1140) to generate a new initial communities layer following the same method as Krofcheck et al. (2019) in R v4.1.2 (R Core Team, 2021). Simulation Analysis To estimate the uncertainty in the initial communities layer that is due to not having coordinates for all CSE plots, we re-ran interpolations randomly selecting one plot for each set of stand coordinates. This led to 31 initial communities layers, which we used to initialize the model with the five climate projections, for a total of 155 simulations. We compared the aboveground carbon following model initialization of these initial communities layers with the initial communities layer that we created using all plot data and that we used for our management simulations. We calculated the difference in aboveground carbon between each layer and the one we used in our simulations to determine how much the initial communities layer is influenced by this source of uncertainty. To determine the influence of the number of plots used in the development of the initial communities layer, we produced five additional initial communities layers with differing numbers of underlying plot data. For four of the five layers, we halved the number of CSE plots used in the interpolation each time (e.g. 536, 268, 134, 67) and combined those with the FIA data. For the fifth initial communities layer, we only used the 68 FIA plots. For each of the layers, we randomly selected plots from each forest type proportional to the prevalence of each forest type on the landscape. We then initialized the model using each of these initial communities layers using the five climate projections and compared the aboveground carbon following model initialization to that of the initial communities layer that we used for our simulations. We quantified differences between our primary initial communities layer and that of Krofcheck et al., (2019) by comparing the difference in quantity and distribution of aboveground carbon at the beginning and end of the simulations. We ran an independent t-test to assess the difference in carbon between the two studies at each site every 10 years for each of the climate models, and computed the percent of area with a significant difference (p < 0.01) in aboveground carbon. We compared treatment location as determined by the probability of high-severity fire between our initial communities layer and that of Krofcheck et al. (2019). We calculated Net Ecosystem Carbon Balance (NECB) by subtracting carbon lost from the system (treatment and wildfires) from carbon gained (photosynthesis) and then relativized the treatment scenario NECB values to the no-management scenario for both our simulations and those of Krofcheck et al. (2019). Data processing and analysis was conducted using R v4.1.2 (R Core Team, 2021). Treatment scenarios To develop the optimized treatment placement scenario, we first ran simulations that included no management to identify locations where landscape conditions were such that there was a high probability of high-severity wildfire. We ran the no-management simulations using the same five projected climate data sets and fire weather data described above. We ran 25 replicate simulations using each of five projected climate data sets, for a total of 6250 simulation years. We used fire severity raster data from these model outputs to quantify the probability of high-severity by dividing the number of years with high-severity fires by the total number of fire years per site. We then identified sites with a probability of high-severity fire greater than 0.3 and targeted those locations in the treatment scenario simulations, assigning treatment to those areas first. To determine the type of treatment we used the probability of high-severity fire in combination with slope and forest type. We limited our management simulations to the ponderosa pine and dry mixed-conifer forest where the combined ponderosa pine and Douglas-fir aboveground carbon was at least 65% of the total. We used the same thinning and prescribed burning treatments as Krofcheck et al. (2019), which were designed to approximate common treatments for the region. Thinning treatments simulated thinning from below by removing approximately 30% of the biomass, preferentially removing the youngest cohorts (Hurteau et al., 2011, 2016) and was only applied to ponderosa pine forest and confined to slopes <30%, to account for a common limitation on mechanical thinning. We simulated prescribed burning based on historic mean fire return intervals, with all ponderosa pine burned using a 10-year return interval and forests co-dominated by ponderosa pine and Douglas-fir burned using a 15-year return interval. The forest type and probability of high-severity fire are highly dependent on the initial communities layer which defines the initial forest conditions. As a result, our treatment placement map differed substantially from the one in Krofcheck et al. (2019). To examine the effects of the treatment on the landscape we produced a new probability of high-severity fires raster and calculated the difference in aboveground carbon between the management and no management scenarios of this study at the end the simulations. We ran simulations over a 50-year period, using climate model projections for years 2000-2050. We ran 25 replicates for each of the five climate projections, totaling 125 simulations each for the no-management and management scenarios. Fire weather distributions tracked projected climate and were updated each decade to account for changes in temperature and precipitation.

Oregon saw the largest area burned by wildfires across the United States in 2024. That year, about 2,232 individual wildfires burned in the northwestern state, ravishing almost 1.89 million acres. Texas followed second, with roughly 1.3 million acres burned due to wildfires that year. Fire season 2021 and California’s wildfire suppression costs As one of the most wildfire-prone states in the country, California spends a significant amount of money on their suppression. Estimates suggest wildfire suppression expenditure in California climbed to 1.2 billion U.S. dollars in the fiscal year ending June 2022. The fiscal year, which includes the summer and fall months of 2021, was among the most devastating fire seasons on record, with that year’s Dixie fire becoming the second-largest California wildfire by acres burned. The Dixie fire was responsible for over 963,000 acres burned across the state that year. Wildfire causes Wildfires are uncontrolled fires burning across any type of combustible vegetation such as grass- and brushland, forests, and agricultural fields. They are also referred to as wildland fires, forest fires, or bushfires, with the latter term particularly common in Australia. Wildfires regularly occur on all continents of the world, except for Antarctica, but are particularly common in dry regions with dense vegetation. As the rise in average global temperatures is changing weather patterns and resulting in more and more countries being affected by dry, hot weather conditions, the severity and rapid spread of wildfires have increased in recent years. The most common causes of wildfires are natural phenomena such as lightning strikes as well as human activity. The area burned due to human-caused wildfires in the U.S. surpassed 1.5 million acres in 2023.

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires. Methods This dataset provides estimates of 30 m resolution burn severity, and emissions of CO2, CO, CH4, non-methane organic compounds (NMOC), SO2, NH3, NO, NO2, nitrogen oxides (NOx = NO + NO2), PM2.5, OC, and BC. WBSE was implemented for California large wildfires on a per-fire event scale since 1984 and also a daily scale since 2002. The inventory implementation steps, input datasets, and output data are summarized in figure 1 in Xu et al, 2022. Burn severity calculation Fire records for California from 1984 to 2019 were retrieved from MTBS (https://mtbs.gov/viewer/index.html) via interactive viewer on 8 May 2021, resulting in a dataset with a total of 1623 wildfires. We also acquired fire perimeters for 74 large wildfires in 2020 from CAL FIRE (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) and calculated dNBR for each 2020 fire using the dNBR calculation tool with Google Earth Engine (GEE). This process first selects either initial assessment or extended assessment for each fire. The initial assessment utilizes Landsat images acquired immediately after a fire to capture first-order fire effects. The extended assessment uses images obtained during the growing season following the fire to identify delayed first-order effects and dominant second-order effects (Eidenshink et al 2007). We utilized LANDFIRE Biophysical Settings (BPS) to determine which assessment type to apply for each fire burned in 2020. After Picotte et al (2021), we used extended assessment if the majority of general vegetation groups within the fire perimeter are forests, while initial assessment is used when the majority of general vegetation groups are grassland/shrubland. By contrast, MTBS uses extended assessment for forest and shrubland types. We did not delineate grasslands into burn severity categories. Instead, we classified them as burned ('grass burn') because of difficulties in assessing vegetation change. Post-fire images for extended assessment were selected during the next peak of the green season (June–September) using the mean compositing approach suggested by Parks et al (2018). Composite post-fire images acquired immediately within two months after the fire containment dates were used for the initial assessment. Composite pre-fire images for extended and initial assessments were acquired with the matching periods from the preceding year. The dNBR images were produced by quantifying the spectral difference between composite pre-fire and post-fire Landsat scenes. We calculated the unitless, continuous CBI variable from dNBR/NBR values using the linear and Sigmoid B regression models developed for the CONUS by Picotte et al (2021). CBI values were then classified following thresholds modified based on Crotteau et al(2014) into six severity classes: unburned, low severity, moderate severity, high severity, grass burn, and non-processing area. Emissions calculation Emissions of all species are calculated as a function of area burned, fuel loading, the fraction of vegetation burned based on burn severity, and an emissions factor specific to each vegetation type using the following equation modified from the FINN model (Wiedinmyer et al 2011). Fuel categories were assigned from LANDFIRE EVT products. For emissions calculations, EVT data were then categorized into five general vegetation categories: grass, shrub, forest under 5500 feet (1676 m), forest between 5500–7500 feet (1676–2286 m), and forest above 7500 feet (2286 m), updated for California ecosystems. Fuel consumption was determined following Hurteau et el 2014 assigning fuel loading and consumption values for each severity class for the five general vegetation categories based on the First Order Fire Effects Model v5 (Reinhardt et al 1997). Emission factors for greenhouse gases, particulate matter, and reactive trace gases were updated with recent data for each general vegetation class using results from recent field campaigns and studies specific for California ecosystems and Western U.S. ecosystems. Day of burning and daily emissions To assign the day of burning for individual pixels, NASA fire information for resource management system (FIRMS) active fire products from MODIS (Collection 6) within 750 m of the fire perimeter shapefiles supplied by MTBS or CAL FIRE were selected for interpolation to account for detections that might be outside the boundary due to detection radius. VIIRS 375 m data, when available since 2012, was added to complement MODIS data with improved performance to assign burn dates using the fire progression raster tool (figure 4). We filtered the MODIS/VIIRS detection points to the date range of interest and created a 500 m buffer around each point. Points were then converted to circle polygons to represent each point's detection extent properly. The average date was selected as the proper date in regions of overlapping buffers. We then calculated daily emissions and assigned them to the centroids of the aggregated daily progression polygons.

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL).

Brief description of project

This project has two main goals:

1. Examine the key factors influencing global economic wildfire damages
2. Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)

Repository structure

• /data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript
  • /data/historical: contains the historical (training) data that was used for fitting the linear regression model
  • /data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages
  • /data/source: contains all raw data used in this study
• /scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript
  • /scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps
    • run_linregress.py: script to run the linear regression model
    • run_nonlinregress.py: script to run the nonlinear models (preliminary)
    • run_plm.py: script to run the supplementary panel regression model (plm)
    • run_gdp_linregress.py: script to run the alternative linear regression model using absolute damages as outcome variable and GDP as additional independent predictor
    • inspect_model.py: script to conduct model validation
  • /scripts/plotting: contains the scripts to plot all figures presented in the manuscript
    • plot_map_y_X_hist.py: script to plot Figure 1 (world maps of historical wildfire damage and predictors used in this study)
    • plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)
    • plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)
    • plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)
    • plot_map_X_ssp.py: script to plot Figure 5 (world maps of predictor values for the three SSPs explored in this study)
    • plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)
    • plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)
    • plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)
    • SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)
    • SI_plot_ssp_damage_projection_xterm.py: script to plot Figure S2 (comparison of time-series of mean projected wildfire damages using models with and without interaction terms)
    • SI_plot_beta_coef_pop_wui.py: script to plot Figure S3 (same as Figure 3 but for the model using pop_wui instead of PDforest)
    • SI_plot_ssp_population.py: script to plot Figure S4 (population projection under the three SSP scenarios)
    • SI_plot_ssp_map_pop_wui.py: script to plot Figure S5 (world maps of the pop_wui predictor under three SSP scenarios)
    • SI_plot_ssp_map_damage.py: script to plot Figure S6 (world maps of projected wildfire damages under the three SSP scenarios and for the years 2030, 2050 and 2070)
    • SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S7 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)
    • SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S8 (time-series of the SSP projections of the predictors by developmental regions)

The Portuguese Large Wildfire Spread Database (PT-FireSprd) includes the reconstruction of the spread of 80 large wildfires that occurred in Portugal between 2015 and 2021. It includes a detailed set of fire behaviour descriptors, such as rate-of-spread (ROS), fire spread direction, fire growth rate (FGR), fire radiative energy (FRE) and FRE flux.

The wildfires were reconstructed by converging evidence from complementary data sources, such as satellite imagery/products, airborne and ground data collected by fire personnel, official fire data and information in external reports. We then implemented a digraph-based algorithm to estimate the fire behaviour descriptors and combined it with MSG-SEVIRI fire radiative power estimates. A total of 1197 observations of ROS and FGR were estimated along with 609 FRE estimates.

PT-WFireSprd database is organized in 3 levels:

• L1: Wildfire Progression, representing the spatial and temporal evolution of the wildfire spread (i.e. where and when).

• L2: Wildfire Behavior, including quantitative behavior descriptors of how a wildfire burned, such as the rate-of-spread (ROS), fire growth rate (FGR), fire radiative energy (FRE), and FRE flux;

• L3: Simplified Wildfire Behavior, averaging fire behavior over longer periods that represent relatively homogenous fire runs.

The data from the different levels is composed by a large set of maps that can be useful for several applications and target users.

The PT-FireSprd is the first open access fire progression and behaviour database in Mediterranean Europe, dramatically expanding the extant information. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel.

This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.

In 2023, more than 17.3 million hectares of land had burned in Canada because of forest fires. This was the largest annual land loss due to wildfires since records started. The number of forest fires in Canada stood at around 5,475 in 2023. The cost of Canadian wildfires In Canada, estimated property losses due to forest fires from 1970 to 2020 amounted to almost 250 million Canadian dollars. The province of British Columbia was by far the most affected, with losses of 115.4 million Canadian dollars, followed by Ontario with 57.9 million Canadian dollars.On the human side, the largest evacuation caused by wildfires in the North American country from 1980 to 2019 occurred in 2016, when more than 92,000 people were displaced. The Fort McMurray wildfire – the costliest natural catastrophe in Canadian history – took place that year. A worldwide picture Wildfires have been wreaking havoc around the world in recent years. In 2022 alone, around 5.2 million hectares of tree cover were lost due to wildfires. A year earlier, wildfire tree cover loss reached the peak of the century so far, with more than seven million hectares. In the past century, Russia has seen the largest annual tree cover loss due to wildfires, with an average of 2.5 million hectares. Canada is the second most impacted country in the world, with an average annual loss of roughly 1.3 million hectares during the same period.

This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.

Protecting Forests and Communities from Wildfire Risk (FIGURE 1b-3) The Protecting Forests and Communities from Wildfire Risk map shows areas of North Carolina where wildfire mitigation and preparedness efforts can reduce loss of life and property, and prevent degradation of the forest resource due to intense fires typical of southern forests. These lands rank high for wildfire susceptibility in the Southern Wildfire Risk Assessment System (ArcGIS software). Many of these areas are considered to be within the wildland-urban interface, and many are owned by individuals who may be unfamiliar with the role of fire in southern forests and firewise building principles.

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL). Brief description of project This project has two main goals: Examine the key factors influencing global economic wildfire damages Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370) Repository structure /data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript /data/historical: contains the historical (training) data that was used for fitting the linear regression model /data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages /data/source: contains all raw data used in this study /scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript /scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps run_linregress.py: script to run the linear regression model run_nonlinregress.py: script to run the nonlinear models (preliminary) run_plm.py: script to run the supplementary panel regression model (plm) run_gdp_linregress.py: script to run the alternative linear regression model using absolute damages as outcome variable and GDP as additional independent predictor inspect_model.py: script to conduct model validation /scripts/plotting: contains the scripts to plot all figures presented in the manuscript plot_map_y_X_hist.py: script to plot Figure 1 (world maps of historical wildfire damage and predictors used in this study) plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model) plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages) plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors) plot_map_X_ssp.py: script to plot Figure 5 (world maps of predictor values for the three SSPs explored in this study) plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions) plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing) plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370) SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models) SI_plot_ssp_damage_projection_xterm.py: script to plot Figure S2 (comparison of time-series of mean projected wildfire damages using models with and without interaction terms) SI_plot_beta_coef_pop_wui.py: script to plot Figure S3 (same as Figure 3 but for the model using pop_wui instead of PDforest) SI_plot_ssp_population.py: script to plot Figure S4 (population projection under the three SSP scenarios) SI_plot_ssp_map_pop_wui.py: script to plot Figure S5 (world maps of the pop_wui predictor under three SSP scenarios) SI_plot_ssp_map_damage.py: script to plot Figure S6 (world maps of projected wildfire damages under the three SSP scenarios and for the years 2030, 2050 and 2070) SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S7 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor) SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S8 (time-series of the SSP projections of the predictors by developmental regions)

This scatter chart displays brightness temperature mean (kelvin) against total fire area. The data is about wildfires.

This bar chart displays brightness temperature mean (kelvin) by wildfire using the aggregation sum. The data is about wildfires.

Plot data from: Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration

This file contains annual recruitment rates for post-fire sites in Montana, Idaho, Colorado, New Mexico, Arizona, and California. The rates were determined by destructively sampling trees to age precisely at the root-shoot boundary (see manuscript for more details). Also included in this file is relevant site information.

Davis_et_al_recruitment_plot_data.csv

Davis_et_al_climate_timeseries

This file contains annual climate timeseries for each site for the climate variables used in the boosted regression tree models. See manuscript for detailed information about how climate data was obtained and/or modeled.

Davis_et_al_code

This file contains the R code used in the analysis.

Davis_et_al_figure_code

This file contains the R code used to make the figures in the manuscript.

The geospatial dataset includes raster and vector data for visualizing the spatial distribution of risk of wildfire-caused carbon loss in Peeler et al. 2023. Raster data evaluate carbon exposure, sensitivity, and vulnerability at the pixel-level across western US carbon forests. Vector data aggregate pixel-level findings into project area and fireshed spatial units to identify target geographies (or “opportunity hot spots”) where proactive forest management could reduce the greatest risk from wildfire to carbon. Vector data also identifies firesheds in which proactive forest management could simultaneously reduce the risk from wildfire to carbon and human communities. Methods To form a composite indicator for exposure at the full extent of western US conifer forests, we aggregated individual indicators for annual burn probability (30 m resolution) and total carbon (tons/acre, 30 m resolution). Annual burn probability was extracted from a gridded dataset on wildfire hazard. Total carbon was estimated by matching plot IDs in gridded tree and fuel lists to corresponding plots in the US Forest Inventory and Analysis (FIA) program. Living and dead biomass in the corresponding FIA plot were converted to units of carbon using a conversion factor of 0.5, while litter and duff used a conversion factor of 0.37. All carbon stocks were summated to quantify total carbon. We used min-max normalization to scale minimum and maximum values of annual burn probability and total carbon to 0 and 1. Afterward, we weighted the normalized individual indicators equally and added them together to create a gridded dataset for exposure that varied from 0 to 1. We interpreted carbon in pixels with values near 1 as most exposed to wildfire, as these locations contained the most total carbon and experienced the highest annual burn probability. We developed a composite indicator for sensitivity using indicators on potential carbon loss and carbon recovery following wildfire. To quantify carbon loss, we combined the gridded tree and fuel lists and pixel-specific flame length probabilities with the Fire and Fuels Extension Forest Vegetation Simulator (FFE-FVS) to estimate how much carbon would be emitted directly to the atmosphere during a wildfire event (tons/acre, 30 m resolution). Additionally, we applied a 50-year half-life over 30 years to fire-killed biomass to estimate how much carbon would be released indirectly through decomposition over time (tons/acre, 30 m resolution). For carbon recovery, we extracted individual indicators from gridded datasets on site productivity (30 m resolution, site index extracted from tree list and used as proxy) and post-wildfire tree regeneration probability (480 m resolution). Given that site productivity and post-wildfire tree regeneration influence forest recovery and growth after wildfire, we assumed both to be good proxies for likelihood of associated carbon recovery. To ensure individual indicators contributed equally to the composite indicator, we log-transformed total carbon loss and site productivity because both were right-skewed. Afterward, all indicators were min-max normalized, weighted equally, and added together to create a gridded dataset for sensitivity that varied from 0 to 1 (30 m resolution). We interpreted carbon in pixels with values near 1 as most sensitive to wildfire, as these locations would lose the most carbon due to wildfire and had the lowest likelihood of carbon recovery. To aggregate pixel-level findings to broader spatial extents, we estimated whether need and feasibility for proactive forest management could reduce wildfire hazard at project area-levels if communities and agencies leveraged their social adaptive capacity. To estimate need, we excluded infrequent-fire forests because forest thinning in these historically dense forests is not justified based on historical ecological conditions. For remaining frequent-fire forests, we matched plot IDs in the gridded tree and fuels list to corresponding FIA plots to calculate stand density index (SDI) for each pixel. We then divided a pixel’s SDI by the maximum SDI corresponding to its forest type and geography – thereby calculating a relative SDI. We assumed that a relative SDI ≤25% indicated a forest structure that was generally climate- and fire-resilient and could be burned safely under moderate weather conditions, whereas a relative SDI >25% suggested high fuel loads that could justify forest thinning. Finally, we applied ecological (e.g. fire behavior fuel models, previous wildfire severity), legal (e.g. land ownership, wilderness areas, distance to perennial stream or wetland), and operational (e.g. distance to road) constraints to identify locations where treatments were not feasible and excluded them from our estimate of social adaptive capacity.

This data publication contains post-fire tree regeneration and other data for 807 plots distributed within and adjacent to 11 recent (10-18 year-old) wildfires in the Black Hills of South Dakota and Wyoming, the Laramie Mountains of Wyoming, and the Front Range of Colorado. Data collection occurred in 2014 and 2015. The overstory of all plots had been dominated or co-dominated by ponderosa pine at the time of burning. Plots were primarily located in areas that burned with high severity, although some plots were located in areas that burned less severely or were unburned. At each plot, we recorded species, height, and other attributes for all post-fire regenerating trees, and we recorded species, status, diameter at breast height, and other attributes for all overstory trees that were thought to have been alive prior to the fire. Additionally, at each plot we recorded numerous plot attributes, including (but not limited to) fire severity, distance from surviving forest, elevation, and coarse wood cover.These data were collected to quantify post-fire tree regeneration in ponderosa pine forests of the Black Hills of South Dakota and Wyoming, the Laramie Mountains of Wyoming, and the Front Range of Colorado, and to examine the site factors that govern it.For more information about these data and this study, see Chambers et al. (2016) and Rodman et al. (2020).

The metadata for this data publication was originally published on 10/25/2022 and the associated data were under an embargo. On 04/14/2023 the metadata was updated to include a citation for a recently published article that is related to these data. On 01/02/2024 the data embargo was lifted.

Map and charts showing all ODF statistical wildfire occurrence data from 2000-2022. Statistical fires are those where ODF has primary protection responsibility. Acres burned are ODF protected acres only (not total fire acres).

Anthropogenic climate change has increased the frequency of extreme drought, wildfires, and invasions of non-native species. Studying interactions amongst these multiple stressors is rarely done at the local scale yet is key to anticipating impacts on vulnerable ecosystems. We leveraged an existing experimental rainfall manipulation to evaluate the relationship between precipitation, fuel load, and fire severity in a Southern California coastal sage scrub system. We asked whether pre-fire rainfall manipulation influenced fuel load and vegetation cover with consequences for fire severity and post-fire community composition. We measured plant biomass and community composition before and after the 2020 Santiago wildfire in experimental plots with three rainfall treatments. After fire, we measured number of branches, diameter of the smallest terminal branch, leaf percent cover, and height on three dominant native shrub species (Malosma laurina, Artemisia californica, and Salvia mellifera) to assess fire severity. Native shrubs had greater pre-fire cover in added water plots compared to reduced water plots. Experimental drought led to less fuel build-up, less native shrub cover, and more invasive grass cover. The decreased biomass led to lower fire severity indicated by smaller branch diameters and more terminal branches after burning. Post-fire shrub cover was low in all plots, and lowest in added and ambient plots compared to reduced water plots. There were fewer native and more invasive species in post-fire droughted plots compared to post-fire irrigated and ambient water plots. Our results demonstrate the importance of fuel load to fire severity and plant community composition on an ecosystem scale. Management strategies should focus on reducing fire frequency to maintain the resilience of coastal sage scrub communities facing drought. Control burns are not recommended for coastal sage scrub communities because they will promote the growth of non-native plants. Methods The Loma Ridge manipulation consists of replicate experimental blocks established in CSS and grassland communities. Each experimental block includes 6 plots, with each plot randomly assigned to a unique water (droughted, ambient, and added) and nitrogen (ambient or added) manipulation treatment. The drought involves an approximately 40% reduction in precipitation, achieved by covering plots with plastic during large storms. Water is funneled into storage tanks and is later pumped on water addition plots that receive approximately 30% more water through irrigation lines. To measure plant community composition, each plot was divided into three 4x4m quadrats, within which all species were identified and % cover was visually estimated. For all shrubs, separate cover values were recorded for crown-sprouting individuals, dead shrubs, and seedlings. Overlapping plants meant that plant cover could total >100%. Ground cover values (bare ground, thatch, litter, cryptobiotic crust, rock, or moss) were separately estimated so that they totaled 100%. We collected herbaceous biomass and litter within four 14 cm by 50 cm sampling frames per plot each April. Within each of these frames, the cover of any rooted plants, along with ground cover, was recorded by functional group (forbs, grasses, shrubs, litter, or bare ground) to total 100%. It was extremely rare for shrubs to be rooted within this small frame, so shrub cover was typically close to 0%. After estimating cover, all litter within each frame was collected and all herbaceous material was cut and harvested. Material was dried in an oven at 60°C degrees for 4 days and weighed. The herbaceous mass measurements include separate and combined totals of all living biomass and dead (litter) mass within each sampling frame. Shrub biomass was estimated pre-fire by measuring all shrubs greater than 20 cm in height that were rooted within the 4x4 m subplot. Total height (H, height at the tallest point), width 1 (W1, width at the widest point) and width 2 (W2, width perpendicular to width1) were used to estimate total shrub volume (V=H*pi(W1*W2/4)). Biomass was estimated from volume using regression equations developed for each species or generalized shrub regression equations (Vourlitis and Pasquini 2009). After fire in Spring 2021, impacts of fire severity on CSS were measured in each of the 24 subplots representing added, ambient, and reduced precipitation with ambient nitrogen. We measured three individuals of each of the three most abundant species (Malosma laurina, Artemisia californica, and Salvia mellifera) in each plot. For each individual shrub, we counted the number of branches remaining, the percentage of branches with leaves, the diameter of the smallest terminal branch (branch width), the height of the burned shrub, the number of burned shrubs that were crown sprouting, and the height of the basal crown sprouting new growth. Higher fire severity results in lower branch count and leaf percent cover, and greater branch thickness (Perez and Moreno 1998).

These data are used to represent the spatial, temporal and thermal characteristics of wildfires in southeast Louisiana beginning in 2003 and extending until 2011. The fire detection points are placed at the centroids of the 1km*1km MODIS grid cells and contain the date collected as well as the derived Fire Radiative Potential which was used to represent intensity. Graphs of normalized and non-normalized wildfire detection frequency were created to examine the temporal trends associated with wildfires in both the marine wetland environment as well southeast Louisiana in general. These temporal trends were useful in identifying periods of peak wildfire activity as well as defining a fire season or seasons to be used in both in statistical analysis of wildfire intensity as well as the kernel density maps. This data was also created in support of a GoMRI RFP III Grant and is also registered as dataset R3.x174.000:0002.

The dataset contains Supplementary Materials for the article ''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis'' in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis.

Supplementary Figures:
- Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line);

- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha.

- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);

- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;

- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;

- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region;

- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory;

- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory.

We share Copernicus Atmosphere Monytoring Service PM2.5 emissions maps (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594.

To determine emissions from the territory of Russia, we provide shapefiles with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334).

Also, there are initial dataset for analysis (Initital data_SARIMAX archive) and SARIMAX model settings (doc.).