The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data.

This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2025, it represents fire24_1.

Please help improve this dataset by filling out this survey with feedback:

Historic Fire Perimeter Dataset Feedback (arcgis.com)

Current criteria for data collection are as follows:

CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.

All cooperating agencies submit perimeters ≥10 acres.

Version update:

Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.

Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports.

Includes separate layers filtered by criteria as follows:

California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale.

Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.

California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep24_1) Metadata

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data.

This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2025, it represents fire24_1.

Please help improve this dataset by filling out this survey with feedback:

Historic Fire Perimeter Dataset Feedback (arcgis.com)

Current criteria for data collection are as follows:

CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.

All cooperating agencies submit perimeters ≥10 acres.

Version update:

Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.

Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports.

Includes separate layers filtered by criteria as follows:

California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale.

Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.

California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep24_1) Metadata

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at https://github.com/river-corridors-sfa/rc_sfa-rc-3-wenas-meta . This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

Climate change is increasing the frequency and severity of wildfires worldwide. Although wildfires are typically viewed as destructive, emerging research suggests they may have benefits for some species, including some pollinators. One reason for this is that wildfires can increase floral resource availability in the years immediately following the burn, potentially creating more favorable conditions for pollinator foraging and reproduction. In this study, we focused on how the 2021 KNP Complex Fire impacted the bumble bee Bombus vosnesenskii in the Southern Sierra Mountains, where the effects of fire on this pollinator species have not been previously explored. Consistent with bumble bee studies in other areas, we found an increase in the size of B. vosnesenskii workers in recently burned areas. This effect was detectable despite a limited number of sampling events and locations in our study, and irrespective of the habitat type (meadow versus forest) in which sampling occurred. We fai..., , , # Fire is associated with positive shifts in bumble bee (Bombus vosnesenskii) body size and bee abundance in the Southern Sierra Nevada Mountains

https://doi.org/10.5061/dryad.1c59zw463

Description of the data and file structure

Contains all analyses performed on datasets regarding how wildfires impact bumble bee populations in Sierra Nevada, California.

Synopsis

We examined the body sizes of bumble bees in a large, recently burned forest-meadow complex, specifically in the Southern Sierra Nevada Mountains, to understand whether the habitat type (forest versus meadow) mediates the impact of fire on bee size. We detected evidence that bees are larger in recently burned areas, irrespective of habitat type.

Sample Information

*see Seki.dataset.csv for all bee data and see [Site.Information.csv](https://github.com/claudinpcosta/2024-Wildfires.B...,

The FIRESTAT (Fire Statistics System) Fire Occurrence point layer represents ignition points, or points of origin, from which individual wildland fires started on National Forest System lands. The source is the FIRESTAT database, which contains records of fire occurrence, related fire behavior conditions, and the suppression actions taken by management taken from the Individual Wildland Fire Report. This publicly available dataset is updated annually for all years previous to January 1 on or after February 16th.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This data package is associated with the publication “Radiative impact of record-breaking wildfires from integrated ground-based data” submitted to Nature Scientific Reports (Kassianov et al., 2024). Data from ground-based measurements of shortwave and spectrally resolved irradiance and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges were assessed to quantify the radiative impact of the September 2020 wildfires that occurred in the Western United States. Data were collected in September 2020 by several ground-based instruments at the Atmospheric Measurements Laboratory (AML) located in Richland, Washington (46.3451, -119.2792). These data include (1) Aerosol Optical Depth (AOD); (2) spectrally resolved and shortwave (SW) irradiances; (3) backscatter profiles; (4) total sky images; and (5) near-surface ambient air temperatures. The data package consists of five sub-directories: (1) “AML_Ceilometer_”; (2)” AML_CSPHOT_”; (3) “AML_MFRSR_irradiances_”; (4) “AML_SW_irradiances_and_Temp_”; (5) “AML_TSI_images_”; and 6 files stored at the directory level, including the readme, file-level metadata file, and data dictionary. The file-level metadata file (the file ending in “_flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (the file ending in “_dd.csv”) describes each tabular column header’s unit, definition, and structure. Below are descriptions of each sub-directory: “AML_Ceilometer_” includes ceilometer data collected at the AML. These files contain the corresponding narratives of data. Details related to the ceilometer data can be found in Morris (2016). “AML_CSPHOT_” includes ascii files with high-temporal resolution (about 10-15 min) AML CSPHOT data and their daily-averaged counterparts. These two files contain the corresponding narratives of data. Details related to the CSPHOT data can be found in Gregory (2011). “AML_MFRSR_irradiances_” includes ascii files with the AML MFRSR-measured diffuse, normal, and total spectrally resolved irradiance. Details related to the MFRSR data can be found in Hodges and Michalsky (2016) and Koontz et al. (2013). “AML_SW_irradiances_+_Temp_” includes near-surface ambient air temperature and SW irradiances, namely direct normal, diffuse hemispherical, and total hemispheric (global), measured at the AML. These files also incorporate the corresponding narratives of data. Details related to the SW irradiances can be found in Andreas et al. (2018). “AML_TSI_images_” includes Total Sky Images (TSIs) collected at the AML. Details related to the TSI data can be found in Morris (2005).

This structured dataset comprises four text (csv) files that represent four categories of wildfire information extracted from the original PDFs of the US Incident Management Situation Reports between 2007 and 2021. The dataset was generated by IMSR-Tool version 1.09, which is available at https://doi.org/10.5281/zenodo.15039313For updates, please see the attached "2007-2024 _IMSR_Note.txt"

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

Precipitation, volumetric soil-water content, videos, and geophone data characterizing postfire debris flows were collected at the 2022 Hermit’s Peak Calf-Canyon Fire in New Mexico. This dataset contains data from June 22, 2022, to June 26, 2024. The data were obtained from a station located at 35° 42’ 28.86” N, 105° 27’ 18.03” W (geographic coordinate system). Each data type is described below. Raw Rainfall Data: Rainfall data, Rainfall.csv, are contained in a comma separated value (.csv) file. The data are continuous and sampled at 1-minute intervals. The columns in the csv file are TIMESTAMP(UTC), RainSlowInt (the depth of rain in each minute [mm]), CumRain (cumulative rainfall since the beginning of the record [mm]), and VWC# (volumetric water content [V/V]) at three depths (1 = 10 cm, 2=30 cm, and 3=50 cm). VWC values outside of the range of 0 to 0.5 represent sensor malfunctions and were replaced with -99999 . Storm Record: We summarized the rainfall, volumetric soil-water content, and geophone data based on rainstorms. We defined a storm as rain for a duration >= 5 minutes or with an accumulation > 2.54 mm. Each storm was then assigned a storm ID starting at 0. The storm record data, StormRecord.csv, provides peak rainfall intensities and times and volumetric soil-water content information for each storm. The columns from left to right provide the information as follows: ID, StormStart yyyy-mm-dd hh:mm:ss-tz, StormStop yyyy-mm-dd hh:mm:ss-tz, StormDepth mm, StormDuration h, I-5 mm h-1, I-10 mm h-1, I-15 mm h-1, I-30 mm h-1, I-60 mm h-1, I-5 time yyyy-mm-dd hh:mm:ss-tz, I-10 time yyyy-mm-dd hh:mm:ss-tz, I-15 time yyyy-mm-dd hh:mm:ss-tz] ([UTC], the time of the peak 15-minute rainfall intensity), I-30 time yyyy-mm-dd hh:mm:ss-tz] ] ([UTC], the time of the peak 30-minute rainfall intensity), I-60 time [yyyy-mm-dd hh:mm:ss-tz] [UTC], (the time of the peak 60-minute rainfall intensity), VWC (volumetric water content [V/V] at three depths (1 = 10 cm, 2 = 30 cm, 3 = 50 cm) at the start of the storm, the time of the peak 15-minute rainfall intensity, and the end of the storm), Velocity [m s-1] of the flow, and Event (qualitative observation of type of flow from video footage). VWC values outside of the range of 0 to 0.5 represent sensor malfunctions and were replaced with -99999. Velocity was only calculated for flows with a noticeable surge as the rest of the signal is not sufficient for a cross-correlation, and Event was only filled for storms with quality video data. Values of -99999 were assigned for these columns for all other storms. Geophone Data: Geophone data, GeophoneData.zip, are contained in comma separated value (.csv) files labeled by ‘storm’ and the corresponding storm ID in the storm record and labeled IDa and IDb if the geophone stopped recording for more than an hour during the storm. The data was recorded at two geophones sampled at 50 Hz, one 11.5 m upstream from the station and one 9.75 m downstream from the station. Geophones were triggered to record when 1.6 mm of rain was detected during a period of 10 minutes, and they continued to record for 30 minutes past the last timestamp when this criteria was met. The columns in each csv file are TIMESTAMP [UTC], GeophoneUp_mV (the upstream geophone [mV]), GeophoneDn_mV (the downstream geophone [mV]). Note that there are occasional missed samples when the data logger did not record due to geophone malfunction when data points are 0.04 s or more apart. Videos: The videos stormID_mmdd.mp4 (or .mov) are organized by storm ID where one folder contains data for one storm. Within folders for each storm, videos are labeled by the timestamp in UTC of the end of the video as IMGPhhmm. Some videos in the early mornings or late evenings, or in very intense rainfall, have had brightness and contrast adjustments in Adobe Premiere Pro for better video quality and are in MP4 format. All raw videos are in MOV format. The camera triggered when a minimum of 1.6 mm of rain fell in a 10-minute interval and it recorded in 16-minute video clips until it was 30 minutes since the last trigger. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

This dataset includes processed organic matter chemistry data from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and vegetation materials representative of major land cover types of the Pacific Northwest, USA. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. Source data and associated metadata (including methods and geospatial information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022). This dataset provides processing scripts and processed data for both solid and dissolved phase organic matter characterization data from experimentally generated chars. These processed data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. The processed data were subsequently analyzed; and the results and ecological implications of the findings were published in peer-reviewed manuscripts. The scripts and workflows used to develop the manuscripts are also included in this data package. This data package was originally published June 2024. It was updated September 2024 (new and modified files). See the change history section in the readme for more details. This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), and folders containing (A) processed data; (B) general processing scripts; and (C) additional folders with specific manuscript analysis scripts and processed data. Step-by-step instructions to assist the user in recreating the workflow used to generate the results in the manuscripts is also provided. The processed data folder includes (1) a folder of processed Parallel Factor Analysis (PARAFAC) and spectra indices outputs from excitation emissions matrix (EEM) fluorescence and absorbance data; (2) a folder of processed solid state carbon-13 (13-C NMR) integrals; (3) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) processed data outputs from Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude), blank corrections and data aggregation, and calculated molecular indices. All files are .pdf, .csv, .html, .Rmd, .R, or .RData.

The following datasets are included in this record. Directories are contained with .tar files of the same name. Sources in parentheses (may be outdated).

• CA-HPI.csv: Home price index data for California (https://fred.stlouisfed.org/series/CASTHPI)
• ca_places/: CA Geographic Boundaries (https://data.ca.gov/dataset/ca-geographic-boundaries)
• CensusTiger/: Census TIGER Shapefiles (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html)
• DamageData/: Post-fire damage data from CAL FIRE DINS (https://data.ca.gov/dataset/cal-fire-damage-inspection-dins-data) and other sources (various)
• FirePerimeters/: Fire perimeter data from FRAP(https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program), NIFC (https://www.nifc.gov/fire-information/maps), MTBS (https://www.mtbs.gov/direct-download), and other sources (various)
• HAZUS/: FEMA HAZUS data on rebuilding costs by Census block (https://www.fema.gov/flood-maps/products-tools/hazus)
• LANDFIRE/: LANDFIRE data on aspect, elevation, slope, and Anderson 13 fuel model (https://www.landfire.gov/)
• NolteEtAl/: Appendix Tables from Nolte et al. 2024 (https://le.uwpress.org/content/100/1/200.abstract)
• simplemaps_uscities_basicv1.72/: Simplemaps US Cities Database (https://simplemaps.com/data/us-cities)
• SRAandFHSZ: Collection of SRA and FHSZ shapefiles to identify homes in building code areas (CAL FIRE)

This database includes wildland fuels data for Mediterranean basin vegetation stands types and in particular for those in Corsica, for fire/forest management, risk assessment and decision-support purposes. It gathers together some of the most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). It has been conceived by using a layering approach, this is, assuming that a vegetation stand type is constituted by one or more structurally distinct pseudo-homogenous layers of vegetation. Vegetation stand types considered are based on the fuel classification and mapping of the BDForêt® 2.0 – Corsica. Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The building methods and the different data sources have been detailed in a paper which is under review.

The attached dataset consists of two tables and one text document:

• The first table (FuelLayersData.csv) contains fuel layers and fuel elements attributes for each vegetation stand type. The table has 15 columns. The first one (CODE_TFV) corresponds to the code assigned to each vegetation stand type following the BDForêt® nomenclature. Next columns, refer to the layer numbering, the stratum of the layer and the species scientific name. After that, next six columns correspond to the layer attributes and four columns correspond to the fuel element attributes. The last column is the diameter at breast height (DBH) for canopy layers. The empty cells in the table indicate that the corresponding attribute is not applicable for this particular layer.

• The second table (FuelParticlesData.csv) contains the particle attributes, this is, the surface-to-volume ratio, particles density and low heat content.

• The text document (StandTypesDescription.docx) is derived from BDForêt® version 2.0 – Corsica (https://geo.isula.corsica/wp-content/uploads/2021/01/descriptif-contenu-bd_foret-IGN.pdf) and contains a short description of the different stand types considered according to the CODE_TFV.

This work was supported by the Agence Nationale de la Recherche, France (grant number ANR-16-CE04-0006 FIRECASTER) and by H2020-EU.3.5. Programme (FIRE-RES, Grant agreement ID: 101037419).

Pérez-Ramirez Y, Ferrat L, Filippi JB. (2024) Wildland Fire Fuels Database for Corsican – Mediterranean Forest stand types. Forest Ecology and Management, 565, 122002.

Linked respiratory prescriptions for children, along with information on birth and MSA of birth, and smoke days assigned through pregnancy. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Access to underlying health data may be purchased through MarketScan. Smoke data (Hazard Mapping System) is available through NOAA. Format: Data is in csv and R dataset formats. This dataset is associated with the following publication: Jardel, H., K. Rappazzo, T. Luben, C. Keeler, B. Staley, C. Ward-Caviness, C. O'Lenick, M. Rebuli, Y. Xi, M. Hernandez, A. Chelminski, i. jaspers, A. Rappold, and R. Dhingra. Gestational and postnatal exposure to wildfire smoke and prolonged use of respiratory medications in early life. Environmental Research: Health. IOP Publishing, BRISTOL, UK, 2: 045004, (2024).

Data associated with: Walker, X.J., S. Hart, W.D. Hansen, M. Jean, C.D. Brown, F.S. Chapin, III, R. Hewitt, T.N. Hollingsworth, M.C. Mack, J.F. Johnstone. 2024. Factors limiting the potential range expansion of lodgepole pine in Interior Alaska. Ecological Applications e2983.Understanding the factors influencing species range limits is increasingly crucial in anticipating migrations due to human-caused climate change. In the boreal biome, ongoing climate change and the associated increases in the rate, size and severity of disturbances may alter the distributions of boreal tree species. Notably, Interior Alaska lacks native pine, a biogeographical anomaly that carries implications for ecosystem structure and function. The current range of lodgepole pine (Pinus contorta var. latifolia) in the adjacent Yukon Territory may expand into Interior Alaska, particularly with human assistance. Evaluating the potential for pine expansion in Alaska requires testing constraints on range limits such as dispersal limitations, environmental tolerance limits, and positive or negative biotic interactions. Here, we archive the model results from a multi-disciplinary study. In the study, we used field experiments with pine seeds and transplanted seedlings, complemented by model simulations, to assess the abiotic and biotic factors influencing lodgepole pine seedling establishment and growth after fire in Interior Alaska.File list:README_Hansen_EcolApps_2024.pdf: Contains data dictionaries for all data tables, full list of creators/authors, methodology, and additional project documentation.Stand_LP.csv: Stand level output for trees taller than 4 m from simulations that included lodgepole pine.Stand_No LP.csv: Stand level output for trees taller than 4 m from simulations that did not include lodgepole pine.Saplingdetail_LP.csv: Stand level output for trees shorter than 4 m from simulations that included lodgepole pine.Saplingdetail_NoLP.csv: Stand level output for trees shorter than 4 m from simulations that included lodgepole pine.Sapling_LP.csv: Stand level output recording sapling output in the simulations that included lodgepole pine.Sapling_noLP.csv: Stand level output recording sapling output in the simulations that did not include lodgepole pine.Tree_LP.csv: Individual tree output in the simulations that included lodgepole pine.Tree_noLP.csv: Individual tree output in the simulations that did not include lodgepole pine.carbon_LP.csv: Stand level carbon pools from the simulations that included lodgepole pine.carbon_noLP.csv: Stand level carbon pools from the simulations that did not include lodgepole pine.

MetaSqueeze: A spatially-explicit metapopulation model for Banksia hookeriana in south-west Australia MetaSqueeze is a mechanistic, process-based, spatially explicit metapopulation model developed to investigate the dynamics of metapopulations under the influence of climate change. This model specifically focuses on the fire-killed, serotinous shrub Banksia hookeriana, studying its response to varying fire regimes and climatic conditions. Reference: Souto-Veiga, R., Groeneveld, J., Enright, N. J., Fontaine, J. B., & Jeltsch, F. (2024). Climate change may shift metapopulations towards unstable source-sink dynamics in a fire-killed, serotinous shrub. Ecology and Evolution, 14, e11488. https://doi.org/10.1002/ece3.11488 Corresponding Author: Rodrigo Souto-Veiga Email: rodrigo.souto.veiga@uni-hamburg.de ORCID: 0000-0001-8639-620X ## Requirements The model is developed using C++ 17 and requires gcc compiler version 9.2 or higher for full functionality. ## Setup Instructions ### For Linux Users: 1. Installation: * Install CMake and Make: Most Linux distributions come with CMake and Make. You can install them via your package manager (e.g., sudo apt-get install cmake make). 2. Compilation: * Navigate to the project directory. * Run make to build the program in debug or release mode. ### For Windows Users: 1. Installation: * Install CMake: Download and install CMake from CMake.org. * Install MinGW or another gcc compiler that includes g++ to get gcc version 9.2 or higher. 2. Compilation: * Open CMake GUI and set the source code and build directories. * Configure the project using MinGW Makefiles generator and specify the path to the MinGW compiler. * Click 'Generate' to create Makefiles. * Open Command Prompt and change the directory to your build directory. * Run mingw32-make to compile the project. ## Model Execution * Local Execution: Use the script functions.sh to run models locally. * High-Performance Cluster: Use job_metasqueeze.sh to submit and manage jobs for parallel simulation execution. ## Project Directory Structure ### Simplified Project Directory Structure |- build/ | |- debug/ | |- release/ |- data/ | |- in/ | |- out/ |- documentation/ | |- TRACE_document.pdf |- src/ |- tools/ | |- data_analysis_visualization/ | |- experiment_generator/ | |- parallel/ |- CMakeLists.txt |- functions.sh |- job_metasqueeze.sh |- makefile ### Comprehensive Project Directory Structure |- build/ | |- debug/ | |- release/ |- data/ | |- in/ | | |- climate/ | | | |- climate_baseline.csv | | | |- climate_baseline_cali_1998_2006.csv | | | |- climate_current.csv | | |- dispersal/ | | | |- dispersal_baseline.csv | | | |- dispersal_current.csv | | | |- dispersal_none.csv | | |- fire/ | | | |- fire.csv | | |- flower_distribution/ | | | |- flower_distributions_current.csv | | |- fuzzy_sets/ | | | |- fuzzy_set_01.csv | | | |- fuzzy_set_02_low.csv | | | |- fuzzy_set_03_high.csv | | | |- fuzzy_set_list.csv | | |- habitat_quality/ | | | |- habitat.csv | | |- metapopulation/ | | | |- metapop_cali_02.csv | | | |- metapop_cali_03_mort_scn_0.csv | | | |- metapop_cali_03_mort_scn_2.csv | | | |- metapop_cali_03_mort_scn_3_4.csv | | | |- metapop_baseline.csv | | | |- metapop_baseline_high.csv | | | |- metapop_baseline_medium.csv | | | |- metapop_only_one.csv | | |- plant_types/ | | | |- plant_types.csv | | |- scenarios/ | | | |- scn_baseline.csv | | | |- scn_cali_01.csv | | | |- scn_cali_02_mort_0.csv | | | |- scn_cali_02_mort_2.csv | | | |- scn_cali_02_mort_3.csv | | | |- scn_cali_02_mort_4.csv | | | |- scn_cali_03_mort_0.csv | | | |- scn_cali_03_mort_2.csv | | | |- scn_cali_03_mort_3.csv | | | |- scn_cali_03_mort_4.csv | | | |- scn_current.csv | | |- sim/ | | | |- manuscript/ | | | | |- calibration/ | | | | | |- 01_cali_init_cones_seeds.csv | | | | | |- 02_cali_init_plants.csv | | | | | |- 03_cali.csv | | | | |- experiments/ | | | | | |- 01_experiment/ | | | | | | |- 01_01_experiment.csv | | | | | | |- 01_02_experiment.csv | | | | | | |- 01_03_experiment.csv | | | | | | |- 01_04_experiment.csv | | | | | |- 02_experiment.csv | | | | | |- 02_suppl_experiment.csv | | | | | |- 03_experiment.csv | | | | | |- 03_suppl_experiment.csv | | | | | |- 04_01_experiment.csv | | | | | |- 04_02_experiment.csv | | | | | |- 04_suppl_1_experiment.csv | | | | | |- 04_suppl_2_01_experiment.csv | | | | | |- 04_suppl_2_02_experiment.csv | | | | | |- 04_suppl_2_03_experiment.csv | | | | | |- 05_experiment.csv | | | | | |- fire_size_experiment.csv | | |- species/ | | | |- banksia_base_90_mort_0.csv | | | |- banksia_base_90_mort_1.csv | | | |- banksia_base_90_mort_2.csv | | | |- banksia_base_90_mort_3.csv | | | |- banksia_base_90_mort_4.csv | | | |- banksia_...

This data publication contains vegetation and fuels measurements from 2001-2022 at the Lubrecht Fire & Fire Surrogate Study Site, which was established in 2000 and includes four treatments: an untreated control, prescribed burn, a thinning, and a thinning followed by prescribed burn. This study site is located at the University of Montana's Lubrecht Experimental Forest in western Montana, approximately 50 kilometers east of Missoula. Data span 20 years from pre-treatment in 2001 through 2022, 20 years post-treatment. The publication includes six tabular data sets (comma-separated values (CSV) files): 1) tree level data (≥ 10.16 centimeter (cm) diameter at breast height (DBH), 2) plot level sapling density by species and diameter class, 3) plot level seedling density by species and height class, 4) plot level understory vegetation cover, 5) plot level fuel loading, and 6) plot level aboveground carbon stocks and potential fire severity (i.e., predicted tree mortality if a fire occurs).The purpose of this study was to examine the long-term (15-20 year) treatment effects (i.e., forest vegetation, fuels, potential fire severity, and carbon dynamics) of prescribed fire and mechanical thinning, as a way to reduce high-severity wildfire and restore ponderosa pine forests.For more information about this study and these data, see Hood et al. (2024).

These data were published on 12/01/2023. On 01/17/2024, the metadata was updated to include reference to a newly published article.

This data publication includes the data used to quantify the amount and spatial arrangement of land available for mechanical risk reduction fuel treatments after considering operational constraints within the twenty-one landscapes prioritized in the USDA Forest Service Wildfire Crisis Strategy (WCS) plan which was initiated in 2022. These landscapes are found in the western United States: Arizona, California, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, and Washington. Data were prepared by combining readily available datasets in a Google Earth Engine workflow. This data publication includes two different types of files which were generated using source data from the years 2016-2023: 1) a layered raster dataset (GeoTIFF file) for each of the twenty-one priority landscapes for the three different constraint scenarios considered; and 2) a comma-separated values (CSV) file containing data on the total area remaining available for mechanical operations under the three operational scenarios within individual fireshed project areas (with at least 25% overlap with a priority landscape). The GeoTIFF files can be used to determine the spatial extent of mechanically available land at the priority landscape level and to identify which constraining factor(s) is the most limiting. The CSV file was used to quantify the spatial arrangement of mechanically available and constrained land at the fireshed project area level using the USFS Fireshed Registry nested spatial framework.The USDA Forest Service Wildfire Crisis Strategy was initiated with the goal to implement proactive management actions to foster fire- and climate-adapted forests in the western United States. This plan was backed by billions of dollars in funding by the United States federal government appropriated through the Infrastructure Investment and Jobs Act of 2021 (P.L. 117-581) and the Inflation Reduction Act of 2022 (P.L. 117-1692) made available to the Forest Service to complete the proposed work. Even with substantial funding allocated to complete the fuels reduction work needed, prior research that considered layered legal, operational, and administrative constraints to implementing mechanical operations indicates that there could be major challenges to completing the proposed work on some landscapes, whereas meeting treatment objectives may be more feasible on other landscapes. This analysis and the resulting data were performed to quantify the amount and spatial arrangement of land available for mechanical risk reduction fuel treatments after considering layered operational constraints within the twenty-one landscapes identified in the Wildfire Crisis Strategy.Full details regarding this study and these data can be found in Woolsey et al. (2024). The Fireshed Registry (Ager et al. 2021) contains details on the nested spatial framework created to organize the landscape into units for managing wildfire risk to communities. Source code used to generate these datasets and perform analysis for this project is provided in Woolsey (2024).

After a policy of aggressive fire suppression in most of North America during the 20th century, increasing aridity has driven widespread, synchronous fire occurrence in recent decades. A lack of historical (pre-1880) fire records limits our ability to understand long-term continental fire-climate dynamics. The goal of this study is to use tree-ring reconstructions to determine the relationships between spatio-temporal patterns in historical climate and widespread fire occurrence in North America, and whether they are stable through time. We applied regionalization methods to tree-ring reconstructions of historical summer soil moisture and annual fire occurrence to independently identify broad- and fine-scale climate and fire regions based on common inter-annual variability. We then tested whether the regions were stable through time and for spatial correspondence between the climate and fire regions. Last, we used correlation analysis to quantify the strength of the fire-climate associ..., Our study area includes North America between 20°N and 60°N (Figure 1). We used this latitudinal range because outside of this area there are few fire history sites with sufficient data prior to 1880 CE (Margolis et al. 2022). The fire data reconstructed from tree rings are located in forested regions; consequently, our study may not be representative of fire regimes in non-forest vegetation. We analyzed records of fire occurrence for the period 1750-1880 from NAFSN (Margolis et al. 2022), using tree-level records of the year of fire occurrence from 1,159 sites. The start year of the analysis, 1750, was chosen to optimize the longest possible period with the broadest geographic coverage of sites that were continuously recording fire. Continuously recorded fire data are necessary to prevent biases in the cluster analysis related to decreasing sample depth associated with tree-ring sample decay (Swetnam et al. 1999). The analysis period ended in the year 1880 due to the strong influence o..., , # Data From: Spatiotemporal synchrony of climate and fire across North America (1750-1880)

https://doi.org/10.5061/dryad.280gb5mxh

Primary data are the occurrence of wildfire based on tree ring fire scars (NAFSN_Fire-Binary_1750-1880_10pct_2min.csv), derived from the North American Fire Scar Network (NAFSN, Margolis et al. 2022). Soil moisture anomalies (SMz_recon.nc) derived from tree rings were derived using methods from Williams et al. 2022. Also included are equal area hexagons (NAM_hexels.shp) used in analyses and a shapefile of North America (NAM.shp). A basemap is provided from Nasa's BlueMarble dataset. Metadata relating to the fire scar sites used in this analysis (NAFSS_Master_Metadata_eqm.csv) and the entire NAFSN as of 2/1/2024 (NAFSS_Master_Metadata.csv) for recreating figure 1. Code for recreating analyses and figures are also included.

Description of the data and file structure

Fire scar data represents the binary occ...

Dataset for "Non fire-adapted dry forest of Northwestern Madagascar: escalating and devastating trends revealed by Landsat timeseries and GEDI lidar data" by Percival et al. 2024 (PLOS ONE)This dataset includes:ndvi_pnts.csv: The NDVI time series for each pixel in the study area for BFAST analysis. This was used with the NBR time series to identify potential fire events. nbr_pnts.csv: The NBR time series for each pixel in the study area which was used with the NDVI time series to identify potential fire events.YYYY_train.geojson: The fire record training data for 2014, 2017, 2018, 2019, and 2021. Each training file (geojson) consisted of a binary fire-non-fire class for training the land cover classification algorithm.fire_YYYY.gpkg: The fire history maps that were created using the training data and Planet Imagery (downloadable from planet.com). These included a map of fires for the years 2014, 2017, 2018, 2019, and 2021.aggregated_fires.gpkg: A map of aggregated fires for the study site.gedi_fires.gpkg: The GEDI L2B data combined with the fire history data for the main set analyses on the effects of fire on forest structure.Metadata.