This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Source: Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency: Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Debris/Product Fire, Fire Rehabilitation, Incident/Event Support, Preparedness/Preposition, Prescribed Fire, Wildfire, Wildland Fire Use, Incident Complex, and Out of Area ResponseArea Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.The USA Wildfires web map provides additional layers and information such as Red Flag warnings, wind speed/gust, and satellite thermal detections. This map can be used as a starting point for your own map.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS: A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration. Methods This archive includes field data and various spatial datasets used in Davis et al. (2023). Individual datasets in the Dryad archive include the following: 1) Field data. This data includes post-fire regeneration density for eight conifer species from the western US that were surveyed in plots 2–30 years following fire. Predictors that were used in the manuscript "Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States" are included with each plot including climate data, fire severity, heat insolation load index, surrounding tree cover, and distance to nearest live seed source. The dataset is a compilation of many datasets. The field methods performed to collect the data varied by dataset/study and are described in detail in each individual study. For a list of the publications that produced each individual dataset please see the supplemental information Table S1 from Davis et al. 2023, the provided .csv file called "data_dryad_contributor_key.csv" or the definitions for each of the values of the contributor id column in the metadata. 2) Statistical models. The final models for recruitment probability for each species and for all species combined are included as .rds files. 3) Projections of recruitment probability made with the final models for 10-years post-fire under four climate scenarios (1981–2000, 2001–2020, 2031–2050 RCP 4.5, 2031–2050 RCP 8.5) and two fire severity scenarios (low severity: 10 m to a seed source, 30% surrounding live tree cover within 300-m radius of plot, relativized burn ratio (RBR) of 100; high severity: 150 m to a seed source, 10% surrounding live tree cover within 300-m radius of plot, RBR of 400). Plot size in projections is set to 100 m2 so the projections can be interpreted as the probability of at least one seedling regenerating by 10 years post-fire in a 100 m2 plot under the given climate and fire severity scenario, which is equivalent to a density of 100 trees ha-1 or around 40 trees acre-1. Please note that the threshold probability at which recruitment is considered likely varies between species due to differences in the models. Therefore when interpreting the probabilities it is not appropriate to compare raw probabilities between species. It is best to interpret the probabilities in light of the provided threshold probabilities above which recruitment is most likely.

In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time. However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes. This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire. In this study we demonstrate that both local climate – through limitations posed by fuel dryness (CWD) and availability (AET) – and human activity – through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California. We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly. Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability. Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modelling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California.

Methods Climate data used in this study was drawn from the California Basin Characterization Model v8, and consists of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) from 1951 – 2016. This dataset represents a 270-m grid-based model of water balance calculations that incorporates climate inputs through PRISM data in addition to solar radiation, topographic shading, cloudiness, and soil properties to estimate evapotranspiration. Using these monthly values, we calculated the 1980 – 2009 mean CWD and AET normals, as well as mean deviations from those normals over a three-year period preceding each year of interest.

Cultivated and agricultural areas were identified using the 2016 National Land Cover Database data, which estimated dominant land cover throughout North America at 30-m resolution. The proportion of cultivated area and of water features that covered each 1-km pixel were then calculated by resampling to 1-km scale. Mean housing density data was drawn from the Integrated Climate and Land-Use Scenarios (ICLUS) dataset, which provides decadal estimates of housing density throughout the United states from 1970 - 2020. As precise continuous estimates of housing density were not available, housing density within each pixel was set to the mean of its class. Annual values were estimated from decadal data using linear interpolation. Ecoregions within California (hereafter referred to as “regions”) were delineated using CalVeg ecosystem provinces data.

Road data were drawn from 2018 TIGER layer data, and consisted of all primary and secondary roads across California. Electrical infrastructure data was drawn from 2020 transmission lines data. In both cases, the distance of nearest roads or transmission lines to each pixel were then calculated. Pixels which contained roads or electrical infrastructure were assigned distances of 0 km.

Fire history data was drawn from FRAP fire perimeter data, which incorporates perimeters of all known timber fires >10 acres (>0.04 km2), brush fires >30 acres (>0.12 km2), and grass fires >300 acres (>1.21 km2) from 1878 – 2017. Using this data, the presence of fire in each 1-km pixel was classified in a binary fashion (e.g. 1 for burned, 0 for unburned) for each year of interest. Due to computational limits and the quantity of data involved in this study, we did not calculate the burned area within each pixel, or distinguish pixels in which a single fire occurred in a given year from those in which multiple fires occurred. This data was also used to calculate the number of years since the most recent fire within any pixel, prior to each year in which fire probability was projected. Thus, locations in which no fire was observed throughout the fire record were treated as having gone a maximum of 100 years without a fire event for the purposes of model construction. These pixels comprised 29% - 33% of data annually (depending on year), and included both locations in which fire would not be expected (such as highly xeric regions) as well as locations in fire-prone areas in which no fire had been documented within the FRAP fire perimeter data used in this study.

Context

Forest fires are a serious problem for the preservation of the Tropical Forests. Understanding the frequency of forest fires in a time series can help to take action to prevent them. Brazil has the largest rainforest on the planet that is the Amazon rainforest.

Content

This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of approximately 10 years (1998 to 2017). The data were obtained from the official website of the Brazilian government.

http://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif

Acknowledgements

We thank the brazilian system of forest information

Inspiration

With this data, it is possible to assess the evolution of fires over the years as well as the regions where they were concentrated. The legal Amazon comprises the states of Acre, Amapá, Pará, Amazonas, Rondonia, Roraima, and part of Mato Grosso, Tocantins, and Maranhão.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place. Related datasets representing components of risk across the entire landscape are available in a separate data publication (Scott et al. 2020, https://doi.org/10.2737/RDS-2020-0016). Likewise, transmitted risk to housing units from the source locations where damaging fires originate will be also be delivered in a separate publication.

Vegetation and wildland fuels data from LANDFIRE 2014 (version 1.4.0) form the foundation for wildfire hazard and risk data included in the Wildfire Risk to Communities datasets. As such, the data presented here reflect wildfire hazard from landscape conditions as of the end of 2014. National wildfire hazard datasets of annual burn probability and fire intensity were generated from the LANDFIRE 2014 data by the USDA Forest Service, Rocky Mountain Research Station (Short et al. 2020) using the large fire simulation system (FSim). These national datasets produced with FSim have a relatively coarse cell size of 270 meters (m). To bring these datasets down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30-m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability and intensity into developed areas represented in LANDFIRE fuels data as non-burnable. Additional methodology documentation is provided with the data publication download.

The data products in this publication that represent where people live reflect 2018 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Microsoft (version 1.1), LandScan 2018 where building footprint data were unavailable, USGS building coverage data, and land cover data from LANDFIRE.

The specific raster datasets included in this publication include:

Housing Unit Density (HUDen): HUDen is a nationwide raster of housing-unit density measured in housing units per square kilometer. The HUDen raster was generated using population and housing-unit count and data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate housing unit locations because Microsoft data were not available across the whole state.

Population Density (PopDen): PopDen is a nationwide raster of residential population density measured in persons per square kilometer. The PopDen raster was generated using population count data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate population locations because Microsoft data were not available across the whole state.

Building Coverage (BuildingCover): BuildingCover is a raster of building density measured as the percent cover of buildings within an approximately 5 acre area around each pixel. It includes all buildings and can be used to complement the HUDen raster, which just reflects residential buildings. Building coverage was generated using building footprint data from Microsoft (v1.1), building coverage data from USGS, and land cover data from LANDFIRE. Building Coverage is not available in Alaska because source data were not available across the whole state.

Building Exposure Type (BuildingExposure): Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. The BuildingExposure layer delineates whether buildings at each pixel are directly exposed to wildfire from adjacent wildland vegetation (pixel value of 1), indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition (pixel values between 0 and 1), or not exposed to wildfire due to distance from direct and indirect ignition sources (pixel value of 0). It is similar to Exposure Type in the companion data publication, RDS-2020-0016, but just where HUDen > 0 or BuildingCover > 0. Pixels where both HUDen and BuildingCover rasters are zero are NoData in the BuildingExposure raster.

Housing Unit Exposure (HUExposure): HUExposure is the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. It is calculated as the product of wildfire likelihood and housing unit count. Pixels where the HUDen raster is zero are NoData in the HUExposure raster.

Housing Unit Impact (HUImpact): HUImpact is an index that represents the relative potential impact of fire to housing units at any pixel, if a fire occurs there. It incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire. HUImpact does not include the likelihood of fire occurring, and it does not reflect mitigations done to individual structures that would influence susceptibility. It is conceptually similar to Conditional Risk to Potential Structures in the companion data publication, RDS-2020-0016, but also incorporates housing unit count and exposure type. Pixels where the HUDen raster is zero are NoData in the HUImpact raster.

Housing Unit Risk (HURisk): HURisk is an index that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density > 0. It is conceptually similar to Risk to Potential Structures (i.e., Risk to Homes) in the companion data publication, RDS-2020-0016, but also incorporates housing unit count. Pixels where the HUDen raster is zero are NoData in the HURisk raster.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. These data represent the first time wildfire risk to communities has been mapped nationally with consistent methodology. They provide foundational information for comparing the relative wildfire risk among populated communities in the United States.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information. The suite of seven raster layers included in this publication are downloadable as zip files by U.S. state. Population Density, Building Coverage, Housing Unit Density, Housing Unit Impact, and Housing Unit Risk are also downloadable as national datasets. National datasets of Housing Unit Exposure and Building Exposure Type are too large for download, but users can request them through the point of contact listed in this metadata document.

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Has not been declared contained, controlled, nor outHas not had fire report records completed (certified)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the Current Wildland Fire Locations service."Fall-off" rules are used to ensure that stale records are not retained. Records are removed from this service under the following conditions:If the fire size is less than 10 acres (Size Class A or B) and fire information has not been updated in more than 3 daysFire size is between 10 and 100 acres (Size Class C) and fire information hasn't been updated in more than 8 daysFire size is larger than 100 acres (Size Class D-L) but fire information hasn't been updated in more than 14 days.Fires from previous calendar years are excluded.Fire size used in the fall off rules is from the IRWIN IncidentSize field.Fires that are no longer in the Current Wildland Fire Perimeter service will be displayed in the Wildland Fire Perimeters Year to Date and/or the 'Full History' service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Fall-off rules are enforced hourly.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Full details: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-current-interagency-fire-perimeters/about

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire. Consumption Best Practices:As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable. Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC).  Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute. Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations. Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed. Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed. Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires. Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives. Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities. IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers. Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands. Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created. GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event. Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned. Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters. This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Parks is an essential element in the environment serve for people physical and mental wellbeing. Especially in 2020, people's health has suffered a great crisis under the dual effects of the COVID 19 pandemic and the extensive, severe wildfire in the western and center United State. People had changed their mobility to obtain the recreational opportunities. The parks offer more safer recreation opportunity for people to keep health during this crisis time. This research analyzes spatial and temporal variation on people’s mobility including number of visitors, dwell time, and travel distance to the park under the impact of confluence of two major crises. we applied Geographically and Temporally Weighted Regression (GTWR) Models to explore how the COVID19 and wildfire factor affected on human recreation behaviors and visitations to parks during June – September 2020. The findings indicated that the overall trend of visitation for the park decrease under impact of COVID pandemic and wildfire. In addition, people tended to travel closer from home to parks and spend less time there when more COVID19 cases were reported. However, with the lifted stay-at-home restriction and national park reopen, people travel more distance to the national park (e.g., Yellowstone) under the COVID case peak in June 2020. Moreover, people shorten the time and traveled a long distance to park in the southwest of study area during non-wildfire season (June -July), and then to the whole study area during the wildfire season (August-September). These findings shed new light on the how human mobility to the park during the pandemic and wildfire crisis, which complements practical research on physical activity, ecosystem services, and public health.

Many ecosystems are experiencing increased fire frequencies and species invasions that can erode their resilience and cause a shift to alternative states. In the sagebrush-steppe, a semi-arid shrubland ecosystem in North America, restoration treatments are often implemented following wildfire to enhance their resilience to invasion. However, little is known about the long-term effectiveness of these treatments. We investigated whether repeated restoration efforts provide greater resilience in sagebrush-steppe communities initially dominated by species with different post-fire regeneration traits and subjected to compounding wildfires and invasion by Bromus tectorum over 25 years. We studied 37 permanent transects (Columbia Basin, Washington, USA) in which species abundance was recorded multiple times from 1992 to 2017. We quantified community change and its relationship with fire, restoration, and moisture availability. Resilience was evaluated by quantifying community resistance and stability indices. The greatest change occurred in communities where the obligate seeding shrub Artemisia tridentata was initially common. Repeated fires led to the extirpation of this shrub and eventual dominance of B. tectorum. Herbicide applications temporarily suppressed B. tectorum post-fire. Seeding treatments and above average precipitation initially increased native cover. Although communities where resprouting species were common showed the least change, repeated fires did lead to a gradual but substantial decline (86%) in resprouting shrubs. Synthesis and applications: Our findings show that repeated restoration efforts, together with elevated precipitation, can support native species re-establishment in systems experiencing altered disturbance regimes and species invasions. Our unique long-term dataset demonstrates, however, that many such interventions have short-lived effects due to the strong “unhelpful resilience” of highly invaded systems. This implicitly suggests that many such systems have experienced fundamental shifts in ecosystem state. The likelihood of this occurring is strongly associated with the dominant species post-fire regeneration traits. We predict that community composition and resilience will continue to degrade in the sagebrush-steppe unless management prioritizes fire suppression and an adaptive restoration approach that considers resource availability. Methods See published article in the Journal of Applied Ecology for method details.

Woody encroachment is one of the greatest threats to grasslands globally, depleting a suite of ecosystem services, including forage production and grassland biodiversity. Recent evidence also suggests that woody encroachment increases wildfire danger, particularly in the Great Plains of North America, where highly volatile Juniperus spp. convert grasslands to an alternative woodland state. Spot-fire distances are a critical component of wildfire danger, describing the distance over which embers from one fire can cause a new fire ignition, potentially far away from fire suppression personnel. We assess changes in spot-fire distances as grasslands experience Juniperus encroachment to an alternative woodland state and how spot-fire distances differ under typical prescribed fire conditions compared to conditions observed during wildfire. We use BehavePlus to calculate spot-fire distances for these scenarios within the Loess Canyons Experimental Landscape, Nebraska, U.S.A., a 73,000-ha ecoregion where private-lands fire management is used to reduce woody encroachment and prevent further expansion of Juniperus fuels. We found prescribed fire used to control woody encroachment had lower maximum spot-fire distances compared to wildfires and, correspondingly, a lower amount of land area at risk to spot-fire occurrence. Under more extreme wildfire scenarios, spot-fire distances were 2 times higher in grasslands, and over 3 times higher in encroached grasslands and Juniperus woodlands compared to fires burned under prescribed fire conditions. Maximum spot-fire distance was 450% greater in Juniperus woodlands compared to grasslands and exposed an additional 14,000 ha of receptive fuels, on average, to spot-fire occurrence within the Loess Canyons Experimental Landscape. This study demonstrates that woody encroachment drastically increases risks associated with wildfire, and that spot fire distances associated with woody encroachment are much lower in prescribed fires used to control woody encroachment compared to wildfires. Methods This data was generated using BehavePlus software to calculate maximum spot-fire distance under multiple different wind speeds and woody encroachment scenarios. BehavePlus models were parameterized using wind speeds listed in the data set along with additional fuel parameters outlined in the associated manuscript (10.1371/journal.pone.0283816), which were used to represent 4 different encroachment scenarios: grassland state, encroached grassland, torching tree, and woodland. Prescribed fires were predicted to only occur under wind speeds between 0–20 mph while wildfires could occur between 0–80 mph.

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service contains all wildland fire incidents from the IRWIN (Integrated Reporting of Wildland Fire Information) integration service that meet the following criteria:Categorized in IRWIN as a Wildfire (WF), Prescribed Fire (RX), or Incident Complex (CX) recordHas not been declared contained, controlled, nor outHas not had fire report record completed (certified)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other records"Fall-off" rules are used to ensure that stale records are not retained. Records are removed from this service under the following conditions:Fire size is less than 10 acres (Size Class A or B), and fire information has not been updated in more than 3 daysFire size is between 10 and 100 acres (Size Class C), and fire information hasn't been updated in more than 8 daysFire size is larger than 100 acres (Size Class D-L), but fire information hasn't been updated in more than 14 days.Fire size used in the fall off rules is from the IncidentSize field.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed from IRWIN every 5 minutes.Fall-off rules are enforced hourly.Full details: https://data-nifc.opendata.arcgis.com/datasets/nifc::current-wildland-fire-incident-locations/about

Important: Our technical support team is available to assist you during business hours only. Please keep in mind that we can only address technical difficulties during these hours. When using the product to make decisions, please take this into consideration.

Abstract This spatial product shows consistent ‘near real-time’ bushfire and prescribed burn boundaries for all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. Currency Maintenance of the underlying data is the responsibility of the custodian. Geoscience Australia has automated methods of regularly checking for changes in source data. Once detected the dataset and feeds will be updated as soon as possible. NOTE: The update frequency of the underlying data from the jurisdictions varies and, in most cases, does not line up to this product’s update cycle. Date created: November 2023 Modification frequency: Every 15 Minutes Spatial Extent

West Bounding Longitude: 113° South Bounding Latitude: -44° East Bounding Longitude: 154° North Bounding Latitude: -10°

Source Information The project team initially identified a list of potential source data through jurisdictional websites and the Emergency Management LINK catalogue. These were then confirmed by each jurisdiction through the EMSINA National and EMSINA Developers networks. This Webservice contains authoritative data sourced from:

Australian Capital Territory - Emergency Service Agency (ESA) New South Wales - Rural Fire Service (RFS) Queensland - Queensland Fire and Emergency Service (QFES) South Australia - Country Fire Service (CFS)
Tasmania - Tasmania Fire Service (TFS)
Victoria – Department of Environment, Land, Water and Planning (DELWP)
Western Australia – Department of Fire and Emergency Services (DFES)

The completeness of the data within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Known Limitations:

This dataset does not contain information from the Northern Territory government. This dataset contains a subset of the Queensland bushfire boundary data. The Queensland ‘Operational’ feed that is consumed within this National Database displays a the last six (6) months of incident boundaries. In order to make this dataset best represent a ‘near-real-time’ or current view of operational bushfire boundaries Geoscience Australia has filtered the Queensland data to only incorporate the last two (2) weeks data. Geoscience Australia is aware of duplicate data (features) may appear within this dataset. This duplicate data is commonly represented in the regions around state borders where it is operationally necessary for one jurisdiction to understand cross border situations. Care must be taken when summing the values to obtain a total area burnt. The data within this aggregated National product is a spatial representation of the input data received from the custodian agencies. Therefore, data quality and data completion will vary. If you wish to assess more information about specific jurisdictional data and/or data feature(s) it is strongly recommended that you contact the appropriate custodian.

The accuracy of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Note: Geoscience Australia has, where possible, attempted to align the data to the (as of October 2023) draft National Current Incident Extent Feeds Data Dictionary. However, this has not been possible in all cases. Work to progress this alignment will be undertaken after the publication of this dataset, once this project enters a maintenance period. Catalog entry: Bushfire Boundaries – Near Real-Time Lineage Statement Version 1 and 2 (2019/20): This dataset was first built by EMSINA, Geoscience Australia, and Esri Australia staff in early January 2020 in response to the Black Summer Bushfires. The product was aimed at providing a nationally consistent dataset of bushfire boundaries. Version 1 was released publicly on 8 January 2020 through Esri AGOL software.
Version 2 of the product was released in mid-February as EMSINA and Geoscience Australia began automating the product. The release of version 2 exhibited a reformatted attributed table to accommodate these new automation scripts. The product was continuously developed by the three entities above until early May 2020 when both the scripts and data were handed over to the National Bushfire Recovery Agency. The EMSINA Group formally ended their technical involvement with this project on June 30, 2020. Version 3 (2020/21): A 2020/21 version of the National Operational Bushfire Boundaries dataset was agreed to by the Australian Government. It continued to extend upon EMSINA’s 2019/20 Version 2 product. This product was owned and managed by the Australian Government Department of Home Affairs, with Geoscience Australia identified as the technical partners responsible for development and delivery. Work on Version 3 began in August 2020 with delivery of this product occurring on 14 September 2020. Version 4 (2021/22): A 2021/22 version of the National Operational Bushfire Boundaries dataset was produced by Geoscience Australia. This product was owned and managed by Geoscience Australia, who provided both development and delivery. Work on Version 4 began in August 2021 with delivery of this product occurring on 1 September 2021. The dataset was discontinued in May 2022 because of insufficient Government funding. Version 5 (2023/25): A 2023/25 version of the National Near-Real-Time Bushfire Boundaries dataset is produced by Geoscience Australia under funding from the National Bushfire Intelligence Capability (NBIC) - CSIRO. NBIC and Geoscience Australia have also partnered with the EMSINA Group to assist with accessing and delivering this dataset. This dataset is the first time where the jurisdictional attributes are aligned to AFAC’s National Bushfire Schema.
Work on Version 5 began in August 2023 and was released in late 2023 under formal access arrangements with the States and Territories. Data Dictionary Geoscience Australia has not included attributes added automatically by spatial software processes in the table below.

Attribute Name Description

fire_id ID attached to fire (e.g. incident ID, Event ID, Burn ID).

fire_name Incident name. If available.

fire_type Binary variable to describe whether a fire was a bushfire or prescribed burn.

ignition_date The date of the ignition of a fire event. Date and time are local time zone from the State where the fire is located and stored as a string.

capt_date The date of the incident boundary was captured or updated. Date and time are local time zone from the Jurisdiction where the fire is located and stored as a string.

capt_method Categorical variable to describe the source of data used for defining the spatial extent of the fire.

area_ha Burnt area in Hectares. Currently calculated field so that all areas calculations are done in the same map projection. Jurisdiction supply area in appropriate projection to match state incident reporting system.

perim_km ) Burnt perimeter in Kilometres. Calculated field so that all areas calculations are done in the same map projection. Jurisdiction preference is that supplied perimeter calculations are used for consistency with jurisdictional reporting.

state State custodian of the data. NOTE: Currently some states use and have in their feeds cross border data

agency Agency that is responsible for the incident

date_retrieved The date and time that Geoscience Australia retrieved this data from the jurisdictions, stored as UTC. Please note when viewed in ArcGIS Online, the date is converted from UTC to your local time.

Contact Geoscience Australia, clientservices@ga.gov.au

Important: Our technical support team is available to assist you during business hours only. Please keep in mind that we can only address technical difficulties during these hours. When using the product to make decisions, please take this into consideration.

Abstract This spatial product shows accumulating 3-hourly snapshots of bushfire and prescribed burn boundaries, consistent across all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. This dataset is derived from the National Near-Real-Time Bushfire Boundaries product. Currency Maintenance of the underlying data is the responsibility of the individual custodian. NOTE: The update frequency of the underlying data from the jurisdictions varies and, in most cases, does not line up to this product’s update cycle. Date created: November 2023 Modification frequency: Every 3 Hours Spatial Extent

West Bounding Longitude: 113° South Bounding Latitude: -44° East Bounding Longitude: 154° North Bounding Latitude: -10°

Source Information This dataset is derived from the National Near-Real-Time Bushfire Boundaries product. The project team initially identified a list of potential source data through jurisdictional websites and the Emergency Management LINK catalogue. These were then confirmed by each jurisdiction through the EMSINA National and EMSINA Developers networks. This Webservice contains authoritative data sourced from:

Australian Capital Territory - Emergency Service Agency (ESA) New South Wales - Rural Fire Service (RFS) Queensland - Queensland Fire and Emergency Service (QFES) South Australia - Country Fire Service (CFS)
Tasmania - Tasmania Fire Service (TFS)
Victoria – Department of Environment, Land, Water and Planning (DELWP)
Western Australia – Department of Fire and Emergency Services (DFES)

The completeness of the data within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Known Limitations:

This dataset does not contain information from the Northern Territory government. This dataset contains a subset of the Queensland bushfire boundary data. The Queensland ‘Operational’ feed that is consumed within this National Database displays a the last six (6) months of incident boundaries. In order to make this dataset best represent a ‘near-real-time’ or current view of operational bushfire boundaries Geoscience Australia has filtered the Queensland data to only incorporate the last two (2) weeks data. Geoscience Australia is aware of duplicate data (features) may appear within this dataset. This duplicate data is commonly represented in the regions around state borders where it is operationally necessary for one jurisdiction to understand cross border situations. Care must be taken when summing the values to obtain a total area burnt. The data within this aggregated National product is a spatial representation of the input data received from the custodian agencies. Therefore, data quality and data completion will vary. If you wish to assess more information about specific jurisdictional data and/or data feature(s) it is strongly recommended that you contact the appropriate custodian.

The accuracy of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Note: Geoscience Australia has, where possible, attempted to align the data to the (as of October 2023) draft National Current Incident Extent Feeds Data Dictionary. However, this has not been possible in all cases. Work to progress this alignment will be undertaken after the publication of this dataset, once this project enters a maintenance period. Catalog entry: Bushfire Boundaries – 3-Hourly Accumulation Lineage Statement Version 1 (2021/22): A 2021/22 version of the National 3 Hourly Cumulative Bushfire Boundaries dataset was produced by Geoscience Australia. This product was owned and managed by Geoscience Australia, who provided both development and delivery. Work on Version 1 of this dataset began in August 2021 with delivery occurring in September 2021. The dataset was discontinued in May 2022 due to insufficient Government funding. Version 2 (2023/25): A 2023/25 version of National Near-Real-Time Bushfire Boundaries dataset is produced by Geoscience Australia under funding from the National Bushfire Intelligence Capability (NBIC) - CSIRO. NBIC and Geoscience Australia have also partnered with the EMSINA Group to assist with accessing and delivering this dataset. This dataset is the first time where the jurisdictional attributes are aligned to AFAC’s National Bushfire Schema.
Work on Version 2 began in August 2023 and was released in late 2023 under formal access arrangements with the States and Territories. Data Dictionary Geoscience Australia has not included attributes added automatically by spatial software processes in the table below.

Attribute Name Description

fire_id ID attached to fire (e.g. incident ID, Event ID, Burn ID).

fire_name Incident name. If available.

fire_type Binary variable to describe whether a fire was a bushfire or prescribed burn.

ignition_date The date of the ignition of a fire event. Date and time are local time zone from the State where the fire is located and stored as a string.

capt_date The date of the incident boundary was captured or updated. Date and time are local time zone from the Jurisdiction where the fire is located and stored as a string.

capt_method Categorical variable to describe the source of data used for defining the spatial extent of the fire.

area_ha Burnt area in Hectares. Currently calculated field so that all areas calculations are done in the same map projection. Jurisdiction supply area in appropriate projection to match state incident reporting system.

perim_km ) Burnt perimeter in Kilometres. Calculated field so that all areas calculations are done in the same map projection. Jurisdiction preference is that supplied perimeter calculations are used for consistency with jurisdictional reporting.

state State custodian of the data. NOTE: Currently some states use and have in their feeds cross border data

agency Agency that is responsible for the incident

date_retrieved The date and time that Geoscience Australia retrieved this data from the jurisdictions, stored as UTC. Please note when viewed in ArcGIS Online, the date is converted from UTC to your local time.

Contact Contact: Geoscience Australia clientservices@ga.gov.au

The United States electric grid, a vast and complex infrastructure, has experienced numerous outages from 2002 to 2023, with causes ranging from extreme weather events to cyberattacks and aging infrastructure. The resilience of the grid has been tested repeatedly as demand for electricity continues to grow while climate change exacerbates the frequency and intensity of storms, wildfires, and other natural disasters.

Between 2002 and 2023, the U.S. Department of Energy recorded thousands of power outages, varying in scale from localized blackouts to large-scale regional failures affecting millions. The Northeast blackout of 2003 was one of the most significant, impacting 50 million people across the United States and Canada. A software bug in an alarm system prevented operators from recognizing and responding to transmission line failures, leading to a cascading effect that took hours to contain and days to restore completely.

Weather-related disruptions have been among the most common causes of outages, particularly hurricanes, ice storms, and heatwaves. In 2005, Hurricane Katrina devastated the Gulf Coast, knocking out power for over 1.7 million customers. Similarly, in 2012, Hurricane Sandy caused widespread destruction in the Northeast, leaving over 8 million customers in the dark. More recently, the Texas winter storm of February 2021 resulted in one of the most catastrophic power failures in state history. Unusually cold temperatures overwhelmed the state’s independent power grid, leading to equipment failures, frozen natural gas pipelines, and rolling blackouts that lasted days. The event highlighted vulnerabilities in grid preparedness for extreme weather, particularly in regions unaccustomed to such conditions.

Wildfires in California have also played a significant role in grid outages. The state's largest utility companies, such as Pacific Gas and Electric (PG&E), have implemented preemptive power shutoffs to reduce wildfire risks during high-wind events. These Public Safety Power Shutoffs (PSPS) have affected millions of residents, causing disruptions to businesses, emergency services, and daily life. The 2018 Camp Fire, the deadliest and most destructive wildfire in California history, was ignited by faulty PG&E transmission lines, leading to increased scrutiny over utility maintenance and fire mitigation efforts.

In addition to natural disasters, cyber threats have emerged as a growing concern for the U.S. electric grid. In 2015 and 2016, Russian-linked cyberattacks targeted Ukraine’s power grid, serving as a stark warning of the potential vulnerabilities in American infrastructure. In 2021, the Colonial Pipeline ransomware attack, while not directly targeting the electric grid, demonstrated how critical energy infrastructure could be compromised, leading to widespread fuel shortages and economic disruptions. Federal agencies and utility companies have since ramped up investments in cybersecurity measures to protect against potential attacks.

Aging infrastructure remains another pressing issue. Many parts of the U.S. grid were built decades ago and have not kept pace with modern energy demands or technological advancements. The shift towards renewable energy sources, such as solar and wind, presents new challenges for grid stability, requiring updated transmission systems and improved energy storage solutions. Federal and state governments have initiated grid modernization efforts, including investments in smart grids, microgrids, and battery storage to enhance resilience and reliability.

Looking forward, the future of the U.S. electric grid depends on continued investments in infrastructure, cybersecurity, and climate resilience. With the increasing electrification of transportation and industry, demand for reliable and clean energy will only grow. Policymakers, utility companies, and regulators must collaborate to address vulnerabilities, adapt to emerging threats, and ensure a more robust, efficient, and sustainable electric grid for the decades to come.

A Fire Hazard Severity Zone (FHSZ) is a mapped area that designates zones (based on factors such as fuel, slope, and fire weather) with varying degrees of fire hazard (i.e., moderate, high, and very high). FHSZ maps evaluate wildfire hazards, which are physical conditions that create a likelihood that an area will burn over a 30- to 50-year period. They do not take into account modifications such as fuel reduction efforts.

While FHSZs do not predict when or where a wildfire will occur, they do identify areas where wildfire hazards could be more severe and therefore are of greater concern. FHSZs are meant to help limit wildfire damage to structures through planning, prevention, and mitigation activities/requirements that reduce risk. The FHSZs serve several purposes: they are used to designate areas where California’s wildland urban interface building codes apply to new buildings; they can be a factor in real estate disclosure; and local governments consider fire hazard severity in the safety elements of their general plans.

This service includes proposed Fire Hazard Severity Zones for State Responsibility Area lands and separate draft Very High Fire Hazard Severity Zones for Local Responsibility Area lands. Moderate, high, and very high FHSZs are found in areas where the State has financial responsibility for fire protection and prevention (SRA). Only very high FHSZs are found in Local Responsibility Areas (LRAs).

This service represents the latest release of FHSZ. It will be updated when a new version is released. As of August 2018, it represents fhszl11_1 and fhszs06_3.

This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning, the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, and the susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events. FSim generates stochastic simulation data based on many thousands of iterations and then integrates those into a probabilistic result. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 trend in wildfire occurrence. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Recreation Infrastructure eNVC represents risk integrated across all Recreation Infrastructure sub-HVRAs. The Recreation HVRA is intended to evaluate wildfire risk to outdoor recreation infrastructure. Mapping the extent of recreation infrastructure required a wide range of data sets and methods according to what was available from each landowner or agency. The extent and characterization of Recreation Infrastructure was based on spatial data provided by multiple state and federal agencies including, Oregon State Parks, Oregon Department of Forestry, Washington State Parks and Recreation Commission, U.S. Department of the Interior, and the U.S. Forest Service. Sub-HVRAs included:Low Development Recreation Sites, including toilets, trail heads, and other minimally developed access points.High Development Recreation Sites, including ranger stations, playgrounds, developed campsites, ski areas, etc. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations:Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zMicrosoft, 2018. Computer generated building footprints for the United States GitHub repository.Primary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning, the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, and the susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events. FSim generates stochastic simulation data based on many thousands of iterations and then integrates those into a probabilistic result. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 trend in wildfire occurrence. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Timber eNVC represents risk integrated across all Timber sub-HVRAs. The timber HVRA is intended to evaluate wildfire risk to commercial timber resources. We grouped sub-HVRAs based on three criteria: land manager, assumed management priority, and timber size class. Land managers included private, state, U.S. Forest Service, Bureau of Land Management, and Tribal entities. Sub-HVRAs include:Private, Non-industrial, QMD < 10"Private, Non-industrial, QMD 10" - 20"Private, Non-industrial, QMD > 20"Private, Industrial, QMD < 10"Private, Industrial, QMD 10" - 20"Private, Industrial, QMD > 20"Tribal, Active Management, QMD < 10"Tribal, Active Management, QMD 10" - 20"Tribal, Active Management, QMD > 20"Tribal, Other Management, QMD < 10"Tribal, Other Management, QMD 10" - 20"Tribal, Other Management, QMD > 20"U.S. Forest Service, Active Management, QMD < 10"U.S. Forest Service, Active Management, QMD 10" - 20"U.S. Forest Service, Active Management, QMD > 20"U.S. Forest Service, Other Management, QMD < 10"U.S. Forest Service, Other Management, QMD 10" - 20"U.S. Forest Service, Other Management, QMD > 20"BLM, Active Management, QMD < 10"BLM, Active Management, QMD 10" - 20"BLM, Active Management, QMD > 20"BLM, Other Management, QMD < 10"BLM, Other Management, QMD 10" - 20"BLM, Other Management, QMD > 20"State, QMD < 10"State, QMD 10" - 20"State, QMD > 20"Methods for mapping the extent of each land manager’s timberlands are described in detail in the following sections. We used assumed management priority criteria to distinguish between lands where commercial timber management is the primary objective from those lands where commercial timber management is part of a multiple-use strategy. Tribal Active Management, U.S. Forest Service Active Management, BLM Active Management, and Private Industrial sub-HVRAs all represent timberlands where commercial timber management is assumed to be the primary management objective. Within all other timber sub-HVRAs, commercial timber management is presumed to be one of several equally important management objectives. State and federal agencies made these designations on public land and used available data for Tribally-managed lands. We mapped timber size class data using Quadratic Mean Diameter (QMD) from the most recent forest structure data available which approximates forest structure in 2021 (LEMMA, 2023a). We included the fire regime group (FRG), along with the timber size class, as a covariate to explain the response to fire. We gave all land managers equal relative importance, but within a land manager type, about twice as much importance was placed on active management timberlands compared to timberlands with multiple, equally important management objectives. Additionally, within any sub-HVRA the most relative importance was assigned to the largest size class and the least was assigned to the smallest size class. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations:Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zLEMMA, 2023a. Greatest Nearest Neighbor (GNN): QMD_DOM, unpublished.Primary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov