Wildfires: Difference between revisions

From CRL Wiki
Jump to navigation Jump to search
CRLADMIN (talk | contribs)
 
(79 intermediate revisions by 2 users not shown)
Line 1: Line 1:
[[File:Wildfires.webp|thumb|700px| Wildfires (Source: MIT Technology Review<ref>Retrived from https://www.technologyreview.com/2024/08/28/1103186/canada-wildfire-emissions/ on Oct 24, 2024</ref>)]]
== What is wildfire? ==
== What is wildfire? ==
Wildfires are unplanned fires that occur in wildlands such as forest, rangelands or grasslands. They can occur naturally (ignited by lightning), or be caused by human activities such as campfires, faulty power lines, and burning crop residues. Other than those ignition sources, wildfires also need fuels and the proper meteorological condition to start and spread.  
Wildfires are unplanned fires that occur in wildlands such as forest, rangelands or grasslands. They can occur naturally (ignited by lightning), or be caused by human activities such as campfires, faulty power lines, and burning crop residues. Other than those ignition sources, wildfires also need fuels and the proper meteorological condition to start and spread.  


'''Fuels''' refer to anything that can burn, trees, bushes, grasses, fallen leaves. The availability of fuel is determined in large part by management practices and ecosystem processes and . For example, deforestation leaves behind slash, which are highly inflammable. Expansion of fire-resistant invasive annual grasses is one of the dominant factor in largely increasing the number, frequency, and severity of rangeland wildfires in the Northwest<ref>https://www.climatehubs.usda.gov/hubs/northwest/topic/climate-change-and-wildfire-northwest-rangelands</ref>.  
'''Fuels''' refer to anything that can burn, trees, bushes, grasses, fallen leaves. The availability of fuel is determined in large part by management practices and ecosystem processes and . For example, deforestation leaves behind slash, which are highly inflammable. Expansion of fire-resistant invasive annual grasses is one of the dominant factor in largely increasing the number, frequency, and severity of rangeland wildfires in the Northwest<ref name=":0">https://www.climatehubs.usda.gov/hubs/northwest/topic/climate-change-and-wildfire-northwest-rangelands</ref>.  


'''Meteorological''' '''conditions,''' specifically high temperature, low humidity, and wind play a significant role in triggering and sustaining a fire.  
'''Meteorological''' '''conditions,''' specifically high temperature, low humidity, and wind play a significant role in triggering and sustaining a fire.  
Line 13: Line 15:
Wildfire activities have significantly increased in the past decades in Alaska and the western United States. [https://www.epa.gov/climate-indicators/climate-change-indicators-wildfires#:~:text=Multiple%20studies%20have%20found%20that%20climate%20change,season%20length%2C%20wildfire%20frequency%2C%20and%20burned%20area.&text=The%20wildfire%20season%20has%20lengthened%20in%20many,dry%20seasons%2C%20and%20drier%20soils%20and%20vegetation. Statistics] show that the number of large fire occurrences, fire extent, fire severity, and fire season length have all increased since 1980. These changes are closely related to climate change both directly and indirectly.   
Wildfire activities have significantly increased in the past decades in Alaska and the western United States. [https://www.epa.gov/climate-indicators/climate-change-indicators-wildfires#:~:text=Multiple%20studies%20have%20found%20that%20climate%20change,season%20length%2C%20wildfire%20frequency%2C%20and%20burned%20area.&text=The%20wildfire%20season%20has%20lengthened%20in%20many,dry%20seasons%2C%20and%20drier%20soils%20and%20vegetation. Statistics] show that the number of large fire occurrences, fire extent, fire severity, and fire season length have all increased since 1980. These changes are closely related to climate change both directly and indirectly.   


Climate change drives increase in fire activity directly by inducing higher temperatures, reduced winter snowpack, earlier snowmelt, decreased summer precipitation, and increased evaporation. These conditions creates a more favorable condition for the start and spread of wildfires.   
Climate change drives increase in fire activity directly by inducing higher temperatures, reduced winter snowpack, earlier snowmelt, decreased summer precipitation, and increased evaporation. These conditions creates a more favorable condition for the start and spread of wildfires<ref>https://www.sciencebase.gov/catalog/item/5956a1b5e4b0d1f9f050d917</ref>.   


Indirectly, climate change drives those changes in wildfire by changing the ecosystems. For example, climate change degrades forest, creates conditions that favor the expansion of fire-resistant invasive species, and promotes beetle outbreaks that have killed millions of acres of trees and resulted in more flammable fuels.   
Indirectly, climate change drives those changes in wildfire by changing the ecosystems. For example, climate change degrades forest, creates conditions that favor the expansion of fire-resistant invasive species, and promotes beetle outbreaks that have killed millions of acres of trees and resulted in more flammable fuels.   
Line 22: Line 24:
Wildfires have significant impacts on environment, human health, and infrastructure. (Drought has extensive impacts across multiple sectors'','' affecting ecosystems, agriculture, water resources, energy production, commerce, public health, and infrastructure stability.)   
Wildfires have significant impacts on environment, human health, and infrastructure. (Drought has extensive impacts across multiple sectors'','' affecting ecosystems, agriculture, water resources, energy production, commerce, public health, and infrastructure stability.)   


* '''Public Health:'''  
* '''Public Health''' Wildfire smoke, which contains various air pollutants, poses a major public health risk, primarily due to particulate matter (PM2.5). Inhalation of smoke and this fine particulate matter produced by wildfires causes respiratory issues. These issues can range from irritation of the respiratory system (nose, mouth, throat, and lungs) to serious problems like bronchitis or asthma. The lack of oxygen from inhaling smoke, humans can experience serious cardiovascular issues, including heart attack or heart failure, because of wildfires<ref>https://www.who.int/health-topics/wildfires#tab=tab_2</ref><ref>https://wfca.com/wildfire-articles/negative-effects-of-wildfires/</ref><ref>Health effects of wildfire smoke by EPA: https://www.epa.gov/air-research/research-health-effects-air-pollution#health-effects-wildfire-smoke</ref>.
** impact air, water quality: https://deq.utah.gov/communication/news/wildfires-impact-on-our-environment<nowiki/>https://wfca.com/wildfire-articles/negative-effects-of-wildfires/<nowiki/>https://www.who.int/health-topics/wildfires#tab=tab_2
* '''Ecosystem and Biodiversity''' Wildfires will likely change the '''forests''' composition. Frequent fires can hinder the regeneration of certain tree species, allowing shrubs and grasses to dominate for extended periods. Frequent fire will also likely reduce the abundance of shade-tolerant species and gradually lead to forests dominated by fire-resistant species, such as Douglas-fir and western larch, instead of fire-susceptible species like western hemlock and subalpine fir. Additionally, increase in fire frequency will also likely result in more young forests as older, late-successional forests burn. Frequent fires will likely replace native plants by invasive annual '''grassland''' as invasive grasses produce many seeds and can reestablish more quickly after a wildfire. All these changes will change the number and composition of animal species that depend on forests or grasslands as their habitat<ref>https://uw.maps.arcgis.com/apps/Cascade/index.html?appid=9c0f8668f47c4773b56c9b9ae6c301e3</ref>, which, in turn, may affect the cultural values, as well as the experience of hunters, anglers, and recreationalists.
** impact property and ecology: https://wfca.com/wildfire-articles/negative-effects-of-wildfires/
**


*'''Livestock''': Wildfires impact livestock by disrupting grazing rotations, stocking rates, and rangeland management. They directly damage grazing land, often leading to the closure of public grazing allotments for several years to allow for restoration. This forces ranchers and rangeland managers to find alternative, often costly and time-consuming, sources of summer forage. Additionally, wildfires promote the expansion of invasive annual grasses, which outcompete native grasses that provide late-season forage, further reducing the availability of palatable forage for livestock.<ref name=":0" />
*'''Water resources''' Wildfires can contaminate water quality and impact water supply within watersheds.They bring more sediments, eroded soil, ashes and debris from fires, as well as heavy metals and toxins into nearby water sources. These substances pollute the water and make it unsafe for human or animal consumption, as well as disrupt or destroy aquatic life<ref>https://deq.utah.gov/communication/news/wildfires-impact-on-our-environment</ref>. Additionally, decreased vegetation increases runoff, reduces groundwater recharge, and diminishes overall water availability.
*'''Buildings and other key infrastructures''' Though started in the wildland, wildfires can easily spread and cause large damages to both residential and industrial infrastructures. For example, the Camp Fire occurred in Northern California in November 2018 destroyed more than 18,000 structures, including nearly 14,000 homes, and significant damage to critical infrastructure such as power lines, roads, and communication networks. Roads and highways can also be impacted by the heat, flames, and falling debris and became impassable.
*'''Power and Energy''' Wildfires severely impact the power and energy sector by damaging or destroying energy infrastructure, such as power lines, power plants, transformers, and substations. They also disrupt operations through [https://www.cpuc.ca.gov/psps/ Public Safety Power Shutoffs]. While these shutdowns effectively reduce the likelihood of ignition, they are extremely costly. For example, a study by a scholar at the Stanford Woods Institute for the Environment estimated that the PSPS in October 2019 cost California’s economy up to $2.5 billion<ref>https://www.cnbc.com/2019/10/10/pge-power-outage-could-cost-the-california-economy-more-than-2-billion.html</ref>.
==Data==


=== Historical Wildfire Data ===
<div style="margin-left: 90px;">
{| class="wikitable" style=width:70em
|+
!Dataset
!Description
!Spatial Coverage
!Temporal coverage
!Data Access
|-
|[https://www.nifc.gov/nicc/predictive-services/intelligence Annual Incident Management Situation Report] by The National Interagency Coordination Center
|The [https://www.nifc.gov/nicc/predictive-services/intelligence annual Incident Management Situation Report] by The National Interagency Coordination Center<ref name=":1">https://www.nifc.gov/fire-information</ref> provides comprehensive statistics on various aspects, including burned areas, number of human-caused and lightning-caused fires, detailed suppression and mobilization cost. The following statistics are explicitly extracted and provided:
* [https://www.nifc.gov/fire-information/statistics/wildfires US total burned areas and number of fires]
* [https://www.nifc.gov/fire-information/statistics/suppression-costs US total suppression cost]
* [https://www.nifc.gov/fire-information/statistics/lightning-caused Number of lightning-caused fires by geographic area]
* [https://www.nifc.gov/fire-information/statistics/human-caused Number of human-caused fires by geographic area]
* [https://www.sciencebase.gov/catalog/item/5ee13de982ce3bd58d7be7e7 Combined wildfire datasets for the US and certain territories 1878-2019]
|US
|1983 to present
|(see "Description")
|-
|Hazard Mapping System Fire and Smoke Product
|Fire and smoke of the US  compiled from satellite images by NOAA. Statistics such as fire and smoke frequency are also provided.
|US
|
|[https://www.ospo.noaa.gov/products/land/hms.html#maps View];[https://www.ospo.noaa.gov/products/land/hms.html#data Access]
|-
|MTBS database
|[https://www.mtbs.gov/ MTBS] multi-agency project maintains a database of wildfire occurrence and burned areas of the US from 1984 to present. 
|US
|1984 to present
|[https://www.mtbs.gov/direct-download Access]
|-
|Global Wildfire Information System (GWIS)
|The [https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/ statistics portal] by Global Wildfire Information System (GWIS)<ref name=":2">https://gwis.jrc.ec.europa.eu/</ref> offers the number of fires, burned area, as well as their seasonal trend by country. Its [https://gwis.jrc.ec.europa.eu/apps/country.profile/downloads Country Profile] provides detailed information of numbers of fires, burned area, emission ''etc.'' by landcover class for all countries for years 2002-2023. [https://ourworldindata.org/wildfires Our World in Data] visualizes some of these statistics in chart.
|Global
|2002 -2023
|[https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/ Access];
|-
|Global Fire Emissions Database (GFED)
|Global Fire Emissions Database (GFED) v4<ref name=":3">Randerson, J.T., G.R. van der Werf, L. Giglio, G.J. Collatz, and P.S. Kasibhatla. 2018. Global Fire Emissions Database, Version 4.1 (GFEDv4). ORNL DAAC, Oak Ridge, Tennessee, USA. <nowiki>https://doi.org/10.3334/ORNLDAAC/1293</nowiki></ref> provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types. Data is at 0.25-degree resolution and is available from June 1995 through 2016.
|Global
|06/1995 - 2016
|[https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html Access]
|-
|[https://confluence.ecmwf.int/display/CEMS/Fire+danger+indices+historical+data+from+the+Copernicus+Emergency+Management+Service Fire danger indices] by European Forest Fire Information System (EFFIS)<ref>Vitolo, C., Di Giuseppe, F., Barnard, C. ''et al.'' ERA5-based global meteorological wildfire danger maps. ''Sci Data'' '''7,''' 216 (2020). <nowiki>https://doi.org/10.1038/s41597-020-0554-z</nowiki></ref>
|Fire danger indices at 0.25-degree produced by EFFIS.  The EFFIS incorporates the fire danger indices for three different models developed in Canada, United States and Australia. This dataset is produced by the [https://confluence.ecmwf.int/pages/viewpage.action?pageId=304221148 GEFF] model, using the historical weather information of [https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 ERA5] reanalysis. Fourteen variables are provided for downloading, including fire weather index<ref name=":4">The Fire Weather Index (FWI) evaluates conditions that increase the danger of wildfires, such as the impact of moisture and wind on wildfire intensity and spread. Higher FWI values represent greater danger of wildfires due to weather conditions; the index does not account for land cover or potential ignition sources. This dataset can be used as a regional approach in assessing future wildfire danger and risks from fires. See more information at NWCG: [https://www.nwcg.gov/publications/pms437/cffdrs/fire-weather-index-fwi-system#:~:text=The%20Fire%20Weather%20Index%20(FWI,Again%2C%20unitless%20and%20open%20ended. https://www.nwcg.gov/publications/pms437/cffdrs/fire-weather-index-fwi-system#:~:text=The%20Fire%20Weather%20Index%20(FWI,Again%2C%20unitless%20and%20open%20ended.]</ref> (Canadian rating) and fire danger index (Australian rating). 
|Global
|1940 to present
|[https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview Access]; [https://fire.trainhub.eumetsat.int/docs/figure5_CEMS_GEFF_FWI.html Python code for processing data]; [https://confluence.ecmwf.int/display/CEMS/CEMS-Fire other useful resources]
|-
|Fire weather index<ref name=":4" /> by European Environment Agency (EEA)
|Forest fire danger in the present climate and projected changes under two climate change scenarios, with a spatial resolution of 25 km. Data are derived from the [http://www.euro-cordex.ne EURO-CORDEX runs]. See more details [https://sdi.eea.europa.eu/catalogue/datahub/api/records/d3c0553c-6a4b-49fe-b7ae-9dd58c1d5ea8/formatters/xsl-view?output=pdf&language=eng&approved=true here].
|Europe
|1981 - 2100
|[https://www.eea.europa.eu/en/datahub/datahubitem-view/f2a24bf5-2a7c-42ec-a148-b2a7025a1782 Access]
|-
|Fire data provided by EEA
|
* [https://www.eea.europa.eu/en/datahub/datahubitem-view/3b8c4652-400b-48d8-86ff-3b925fd4bbbb?activeAccordion=1070122 Areas burnt by wildfires] between 2000 and 2017
* [https://www.eea.europa.eu/en/datahub/datahubitem-view/f4d8f368-5707-4b62-aeb5-55d9c8a71a75?activeAccordion=1070123%2C1070121 Average forest fire danger] between 1981 and 2010
|Global
|1981 - 2017
|
|-
|MODIS Active fire
|Active fire data derived from satellite images. The MODIS active fire product<ref>https://modis-fire.umd.edu/ba.html</ref> detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. MODIS C6.1 is available from November 2000 (for Terra) and from July 2002 (for Aqua) to the present.
|Global
|2000 to present
|[https://firms.modaps.eosdis.nasa.gov/download/ Access]; [https://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z View]
|-
|MODIS Burned area<ref>https://lpdaac.usgs.gov/products/mcd64a1v006/</ref>
|Data derived from satellite images. MCD64A1 Version 6 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information.
|Global
|2000 to present
|[https://firms.modaps.eosdis.nasa.gov/download/ Access]; [https://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z View]
|-
|VIIRS Active fire
|Data derived from satellite images at 375 m resolution.
|Global
|2012 to present
|[https://firms.modaps.eosdis.nasa.gov/download/ Access]; [https://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z View]
|-
|VIIRS Burned area
|Data derived from satellite images at 500 m resolution.
|Global
|2012 to present
|[https://viirsland.gsfc.nasa.gov/Products/NASA/BurnedAreaESDR.html Access]; [https://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z View]
|}
</div>


Impacts to human health and safety: Larger, more frequent wildfires threaten '''human safety, infrastructure, and livelihoods'''. For example, in 2023, the Wanes Gray Fire near Medical Lake, Washington burned through rangeland and timber, destroying 259 structures, and burning over 10,000 acres. In 2015, the Soda Fire burned nearly 280,000 acres in southwest Idaho and southeast Oregon, including 200,000 acres of greater sage-grouse habitat and portions of 41 grazing allotments. Fires of this size are becoming more common in the inland Northwest.  
=== Forecast at Near-term (1-7 days ahead) to Seasonal Scale ===
<div style="margin-left: 90px;">
{| class="wikitable" style="width:70em"
|+
!Dataset
!Description
!Spatial Coverage
!Temporal Coverage
!Data Access
|-
|[https://gwis.jrc.ec.europa.eu/applications/data-and-services Fire danger forecast] by EFFIS
|Fire danger forecast produced by the [https://confluence.ecmwf.int/pages/viewpage.action?pageId=304221148 GEFF] model. Variables forecasted include fire weather index, initial spread index, build up index, burning index, and fire danger index.
|Europe
|1 day ahead
|[https://forest-fire.emergency.copernicus.eu/apps/effis_current_situation/ View]; [https://gwis.jrc.ec.europa.eu/applications/data-and-services Access]
|-
|7-day fire potential forecast
|[https://www.nifc.gov/sites/default/files/document-media/7-Day_Product_Description.pdf Fire potential forecast] produced by a fire potential model by NICC. It is a function of fuel conditions, weather, and resource availability. It assesses the daily probability for occurrence of a new large fire and/or the daily potential for significant new growth on existing fires.<ref name=":5">https://www.nifc.gov/sites/default/files/document-media/7-Day_Product_Description.pdf</ref>
|US
|1-7 days ahead
|[https://fsapps.nwcg.gov/psp/npsg/forecast/#/outlooks?state=map View]; [https://www.nifc.gov/nicc/predictive-services/outlooks Access]
|-
|[https://www.usgs.gov/fire-danger-forecast/data USGS fire danger forecast]
|This fire danger forecast (including fire potential index, large fire probability, fire spread probability) forecast is based on the Wildland Fire Potential Index (WFPI) forecast of USGS. WFPI is a numerical rating of fuel availability and ignitability, based on an assessment of the proportion of dead fuel loading and its dryness. It can be used to indicate the “combustibility” of the landscape, with increasing values indicating increasing potential for large fires, defined as fires that burn more than 500 acres. <ref>https://www.usgs.gov/fire-danger-forecast/fire-danger-data-products-and-tools</ref> The forecast is produced by feeding satellite observations and weather forecast into a fuel model.
|US
|1-7 days ahead
|[https://firedanger.cr.usgs.gov/viewer/index.html View]; [https://www.usgs.gov/fire-danger-forecast/data Access]
|-
|Seasonal fire potential outlookby NICC
|Fire potential forecast produced by NICC at the monthly to seasonal scale.
|US
|1-4 months ahead
|[https://www.nifc.gov/nicc/predictive-services/outlooks Access]
|-
|FuleCast
|[https://fuelcast.net/home FuelCast] provides monthly fuel and fire forecasts during the growing season to help users stay up to date on fire danger. It is updated monthly during the growing season.
|
|Monthly
|Probably useful
|}
</div>


Wildfire smoke impacts human health. Smoke degrades air quality, and can severely affect the health of children, the elderly, pregnant individuals, and individuals with respiratory conditions. Wildfires can also reduce water quality. After a fire, runoff and erosion can increase substantially. This can lead to sedimentation and chemical changes that degrade the quality of aquatic habitat and drinking water.
=== Future Projection for One year and beyond ===
 
<div style="margin-left: 90px;">
Loss of sagebrush habitat and conversion to invasive annual grassland: Cheatgrass and other invasive annual grasses produce many seeds and can reestablish very quickly after a wildfire. Native plants like sagebrush and perennial bunchgrasses require more time to reestablish and produce seeds. This leads to a positive feedback loop between invasive annual grasses and wildfire: fire makes room for more cheatgrass, which encourages more fire and so on. Because frequent wildfires make sagebrush recovery nearly impossible, over time, this positive feedback loop can lead to a conversion of sagebrush landscapes to invasive annual grasslands.
{| class="wikitable" style="width:70em"
 
|+
Loss of wildlife habitat: Greater sage-grouse (the largest grouse in North America) depend on sagebrush for breeding habitat and forage. More frequent and severe wildfires can reduce sage-grouse habitat. Since 1984, over 22 million acres of sage-grouse habitat have burned in the Great Basin. If current fire trends continue, half of the sage-grouse population in the Great Basin could be gone by the mid-2040s. Other species that are affected by sagebrush habitat reduction include pygmy rabbits, sage thrashers, and sharp-tailed grouse. Loss of wildlife habitat can affect cultural values, and impact the experience of hunters, anglers, and recreationalists.
!Dataset
 
!Description
Impacts to livestock operations: Both wildfire and annual grasses can impact yearly livestock grazing rotations, stocking rates, and rangeland management. Though invasive annual grasses can provide forage for a short period in spring, they dry out quickly and become unpalatable to livestock. Because they outcompete native grasses that are palatable later in the season, invasive annual grasses reduce the availability of late-season forage. The increasing frequency and severity of rangeland fires can also reduce forage amounts. Following a wildfire, public grazing allotments can be closed for several years to allow restoration of burned areas. In these conditions, ranchers and rangeland managers must find alternate sources of summer forage, which can be expensive and time consuming.
!Spatial Coverage
 
!Temporal Coverage
Impacts to rangeland carbon sequestration potential: The combination of the invasive grass-fire cycle and the loss of woody plants like sagebrush suggest that less carbon can be stored in annual grass-dominated ecosystems than sagebrush systems. Conversion of deep-rooted perennial systems to shallow-rooted annual grasses can result in the loss of persistent below-ground carbon. Because most of the carbon stored in rangeland systems is stored in the soil, losing below-ground carbon has serious implications for rangeland carbon storage potential.
!Data Access
 
|-
 
|CMIP6
=== How will changing wildfire patterns affect Northwest forests? ===
|'''Coupled Model Intercomparison Project Phase 6 (CMIP6)'''<ref name=":6" /> provides a comprehensive set of climate model simulations to understand past, present, and future climate changes. It is currently the leading state-of-the-art resource for future climate projections. The data can be downloaded from http://esgf-node.llnl.gov/search/cmip6/. Navigating the data portal and finding the necessary variables can be challenging, so we provide some guidance below. The model resolution is coarser than 100 km for most models.  
More frequent and severe fires can slow the regrowth of vegetation and alter the species composition of Northwest forest ecosystems. High-severity fire creates opportunities for establishment of invasive species, such as cheatgrass, and can limit tree regeneration. In Northwest forests, a warming climate coupled with more frequent wildfires will lead to a shift away from shade-tolerant, thin-barked, or fire-intolerant species such as western hemlock, subalpine fir, and Engelmann spruce. Species that are fire-tolerant, thick-barked, and have high seed-dispersal rates, like Douglas-fir and ponderosa pine, are likely to fare well. However, with warmer and drier conditions and more frequent disturbance, there are some locations that will likely shift from forest to shrubland or grassland.
|Global
 
|2015 to 2100
Short interval reburns (fires in areas burned within the last 15-20 years) are also likely to occur with increasing frequency. Frequent reburns can shift species composition toward species that are adapted to frequent fire. Some tree species will have a difficult time regenerating if intervals between fires are short, and shrubs and grasses may dominate for extended periods.
|[http://esgf-node.llnl.gov/search/cmip6/ Access]
 
|-
The increasing frequency of wildfire, particularly high-severity wildfires, will usher in more young forests as older, late-successional forests burn. This shift has major implications for species that depend on late-successional forests, such as the Northern spotted owl. However, some species, like deer and elk, could prosper in younger forests.
|CORDEX
 
|Note that the resolution of CMIP6 simulations (coarser than 100 km for most models) is usually too coarse for climate risk analysis and other downstream applications, the '''downscaled CMIP''' data using regional climate modeling by Coordinated Regional Climate Downscaling Experiment (CORDEX) is also available. Note that no direct wildfire indicators are provided in the CORDEX data. Users will need to calculate these indicators using the available variables.
Fire-intolerant species could be replaced by species that are better able to survive fires. The number of trees in dry forests could decrease, and it could be harder for new trees to grow. Areas that have already burned could burn again more easily, which could affect conifer regrowth. As old forests burn, young forests could become more common, which could harm species that live in old forests. Invasive plants could establish more easily after fires and could outcompete native plants. Fire, drought, insect outbreaks, and invasive species could interact to drive forest change in a warming
|Regional
 
|
== Wildfires under climate change ==
|[https://cordex.org/data-access/cordex-cmip5-data/bias-adjusted-rcm-data/. Access]; [https://na-cordex.org/index.html CORDEX-CMIP5 data for North America];
Wildfire activities have significantly increased in the past decades in Alaska and the western United States. [https://www.epa.gov/climate-indicators/climate-change-indicators-wildfires#:~:text=Multiple%20studies%20have%20found%20that%20climate%20change,season%20length%2C%20wildfire%20frequency%2C%20and%20burned%20area.&text=The%20wildfire%20season%20has%20lengthened%20in%20many,dry%20seasons%2C%20and%20drier%20soils%20and%20vegetation. Statistics] show that the number of large fire occurence, fire extent, fire severity, and fire season length have all increased since 1980. Increases in large fire activity and area burned have been driven by rising temperatures, reduced winter snowpack, earlier snowmelt, reduced summer precipitation and increased evaporation. Under climate change we can expect the wildfire activity to increase as temperatures continue to warm, lengthening the fire season further, and as drought continues to afflict wildland ecosystems.
[https://registry.opendata.aws/ncar-na-cordex/ AWS link for EURO-CORDEX]
 
[https://registry.opendata.aws/euro-cordex/ AWS link]
== Wildfire-related risk analysis ==
|-
 
|HighResMIP
=== Wildfire data ===
|Higher-resolution (25 km) CMIP6-like simulations (HighResMIP) is also available for some models. Note that same as CORDEX, HighResMIP data has no direct wildfire indicators.
 
|Global
==== Historical data ====
|
Real-time monitoring data
|[https://esgf-ui.ceda.ac.uk/cog/search/cmip6-ceda/ Access]
 
|-
Forecast data (or called "daily to seasonal scale forecast")
|NARCCAP data
 
|The North American Regional Climate Change Assessment Program (NARCCAP) also provides future projection simulations.
Future projection
|
 
|
 
|[https://www.narccap.ucar.edu/data/access.html Access]
[https://rangelands.app/rap/?biomass_t=herbaceous&ll=39.0000,-98.0000&z=5&landcover_t=afg The Rangeland Analysis Platform] is an online tool that visualizes and analyzes vegetation data (including annual forb and grass coverage) for the United States, including the Northwest.
|-
 
|Fire danger by EEA
[https://rangelands.app/great-basin-fire/ Great Basin Rangeland Fire Probability Map] represents the relative probability of large (> 1,000 acres) rangeland fire given an ignition in a given year. Maps are updated yearly.
|Projected forest fire danger by EEA
 
|Europe
[https://research.fs.usda.gov/rmrs/products/multimedia/webinars/rttl FuelCast] provides monthly fuel and fire forecasts during the growing season to help users stay up to date on fire danger. It is updated monthly during the growing season.
|2071-2100
 
|[https://www.eea.europa.eu/en/datahub/datahubitem-view/1e006660-816d-49da-aaa8-d44cb305efee?activeAccordion=1070108%2C1070123 Access]
 
|-
=== Data Sources ===
|Fire weather index<ref name=":4" /> by European Environment Agency (EEA)
The full set of wildfire frequency and burned acreage data in Figures 1 and 2 comes from the National Interagency Fire Center, which compiles wildfire reports sent from local, state, and federal entities that are involved in fighting fires. These data are available online at: www.nifc.gov/fire-information/statistics. Additional data were provided by the U.S. Forest Service based on a different set of records, referred to as Smokey Bear Reports. Burn severity data, state-by-state acreage totals, and monthly acreage data in Figures 3 through 7 come from the MTBS multi-agency project, which maintains a database of wildfire events across the United States. These data are publicly available at: www.mtbs.gov/direct-download.https://www.mtbs.gov/direct-download
|Forest fire danger in the present climate and projected changes under two climate change scenarios, with a spatial resolution of 25 km. Data are derived from the [http://www.euro-cordex.ne EURO-CORDEX runs]. See more details [https://sdi.eea.europa.eu/catalogue/datahub/api/records/d3c0553c-6a4b-49fe-b7ae-9dd58c1d5ea8/formatters/xsl-view?output=pdf&language=eng&approved=true here].
 
|Europe
 
|1981 - 2100
 
|[https://www.eea.europa.eu/en/datahub/datahubitem-view/f2a24bf5-2a7c-42ec-a148-b2a7025a1782 Access]
=== How does fire make an impact? ===
|-
==Wildfire Data Analysis==
|Fire weather index by ANL
 
|Projected fire weather index over the US by [https://climrr.anl.gov/ Argonne National Laboratory (ANL)] using Argonne’s downscaled 12km climate data.  
 
|US
U.S. Wildfire '''statistics''':
|up until 2050
 
|[https://disgeoportal.egs.anl.gov/portal/apps/webappviewer/index.html?id=e61a6dbeca8c48e9b2309780807ead33 View]; [https://climrr.anl.gov/datacatalog Access]
* [https://www.nifc.gov/fire-information/statistics/wildfires Wildfires and Acres] (burned areas and number of fires)
|-
* [https://www.nifc.gov/fire-information/statistics/suppression-costs Suppression Cost]
|[https://rangelands.app/great-basin-fire/ Great Basin Rangeland Fire Probability Map]
* ([https://www.nifc.gov/nicc/sitreprt.pdf Incident management situation report]) by National Interagency Coordination Center has a lot of statistics. LLM or even some simple coding is useful in extracting this resources
|It represents the relative probability of large (> 1,000 acres) rangeland fire given an ignition in a given year. Maps are updated yearly.
 
|Great basin of the US
=== Global fire statistics: ===
|one year ahead
 
|[https://rangelands.app/great-basin-fire/ Access]
* [https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/estimates burned area by country]
|}
* number of fires by country
</div>
* seasonal trend
 
 
'''Geospatial Data:'''
 
* Active fires (MODIS and VIIRS)
* Burnt Areas (MODIS and VIIRS NRT)
* [https://gwis.jrc.ec.europa.eu/apps/gwis.longterm.forecasts/Seasonal/Europe Monthly and seasonal forecast of temperature and precipitation by regions]
* [https://gwis.jrc.ec.europa.eu/applications/data-and-services Fire danger forecast (+1 day)]
* [https://gwis.jrc.ec.europa.eu/apps/country.profile/downloads Historical data:]  
* [https://ourworldindata.org/wildfires ourworldindata]
* [https://www.sciencebase.gov/catalog/item/5ee13de982ce3bd58d7be7e7 combined wildfire datasets for the US and certain territories 1878-2019]
* wildfire risk data: EFFIS Wildfire Risk Viewer (copernicus.eu)
* [https://gwis.jrc.ec.europa.eu/projects EU and LAC collaboration]
 
=== Fire forecast: ===
 
* [https://www.weather.gov/fire/ fire weather outlook]
* [https://fsapps.nwcg.gov/psp/npsg/forecast/#/outlooks?state=map 7-day fire potential forecast]
* [https://www.nifc.gov/nicc/predictive-services/outlooks 7-day fire outlook]; [https://www.nifc.gov/sites/default/files/document-media/7-Day_Product_Description.pdf documentation of the fire potential model]
* [https://www.usgs.gov/fire-danger-forecast fire danger forecast by USGS]
** [https://www.usgs.gov/fire-danger-forecast/wildland-fire-potential-index-wfpi fire potential index]
** [https://firedanger.cr.usgs.gov/viewer/index.html 7-day forecast]
 
 
Current Situation Viewer: [https://gwis.jrc.ec.europa.eu/apps/gwis_current_situation/index.html]
 
== Resources: ==
 
* '''[https://www.nifc.gov/fire-information National Interagency Fire Center (NIFC)]'''
** '''Description:''' Provides comprehensive information on wildfire management and coordination among various agencies in the United States, including useful maps of the historical and current fires.
* '''[https://www.fs.usda.gov/managing-land/fire U.S. Forest Service (USFS)]'''
** '''Description:''' Offers extensive resources on wildfire prevention, suppression, and research, including detailed reports and data.
* '''[https://www.earthdata.nasa.gov/learn/find-data/near-real-time/firms Fire Information for Resource Management System (FIRMS)]''' by National Aeronautics and Space Administration (NASA)  
** '''Description:''' Uses satellite data to provide near real-time active fire data and tools for monitoring wildfires globally.
* '''[https://gwis.jrc.ec.europa.eu/apps/gwis_current_situation/index.html Global Wildfire Information System (GWIS)]:''' A joint initiative by the European Commission and partners providing global wildfire information, including risk assessments, historical data, and monitoring tools.
* '''[https://www.noaa.gov/noaa-wildfire National Oceanic and Atmospheric Administration (NOAA) Wildfire]'''
** '''Description:''' Offers information on wildfire weather, satellite imagery, and forecasting tools to support wildfire management and research.
* [https://www.epa.gov/climate-indicators/climate-change-indicators-wildfires#:~:text=Multiple%20studies%20have%20found%20that%20climate%20change,season%20length%2C%20wildfire%20frequency%2C%20and%20burned%20area.&text=The%20wildfire%20season%20has%20lengthened%20in%20many,dry%20seasons%2C%20and%20drier%20soils%20and%20vegetation. EPA]  
* USDA
 
 
 
The Wildfire dataset encompasses occurrences of wildfires across the USA spanning from 2000 to 2023. The dataset includes information on the total count of deaths and the number of individuals affected, providing quantitative insights.
 
===Sample Data===
 
{| class="wikitable"
|'''Disaster Type'''
|'''Disaster Subype'''
|'''Location'''
|'''Total Deaths'''
|'''Total Affected'''


==== Instructions for Downloading CMIP6 Fire Data ====
'''CMIP6'''<ref name=":6">https://pcmdi.llnl.gov/CMIP6/</ref> is currently the leading state-of-the-art resource for future climate projections. The data can be downloaded from http://esgf-node.llnl.gov/search/cmip6/. Navigating the data portal and finding the necessary variables can be challenging, so we provide some guidance below:
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none; max-width:60em"
|+
!Filter with Facets
!Value
!Explanation<ref> [https://gmd.copernicus.org/articles/9/1937/2016/ Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.]</ref>
|-
|Classifications --> Realm
|"'''land'''"
|land component of CMIP6 model
|-
|Classifications --> Variable ID
|"'''burntFractionAll'''"
|burnt area fraction
|-
| rowspan="6" |Identifiers --> Experiment ID
|"'''esm-hist'''" or "'''hist'''"
|historical simulation
|-
|"'''esm-piControl'''" or "'''piControl'''"
|pre-industrial simulation
|-
|"'''SSP119'''"
|1.5 degree Paris Agreement goal
|-
|"'''SSP126'''"
|sustainable pathway
|-
|-
|Wildfire
|"'''SSP245'''"
|Forest fire
|middle of the road
|Gainesville, Alachua areas (Alachua district, Florida province), Lafayette, Gulf districts (Florida province)
|0
|600
|-
|-
|Wildfire
|"'''SSP585'''"
|Forest fire
|fossil fuel-rich development
|Los Alamos, Rio Arriba, Sandoval, Santa Fe districts (New Mexico province)
|0
|25400
|-
|-
|Wildfire
|Resolutions --> Nominal Resolution
|Forest fire
|choose your desired option from the available selections after applying the above filters.
|Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, South Dakota, Texas, Utah, Washington, Wyoming, Florida, North Dakota provinces
|
|14
|1000
|}
|}


=== Pre-existing Fire Risk Data ===
* [https://www.fs.usda.gov/rds/archive/catalog/RDS-2016-0034-3 Spatial datasets of probabilistic wildfire risk components for the United States (270m) (3rd Edition)]by USDA
* [https://www.arcgis.com/apps/Styler/index.html?appid=5e96315793d445419b6c96f89ce5d153 Fire hazard severity zone of California] by FEMA


<div style="border: 4px solid #aaa; padding: 7px;">
=== Fire-related Loss or fatality data ===
'''Access the whole dataset here:''' https://docs.google.com/spreadsheets/d/1L_EbjiHZYTChjEllwEG0LX_4HavbomD3/edit#gid=1887285575
</div>


==Deaths and Affected numbers on the basis of different Climatological disasters in USA from 2000-2023==
* [https://climate-adapt.eea.europa.eu/en/observatory/evidence/health-effects/wildfires Fatalities associated with wildfire in Europe (1981-2022)]


== Other resources ==


<div style="margin-left: 275px;">
* [https://www.nifc.gov/fire-information '''National Interagency Fire Center (NIFC)'''] provides comprehensive information on wildfire management and coordination among various agencies in the United States, including useful maps of the historical and current fires.
[[File:wildfirechart.jpg||Shows death and affected data for climatological disasters|500px]]
* '''[https://www.nifc.gov/nicc National Interagency Coordination Center]''' serves as the focal point for coordinating the mobilization of resources to wildland fires and other incidents throughout the United States. It also provides Predictive Services related products.
</div>
* [https://www.fs.usda.gov/managing-land/fire '''U.S. Forest Service (USFS)'''] offers extensive resources on wildfire prevention, suppression, and research, including detailed reports and data.
 
* [https://www.earthdata.nasa.gov/learn/find-data/near-real-time/firms '''Fire Information for Resource Management System (FIRMS)'''] by National Aeronautics and Space Administration (NASA) uses satellite data to provide near real-time active fire data and tools for monitoring wildfires globally.
* [https://www.noaa.gov/noaa-wildfire '''National Oceanic and Atmospheric Administration (NOAA) Wildfire'''] offers information on wildfire weather, satellite imagery, and forecasting tools to support wildfire management and research.
* '''[https://gwis.jrc.ec.europa.eu/apps/gwis_current_situation/index.html Global Wildfire Information System (GWIS)]:''' A joint initiative by the European Commission and partners providing global wildfire information, including risk assessments, historical data, and monitoring tools.
** [https://gwis.jrc.ec.europa.eu/projects Wildfire management in Latin American and Caribbean by GWIS]
** [https://forest-fire.emergency.copernicus.eu/apps/fire.risk.viewer/ Pan-Europe wildfire risk assessment by EFFIS]
** https://forest-fire.emergency.copernicus.eu/<nowiki/>EFFIS
* [https://rangelands.app/rap/?biomass_t=herbaceous&ll=39.0000,-98.0000&z=5&landcover_t=afg The Rangeland Analysis Platform] is an online tool that visualizes and analyzes vegetation data (including annual forb and grass coverage) for the United States, including the Northwest.
* Other datasets:
** Sheehan et al. 2015<ref>Sheehan, T., D. Bachelet, and K. Ferschweiler. "Projected major fire and vegetation changes in the Pacific Northwest of the conterminous United States under selected CMIP5 climate futures." ''Ecological Modelling'' 317 (2015): 16-29.</ref>: Fire variables such as burned area, fire interval simulated by a dynamic vegetation model
** Abatzoglou and Brown et al. 2011<ref>Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. ''International journal of climatology'', ''32''(5), 772-780.</ref>: Energy Release Component and Fire Danger Index produced by statistical downscaling methods
==References==
==References==
1. https://climatedata.imf.org/

Latest revision as of 18:27, 21 November 2024

Wildfires (Source: MIT Technology Review[1])

What is wildfire?

Wildfires are unplanned fires that occur in wildlands such as forest, rangelands or grasslands. They can occur naturally (ignited by lightning), or be caused by human activities such as campfires, faulty power lines, and burning crop residues. Other than those ignition sources, wildfires also need fuels and the proper meteorological condition to start and spread.

Fuels refer to anything that can burn, trees, bushes, grasses, fallen leaves. The availability of fuel is determined in large part by management practices and ecosystem processes and . For example, deforestation leaves behind slash, which are highly inflammable. Expansion of fire-resistant invasive annual grasses is one of the dominant factor in largely increasing the number, frequency, and severity of rangeland wildfires in the Northwest[2].

Meteorological conditions, specifically high temperature, low humidity, and wind play a significant role in triggering and sustaining a fire.

  • Low Humidity: Low humidity levels dry out vegetation, making it more susceptible to ignition and promoting the rapid spread of fires.
  • High Temperatures: Hot temperatures contribute to the drying of vegetation, creating favorable conditions for fires.
  • Wind: Wind can carry embers over long distances, accelerate the spread of flames, and make firefighting efforts more challenging.

Wildfires under climate change

Wildfire activities have significantly increased in the past decades in Alaska and the western United States. Statistics show that the number of large fire occurrences, fire extent, fire severity, and fire season length have all increased since 1980. These changes are closely related to climate change both directly and indirectly.

Climate change drives increase in fire activity directly by inducing higher temperatures, reduced winter snowpack, earlier snowmelt, decreased summer precipitation, and increased evaporation. These conditions creates a more favorable condition for the start and spread of wildfires[3].

Indirectly, climate change drives those changes in wildfire by changing the ecosystems. For example, climate change degrades forest, creates conditions that favor the expansion of fire-resistant invasive species, and promotes beetle outbreaks that have killed millions of acres of trees and resulted in more flammable fuels.

As climate change continues, we can expect wildfire activity to increase, with rising temperatures and persistent droughts affecting wildland ecosystems​.

Impacts of wildfire

Wildfires have significant impacts on environment, human health, and infrastructure. (Drought has extensive impacts across multiple sectors, affecting ecosystems, agriculture, water resources, energy production, commerce, public health, and infrastructure stability.)

  • Public Health Wildfire smoke, which contains various air pollutants, poses a major public health risk, primarily due to particulate matter (PM2.5). Inhalation of smoke and this fine particulate matter produced by wildfires causes respiratory issues. These issues can range from irritation of the respiratory system (nose, mouth, throat, and lungs) to serious problems like bronchitis or asthma. The lack of oxygen from inhaling smoke, humans can experience serious cardiovascular issues, including heart attack or heart failure, because of wildfires[4][5][6].
  • Ecosystem and Biodiversity Wildfires will likely change the forests composition. Frequent fires can hinder the regeneration of certain tree species, allowing shrubs and grasses to dominate for extended periods. Frequent fire will also likely reduce the abundance of shade-tolerant species and gradually lead to forests dominated by fire-resistant species, such as Douglas-fir and western larch, instead of fire-susceptible species like western hemlock and subalpine fir. Additionally, increase in fire frequency will also likely result in more young forests as older, late-successional forests burn. Frequent fires will likely replace native plants by invasive annual grassland as invasive grasses produce many seeds and can reestablish more quickly after a wildfire. All these changes will change the number and composition of animal species that depend on forests or grasslands as their habitat[7], which, in turn, may affect the cultural values, as well as the experience of hunters, anglers, and recreationalists.
  • Livestock: Wildfires impact livestock by disrupting grazing rotations, stocking rates, and rangeland management. They directly damage grazing land, often leading to the closure of public grazing allotments for several years to allow for restoration. This forces ranchers and rangeland managers to find alternative, often costly and time-consuming, sources of summer forage. Additionally, wildfires promote the expansion of invasive annual grasses, which outcompete native grasses that provide late-season forage, further reducing the availability of palatable forage for livestock.[2]
  • Water resources Wildfires can contaminate water quality and impact water supply within watersheds.They bring more sediments, eroded soil, ashes and debris from fires, as well as heavy metals and toxins into nearby water sources. These substances pollute the water and make it unsafe for human or animal consumption, as well as disrupt or destroy aquatic life[8]. Additionally, decreased vegetation increases runoff, reduces groundwater recharge, and diminishes overall water availability.
  • Buildings and other key infrastructures Though started in the wildland, wildfires can easily spread and cause large damages to both residential and industrial infrastructures. For example, the Camp Fire occurred in Northern California in November 2018 destroyed more than 18,000 structures, including nearly 14,000 homes, and significant damage to critical infrastructure such as power lines, roads, and communication networks. Roads and highways can also be impacted by the heat, flames, and falling debris and became impassable.
  • Power and Energy Wildfires severely impact the power and energy sector by damaging or destroying energy infrastructure, such as power lines, power plants, transformers, and substations. They also disrupt operations through Public Safety Power Shutoffs. While these shutdowns effectively reduce the likelihood of ignition, they are extremely costly. For example, a study by a scholar at the Stanford Woods Institute for the Environment estimated that the PSPS in October 2019 cost California’s economy up to $2.5 billion[9].

Data

Historical Wildfire Data

Dataset Description Spatial Coverage Temporal coverage Data Access
Annual Incident Management Situation Report by The National Interagency Coordination Center The annual Incident Management Situation Report by The National Interagency Coordination Center[10] provides comprehensive statistics on various aspects, including burned areas, number of human-caused and lightning-caused fires, detailed suppression and mobilization cost. The following statistics are explicitly extracted and provided: US 1983 to present (see "Description")
Hazard Mapping System Fire and Smoke Product Fire and smoke of the US compiled from satellite images by NOAA. Statistics such as fire and smoke frequency are also provided. US View;Access
MTBS database MTBS multi-agency project maintains a database of wildfire occurrence and burned areas of the US from 1984 to present. US 1984 to present Access
Global Wildfire Information System (GWIS) The statistics portal by Global Wildfire Information System (GWIS)[11] offers the number of fires, burned area, as well as their seasonal trend by country. Its Country Profile provides detailed information of numbers of fires, burned area, emission etc. by landcover class for all countries for years 2002-2023. Our World in Data visualizes some of these statistics in chart. Global 2002 -2023 Access;
Global Fire Emissions Database (GFED) Global Fire Emissions Database (GFED) v4[12] provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types. Data is at 0.25-degree resolution and is available from June 1995 through 2016. Global 06/1995 - 2016 Access
Fire danger indices by European Forest Fire Information System (EFFIS)[13] Fire danger indices at 0.25-degree produced by EFFIS. The EFFIS incorporates the fire danger indices for three different models developed in Canada, United States and Australia. This dataset is produced by the GEFF model, using the historical weather information of ERA5 reanalysis. Fourteen variables are provided for downloading, including fire weather index[14] (Canadian rating) and fire danger index (Australian rating). Global 1940 to present Access; Python code for processing data; other useful resources
Fire weather index[14] by European Environment Agency (EEA) Forest fire danger in the present climate and projected changes under two climate change scenarios, with a spatial resolution of 25 km. Data are derived from the EURO-CORDEX runs. See more details here. Europe 1981 - 2100 Access
Fire data provided by EEA Global 1981 - 2017
MODIS Active fire Active fire data derived from satellite images. The MODIS active fire product[15] detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. MODIS C6.1 is available from November 2000 (for Terra) and from July 2002 (for Aqua) to the present. Global 2000 to present Access; View
MODIS Burned area[16] Data derived from satellite images. MCD64A1 Version 6 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. Global 2000 to present Access; View
VIIRS Active fire Data derived from satellite images at 375 m resolution. Global 2012 to present Access; View
VIIRS Burned area Data derived from satellite images at 500 m resolution. Global 2012 to present Access; View

Forecast at Near-term (1-7 days ahead) to Seasonal Scale

Dataset Description Spatial Coverage Temporal Coverage Data Access
Fire danger forecast by EFFIS Fire danger forecast produced by the GEFF model. Variables forecasted include fire weather index, initial spread index, build up index, burning index, and fire danger index. Europe 1 day ahead View; Access
7-day fire potential forecast Fire potential forecast produced by a fire potential model by NICC. It is a function of fuel conditions, weather, and resource availability. It assesses the daily probability for occurrence of a new large fire and/or the daily potential for significant new growth on existing fires.[17] US 1-7 days ahead View; Access
USGS fire danger forecast This fire danger forecast (including fire potential index, large fire probability, fire spread probability) forecast is based on the Wildland Fire Potential Index (WFPI) forecast of USGS. WFPI is a numerical rating of fuel availability and ignitability, based on an assessment of the proportion of dead fuel loading and its dryness. It can be used to indicate the “combustibility” of the landscape, with increasing values indicating increasing potential for large fires, defined as fires that burn more than 500 acres. [18] The forecast is produced by feeding satellite observations and weather forecast into a fuel model. US 1-7 days ahead View; Access
Seasonal fire potential outlookby NICC Fire potential forecast produced by NICC at the monthly to seasonal scale. US 1-4 months ahead Access
FuleCast FuelCast provides monthly fuel and fire forecasts during the growing season to help users stay up to date on fire danger. It is updated monthly during the growing season. Monthly Probably useful

Future Projection for One year and beyond

Dataset Description Spatial Coverage Temporal Coverage Data Access
CMIP6 Coupled Model Intercomparison Project Phase 6 (CMIP6)[19] provides a comprehensive set of climate model simulations to understand past, present, and future climate changes. It is currently the leading state-of-the-art resource for future climate projections. The data can be downloaded from http://esgf-node.llnl.gov/search/cmip6/. Navigating the data portal and finding the necessary variables can be challenging, so we provide some guidance below. The model resolution is coarser than 100 km for most models. Global 2015 to 2100 Access
CORDEX Note that the resolution of CMIP6 simulations (coarser than 100 km for most models) is usually too coarse for climate risk analysis and other downstream applications, the downscaled CMIP data using regional climate modeling by Coordinated Regional Climate Downscaling Experiment (CORDEX) is also available. Note that no direct wildfire indicators are provided in the CORDEX data. Users will need to calculate these indicators using the available variables. Regional Access; CORDEX-CMIP5 data for North America;

AWS link for EURO-CORDEX AWS link

HighResMIP Higher-resolution (25 km) CMIP6-like simulations (HighResMIP) is also available for some models. Note that same as CORDEX, HighResMIP data has no direct wildfire indicators. Global Access
NARCCAP data The North American Regional Climate Change Assessment Program (NARCCAP) also provides future projection simulations. Access
Fire danger by EEA Projected forest fire danger by EEA Europe 2071-2100 Access
Fire weather index[14] by European Environment Agency (EEA) Forest fire danger in the present climate and projected changes under two climate change scenarios, with a spatial resolution of 25 km. Data are derived from the EURO-CORDEX runs. See more details here. Europe 1981 - 2100 Access
Fire weather index by ANL Projected fire weather index over the US by Argonne National Laboratory (ANL) using Argonne’s downscaled 12km climate data. US up until 2050 View; Access
Great Basin Rangeland Fire Probability Map It represents the relative probability of large (> 1,000 acres) rangeland fire given an ignition in a given year. Maps are updated yearly. Great basin of the US one year ahead Access

Instructions for Downloading CMIP6 Fire Data

CMIP6[19] is currently the leading state-of-the-art resource for future climate projections. The data can be downloaded from http://esgf-node.llnl.gov/search/cmip6/. Navigating the data portal and finding the necessary variables can be challenging, so we provide some guidance below:

Filter with Facets Value Explanation[20]
Classifications --> Realm "land" land component of CMIP6 model
Classifications --> Variable ID "burntFractionAll" burnt area fraction
Identifiers --> Experiment ID "esm-hist" or "hist" historical simulation
"esm-piControl" or "piControl" pre-industrial simulation
"SSP119" 1.5 degree Paris Agreement goal
"SSP126" sustainable pathway
"SSP245" middle of the road
"SSP585" fossil fuel-rich development
Resolutions --> Nominal Resolution choose your desired option from the available selections after applying the above filters.

Pre-existing Fire Risk Data

Fire-related Loss or fatality data

Other resources

References

  1. Retrived from https://www.technologyreview.com/2024/08/28/1103186/canada-wildfire-emissions/ on Oct 24, 2024
  2. 2.0 2.1 https://www.climatehubs.usda.gov/hubs/northwest/topic/climate-change-and-wildfire-northwest-rangelands
  3. https://www.sciencebase.gov/catalog/item/5956a1b5e4b0d1f9f050d917
  4. https://www.who.int/health-topics/wildfires#tab=tab_2
  5. https://wfca.com/wildfire-articles/negative-effects-of-wildfires/
  6. Health effects of wildfire smoke by EPA: https://www.epa.gov/air-research/research-health-effects-air-pollution#health-effects-wildfire-smoke
  7. https://uw.maps.arcgis.com/apps/Cascade/index.html?appid=9c0f8668f47c4773b56c9b9ae6c301e3
  8. https://deq.utah.gov/communication/news/wildfires-impact-on-our-environment
  9. https://www.cnbc.com/2019/10/10/pge-power-outage-could-cost-the-california-economy-more-than-2-billion.html
  10. https://www.nifc.gov/fire-information
  11. https://gwis.jrc.ec.europa.eu/
  12. Randerson, J.T., G.R. van der Werf, L. Giglio, G.J. Collatz, and P.S. Kasibhatla. 2018. Global Fire Emissions Database, Version 4.1 (GFEDv4). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1293
  13. Vitolo, C., Di Giuseppe, F., Barnard, C. et al. ERA5-based global meteorological wildfire danger maps. Sci Data 7, 216 (2020). https://doi.org/10.1038/s41597-020-0554-z
  14. 14.0 14.1 14.2 The Fire Weather Index (FWI) evaluates conditions that increase the danger of wildfires, such as the impact of moisture and wind on wildfire intensity and spread. Higher FWI values represent greater danger of wildfires due to weather conditions; the index does not account for land cover or potential ignition sources. This dataset can be used as a regional approach in assessing future wildfire danger and risks from fires. See more information at NWCG: https://www.nwcg.gov/publications/pms437/cffdrs/fire-weather-index-fwi-system#:~:text=The%20Fire%20Weather%20Index%20(FWI,Again%2C%20unitless%20and%20open%20ended.
  15. https://modis-fire.umd.edu/ba.html
  16. https://lpdaac.usgs.gov/products/mcd64a1v006/
  17. https://www.nifc.gov/sites/default/files/document-media/7-Day_Product_Description.pdf
  18. https://www.usgs.gov/fire-danger-forecast/fire-danger-data-products-and-tools
  19. 19.0 19.1 https://pcmdi.llnl.gov/CMIP6/
  20. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
  21. Sheehan, T., D. Bachelet, and K. Ferschweiler. "Projected major fire and vegetation changes in the Pacific Northwest of the conterminous United States under selected CMIP5 climate futures." Ecological Modelling 317 (2015): 16-29.
  22. Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International journal of climatology, 32(5), 772-780.