10 datasets found
  1. State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire...

    • zenodo.org
    csv
    Updated Jun 3, 2024
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    Matthew William Jones; Matthew William Jones; Esther Brambleby; Esther Brambleby; Niels Andela; Niels Andela; Guido van der Werf; Guido van der Werf; Mark Parrington; Mark Parrington; Louis Giglio; Louis Giglio (2024). State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions [Dataset]. http://doi.org/10.5281/zenodo.11400540
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    csvAvailable download formats
    Dataset updated
    Jun 3, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Matthew William Jones; Matthew William Jones; Esther Brambleby; Esther Brambleby; Niels Andela; Niels Andela; Guido van der Werf; Guido van der Werf; Mark Parrington; Mark Parrington; Louis Giglio; Louis Giglio
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data Discussions (Jones et al., under review, https://doi.org/10.5194/essd-2024-218). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2023-February 2024), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Jones et al. (2024).

    Citation

    Work utilising our regional summaries should cite both Jones et al. (2024, under review, ESSD) AND the primary reference for the variable(s) of interest as follows:

    • Giglio et al. (2018) for MODIS MCD64A1 BA.
    • van der Werf et al. (2017) for GFED4.1s fire C emissions.
    • Kaiser er al. (2012) for GFAS fire C emissions.
    • van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.
    • Andela et al. (2019) for the Global Fire Atlas.

    Input Data

    Burned Area (BA)

    Fire Carbon (C) Emissions

    • GFED4.1s fire C emissions data are extended from van der Werf and are available at https://globalfiredata.org/.
      • Period: 2003-February 2024
      • Resolution: 0.25 degree, daily

    Global Fire Atlas (Individual Fire Atlas and Properties)

    • Global Fire Atlas are extended from Andela et al. (2019) and are available at Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062, last access: 31 May 2024).
      • Period: 2002-February 2024
      • Driven by 500m MODIS BA data (collection 6.1)

    Regional Analysis

    We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer).

    The statistics derived from cookie-cutting are listed below. Full details in Jones et al. (2024).

    Layer

    Short Form

    Source

    Biomes

    NA

    Olson et al. (2001)

    Continents

    NA

    ArcGIS Hub (2024)

    Continental Biomes

    NA

    See above

    Countries

    NA

    EU Eurostat (2020)

    UC Davis Global Administrative Areas (GADM) Level 1

    GADM-L1

    UC Davis (2022)



    Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions

    IPCC AR6 WGI Regions

    IPCC (2021); SantanderMetGroup (2021)

    Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions

    RECCAP2 Regions

    Ciais et al. (2022)

    Global Fire Emissions Database (GFED) Basis Regions

    GFED4.1s Regions

    van der Werf et al. (2006)

    Regional Statistics and Anomalies

    • Burned Area (BA)
      • Calculated regional totals for each fire season.
      • Relative and standardized anomalies from historical data (since 2001).
      • Ranking amongst all recorded fire seasons.
      • Onset, peak, and cessation based on monthly deviations from climatological means.
    • Carbon Emissions
      • Calculated regional totals for each fire season.
      • Relative and standardized anomalies from historical data (since 2003).
      • Ranking amongst all recorded fire seasons.
      • Onset, peak, and cessation based on monthly deviations from climatological means.
      • Statistics available for GFAS, GFED, and their mean.
    • Individual Fire Properties
      • Based on ignition point vectors from the Global Fire Atlas.
      • Calculated regional count.
      • Calculated regional maxima and 95th percentiles for each fire season.
      • Relative and standardized anomalies from historical data (since 2002).
      • Ranked anomalies among all recorded fire seasons.
  2. g

    Global Fire Emissions Database, Version 3.1

    • data.globalchange.gov
    • search.dataone.org
    • +1more
    Updated Sep 23, 2013
    + more versions
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    (2013). Global Fire Emissions Database, Version 3.1 [Dataset]. https://data.globalchange.gov/dataset/nasa-ornldaac-1191
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    Dataset updated
    Sep 23, 2013
    Description

    ABSTRACT: This data set provides monthly burned area, and monthly, and annual fire emissions data from July 1996 to February 2012. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter 2.5 micron (PM2p5), total particulate matter (TPM), and sulfur dioxide (SO2). The C4 fraction of carbon emissions is also provided. The annual C emissions estimates were derived by combining burned area data with a biogeochemical model, CASA-Global Fire Emissions Database (CASA-GFED), that estimates fuel loads and combustion completeness for each monthly time step. The fuel loads were based on satellite derived information on vegetation characteristics and productivity to estimate carbon input and carbon outputs through heterotrophic respiration, herbivory, and fires. Note that while most emissions estimates included data for 32 variables (trace gases, aerosols, and carbon), not all data are available for all years, and not all variables (emission species) are included in each data product. Additional information may be obtained from the Global Fire Data website: http://www.globalfiredata.org/index.html. Data products include: - 0.5 degree x 0.5 degree gridded monthly burned area data (ha) for 1996 to 2012 provided as text files and as GeoTIFF files for 1996 to 2012. - 3-Hourly emssions (fraction) for 2003 to 2010 in NetCDF (.nc) format. - Daily emssions (fraction) for 2003 to 2010, in NetCDF (.nc) format. - Monthly emissions for 32 variables from 1997 to 2011, in text and GeoTIFF format. - Monthly emissions for 31 variables from specific sources (grassland and savanna, woodland, deforestation & degradation, forest, agricultural waste burning, and peat fires), both as absolute and relative emissions. The time period is for 2007 to 2011, and the files are provided in text and GeoTIFF format. - Global emission totals of C and other species from all sources, and from each individual source (forest fires, peat fires, agricultural waste burning, etc). - Annual emissions of carbon and other trace gases for all countries, for the period 1997 to 2010, provided as text files. These files are for indicative use only; they are not suitable for official reporting due to large uncertainties and potential for missing key regional aspects in the global approach used. - Ancillary data for monthly biosphere fluxes. The CASA-GFED biosphere flux sources include Net Primary Production (NPP), Heterotrophic respiration (Rh), and fires (biomass burning). These files are for the time period 1997 to 2009 and are provided as text files and in GeoTIFF format. There are 12 compressed (*.zip) files with this data set. The data are in text, NetCDF (.nc), and GeoTIFF (.tiff) formats as described above.

  3. Data from: Reconstructed Global Monthly Burned Area Maps from 1901 to 2020

    • zenodo.org
    zip
    Updated Nov 23, 2024
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    Zhixuan Guo; Zhixuan Guo; Wei Li; Wei Li (2024). Reconstructed Global Monthly Burned Area Maps from 1901 to 2020 [Dataset]. http://doi.org/10.5281/zenodo.14191467
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    zipAvailable download formats
    Dataset updated
    Nov 23, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Zhixuan Guo; Zhixuan Guo; Wei Li; Wei Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Satellites provide direct observations of fire activities (e.g., burned area, fire radiative power) (Andela et al., 2017; Giglio et al., 2006; Luo et al., 2024), but their temporal coverages are limited because most satellite data were available only after 1980s (Chuvieco et al., 2019). Fire modules in dynamic global vegetation models (DGVMs) are able to simulate long-term burned area and the interactions with vegetation dynamics based on climate conditions and soil properties (Sitch et al., 2015; Sitch et al., 2024), but the spatial resolution at the global scale is usually coarse due to the coarse resolution of the input meteorological forcing data, and most models failed in capturing the global trends of burned area (Andela et al., 2017; Hantson et al., 2020). The processes included and the parameterizations of fire processes are widely different across fire models, resulting in a large range of simulated burned area at both the regional and global scales (Hantson et al., 2020). Considering the limitations of satellite observations and fire models, a spatiotemporally consistent burned area dataset over the 20th century trained from present-day observations, is essential for fire modelling and can serve as publicly available benchmark for fire ecology and carbon cycle studies.

    This study produced a global monthly 0.5°×0.5° burned area fraction (BAF) dataset from1901 to 2020 using machine learning models based on climate conditions, vegetation states, population density and land use data. We first divided the globe into 14 regions following the Global Fire Emission Dataset (GFED regions) (Giglio et al., 2006; van Der Werf et al., 2017) and conducted all steps described below in each GFED region individually. To better capture extreme fires, we first developed a classification model to distinguish grid cells with extreme and regular fires, using the 90th percentile of all burned area fractions within a region as the threshold to define extreme fires. We then trained separate regression models for grid cells categorized as having extreme or regular fires. The models were trained against the satellite-based burned area product (FireCCI51) during 2003-2020 excluding cropland fires, and then used to reconstruct the burned area from 1901 to 2020. In addition to validation against satellite observations that were not used for model training, we also compared our burned area predictions with charcoal records and other independent global and regional burned area datasets. In addition to the historical reconstructed burned area dataset based on the FireCCI51 mentioned above, we also produced two additional products of historical burned area with the same spatiotemporal resolution as the FireCCI51-based burned area reconstruction: 1) the GFED5-based data version, which is based on machine-learning models trained by the burned area from GFED5 which has much more fires than GFED4 (Chen et al., 2023) instead of FireCCI51, and 2) the FireCCI51-based data with burned area further calibrated using the relationship between statistic-based burned area (Mouillot and Field, 2005) and GDP (Bolt and Van Zanden, 2024) at the regional scale before 2000 (named as FireCCI51-GDP version).

    This dataset can be used to benchmark historical simulations from fire modules in DGVMs, re-calculate historical fire emissions and estimate legacy effects of vegetation recovery after fires on terrestrial carbon sink. Though the temporal coverage of our product is long enough to support studies related to fire disturbance, carbon dynamics and climate change, more reliable explanatory data for model training and burned area data for validation would help further improve the accuracy of the reconstructed burned area product.

    Description of variable names in netcdf files:
    'lat' -- latitude of the center of grid cells
    'lon' -- longitude of the center of grid cells
    'ba' -- burned area fraction in 0.5°×0.5°grid cells
    'ba_type' -- 0=no fire; 1=regular burned area; 2=extreme burned area. Note that regular & extreme BA is defined in each GFED region individually. Extreme BA refers to that burned area within grid cells surpassing the 90th percentile of all grid cells with burned area in each GFED region.

    This dataset is currently for manuscript submission to the scientifc journal

  4. Z

    Global GFED-based monthly burned area time series (1996-2016) at 1 km and...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Dec 18, 2021
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    Hengl, T. (2021). Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5788671
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    Dataset updated
    Dec 18, 2021
    Dataset authored and provided by
    Hengl, T.
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Contains two separate datasets:

    Global GFED-based monthly burned area (in ha) time series (1996-2016) at 1 km (downscaled using cubic-splines from 25 km);

    Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the ESA CCI burned area accumulated weekly product;

    Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in Giglio et al. (2013). Processing steps are available here. Antarctica is not included.

    To access and visualize global datasets use: https://openlandmap.org or watch this video.

    If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:

    Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues

    All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

    nhz = theme: natural hazards,

    monthly.burned.ha = variable: estimated monthly burned area in ha,

    gfed = data source GFED data,

    m = mean value,

    1km = spatial resolution / block support: 1 km,

    s0..0cm = vertical reference: land surface,

    2000.02 = time reference aggregated: month Feb of year 2000,

    v4 = version number: GFEDv4,

  5. d

    Data Release for the Validation of the USGS Landsat Burned Area Essential...

    • datadiscoverystudio.org
    • dataone.org
    • +1more
    025
    Updated Jun 8, 2018
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    (2018). Data Release for the Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous U.S.. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/6621795d8a9b4c87af6a568f3e379547/html
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    025Available download formats
    Dataset updated
    Jun 8, 2018
    Description

    description: The Landsat Burned Area Product Validation dataset was collected to determine the accuracy of The Landsat Burned Area Essential Climate Variable (BAECV) product, developed by the U.S. Geological Survey (USGS). The BAECV maps burned areas across the conterminous United States (CONUS) for the entire Landsat archive (1984 2015). Rigorous validation of such products is critical for their proper usage and interpretation. The sampling design used to derive this validation dataset was adapted from the methods used by European Space Agency s (ESA) Climate Change Initiative (CCI) fire_cci project to generate the first statistically rigorous global reference dataset for a burned area product that meets the CEOS LPVS stage 3 validation requirements. Our validation dataset consists of 28 Landsat path/rows across the CONUS which were selected using a stratified sampling scheme across the major Olson biomes, as summarized by the fire_cci project (Olson et al., 2001; Padilla et al. 2014). Within the CONUS this included temperate forest, Mediterranean forest, temperate grassland and savannah, tropical and subtropical grasslands and savannah, and other which included desert/xeric shrub and flooded grasslands (Padilla et al. 2014). Path/rows selected within each biome were meant to represent high and low burned areas as specified by the Global Fire Emissions Database (GFED) version 3 (Giglio et al, 2009, 2010). We used systematic sampling to select 5 validation years spaced out in 5 year increments (2008, 2003, 1998, 1993 and 1988). The validation dataset was then independently generated by three different analysts. Each analyst mapped new burned areas using Landsat pre-fire and post-fire image pairs. The burned area polygons were generated using the Burned Area Mapping Software (BAMS), which is a semi-automated algorithm developed by the University of Alcala, Madrid, and implemented by the fire_cci project (Bastarrika et al., 2014; Padilla et al., 2014). The outputs were manually edited using visual interpretation. From these outputs, three renditions of the validation datasets were generated in which burned area extent ranged from liberal (or inclusive) (Level 1) to conservative (Level 3). Burned area extent was defined as (1) at least one analyst identified a given pixel as burned (Level 1), (2) at least two of the three analysts were required to agree a given pixel was burned (Level 2), (3) all three analysts were required to agree a pixel was burned (Level 3). Full details of the methods used to derive this validation dataset are provided in Vanderhoof et al. (2017).; abstract: The Landsat Burned Area Product Validation dataset was collected to determine the accuracy of The Landsat Burned Area Essential Climate Variable (BAECV) product, developed by the U.S. Geological Survey (USGS). The BAECV maps burned areas across the conterminous United States (CONUS) for the entire Landsat archive (1984 2015). Rigorous validation of such products is critical for their proper usage and interpretation. The sampling design used to derive this validation dataset was adapted from the methods used by European Space Agency s (ESA) Climate Change Initiative (CCI) fire_cci project to generate the first statistically rigorous global reference dataset for a burned area product that meets the CEOS LPVS stage 3 validation requirements. Our validation dataset consists of 28 Landsat path/rows across the CONUS which were selected using a stratified sampling scheme across the major Olson biomes, as summarized by the fire_cci project (Olson et al., 2001; Padilla et al. 2014). Within the CONUS this included temperate forest, Mediterranean forest, temperate grassland and savannah, tropical and subtropical grasslands and savannah, and other which included desert/xeric shrub and flooded grasslands (Padilla et al. 2014). Path/rows selected within each biome were meant to represent high and low burned areas as specified by the Global Fire Emissions Database (GFED) version 3 (Giglio et al, 2009, 2010). We used systematic sampling to select 5 validation years spaced out in 5 year increments (2008, 2003, 1998, 1993 and 1988). The validation dataset was then independently generated by three different analysts. Each analyst mapped new burned areas using Landsat pre-fire and post-fire image pairs. The burned area polygons were generated using the Burned Area Mapping Software (BAMS), which is a semi-automated algorithm developed by the University of Alcala, Madrid, and implemented by the fire_cci project (Bastarrika et al., 2014; Padilla et al., 2014). The outputs were manually edited using visual interpretation. From these outputs, three renditions of the validation datasets were generated in which burned area extent ranged from liberal (or inclusive) (Level 1) to conservative (Level 3). Burned area extent was defined as (1) at least one analyst identified a given pixel as burned (Level 1), (2) at least two of the three analysts were required to agree a given pixel was burned (Level 2), (3) all three analysts were required to agree a pixel was burned (Level 3). Full details of the methods used to derive this validation dataset are provided in Vanderhoof et al. (2017).

  6. Z

    SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Sep 26, 2024
    + more versions
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    Panagiotou, Eleannna (2024). SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6834584
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    Dataset updated
    Sep 26, 2024
    Dataset provided by
    Papoutsis, Ioannis
    Weber, Ulrich
    Alonso, Lazaro
    Prapas, Ioannis
    Mihail, Dimitrios
    Ahuja, Akanksha
    Gans, Fabian
    Cremer, Felix
    Kondylatos, Spyros
    Carvalhais, Nuno
    Karasante, Ilektra
    Panagiotou, Eleannna
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Earth
    Description

    The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

    It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

    It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

    Datacube properties
    

    Feature

    Value

    Spatial Coverage

    Global

    Temporal Coverage

    2001 to 2021

    Spatial Resolution

    0.25 deg x 0.25 deg

    Temporal Resolution

    8 days

    Number of Variables

    54

    Tutorial Link

    https://github.com/SeasFire/seasfire-datacube

        Full name
        DataArray name
        Unit
        Contact *
    
    
    
    
        Dataset: ERA5 Meteo Reanalysis Data
    
    
    
    
    
        Mean sea level pressure
        mslp
        Pa
        NOA
    
    
        Total precipitation
        tp
        m
        MPI
    
    
        Relative humidity
        rel_hum
        %
        MPI
    
    
        Vapor Pressure Deficit
        vpd
        hPa
        MPI
    
    
        Sea Surface Temperature
        sst
        K
        MPI
    
    
        Skin temperature
        skt
        K
        MPI
    
    
        Wind speed at 10 meters
        ws10
        m*s-2
        MPI
    
    
        Temperature at 2 meters - Mean
        t2m_mean
        K
        MPI
    
    
        Temperature at 2 meters - Min
        t2m_min
        K
        MPI
    
    
        Temperature at 2 meters - Max
        t2m_max
        K
        MPI
    
    
        Surface net solar radiation
        ssr
        MJ m-2
        MPI
    
    
        Surface solar radiation downwards
        ssrd
        MJ m-2
        MPI
    
    
        Volumetric soil water level 1
        swvl1
        m3/m3
        MPI
    
    
    
    
    
    
    
              Volumetric soil water level 2
    
    
    
    
        swvl2
        m3/m3
        MPI
    
    
        Volumetric soil water level 3
        swvl3
        m3/m3
        MPI
    
    
        Volumetric soil water level 4
        swvl4
        m3/m3
        MPI
    
    
        Land-Sea mask
        lsm
        0-1
        NOA
    
    
        Dataset: Copernicus
    

    CEMS

        Drought Code Maximum
        drought_code_max
        unitless
        NOA
    
    
        Drought Code Average
        drought_code_mean
        unitless
        NOA
    
    
        Fire Weather Index Maximum
        fwi_max
        unitless
        NOA
    
    
        Fire Weather Index Average
        fwi_mean
        unitless
        NOA
    
    
        Dataset: CAMS: Global Fire Assimilation System (GFAS)
    
    
    
    
    
        Carbon dioxide emissions from wildfires
        cams_co2fire
        kg/m²
        NOA
    
    
        Fire radiative power
        cams_frpfire
        W/m²
        NOA
    
    
        Dataset: FireCCI - European Space Agency’s Climate Change Initiative
    
    
    
    
    
        Burned Areas from Fire Climate Change Initiative (FCCI)
        fcci_ba
        ha
        NOA
    
    
        Valid mask of FCCI burned areas
        fcci_ba_valid_mask
        0-1
        NOA
    
    
    
        Fraction of burnable area
        fcci_fraction_of_burnable_area
        %
        NOA
    
    
        Number of patches
        fcci_number_of_patches
        N
        NOA
    
    
        Fraction of observed area
        fcci_fraction_of_observed_area
        %
        NOA
    
    
        Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2
    
    
    
    
    
        Land Surface temperature at day
        lst_day
        K
        MPI
    
    
        Leaf Area Index
        lai
        m²/m²
        MPI
    
    
        Normalized Difference Vegetation Index
        ndvi
        unitless
        MPI
    
    
        Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4
    
    
    
    
    
        Population density
        pop_dens
        persons per square kilometers
        NOA
    
    
        Dataset: Global Fire Emissions Database (GFED)
    
    
    
    
    
        Burned Areas from GFED (large fires only)
        gfed_ba
        hectares (ha)
        MPI
    
    
        Valid mask of GFED burned areas
        gfed_ba_valid_mask
        0-1
        NOA
    
    
        GFED basis regions
        gfed_region
        N
        NOA
    
    
        Dataset: Global Wildfire Information System (GWIS)
    
    
    
    
    
        Burned Areas from GWIS
        gwis_ba
        ha
        NOA
    
    
        Valid mask of GWIS burned areas
        gwis_ba_valid_mask
        0-1
        NOA
    
    
        Dataset: NOAA Climate Indices
    
    
    
    
    
        Arctic Oscillation Index
        oci_ao
        unitless
        NOA
    
    
        Western Pacific Index
        oci_wp
        unitless
        NOA
    
    
        Pacific North American Index
        oci_pna
        unitless
        NOA
    
    
        North Atlantic Oscillation
        oci_nao
        unitless
        NOA
    
    
        Southern Oscillation Index
        oci_soi
        unitless
        NOA
    
    
        Global Mean Land/Ocean Temperature
        oci_gmsst
        unitless
        NOA
    
    
        Pacific Decadal Oscillation
        oci_pdo
        unitless
        NOA
    
    
        Eastern Asia/Western Russia
        oci_ea
        unitless
        NOA
    
    
        East Pacific/North Pacific Oscillation
        oci_epo
        unitless
        NOA
    
    
        Nino 3.4 Anomaly
        oci_nino_34_anom
        unitless
        NOA
    
    
        Bivariate ENSO Timeseries
        oci_censo
        unitless
        NOA
    
    
        Dataset: ESA CCI
    
    
    
    
    
        Land Cover Class 0 - No data
        lccs_class_0
        %
        NOA
    
    
        Land Cover Class 1 - Agriculture
        lccs_class_1
        %
        NOA
    
    
        Land Cover Class 2 - Forest
        lccs_class_2
        %
        NOA
    
    
        Land Cover Class 3 - Grassland
        lccs_class_3
        %
        NOA
    
    
        Land Cover Class 4 - Wetlands
        lccs_class_4
        %
        NOA
    
    
        Land Cover Class 5 - Settlement
        lccs_class_5
        %
        NOA
    
    
        Land Cover Class 6 - Shrubland
        lccs_class_6
        %
        NOA
    
    
        Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
        lccs_class_7
        %
        NOA
    
    
        Land Cover Class 8 - Water Bodies
        lccs_class_8
        %
        NOA
    
    
        Dataset: Biomes
    
    
    
    
    
        Dataset: Calculated
    
    
    
    
    
        Grid Area in square meters
        area
        m²
        NOA
    

    *The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

  7. MiCASA Daily NPP Rh ATMC NEE FIRE FUEL Fluxes 0.1 degree x 0.1 degree

    • catalog.data.gov
    • s.cnmilf.com
    • +3more
    Updated Jul 10, 2025
    + more versions
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    NASA/GSFC/SED/ESD/TISL/GESDISC (2025). MiCASA Daily NPP Rh ATMC NEE FIRE FUEL Fluxes 0.1 degree x 0.1 degree [Dataset]. https://catalog.data.gov/dataset/micasa-daily-npp-rh-atmc-nee-fire-fuel-fluxes-0-1-degree-x-0-1-degree-20af0
    Explore at:
    Dataset updated
    Jul 10, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately.

  8. Field data synthesis accompanying "Global biomass burning fuel consumption...

    • zenodo.org
    Updated Nov 15, 2023
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    Dave van Wees; Dave van Wees; Guido R. van der Werf; Guido R. van der Werf; James T. Randerson; James T. Randerson; Brendan M. Rogers; Brendan M. Rogers; Yang Chen; Yang Chen; Sander Veraverbeke; Sander Veraverbeke; Louis Giglio; Louis Giglio; Douglas C. Morton; Douglas C. Morton (2023). Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" [Dataset]. http://doi.org/10.5281/zenodo.6670870
    Explore at:
    Dataset updated
    Nov 15, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Dave van Wees; Dave van Wees; Guido R. van der Werf; Guido R. van der Werf; James T. Randerson; James T. Randerson; Brendan M. Rogers; Brendan M. Rogers; Yang Chen; Yang Chen; Sander Veraverbeke; Sander Veraverbeke; Louis Giglio; Louis Giglio; Douglas C. Morton; Douglas C. Morton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Synthesis of fuel load and fuel consumption field measurements accompanying the publication:

    "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"

    Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5

    1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands
    2Department of Earth System Science, University of California, Irvine, CA 92697, USA
    3Woodwell Climate Research Center, Falmouth, MA 02540, USA
    4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
    5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

    DOI: https://doi.org/10.5194/gmd-15-8411-2022

    Units are g C / m2

  9. CESM2 SSP2-4.5 with Modified Biomass Burning Emissions

    • data.ucar.edu
    netcdf
    Updated Nov 12, 2023
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    Adam Phillips; Isla Simpson; Nan Rosenbloom (2023). CESM2 SSP2-4.5 with Modified Biomass Burning Emissions [Dataset]. http://doi.org/10.26024/j23t-pc83
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    netcdfAvailable download formats
    Dataset updated
    Nov 12, 2023
    Dataset provided by
    University Corporation for Atmospheric Research
    Authors
    Adam Phillips; Isla Simpson; Nan Rosenbloom
    Description

    This is a 16 member ensemble of simulations with CESM2 under the SSP2-4.5 forcing scenario from 2015 to 2100. These simulations can be compared with the CESM2 Large Ensemble and provide the opportunity to compare and contrast climate change under a lower forcing scenario. One difference from the official CMIP6 SSP2-4.5 forcing is that slightly modified biomass burning emissions are used at the beginning of the simulation. As is discussed in the CESM2 Large Ensemble reference paper (Rodgers et. al. 2021), the second 50 members of the CESM2 Large Ensemble use smoothed biomass burning emissions over the GFED era of the late 20th/early 21st centuries. While the GFED emissions were not prescribed in the SSP scenario, there is a minor effect of the smoothing into the first years of the SSP scenario and these simulations have been branched from historical simulations that used the smoothed biomass burning emissions. As such, this medium ensemble is complementary to the second 50 member of the CESM2 Large Ensemble. More information about these simulations can be found on the CESM CVCWG's CESM2 2-4.5 ensemble website.

  10. u

    Subtropical southern Africa top-down biomass burning NOx and NH3 emissions...

    • rdr.ucl.ac.uk
    jpeg
    Updated Apr 3, 2025
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    Eloise Marais (2025). Subtropical southern Africa top-down biomass burning NOx and NH3 emissions for the 2019 fire season derived with satellite observations and the GEOS-Chem model [Dataset]. http://doi.org/10.5522/04/28712444.v1
    Explore at:
    jpegAvailable download formats
    Dataset updated
    Apr 3, 2025
    Dataset provided by
    University College London
    Authors
    Eloise Marais
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Data generated for the paper entitled "Subtropical southern Africa fire emissions of nitrogen oxides and ammonia obtained with satellite observations and GEOS-Chem" submitted for peer-review to Royal Society's Environmental Science: Atmospheres journal.All the data are gridded to 0.25 degree latitude by 0.3125 degree longitude for the year 2019.The data include:(1) IASI NH3 monthly mean column densities for June-October from IASI instruments onboard MetOp-A and MetOp-B sensors reprocessed using GEOS-Chem a priori profiles. The IASI retrieval product used is version 4.0.0. Data are in files named with the format "iasi-metopa-metopb-gc-prior-[inventory]-nh3-cols.nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2 for the three separate biomass burning inventories that were used to drive the model. (2) Top-down NOx and NH3 biomass burning emissions for 2019 obtained by applying a mass-balance approach to satellite observations and GEOS-Chem. NOx emissions are for June-October. NH3 emissions are for July-October. Only non-negative NH3 emissions are summed and use to report monthly and biomass burning season totals in the accompanying manuscript. Emissions data are in files named with the format "top-down-bb-[compound]-emis.nc", where [compound] is either NOx or NH3.(3) GEOS-Chem monthly mean tropospheric column densities of NO2 and total column densities of NH3 for June-October 2019. These are obtained by co-sampling with TROPOMI and applying the TROPOMI averaging kernel for NO2 and co-sampling with IASI for NH3. Model data are averaged over 13h00-14h00 local solar time (LST) for coincidence with IASI and 09h00-10h00 LST for coincidence with IASI. Model data are in files named with the format "geos-chem-no2-nh3-cols-[inventory].nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2.

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Matthew William Jones; Matthew William Jones; Esther Brambleby; Esther Brambleby; Niels Andela; Niels Andela; Guido van der Werf; Guido van der Werf; Mark Parrington; Mark Parrington; Louis Giglio; Louis Giglio (2024). State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions [Dataset]. http://doi.org/10.5281/zenodo.11400540
Organization logo

State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions

Explore at:
csvAvailable download formats
Dataset updated
Jun 3, 2024
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Matthew William Jones; Matthew William Jones; Esther Brambleby; Esther Brambleby; Niels Andela; Niels Andela; Guido van der Werf; Guido van der Werf; Mark Parrington; Mark Parrington; Louis Giglio; Louis Giglio
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data Discussions (Jones et al., under review, https://doi.org/10.5194/essd-2024-218). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2023-February 2024), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Jones et al. (2024).

Citation

Work utilising our regional summaries should cite both Jones et al. (2024, under review, ESSD) AND the primary reference for the variable(s) of interest as follows:

  • Giglio et al. (2018) for MODIS MCD64A1 BA.
  • van der Werf et al. (2017) for GFED4.1s fire C emissions.
  • Kaiser er al. (2012) for GFAS fire C emissions.
  • van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.
  • Andela et al. (2019) for the Global Fire Atlas.

Input Data

Burned Area (BA)

Fire Carbon (C) Emissions

  • GFED4.1s fire C emissions data are extended from van der Werf and are available at https://globalfiredata.org/.
    • Period: 2003-February 2024
    • Resolution: 0.25 degree, daily

Global Fire Atlas (Individual Fire Atlas and Properties)

  • Global Fire Atlas are extended from Andela et al. (2019) and are available at Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062, last access: 31 May 2024).
    • Period: 2002-February 2024
    • Driven by 500m MODIS BA data (collection 6.1)

Regional Analysis

We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer).

The statistics derived from cookie-cutting are listed below. Full details in Jones et al. (2024).

Layer

Short Form

Source

Biomes

NA

Olson et al. (2001)

Continents

NA

ArcGIS Hub (2024)

Continental Biomes

NA

See above

Countries

NA

EU Eurostat (2020)

UC Davis Global Administrative Areas (GADM) Level 1

GADM-L1

UC Davis (2022)



Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions

IPCC AR6 WGI Regions

IPCC (2021); SantanderMetGroup (2021)

Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions

RECCAP2 Regions

Ciais et al. (2022)

Global Fire Emissions Database (GFED) Basis Regions

GFED4.1s Regions

van der Werf et al. (2006)

Regional Statistics and Anomalies

  • Burned Area (BA)
    • Calculated regional totals for each fire season.
    • Relative and standardized anomalies from historical data (since 2001).
    • Ranking amongst all recorded fire seasons.
    • Onset, peak, and cessation based on monthly deviations from climatological means.
  • Carbon Emissions
    • Calculated regional totals for each fire season.
    • Relative and standardized anomalies from historical data (since 2003).
    • Ranking amongst all recorded fire seasons.
    • Onset, peak, and cessation based on monthly deviations from climatological means.
    • Statistics available for GFAS, GFED, and their mean.
  • Individual Fire Properties
    • Based on ignition point vectors from the Global Fire Atlas.
    • Calculated regional count.
    • Calculated regional maxima and 95th percentiles for each fire season.
    • Relative and standardized anomalies from historical data (since 2002).
    • Ranked anomalies among all recorded fire seasons.
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