This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions. The selected data records in this data set are regularly extended with time as ERA5 forcing data become available. This dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service.

The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately on Zenodo. Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset. Details: File format: netcdf4 Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326) Longitude range: [-180, +180] Latitude range: [-90, +90] Temporal resolution: 1 day (at 12 local noon) Spatial resolution: 0.28 degrees (~31 Km) Spatial coverage: Global Time span: from 1980-01-01 to 2019-06-30 Stream: Deterministic forecasts

This dataset provides fine-scale gridded data of the daily components of the Forest Fire Weather Index (FWI) System, that best reflect physical processes, and also includes additional fire season-related indices. Designed for retrospective monitoring (6-days lag), the dataset enables daily tracking of fire danger across Canada. The dataset includes the six daily FWI components, namely the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC), the Drought Code (DC), the Initial Spread Index (ISI), the Buildup Index (BUI) and the Fire Weather Index (FWI), as well as the Daily Severity Rating (DSR), the cumulated DSR (DSRc), the fire season onset (Onset), the end of fire season (WinterOnset) and the fire season length (FSL) indices. Data, spanning from 1950 to the present year and covering the land surfaces of Canada, are provided in NetCDF and GeoTIFF file formats for each year, at a spatial resolution of 0.25° (approximatively 31 km). These data were calculated using ERA5 reanalysis hourly data. FWI components were computed using the solar noon (SN) method, which accounts for local insolation conditions. ​This method reduces discontinuities in indices value across longitudes-an issue that arises when the standard local noon method is used- particularly at higher spatial resolutions. Overall, this dataset aims to improve our collective capacity to anticipate and respond to the spatiotemporal variability in fire danger conditions that may trigger severe and widespread forest fires across Canada.

The dataset integrates a 30-year Canadian Fire Weather Index (FWI), generated using the Global ECMWF Fire Forecast model, forced by ERA5 reanalysis data (1981-2010). These simulations incorporate perturbations in temperature and precipitation forcings based on CMIP6 climate projections under the SSP2-4.5 medium mitigation scenario. The perturbed forcings were produced by modifying the daily temperature and precipitation data from ERA5 for the period of control from 1980 to 2010, using monthly factors that were estimated from a combination of climate change signals obtained from CMIP6 multi-model simulations, along with mean annual perturbations. The perturbation for temperature was constructed as an additive factor, varying from 0°C to 5°C with an increment of 1°C, added to the projected temperature under the SSP2-4.5 climate scenario. The perturbation for precipitation was constructed as a multiplicative factor, ranging from 0.6 to 1.6 with an increment of 0.2. The dataset offers daily FWI values across Europe, with a 31 km resolution, for each perturbation scenario over three decades.

This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).

References:

Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20^th 2019. https://cds.climate.copernicus.eu/cdsapp#!/home

Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.

Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.

Timeseries and Trend statistics (Sen-Theil slope estimates, Mann-Kendall S statistics, and associated Z statistics) for fine fuel moisture content (FMC) as calculated using the Canadian Forest Fire Weather Index (FWI) System and ERA5 data for the period 1979 to 2019. The underlying metrics use the FWI System's Fine Fuel Moisture Code (FFMC), transformed to a percentage of the fire season falling under 10% fuel moisture content, which has been identified as a critical threshold controlling fire potential. Also includes associated realm, biome, and ecoregion data using definitions from Olson et al. (2001).

The dataset presents projections of fire danger indicators for Europe based upon the Canadian Fire Weather Index System (FWI) under future climate conditions. The FWI is a meteorologically based index used worldwide to estimate the fire danger and is implemented in the Global ECMWF Fire Forecasting model (GEFF). In this dataset, daily FWI values, seasonal FWI values, and other FWI derived, threshold-specific, indicators were modelled using the GEFF model to estimate the fire danger in future climate scenarios. These indicators include the number of days with moderate, high, or very high fire danger conditions as classified by the European Forest Fire Information System (EFFIS) during the northern hemisphere's fire season (June-September):

very low: <5.2 low: 5.2 - 11.2 moderate: 11.2 - 21.3 high: 21.3 - 38.0 very high: 38.0 - 50 extreme: >=50.0

This dataset may serve to assess future fire danger conditions for regions across Europe and support the development of a long-term tourism strategy to reduce the risk of forest fires on nature-based tourism infrastructure. The FWI is a meteorologically based index that accounts for the effect of fuel moisture and weather conditions on fire behaviour. Daily noon values of air temperature, relative humidity, wind speed and 24-h accumulated precipitation are required for the calculation of the index. In order to attain the meteorological variables, projections from multiple global climate models downscaled to a regional climate model were used as input to the GEFF model. The climate models were developed within the EURO-CORDEX initiative, providing high-resolution and comparable model output centered on the European domain. In order to assess the impact of climate change, the GEFF model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5). Historical simulations, for the period 1970-2005, are included to provide a reference for the FWI projections. An estimate of the statistical uncertainty associated with climate projections may be derived with the use of multiple climate model outcomes. This may be achieved by the user both implicitly or explicitly by selecting from a choice of mean, best case, or worst case multi-model outcomes. It should be noted, however, that the multi-model approach may improve the robustness of the outcomes but does not take into account all possible aspects of uncertainty associated with modelling a future climate. This dataset was produced on behalf of the Copernicus Climate Change Service.

This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).

References:

Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20^th 2019. https://cds.climate.copernicus.eu/cdsapp#!/home

Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.

Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.

dataset_greece.nc

This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains the following variables for the years 2009 to 2021 at a daily 1km x 1km grid.

    Variable
    Units
    Long Name
    Description




    avg_d2m
    K
    Avg 2 metre dewpoint temperature
    Daily Average 2 metre dewpoint temperature ERA5-Land


    avg_rh
    %
    Avg Relative humidity
    Daily Average Relative humidity calculated from t2m and d2m


    avg_sp
    Pa
    Avg Surface pressure
    Daily Average Surface pressure ERA5-Land


    avg_t2m
    K
    Avg 2 metre temperature
    Daily Average 2 metre temperature ERA5-Land


    avg_tp
    m
    Avg total precipitation
    Daily Average Total precipitation ERA5-Land


    avg_u10
    m/s
    Avg 10 metre U wind component
    Daily Average 10 metre U wind component ERA5-Land


    avg_v10
    m/s
    Avg 10 metre V wind component
    Daily Average 10 metre V wind component ERA5-Land


    burned_areas
    unitless
    Rasterized burned polygons
    EFFIS (https://effis.jrc.ec.europa.eu/) burned areas burned as raster (value 1). Starting date retrieved with intersection with MODIS active fires


    et
    kg/m^2/8day
    8-day Evapotranspiration
    Total Evapotranspiration - MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid (MOD16A2)


    evi
    unitless
    16-day EVI
    Enhanced vegetation index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2)


    fapar
    %
    Fraction of Absorbed Photosynthetically Active Radiation
    FPAR - MOD15A2H MODIS/Terra Gridded 500M (8-day composite)


    fwi
    unitless
    Fire Weather Index
    Fire Weather Index 0.25 deg - https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview


    ignition_points
    unitless
    Rasterized ignition points
    Ignition points burned as raster (value 1) on the map calculated from intersection of MODIS active fires and EFFIS (https://effis.jrc.ec.europa.eu/) burned areas


    lai
    unitless
    Leaf Area Index
    Leaf Area Index (LAI) - MOD15A2H MODIS/Terra Gridded 500M Leaf Area Index LAI (8-day composite)


    lst_day
    K
    Day Land Surface Temperature
    Day Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)


    lst_night
    K
    Night Land Surface Temperature
    Night Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)


    max_d2m
    K
    Max 2 metre dewpoint temperature
    Daily Maximum 2 metre dewpoint temperature ERA5-Land


    max_rh
    %
    Max Relative humidity
    Daily Maximum Relative humidity calculated from t2m and d2m


    max_sp
    Pa
    Max Surface pressure
    Daily Maximum Surface pressure ERA5-Land


    max_t2m
    K
    Max 2 metre temperature
    Daily Maximum 2 metre temperature ERA5-Land


    max_tp
    m
    Max Total precipitation
    Daily Maximum Total precipitation ERA5-Land


    max_u10
    m/s
    Max 10 metre U wind component
    Daily Maximum 10 metre U wind component ERA5-Land


    max_v10
    m/s
    Max 10 metre V wind component
    Daily Maximum 10 metre V wind component ERA5-Land


    max_wind_direction
    degrees
    Wind direction of Max Wind
    Daily Maximum wind speed direction calculated from the U, V components


    max_wind_speed
    m/s
    Max wind speed norm
    Daily Maximum wind speed calculated from the U, V components


    max_wind_u10
    m/s
    10 metre U wind of Max Wind
    Daily 10 metre U wind component of Maximum Wind


    max_wind_v10
    m/s
    10 metre V wind of Max Wind
    Daily 10 metre V wind component of Maximum Wind


    min_d2m
    K
    Min 2 metre dewpoint temperature
    Daily Minimum 2 metre dewpoint temperature ERA5-Land


    min_rh
    %
    Min Relative humidity
    Daily Minimum Relative humidity calculated from t2m and d2m


    min_sp
    Pa
    Min Surface Pressure
    Daily Minimum Surface pressure ERA5-Land


    min_t2m
    K
    Min 2 metre temperature
    Daily Minimum 2 metre temperature ERA5-Land


    min_tp
    m
    Min Total precipitation
    Daily Minimum Total precipitation ERA5-Land


    min_u10
    m/s
    Min 10 metre U wind component
    Daily Minimum 10 metre U wind component ERA5-Land


    min_v10
    m/s
    Min 10 metre V wind component
    Daily Minimum 10 metre V wind component ERA5-Land


    ndvi
    unitless
    16-day NDVI
    Normalized Difference Vegetation Index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid


    number_of_fires
    unitless
    Daily number of fires
    Daily number of fires


    smian
    unitless
    Soil moisture index anomaly
    Soil Moisture Index Anomaly 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112


    sminx
    unitless
    Soil moisture index
    Soil Moisture Index 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112


    ASPECT
    degrees
    Aspect
    Aspect calculated from Digital Elevation Model EU-DEM


    CLC_2006
    unitless
    Mode of Corine Land Cover 2006
    Mode of Corine Land Cover 2006


    CLC_2006_0
    /100 %
    Fraction of lc class 0 (artificial_surfaces)
    Fraction of class 0 (artificial surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_1
    /100 %
    Fraction of lc class 1 (discontinuous_urban)
    Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_2
    /100 %
    Fraction of lc class 2 (arable_land)
    Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_3
    /100 %
    Fraction of lc class 3 (permanent_crops)
    Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_4
    /100 %
    Fraction of lc class 4 (pastures)
    Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_5
    /100 %
    Fraction of lc class 5 (general_agricultural)
    Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_6
    /100 %
    Fraction of lc class 6 (forest)
    Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_7
    /100 %
    Fraction of lc class 7 (misc_vegetation)
    Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_8
    /100 %
    Fraction of lc class 8 (misc_no_vegetation)
    Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2006


    CLC_2006_9
    /100 %
    Fraction of lc class 9 (water)
    Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2006


    CLC_2012
    unitless
    Mode of Corine Land Cover 2012
    Mode of Corine Land Cover 2012


    CLC_2012_0
    /100 %
    Fraction of lc class 0 (artificial_surfaces)
    Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_1
    /100 %
    Fraction of lc class 1 (discontinuous_urban)
    Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_2
    /100 %
    Fraction of lc class 2 (arable_land)
    Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_3
    /100 %
    Fraction of lc class 3 (permanent_crops)
    Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_4
    /100 %
    Fraction of lc class 4 (pastures)
    Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_5
    /100 %
    Fraction of lc class 5 (general_agricultural)
    Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_6
    /100 %
    Fraction of lc class 6 (forest)
    Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_7
    /100 %
    Fraction of lc class 7 (misc_vegetation)
    Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_8
    /100 %
    Fraction of lc class 8 (misc_no_vegetation)
    Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2012


    CLC_2012_9
    /100 %
    Fraction of lc class 9 (water)
    Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2012


    CLC_2018
    unitless
    Mode of Corine Land Cover 2018
    Mode of Corine Land Cover 2018


    CLC_2018_0
    /100 %
    Fraction of lc class 0 (artificial_surfaces)
    Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2018


    CLC_2018_1
    /100 %
    Fraction of lc class 1 (discontinuous_urban)
    Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2018


    CLC_2018_2
    /100 %
    Fraction of lc class 2 (arable_land)
    Fraction of class 2 (arable_land), Corine

The European Centre for Medium-Range Weather Forecasts (ECMWF) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (GEFF) model. Reanalysis is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms.

This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:

ECMWF_EFFIS_20180723_1200_en.tar (ensemble forecasts issued on 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723_1200_hr.tar (deterministic forecasts issued on 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_1200_hr_e5.tar (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_1200_en_e5.tar (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_e5.tar (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)

bbox.tar, containing 1 index (FWI) for the bounding box:

GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018

e5_hr, this folder contains deterministic model outputs

e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)

GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).

rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)

rt_en, this folder contains probabilistic forecasts (~18Km)

lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39

Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).

These products have been developed as part of the EU-funded Copernicus Emergency Management Services (CEMS) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (CAMS). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (JRC).

GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.

For more information, please refer to the documentation on the CDS and on the EFFIS website.

**Description of GlobalRx Dataset:

204,517 records of prescribed burns in 16 countries. In the information below, the name of the variable's column within the dataset is given in parentheses () in code font. For example, the column with the Drought Code data is titled DC.

For each record, the following general information (derived from the original burn records sources) is included, where available:

• Latitude (Latitude)
• Longitude (Longitude)
• Year (Year)
• Month (Month)
• Day (Day)
• Time* (Time)
• DOY (DOY)
• Date (Date)
• Country (Country)
• State/Province (State/Province)
• Agency/Organisation (Agency/Organisation)
• Burn Objective* (Burn Objective)
• Area Burned (Ha)* (Area Burned (Ha))
• Data Repository (Data Repository)
• Citation (Citation)

* Not available for every record

For each record, the following meteorological information (derived from the ERA5 single levels reanalysis product) is also included:

• Daily total accumulated precipitation (PPT_tot)
• Daily minimum and mean relative humidity (RH_min, RH_mean)*
• Daily maximum 2-meter temperature (T_max,T_mean)
• Daily maximum and mean 10-meter wind speed (Wind_max, Wind_mean)
• Daily minimum boundary layer height (BLH_min)
• C-Haines Index (CHI)*
• Vapor pressure deficit (VPD)*

* Computed from other ERA5 meteorological variables.

For each record, the following fire weather indices and components (derived from ERA5 fire weather reanalysis product) are also included:

• Canadian fire weather index (FWI)
• Fine fuel moisture code (FFMC)
• Drought moisture code (DMC)
• Drought code (DC)
• McArthur forest fire danger index (FFDI)
• Keetch-Byram drought index (KBDI)
• US burning index (USBI)

For each record, the following environmental information (derived from various sources, see paper for more information) is also included:

• Ecoregion (Olson et al. 2001) (Ecoregion (Olson))
• Biome (Olson et al. 2001) (Biome (Olson))
• Koppen Climate (Beck et al. 2023) (Koppen Climate)
• Topography (Danielson and Gesch 2011) (Topography)
• Fuelbed Classification (GFD-FCCS) (Pettinari and Chuvieco 2016) (Fuelbed Classification (GFD-FCCS))
• Fuelbed Group, broader groupings of the Fuelbed Classification used in the summary tables above (Fuelbed Group)
• WDPA Name (WDPA 2024) (WDPA Name)
• WDPA Governance (WDPA 2024) (WDPA Governance)
• WDPA Ownership (WDPA 2024) (WDPA Ownership)
• WDPA Designation (WDPA 2024) (WDPA Designation)
• WDPA IUCN Category (WDPA 2024) (WDPA IUCN Category)

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

Past and future weather extremes across Europe

This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [Zhang et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [Christensen et al., 2020, Muñoz, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [Giannakopoulos et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while.

Parameters of the analysis of the percentile-based extreme.


    Type
    Variable
    Percentile
    Window
    Min duration
    Rescaling
    Months
    Bootstrapping




    Heat wave
    tmin and tmax
    90
    5
    3
    max
    6, 7, 8
    yes


    Cold wave
    tmin and tmax
    10
    5
    3
    max
    1, 2, 10, 11, 12
    yes


    Flash drought
    swvl 0-28cm
    30
    5
    5
    mean
    6, 7, 8, 9, 10
    no


    Fire risk
    FWI
    90
    5
    1
    mean
    3, 4, 5, 6, 7, 8, 9
    yes

Xuebin Zhang, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1.

Samuel Jonson Sutanto, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276.

J. Sabater Muñoz. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019.

O. B. Christensen, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf

Barry J. F. Biggs, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677.

Giannakopoulos, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7

Funding Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the BIOCLIMAPATHS project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.

dataset_greece.nc

This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains data from 2009 to 2020 at a 1km x 1km x 1 daily grid.

Check our Jupyter notebook for an example showing how to access the dataset.

====================================================================================

Dynamic Variables

IMPORTANT NOTE: The Fire, Meteorological Variables and Fire Weather Index have been shifted one day back to ease the development of the models. This is to ease the development of our models, because operationally Meteorological variables and the Fire Weather Index are available as forecast and the Fire Variables are what we want our models to forecast given all the other variables.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------

It includes the following dynamic variables resampled at daily temporal resolution and 1km spatial resolution:

1. Previous day Leaf Area Index - MOD15A2H Variables (https://lpdaac.usgs.gov/products/mod15a2hv006/)

Fpar_500m
Lai_500m
FparLai_QC
FparExtra_QC
FparStdDev_500m
LaiStdDev_500m

2. Previous day MOD13A2 Variables (https://lpdaac.usgs.gov/products/mod13a2v006/)
1 km 16 days NDVI
1 km 16 days EVI
1 km 16 days VI Quality

3. Previous daty Evapotranspiration. MOD16A2 Variables (https://lpdaac.usgs.gov/products/mod16a2v006/)
ET_500m
LE_500m
PET_500m
PLE_500m
ET_QC_500m

4. Previous day Land Surface Temperature. MOD11A1 variables (https://lpdaac.usgs.gov/products/mod11a1v006/)
LST_Day_1km
QC_Day
LST_Night_1km
QC_Night

5. Meteorological data. ERA5-Land variables (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview)
era5_max_u10
era5_max_v10
era5_max_t2m
era5_max_tp
era5_min_u10
era5_min_v10
era5_min_t2m
era5_min_tp

6. Fire variables
ignition_points Ignition points derived from the association of burned areas product from EFFIS (effis.jrc.ec.europa.eu/) with FIRMS active fire product.
burned_areas: Burned areas from EFFIS (effis.jrc.ec.europa.eu/), associated with FIRMS active fire product to find ignition date
number_of_fires: Count of fire events for the given day.

7. Fire Weather Index (https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview)
fwi

------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Static Variables

It includes the following static variables resampled at 1km spatial resolution:

1. clc_YYYY for years 2006,, 2012, 2018: Corine Land Cover. (https://land.copernicus.eu/)

2. roads_density_2020: raster derived from OpenStreetMaps polygons for 2020. (https://www.openstreetmap.org/)
3. population_density_YYYY for years 2009-2020: population density at 1km spatial resolution. Source - https://www.worldpop.org/

4. Topography layers derived from EU-DEM. (https://land.copernicus.eu/)

dem_{agg}, aspect_{agg}. slope_{agg}, where agg is mean (mean value), std (standard deviation), max (maximum value), min (minimun value) and specifies the applied aggregation for the resampling to 1km.

The National Bushfire Intelligence Capability (NBIC) is providing national awareness for bushfire hazard and risk. It recognises that disaster risk reduction requires an informed shared understanding of bushfire hazard and risk across the disaster Prevent-Prepare-Response-Recovery (PPRR) continuum. The NBIC approach is unique in that it was conceived and is being implemented as an integrated socio-technical system to ensure that data design and relevance is optimised and to support longer-term climate adaptation decision making.

NBIC Stage 1 achieved the generation of bushfire behaviour maps that consider future climate over a range of timescales using the best available and readily accessible data. This data collection comprises a set of demonstration outputs that show bushfire hazard severity potential and various layers that are used to calculate it. These include draft input mapping rasters for slope and fire weather potential (baseline and projected).

The NBIC workflow uses a powerful cloud-based digital platform to enable the rapid co-development, testing and delivery of national extent products. NBIC Stage 2 will build on this work to produce more refined outputs of bushfire hazard, incorporating ongoing advances in data and science to support medium- to long-term decision support needs.

Further information about NBIC is available at https://research.csiro.au/nbic/ Lineage: Bushfire behaviour, such as its intensity and how quickly it moves, depends on three factors: vegetation, weather, and terrain. NBIC Stage 1 vegetation classification is represented by the Australian Fire Danger Rating System (AFDRS) National Fuel Map and parameter table. Weather is defined by both a historical weather reanalysis dataset (ERA5, baseline scenario) drawing on hourly concurrent data and a climate biased future weather dataset (based on 6 Global Climate Models and 2 Representative Concentrations Pathways from CMIP5, projected scenarios). Terrain is represented by Geoscience Australia’s Shuttle Radar Topographic Mission (SRTM) Smoothed Digital Elevation Model (DEM-S). These are ingested into 8 select fire behaviour models in alignment with the Australian Fire Danger Rating System (AFDRS).

A suite of modelled data was produced, accounting for climate projections and relevant return time intervals (a prediction of future extremes), representing fire behaviour in the form of fire rate of spread (ROS) and fireline intensity (FLI). Additional data such as the Forest Fire Danger Index (FFDI) are also produced to socialise all the projected weather scenarios explored and their effect on weather parameters driving fire behaviour. These data are generated for select return time intervals, derived by applying Extreme Value Analysis and an improved method for threshold evaluation.

For further information, refer to the metadata specification sheets in relevant folder for each dataset.

Please note a change to this version of the NBIC Stage 1 collection: The steady state total fuel load raster used in the derivation of fireline intensity and its data specification sheet are no longer publicly accessible. Further enquiries can be directed to NBICGeneral@csiro.au

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.

<p>Datacube variables</p>
</caption>
<thead>
  <tr>
    <th scope="row">Full name</th>
    <th scope="col">DataArray name</th>
    <th scope="col">Unit</th>
    <th scope="col">Contact *</th>
  </tr>
</thead>
<tbody>
  <tr>
    <th scope="row">Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Mean sea level pressure</th>
    <td>mslp</td>
    <td>Pa</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Total precipitation</th>
    <td>tp</td>
    <td>m</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Relative humidity</th>
    <td>rel_hum</td>
    <td>%</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Vapor Pressure Deficit</th>
    <td>vpd</td>
    <td>hPa</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Sea Surface Temperature</th>
    <td>sst</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Skin temperature</th>
    <td>skt</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Wind speed at 10 meters</th>
    <td>ws10</td>
    <td>m*s-2</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Temperature at 2 meters - Mean</th>
    <td>t2m_mean</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Temperature at 2 meters - Min</th>
    <td>t2m_min</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Temperature at 2 meters - Max</th>
    <td>t2m_max</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Surface net solar radiation</th>
    <td>ssr</td>
    <td>MJ m-2</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Surface solar radiation downwards</th>
    <td>ssrd</td>
    <td>MJ m-2</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Volumetric soil water level 1</th>
    <td>swvl1</td>
    <td>m3/m3</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Land-Sea mask</th>
    <td>lsm</td>
    <td>0-1</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: Copernicus
    <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p>
    </th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Drought Code Maximum</th>
    <td>drought_code_max</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Drought Code Average</th>
    <td>drought_code_mean</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Fire Weather Index Maximum</th>
    <td>fwi_max</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Fire Weather Index Average</th>
    <td>fwi_mean</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Carbon dioxide emissions from wildfires</th>
    <td>cams_co2fire</td>
    <td>kg/m²</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Fire radiative power</th>
    <td>cams_frpfire</td>
    <td>W/m²</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Burned Areas from Fire Climate Change Initiative (FCCI)</th>
    <td>fcci_ba</td>
    <td>ha</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Valid mask of FCCI burned areas</th>
    <td>fcci_ba_valid_mask</td>
    <td>0-1</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row"><br>
    Fraction of burnable area</th>
    <td>fcci_fraction_of_burnable_area</td>
    <td>%</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Number of patches</th>
    <td>fcci_number_of_patches</td>
    <td>N</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Fraction of observed area</th>
    <td>fcci_fraction_of_observed_area</td>
    <td>%</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Land Surface temperature at day</th>
    <td>lst_day</td>
    <td>K</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Leaf Area Index</th>
    <td>lai</td>
    <td>m²/m²</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Normalized Difference Vegetation Index</th>
    <td>ndvi</td>
    <td>unitless</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Population density</th>
    <td>pop_dens</td>
    <td>persons per square kilometers</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Burned Areas from GFED (large fires only)</th>
    <td>gfed_ba</td>
    <td>hectares (ha)</td>
    <td>MPI</td>
  </tr>
  <tr>
    <th scope="row">Valid mask of GFED burned areas</th>
    <td>gfed_ba_valid_mask</td>
    <td>0-1</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">GFED basis regions</th>
    <td>gfed_region</td>
    <td>N</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Burned Areas from GWIS</th>
    <td>gwis_ba</td>
    <td>ha</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Valid mask of GWIS burned areas</th>
    <td>gwis_ba_valid_mask</td>
    <td>0-1</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Western Pacific Index</th>
    <td>oci_wp</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Pacific North American Index</th>
    <td>oci_pna</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">North Atlantic Oscillation</th>
    <td>oci_nao</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Southern Oscillation Index</th>
    <td>oci_soi</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Global Mean Land/Ocean Temperature</th>
    <td>oci_gmsst</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Pacific Decadal Oscillation</th>
    <td>oci_pdo</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Eastern Asia/Western Russia</th>
    <td>oci_ea</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">East Pacific/North Pacific Oscillation</th>
    <td>oci_epo</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Nino 3.4 Anomaly</th>
    <td>oci_nino_34_anom</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Bivariate ENSO Timeseries</th>
    <td>oci_censo</td>
    <td>unitless</td>
    <td>NOA</td>
  </tr>
  <tr>
    <th scope="row">Dataset: ESA CCI</th>
    <td> </td>
    <td> </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">Land Cover