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Soil microbial and physicochemical data from watersheds impacted by different management practices or wildfire in the Southern Appalachian Mountains, 2023
Four forested watersheds in Western North Carolina with different management practices or disturbance were sampled in the summer of 2023 to compare soil physicochemical, microbial, and functional differences. These data include mineral soil physicochemical properties (location, elevation, aspect, gravimetric moisture content, pH, total carbon and nitrogen, total organic carbon, dissolved organic carbon and nitrogen, total dissolved nitrogen, dissolved inorganic nitrogen (NO3 and NH4), and microbial biomass carbon and nitrogen), soil microbial properties (16S ASV community sequences, ITS ASV community sequences, extracellular enzyme activity, carbon mineralization rates, and ammonium mineralization rates), and organic soil properties (total organic carbon, total carbon and nitrogen, 16S ASV sequences, pH, and moisture). Together, this dataset provides context to understanding the impacts of different management practices and relevant disturbances, such as severe wildfire, on soil in the Southern Appalachian region.
Indirect impacts of a novel wildfire on a well-studied desert stream: connectivity, carbon, and communities
In 2020 the Bush Fire burned approximately half of the Sycamore Creek watershed in central Arizona. Sycamore Creek has been subject to >40 years of research and the stream has been monitored by NEON since 2017. We studied the effects of fire on biogeochemistry of the stream and its watershed. We deployed autosamplers to monitor stream chemistry during storms on the mainstem and in ephemeral tributaries draining burned and unburned watersheds. The storm sampling program commenced nearly a year following the fire because absence of summer monsoon or winter storms in 2020-21 resulted in no flow in tributaries and intermittent flow in the mainstem. Water chemistry was measured during 14 monsoon storms of 2021 and winter frontal storms of 2021-22 with samples of baseflow collected in the mainstem during intervening periods. Water samples were analyzed for dissolved organic carbon, nitrogen, phosphorus, and major anions and cations. We also measured nutrient content of ash and chemistry of ash leachate as a potential source of solutes to stream biota.
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). 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 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>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). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece
<p><strong>dataset_greece.nc</strong></p> <p><strong>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.</strong><br> </p> <table> <thead> <tr> <th scope="col"><strong>Variable</strong></th> <th scope="col"><strong>Units</strong></th> <th scope="col"><strong>Long Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td>avg_d2m</td> <td>K</td> <td>Avg 2 metre dewpoint temperature</td> <td>Daily Average 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>avg_rh</td> <td>%</td> <td>Avg Relative humidity</td> <td>Daily Average Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>avg_sp</td> <td>Pa</td> <td>Avg Surface pressure</td> <td>Daily Average Surface pressure ERA5-Land</td> </tr> <tr> <td>avg_t2m</td> <td>K</td> <td>Avg 2 metre temperature</td> <td>Daily Average 2 metre temperature ERA5-Land</td> </tr> <tr> <td>avg_tp</td> <td>m</td> <td>Avg total precipitation</td> <td>Daily Average Total precipitation ERA5-Land</td> </tr> <tr> <td>avg_u10</td> <td>m/s</td> <td>Avg 10 metre U wind component</td> <td>Daily Average 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>avg_v10</td> <td>m/s</td> <td>Avg 10 metre V wind component</td> <td>Daily Average 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>burned_areas</td> <td>unitless</td> <td>Rasterized burned polygons</td> <td>EFFIS (https://effis.jrc.ec.europa.eu/) burned areas burned as raster (value 1). Starting date retrieved with intersection with MODIS active fires</td> </tr> <tr> <td>et</td> <td>kg/m^2/8day</td> <td>8-day Evapotranspiration</td> <td>Total Evapotranspiration - MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid (MOD16A2)</td> </tr> <tr> <td>evi</td> <td>unitless</td> <td>16-day EVI</td> <td>Enhanced vegetation index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2)</td> </tr> <tr> <td>fapar</td> <td>%</td> <td>Fraction of Absorbed Photosynthetically Active Radiation</td> <td>FPAR - MOD15A2H MODIS/Terra Gridded 500M (8-day composite)</td> </tr> <tr> <td>fwi</td> <td>unitless</td> <td>Fire Weather Index</td> <td>Fire Weather Index 0.25 deg - https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview</td> </tr> <tr> <td>ignition_points</td> <td>unitless</td> <td>Rasterized ignition points</td> <td>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</td> </tr> <tr> <td>lai</td> <td>unitless</td> <td>Leaf Area Index</td> <td>Leaf Area Index (LAI) - MOD15A2H MODIS/Terra Gridded 500M Leaf Area Index LAI (8-day composite)</td> </tr> <tr> <td>lst_day</td> <td>K</td> <td>Day Land Surface Temperature</td> <td>Day Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>lst_night</td> <td>K</td> <td>Night Land Surface Temperature</td> <td>Night Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>max_d2m</td> <td>K</td> <td>Max 2 metre dewpoint temperature</td> <td>Daily Maximum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>max_rh</td> <td>%</td> <td>Max Relative humidity</td> <td>Daily Maximum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>max_sp</td> <td>Pa</td> <td>Max Surface pressure</td> <td>Daily Maximum Surface pressure ERA5-Land</td> </tr> <tr> <td>max_t2m</td> <td>K</td> <td>Max 2 metre temperature</td> <td>Daily Maximum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>max_tp</td> <td>m</td> <td>Max Total precipitation</td> <td>Daily Maximum Total precipitation ERA5-Land</td> </tr> <tr> <td>max_u10</td> <td>m/s</td> <td>Max 10 metre U wind component</td> <td>Daily Maximum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>max_v10</td> <td>m/s</td> <td>Max 10 metre V wind component</td> <td>Daily Maximum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>max_wind_direction</td> <td>degrees</td> <td>Wind direction of Max Wind</td> <td>Daily Maximum wind speed direction calculated from the U, V components</td> </tr> <tr> <td>max_wind_speed</td> <td>m/s</td> <td>Max wind speed norm</td> <td>Daily Maximum wind speed calculated from the U, V components</td> </tr> <tr> <td>max_wind_u10</td> <td>m/s</td> <td>10 metre U wind of Max Wind</td> <td>Daily 10 metre U wind component of Maximum Wind</td> </tr> <tr> <td>max_wind_v10</td> <td>m/s</td> <td>10 metre V wind of Max Wind</td> <td>Daily 10 metre V wind component of Maximum Wind</td> </tr> <tr> <td>min_d2m</td> <td>K</td> <td>Min 2 metre dewpoint temperature</td> <td>Daily Minimum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>min_rh</td> <td>%</td> <td>Min Relative humidity</td> <td>Daily Minimum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>min_sp</td> <td>Pa</td> <td>Min Surface Pressure</td> <td>Daily Minimum Surface pressure ERA5-Land</td> </tr> <tr> <td>min_t2m</td> <td>K</td> <td>Min 2 metre temperature</td> <td>Daily Minimum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>min_tp</td> <td>m</td> <td>Min Total precipitation</td> <td>Daily Minimum Total precipitation ERA5-Land</td> </tr> <tr> <td>min_u10</td> <td>m/s</td> <td>Min 10 metre U wind component</td> <td>Daily Minimum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>min_v10</td> <td>m/s</td> <td>Min 10 metre V wind component</td> <td>Daily Minimum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>ndvi</td> <td>unitless</td> <td>16-day NDVI</td> <td>Normalized Difference Vegetation Index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid</td> </tr> <tr> <td>number_of_fires</td> <td>unitless</td> <td>Daily number of fires</td> <td>Daily number of fires</td> </tr> <tr> <td>smian</td> <td>unitless</td> <td>Soil moisture index anomaly</td> <td>Soil Moisture Index Anomaly 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>sminx</td> <td>unitless</td> <td>Soil moisture index</td> <td>Soil Moisture Index 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>ASPECT</td> <td>degrees</td> <td>Aspect</td> <td>Aspect calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>CLC_2006</td> <td>unitless</td> <td>Mode of Corine Land Cover 2006</td> <td>Mode of Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>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</td> </tr> <tr> <td>CLC_2006_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>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</td> </tr> <tr> <td>CLC_2006_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2012</td> <td>unitless</td> <td>Mode of Corine Land Cover 2012</td> <td>Mode of Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>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</td> </tr> <tr> <td>CLC_2012_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>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</td> </tr> <tr> <td>CLC_2012_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2018</td> <td>unitless</td> <td>Mode of Corine Land Cover 2018</td> <td>Mode of Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>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</td> </tr> <tr> <td>CLC_2018_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>DEM</td> <td>m</td> <td>Elevation</td> <td>Averaged Digital Elevation Model EU-DEM</td> </tr> <tr> <td>POP_DENS_2009</td> <td>humans/km^2</td> <td>Population density (2009)</td> <td>Population density for year 2009 from Worldpop</td> </tr> <tr> <td>POP_DENS_2010</td> <td>humans/km^2</td> <td>Population density (2010)</td> <td>Population density for year 2010 from Worldpop</td> </tr> <tr> <td>POP_DENS_2011</td> <td>humans/km^2</td> <td>Population density (2011)</td> <td>Population density for year 2011 from Worldpop</td> </tr> <tr> <td>POP_DENS_2012</td> <td>humans/km^2</td> <td>Population density (2012)</td> <td>Population density for year 2012 from Worldpop</td> </tr> <tr> <td>POP_DENS_2013</td> <td>humans/km^2</td> <td>Population density (2013)</td> <td>Population density for year 2013 from Worldpop</td> </tr> <tr> <td>POP_DENS_2014</td> <td>humans/km^2</td> <td>Population density (2014)</td> <td>Population density for year 2014 from Worldpop</td> </tr> <tr> <td>POP_DENS_2015</td> <td>humans/km^2</td> <td>Population density (2015)</td> <td>Population density for year 2015 from Worldpop</td> </tr> <tr> <td>POP_DENS_2016</td> <td>humans/km^2</td> <td>Population density (2016)</td> <td>Population density for year 2016 from Worldpop</td> </tr> <tr> <td>POP_DENS_2017</td> <td>humans/km^2</td> <td>Population density (2017)</td> <td>Population density for year 2017 from Worldpop</td> </tr> <tr> <td>POP_DENS_2018</td> <td>humans/km^2</td> <td>Population density (2018)</td> <td>Population density for year 2018 from Worldpop</td> </tr> <tr> <td>POP_DENS_2019</td> <td>humans/km^2</td> <td>Population density (2019)</td> <td>Population density for year 2019 from Worldpop</td> </tr> <tr> <td>POP_DENS_2020</td> <td>humans/km^2</td> <td>Population density (2020)</td> <td>Population density for year 2020 from Worldpop</td> </tr> <tr> <td>POP_DENS_2021</td> <td>humans/km^2</td> <td>Population density (2021)</td> <td>Population density for year 2021 from Worldpop</td> </tr> <tr> <td>ROAD_DISTANCE</td> <td>meters</td> <td>Distance from roads</td> <td>Distance from major roads from Worldpop</td> </tr> <tr> <td>ROUGHNESS</td> <td>unitless</td> <td>Roughness</td> <td>Roughness calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>SLOPE</td> <td>degrees</td> <td>Slope</td> <td>Slope calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>WATERWAY_DISTANCE</td> <td>meters</td> <td>Distance from waterways</td> <td>Distance from major waterways from Worldpop</td> </tr> </tbody> </table> <p>temporal_extent : (2009-03-06, 2021-08-29)</p> <p>spatial_extent : (18.7, 34.3, 28.9, 42.3)</p> <p>crs : EPSG:4326</p> <p>license : Creative Commons Attribution v4</p> <p>creators : Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis</p> <p>contact_person : Ioannis Prapas <iprapas (at) noa.gr></p> <p>citation : Ioannis Prapas, Spyros Kondylatos, & Ioannis Papoutsis. (2021). A Datacube for the analysis of wildfires in Greece (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4943354</p> <p>acknowledgements : This work has received funding from the European Union’s Horizon 2020 research and innovation project DeepCube, under grant agreement number 101004188.</p> <p>title : FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece</p>
Probability of wildfire containment
<p>Raster layer depicting the probatility of containing a fire according to the landscape configuration: accessibility, aerial means, relief complexity and vegetation density.</p>
State of Wildfires 2023-24 - ConFire data
<div> <p>This contains driving and output data used by ConFire in the State of Wildfire’s 2023/24 report. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over the three regions used and defined in the report.</p> <p> </p> <p><strong>Driving Data</strong></p> <p>The “Driving_data” directory contains data used to run the ConFire model and produce analyses. This directory is divided into three focal regions, with NW_Amazon corresponding to the report's “Western Amazonia”. Each region contains the following files:</p> </div> <div> <ul> <li><strong>raw_burnt_area.nc</strong>: The original 0.25-degree burnt area dataset before being regridded for use in ConFire.</li> <li><strong>nrt</strong>: Near Real Time (NRT) driving data used for driver identification.</li> <li><strong>isimip3a</strong>: ISIMIP3a data used for attribution.</li> <li><strong>isimip3b</strong>: ISIMIP3b GCM bias-corrected data used for future projections.</li> </ul> </div> <div> <p><strong>NRT</strong></p> <p>Within the <strong>nrt</strong> directory, data is organized by periods, with the numbers corresponding to the year range. The report utilizes the <strong>period_2013_2023</strong> directory, which contains the NetCDF files in the table below.</p> <p>Filename ending with the following show:</p> <p>12Annual – 12 month running mean</p> <ul> <li>12monthMax – 12 month running maximum</li> <li>Deficity – current month over 12 month running mean</li> <li>Quarter – 3 month running mean</li> </ul> <p><br>Not all were used in the final analysis. For full data info, see Table 3 of the report <a href="https://doi.org/10.5194/essd-2024-218" target="_blank" rel="noopener">https://doi.org/10.5194/essd-2024-218</a>:</p> </div> <div> <div> <div> <div> <table> <tbody> <tr> <th>NetCDF File</th> <th>Variable</th> <th>Used/Not Used</th> <th>Source</th> <th>Notes</th> </tr> </tbody> <tbody> <tr> <td>burnt_area.nc</td> <td>Burnt Area</td> <td>As training data</td> <td> </td> <td> </td> </tr> <tr> <td>cropland.nc</td> <td>Cropland</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>d2m.nc</td> <td>2m Dewpoint Temperature</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>DeadFuelFoilage-cvh_C.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelFoilage-cvl_C.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelFoilage.nc</td> <td>Dead Foliage Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood-cvh_C.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood-cvl_C.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>DeadFuelWood.nc</td> <td>Dead Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-12Annual.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-12monthMax.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-Deficity.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Foilage-Quater.nc</td> <td>Dead Foliage Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-12Annual.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-12monthMax.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood-Deficity.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Dead-Wood.nc</td> <td>Dead Wood Fuel Moisture</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-12Annual.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-12monthMax.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-Deficity.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live.nc</td> <td>Live Fuel Moisture Content</td> <td>Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>Fuel-Moisture-Live-Quater.nc</td> <td>Live Fuel Moisture Content</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>grazing_land.nc</td> <td>Grazing Land</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>lightn.nc</td> <td>Lightning</td> <td>Used</td> <td>LIS/OTD</td> <td>Cecil et al., 2014</td> </tr> <tr> <td>LiveFuelFoilage-cvh_C.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelFoilage-cvl_C.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelFoilage.nc</td> <td>Live Leaf Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood-cvh_C.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood-cvl_C.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>LiveFuelWood.nc</td> <td>Live Wood Fuel Load</td> <td>Not Used</td> <td>Fuel Model</td> <td>McNorton et al. 2024a</td> </tr> <tr> <td>pasture.nc</td> <td>Pasture</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>population_density.nc</td> <td>Population Density</td> <td>Used</td> <td> </td> <td> </td> </tr> <tr> <td>rangeland.nc</td> <td>Rangeland</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>rural_population.nc</td> <td>Rural Population</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>snowCover.nc</td> <td>Snow Cover</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>t2m.nc</td> <td>2m Temperature</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>total_irrigated.nc</td> <td>Irrigated Area</td> <td>Not Used</td> <td> </td> <td> </td> </tr> <tr> <td>tp-12Annual.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-12monthMax.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-Deficity.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp.nc</td> <td>Precipitation</td> <td>Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>tp-Quater.nc</td> <td>Precipitation</td> <td>Not Used</td> <td>ERA5-Land</td> <td>Muñoz-Sabater et al. 2021</td> </tr> <tr> <td>urban_population.nc</td> <td>Urban Population</td> <td>Used</td> <td>HYDE</td> <td>Klein Goldewijk et al., 2011</td> </tr> <tr> <td>VOD-12Annual.nc</td> <td>Mean & Max VOD</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-12monthMax.nc</td> <td>Mean & Max VOD</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-Deficity.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Not Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> <tr> <td>VOD-Quater.nc</td> <td>Vegetation Optical Depth (VOD)</td> <td>Not Used</td> <td>Satellite (SMOS)</td> <td>Wigneron et al 2021</td> </tr> </tbody> </table> </div> </div> </div> </div> <div> <div> </div> <div> </div> <div> <div> <p><strong>ISIMIP3a</strong></p> <p>The <strong>isimip3a</strong> directory follows the structure: <strong><<experiment>>/<<reanalysis_source>>/period_yyyy_yyyy/</strong>.</p> </div> <div> <ul> <li><strong><<experiment>></strong>: Can be either:</li> </ul> </div> <div> <ul> <ul> <li><strong>obsclim</strong>: Reanalysis targeting observed climate.</li> <li><strong>counterclim</strong>: Detrended obsclim approximating climate without climate change.</li> </ul> </ul> </div> <div> <ul> <li><strong><<reanalysis_source>></strong>: Currently contains only GSWP3-W5E5, with more sources to follow in subsequent years.</li> <li><strong>yyyy_yyyy</strong>: Corresponds to the year range.</li> </ul> </div> <div> <p>For attribution experiments in the report, the following directories are used:</p> </div> <div> <ul> <li><strong>Factual</strong>: <strong>obsclim/GSWP3-W5E5/period_2002_2019/</strong></li> <li><strong>Counterfactual</strong>: <strong>counterclim/GSWP3-W5E5/period_2002_2019/</strong></li> <li><strong>Early Industrial</strong>: <strong>counterclim/GSWP3-W5E5/period_1901_1920/</strong></li> </ul> </div> <div> <p>Additional details on setting the temporal range for the report can be found <a href="https://github.com/douglask3/Bayesian_fire_models/tree/SoW?tab=readme-ov-file#configuration-settings" target="_new">here</a>.</p> <p><strong>ISIMIP3b</strong></p> <p>The <strong>isimip3b</strong> directory structure is similar to ISIMIP3a: <strong><<experiment>>/<<GCM>>/period_yyyy_yyyy/</strong>.</p> </div> <div> <ul> <li><strong><<experiment>></strong> includes:</li> </ul> </div> <div> <ul> <ul> <li><strong>historical</strong>: Historical GCM output.</li> <li><strong>ssp126</strong></li> <li><strong>ssp370</strong></li> <li><strong>ssp585</strong></li> </ul> </ul> </div> <div> <ul> <li><strong><<GCM>></strong>: Refers to the General Circulation Model used.</li> <li><strong>yyyy_yyyy</strong>: Corresponds to the year range.</li> </ul> </div> <div> <p>Both ISIMIP3a and ISIMIP3b contain the same NetCDF files, as follows:</p> <table> <tbody> <tr> <th><strong>netcdf file</strong></th> <th><strong>variable</strong></th> <th><strong>used/not used</strong></th> <th><strong>source</strong></th> <th><strong>Notes</strong></th> </tr> </tbody> <tbody> <tr> <td>consec_dry_mean.nc</td> <td>Max. consecutive dry days</td> <td>used</td> <td> <p>ISIMIP3a/3b</p> </td> <td>Based on precipitation</td> </tr> <tr> <td>crop_jules-es.nc</td> <td>Cropland</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Interpolated from annual to monthly</td> </tr> <tr> <td>debiased_nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Non-tree vegetated cover simulated by JULES and bias-corrected</td> </tr> <tr> <td>debiased_tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>not used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Annual mean tree cover bias-corrected to VCF</td> </tr> <tr> <td>dry_days.nc</td> <td>No. dry days</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Fractional number of days with rainfall < 0.1mm/m</td> </tr> <tr> <td>filled_debiased_nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected non-tree vegetated cover</td> </tr> <tr> <td>filled_debiased_tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected tree cover</td> </tr> <tr> <td>filled_debiased_vegCover_jules-es.nc</td> <td>Total vegetation cover</td> <td>used</td> <td>JULES-ES-ISIMIP VCF using ibicus</td> <td>Filled and bias-corrected vegetation cover</td> </tr> <tr> <td>lightning.nc</td> <td>Lightning</td> <td>used</td> <td>ISIMIP3a</td> <td>Climatology</td> </tr> <tr> <td>nonetree_cover_jules-es.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>JULES-ES-ISIMIP </td> <td>Non-tree vegetated cover simulated by JULES</td> </tr> <tr> <td>pasture_jules-es.nc</td> <td>Pasture</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Interpolated from annual to monthly</td> </tr> <tr> <td>pr_mean.nc</td> <td>Precipitation</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Monthly mean precipitation</td> </tr> <tr> <td>tas_max.nc</td> <td>Maximum monthly temperature</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Maximum of maximum daily temperature within the month</td> </tr> <tr> <td>tas_mean.nc</td> <td>Mean monthly temperature</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Daily mean temperature</td> </tr> <tr> <td>tree_cover_jules-es.nc</td> <td>Tree Cover</td> <td>not used</td> <td>JULES-ES-ISIMIP </td> <td>Annual mean tree cover bias-corrected to VCF</td> </tr> <tr> <td>urban_jules-es.nc</td> <td>Urban fraction</td> <td>used</td> <td>JULES-ES</td> <td>Urban area fraction</td> </tr> <tr> <td>vpd_max.nc</td> <td>Maximum monthly VPD</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Maximum of daily VPD values</td> </tr> <tr> <td>vpd_mean.nc</td> <td>Mean monthly VPD</td> <td>used</td> <td>ISIMIP3a/3b</td> <td>Mean of daily VPD values</td> </tr> <tr> <td>nontree_cover_VCF-obs.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td>VCF</td> <td>Non-tree vegetated cover observed</td> </tr> <tr> <td>nontree_raw_VCF-obs.nc</td> <td>Total vegetation cover</td> <td>not used</td> <td> VCF</td> <td>Raw non-tree vegetated cover observed</td> </tr> <tr> <td>nonveg_cover_VCF-obs.nc</td> <td>Non-vegetated cover</td> <td>not used</td> <td> VCF</td> <td>Observed non-vegetated cover</td> </tr> <tr> <td>nonveg_raw_VCF-obs.nc</td> <td>Non-vegetated cover</td> <td>not used</td> <td> VCF</td> <td>Raw observed non-vegetated cover</td> </tr> <tr> <td>tree_cover_VCF-obs.nc</td> <td>Tree Cover</td> <td>not used</td> <td> VCF</td> <td>Observed tree cover</td> </tr> <tr> <td>tree_raw_VCF-obs.nc</td> <td>Tree Cover</td> <td>not used</td> <td> VCF</td> <td>Raw observed tree cover</td> </tr> </tbody> </table> </div> <p>ISIMIP3a/3b is detailed in Frieler et al. (2024) and raw data can be obtained from <strong><a href="https://data.isimip.org/">https://data.ISIMIP.org</a></strong></p> <p>While not used as driving data, VCF is used to proceed bias corrected driving data. VCF is taken from MODIS Vegetation Continuous Fields collection 6.1 remote sensed data for <60॰N DiMiceli et al. (2022) and collection 6 for <60॰N DiMiceli et al. (2015). JULES-ES (Mathison et al. 2023) was driven using the corresponding ISIMIP datasets.</p> <div> <p><strong>Outputs</strong></p> <p>Outputs contain the ConFire outputs when driven with the provided datasets. The directories are named according to the regions, and for each region, there are four sets of outputs:</p> </div> <div> <ul> <li>isimip-evaluation1</li> <li>isimip-final.tar</li> <li>nrt-evaluation1</li> <li>nrt-final</li> </ul> </div> <div> <p>Each of these directories contains the following files necessary for rerunning the model without redoing the optimization. While you are unlikely to need to look at these files, they are useful for setting up your own model experiments (see the <a href="https://github.com/douglask3/Bayesian_fire_models/tree/SoW?tab=readme-ov-file#configuration-settings" target="_new">ConFire configuration settings</a>):</p> </div> <div> <ul> <li>scalers-_*.csv</li> <li>trace-_*.nc</li> <li>variables_info-_*.txt</li> </ul> </div> <div> <p>Additionally, there are two other directories:</p> </div> <div> <ul> <li><strong>figs</strong>: Contains automatically generated figures and some of their outputs.</li> <li><strong>sample</strong>: Contains model outputs.</li> </ul> </div> <div> <p>Within the <strong>sample</strong> directory, there is a subdirectory indicating the model run name, which contains a series of experiments. These experiments differ for each run (see below), and each experiment contains some or all of the following directories:</p> </div> <div> <ul> <li><strong>Evaluate</strong>: Contains the burnt area from the full model including stochastic parameters. Often used for evaluation (see report supplement for more information).</li> <li><strong>Control</strong>: Contains burnt area driven purely by driving datasets with stochasticity. Used as the control in most of the analysis.</li> <li><strong>Standard_X</strong>: A series of directories with burnt areas from individual controls. This describes the burnt area in the presence of that control in otherwise ideal burning conditions. The numbers are:</li> </ul> </div> <div> <ul> <ul> <li>0: Fuel load for all runs</li> <li>1: Fuel moisture for all runs</li> <li>2: Fire weather for NRT and ignitions for ISIMIP3a</li> <li>3: Ignitions for ISIMIP3a and suppression for ISIMIP3a</li> <li>4: Suppression for NRT</li> <li>5: Snow for NRT</li> </ul> </ul> </div> <div> <p>Within each of these directories is a series of ensemble members <strong>sample-predX.nc</strong>. Within the same optimization (i.e., the same model run, so across all experiments), samples are paired, meaning the <strong>sample-predX.nc</strong> corresponds to the sample in another experiment.</p> <p><strong>Experiments</strong></p> <p><strong>isimip-evaluation1 & nrt-evaluation1</strong></p> <p>The only experiment for evaluation is called <strong>baseline</strong>, which has an 'Evaluate' and 'Control' run and is used to evaluate the model. Automatically generated evaluation figures can be found in the <strong>figs/</strong> directory.</p> <p><strong>nrt-final</strong></p> <p>This also contains only one run, <strong>baseline</strong>, but includes runs for each of the controls.</p> <p><strong>isimip-final</strong></p> <p>This has more runs:</p> </div> <div> <ul> <li><strong>factual</strong>: Uses the ISIMIP3a obsclim driving dataset (see driving dataset above).</li> <li><strong>counterfactual</strong>: Uses the ISIMIP3a counterclim dataset.</li> <li><strong>early_industrial</strong>: Uses the early period ISIMIP3a counterclim dataset.</li> <li><strong>historical/<<GCM>>/</strong>, <strong>ssp126/<<GCM>>/</strong>, <strong>ssp370/<<GCM>>/</strong>, <strong>ssp585/<<GCM>>/</strong>: Uses the ISIMIP3b datasets outlined above, where <<GCM>> is one of each of the five GCMs used in ISIMIP3b.</li> </ul> </div> <div> <p><strong>Additional Analysis</strong></p> <p>The analysis in the report also utilizes 95th and 90th percentile burnt area totals. These aren't as neatly organized as the NetCDF files yet, but we’re getting there. They can be found in:</p> <p>figs/ _13-frac_points_0.5-<<experiment>>-control_TS/<<GCM>>-control_TS/pc-%%/ points-<<run>>.csv</p> </div> </div> </div>
PALEODEM/ What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula
<p>This repository contains new XRD data from the Villena paleolake, archaeological radiocarbon evidence from the Villena area and the R code used to produce Summed Probability distribution analyses. </p> <p>They correspond to the following reference: </p> <p>Sánchez-García, C., Revelles, J., Burjachs, F., Euba, I., Expósito, I., Ibáñez, J., Schulte, L., Fernández-López de Pablo, J. What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula (submitted to Catena). </p> <p>We specify the content of file further down:</p> <ul> <li>Vinalopo.csv: the list of radiocarbon dates from Villena spanning ca.9500-5500 cal BP from the following sites: Arenal de la Virgen, Cueva del Lagrimal and Casa Corona. </li> <li>ngrip.csv: NGRIP GICC05 paleotemperature record based on oxygen isotope series from Rasmussen SO <em>et al.</em>2006 A new Greenland ice core chronology for the last glacial termination. <em>J. Geophys. Res. Atmos.</em><strong>111</strong>. (doi:10.1029/2005JD006079) and Andersen KK <em>et al.</em>2006 The Greenland Ice Core Chronology 2005, 15–42ka. Part 1: constructing the time scale. <em>Quat. Sci. Rev.</em>25, 3246–3257.</li> <li>Char.csv: Sedimentary charcoal data set from the Villena Paleolake (VL3 core) published by Jones, S.E., Burjachs, F., Fernández-López de Pablo (2018) DOI/10.5281/zenodo.1244003, according to the new Bacon chronological model of the Villena paleolake (Fernández-López de Pablo et al., 2022 . Impacts of Early Holocene environmental dynamics on open-air occupation patterns in the Western Mediterranean: insights from El Arenal de la Virgen (Alicante, Spain). <a href="https://doi.org/10.31235/osf.io/5yqsr">https://doi.org/10.31235/osf.io/5yqsr</a>)</li> <li>SPD_analysis.R: R script with the code to reproduce the SPD analysis presented in the manuscript. </li> <li>SupplMat1xlsl: an excel file This file is composed by 8 spreadsheets:</li> </ul> <ol> <li>‘Selected variables 12.6-5.5’: all the data included in the time frame 12600-5500 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 12.6-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 5_DCA 12.6-5.5’ to track the results) and have been plotted in Figure 3 and 7. </li> <li>'Selected variables 9.1-5.5’: data included in the analysis focused on the time period 9.1-5.5 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 9.1-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 6_DCA 9.1-5.5’ to track the results) and have been plotted in Figure 8.</li> <li>‘Spearmans’rs 12.6-5.5’: Spearmans’rs correlation analysis applied to the 12600-5500 cal BP dataset (data from ‘Selected variables 12.6-5.5’).</li> <li>‘Spearmans’rs 9.1-5.5 cal BP’ Spearmans’rs correlation analysis applied to the 9100-5500 cal BP dataset, including here high-resolution XRD data (data from ‘Selected variables 9.1-5.5’).</li> <li>‘Figure 2 charcoal results’: original sedimentary charcoal results provided in this work. Data plotted in Figure 2. </li> <li>‘Figure 4 XRD results’: original XRD results provided in this work. Data plotted in Figure 4.</li> <li>‘Figure 5 DCA 12.6-5.5’: results of Detrended Correspondence analysis focused on the time period from 12600 to 5500 cal BP. Data plotted in Figure 5.</li> <li>‘Figure 6 DCA 9.1-5.5’ results of Detrended Correspondence analysis focused on the time period from 9100 to 5500 cal BP, including here high-resolution XRD data. Data plotted in Figure 6.</li> </ol>
Online Data for 'The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century'
<p>This data file (.xlsx) contains all data used to create table 1, figures 1a-d, figure 2, figure S1, S2, and S5 of the study "The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century". Main article is available under: https://doi.org/10.1029/2023GB007813</p>
Fire-severity effects on plant-fungal interactions after a novel tundra wildfire disturbance: implications for arctic shrub and tree migration
Background-Vegetation change in high latitude tundra ecosystems is expected to accelerate due to increased wildfire activity. High-severity fires increase the availability of mineral soil seedbeds, which facilitates recruitment, yet fire also alters soil microbial composition, which could significantly impact seedling establishment. Results - We investigated the effects of fire severity on soil biota and associated effects on plant performance for two plant species predicted to expand into Arctic tundra. We inoculated seedlings in a growth chamber experiment with soils collected from the largest tundra fire recorded in the Arctic and used molecular tools to characterize root-associated fungal communities. Seedling biomass was significantly related to the composition of fungal inoculum. Biomass decreased as fire severity increased and the proportion of pathogenic fungi increased. Conclusions - Our results suggest that effects of fire severity on soil biota reduces seedling performance and thus we hypothesize that in certain ecological contexts fire-severity effects on plant-fungal interactions may dampen the expected increases in tree and shrub establishment after tundra fire.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022
This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022
This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022
This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022
This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022
This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
GIS13 GIS Coverages Defining Konza Wildfire and Supplementary Burn History (1977-present)
This dataset contains a comprehensive record of supplemental burns, wildfires, wildfire cleanup burns for the Konza Prairie Biological Station (KPBS) dating from 1972. Burn history data contains date burned, area burned and type of treatment (wildfires, wildfire cleanup, and supplemental burns). Burn histories for planned, prescribed burns are available in dataset GIS05. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
2018 Carr Wildfire Evacuation Survey Data
<p>Following the 2018 Carr Wildfire, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the fires in the Redding, California area. Collected from March to April 2019, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 647 valid responses, of which 338 were completed. Subsequent papers using this data retained 284 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of ten $200 gift cards. The survey questions are included in a separate Word document.</p> <p>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</p> <p>Wong, S., Broader, J., Walker, J. & Shaheen, S. (2021). Understanding California Wildfire Evacuee Behavior and Joint Choice-Making. Retrieved from <a href="https://escholarship.org/uc/item/4fm7d34j">https://escholarship.org/uc/item/4fm7d34j</a></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Role of Trust and Compassion in Resource Sharing in Evacuations: A Case Study of the 2017 and 2018 California Wildfire. <em>International Journal of Disaster Risk Reduction. </em><a href="https://escholarship.org/content/qt1zm0q2qc/qt1zm0q2qc.pdf">https://www.sciencedirect.com/science/article/abs/pii/S2212420920314023</a></p> <p>Wong, S., Broader, J., Shaheen, S. (2020). Review of California Wildfire Evacuations from 2017 to 2019. Retrieved from <a href="https://escholarship.org/uc/item/5w85z07g">https://escholarship.org/uc/item/5w85z07g</a></p> <p>Wong, S. & Shaheen, S. (2019). Current State of the Sharing Economy and Evacuations: Lessons from California. SB 1 Report. Retrieved from <a href="https://escholarship.org/uc/item/16s8d37x">https://escholarship.org/uc/item/16s8d37x</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S. (2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a></p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.