CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a>

Raster of Active Fires frequency per square kilometer for the period 01/01/2019 - 25/09/2019, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This raster layer was produced by GRID-Geneva. Data accessed in October 2019, at https://modis.gsfc.nasa.gov/data/dataprod/mod14.php. <br> <br> The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. <br> <br> Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.

Anomaly for the period 2041-2060 compared to climatological data (1979-2013) on precipitation and temperature data based on two different scenarios (RCP4.5 and RCP8.5). The layer is calculated at UNEP/GRID-Geneva from the layers on annual mean temperature and annual precipitations provided in the products CHELSA V1.2 and CHELSA-[CMIP5]. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2

These layers show the average values by decade (60s, 70s, 80s, 90s, 00s, 10s) over the time interval from 1960 to 2018 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br> Please read <a href="https://crudata.uea.ac.uk/cru/data/drought/scpdsi.global2018.readme.txt"target=_blank>this document</a> for more information. <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>

CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2

Raster of Active Fires frequency per square kilometer, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. This raster layer was produced by GRID-Geneva. Data accessed in December 2018, at <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod14.php" target="_blank">https://modis.gsfc.nasa.gov/data/dataprod/mod14.php</a>. The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.