EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The ecosystem data-set contains area percentage of each considered ecosystem in a 100 square kilometer cell. For a specific ecosystem, a 0.01 degree resolution raster of coverage real area is generated. In the case of forest coverage, the classification of the source datasets was grouped in three classes: woodland, open forest and closed forest. The quality of ecosystem in a 100 km2 grid cell is expressed as its area percentage, considering only cell land area for forest ecosystem. Sources: This dataset describes the current status of land areas that could potentially be forested according to climate (includes forest, open forest, woodlands). Intact forests and Fragmented/managed forests were not considered to need restoration. Potential forest lands that are currently non-forest were assumed to be deforested. Forest lands with significantly reduced canopy coverage were considered to be partially deforested (for example, potential closed forest with canopy coverage less than 45%). Both deforested and partially deforested lands considered to be restoration opportunity areas. Credit: Peter Potapov, Lars Laestadius, and Susan Minnemeyer. 2011. Global map of forest cover and condition. World Resources Institute: Washington, DC. Online at www.wri.org/forest-restoration-atlas.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The population raster has the same resolution and represents the absolute number of inhabitants in a 0.01 degree cell. The physical exposure in a 100 km2 grid cell is the sum of the included physical exposure raster cells. Sources: Estimate of surges triggered by tropical cyclone frequency of Saffir-Simpson category 1. It is based on three sources: 1) A compilation of best tracks dataset from WMO Regional Specialised Meteorological Centres (RSMCs) and Tropical Cyclone Warning Centres (TCWCs). As well as personal communication with Dr. Varigonda Subrahmanyam, Dr. James Weyman, Kiichi Sasaki, Philippe CAROFF, Jim Davidson, Simon Mc Gree, Steve Ready, Peter Kreft, Henrike Brecht. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. 3) A Digital Elevation Model (SRTM). Unit is expected average number of event per 1000 years. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe. GHS Population GRID. The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Credit: European Commission, Joint Research Centre; Columbia University, Center for International Earth Science Information Network (2015): GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

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.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The population raster has the same resolution and represents the absolute number of inhabitants in a 0.01 degree cell. The physical exposure in a 100 km2 grid cell is the sum of the included physical exposure raster cells. Sources: Probabilistic approach for modelling riverine flood of major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from Dartmouth Flood Observatory archive. This product was designed by UNEP/GRID-Europe and CIMA Research Foundation for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe and CIMA Research Foundation. GHS Population GRID. The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Credit: European Commission, Joint Research Centre; Columbia University, Center for International Earth Science Information Network (2015): GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The population raster has the same resolution and represents the absolute number of inhabitants in a 0.01 degree cell. The physical exposure in a 100 km2 grid cell is the sum of the included physical exposure raster cells. Sources: Estimate of the annual frequency of landslide triggered by earthquakes or precipitation. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. Unit is expected annual probability and percentage of pixel of occurrence of a potentially destructive landslide event x 1000000. This product was designed by International Centre for Geohazards /NGI for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards /NGI. GHS Population GRID. The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Credit: European Commission, Joint Research Centre; Columbia University, Center for International Earth Science Information Network (2015): GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The ecosystem data-set contains area percentage of each considered ecosystem in a 100 square kilometer cell. For a specific ecosystem, a 0.01 degree resolution raster of coverage real area is generated. The quality of ecosystem in a 100 km2 grid cell is expressed as its area percentage, considering only cell ocean area for sea grass ecosystem. Sources: This dataset shows the global distribution of seagrasses, and is composed of two subsets of point and polygon occurence data. The data were compiled by UNEP World Conservation Monitoring Centre in collaboration with many collaborators (e.g. Frederick Short of the University of New Hampshire), organisations (e.g. the OSPAR Convention for the Northeast Atlantic sea), and projects (e.g. the European project Mediterranean Sensitive Habitats "Mediseh"), across the globe (full list available in "Metadata_Seagrass.dbf"). Credit: UNEP-WCMC, Short FT (2017). Global distribution of seagrasses (version 6.0). Sixth update to the data layer used in Green and Short (2003). Cambridge (UK): UN Environment World Conservation Monitoring Centre. URL: http://data.unep-wcmc.org/datasets/7

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The population raster has the same resolution and represents the absolute number of inhabitants in a 0.01 degree cell. The physical exposure in a 100 km2 grid cell is the sum of the included physical exposure raster cells. Sources: Estimate of tropical cyclone frequency of Saffir-Simpson category 1. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe. GHS Population GRID. The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Credit: European Commission, Joint Research Centre; Columbia University, Center for International Earth Science Information Network (2015): GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The tropical cyclone surge frequency is based on a model that estimates surges triggered by tropical cyclone frequency of Saffir-Simpson category one. Sources: The dataset includes an estimate of surges triggered by tropical cyclone frequency of Saffir-Simpson category 1. It is based on three sources: 1) A compilation of best tracks dataset from WMO Regional Specialised Meteorological Centres (RSMCs) and Tropical Cyclone Warning Centres (TCWCs). As well as personal communication with Dr. Varigonda Subrahmanyam, Dr. James Weyman, Kiichi Sasaki, Philippe CAROFF, Jim Davidson, Simon Mc Gree, Steve Ready, Peter Kreft, Henrike Brecht. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. 3) A Digital Elevation Model (SRTM). Unit is expected average number of event per 1000 years. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The ecosystem data-set contains area percentage of each considered ecosystem in a 100 square kilometer cell. For a specific ecosystem, a 0.01 degree resolution raster of coverage real area is generated. In the case of forest coverage, the classification of the source datasets was grouped in three classes: woodland, open forest and closed forest. The quality of ecosystem in a 100 km2 grid cell is expressed as its area percentage, considering only cell land area for forest ecosystem. Sources: This dataset describes the current status of land areas that could potentially be forested according to climate (includes forest, open forest, woodlands). Intact forests and Fragmented/managed forests were not considered to need restoration. Potential forest lands that are currently non-forest were assumed to be deforested. Forest lands with significantly reduced canopy coverage were considered to be partially deforested (for example, potential closed forest with canopy coverage less than 45%). Both deforested and partially deforested lands considered to be restoration opportunity areas. Credit: Peter Potapov, Lars Laestadius, and Susan Minnemeyer. 2011. Global map of forest cover and condition. World Resources Institute: Washington, DC. Online at www.wri.org/forest-restoration-atlas.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. Tsunamis annual frequency is based on a model which output raster shows expected affected percentage of each pixel over a 500 years return period. Sources: The dataset includes an estimate of tsunami frequency. It is based on two sources: 1) A comprehensive list of reports and scientific papers compiled and utilized in producing tsunami hazard maps as well as finding return periods of future events. 2) Applying numerical tsunami models and zooming on selected areas. Unit is expected affected percentage of each pixel over a minimum return period of 500 years. This product was designed by International Centre for Geohazards /NGI for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards /NGI.