This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a high sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

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 total cell area for mangrove ecosystem. Sources: This dataset shows the global distribution of mangrove forests, derived from earth observation satellite imagery. It was created using Global Land Survey (GLS) data and the Landsat archive. Approximately 1,000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al. (2011) for full details. Credit: Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data (version 1.3, updated by UNEP-WCMC). Global Ecology and Biogeography 20: 154-159. Paper DOI: 10.1111/j.1466-8238.2010.00584.x; Data URL: http://data.unep-wcmc.org/datasets/4

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.

Indicator based upon the Land-Use Harmonization 2 (LUH2) gridded global land use maps produced by advanced Earth System Models (ESM) which model the combined pressures of land use conversion and fossil fuel emissions on the carbon-climate system (Hurtt et al. in prep). The pressure data is derived from the History Database of the Global Environment (HYDE). Primary vegetation is defined as natural vegetation (either forest or non-forest) that has never been impacted by human activities (e.g. agriculture or wood harvesting) since the start of the time series (850). However, such areas may be indirectly impacted by humans, for instance, through hunting, pollution or the introduction of invasive alien species. They still represent modelled estimates, and the uncertainty associated with the land use present within each particular grid cell increases as we step back in time through the series.

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

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. 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 combination of ecosystem and hazard physical exposure, a data-set sample is obtained by selecting any 100 square kilometer cell that contain positive values for both physical exposure and ecosystem percentage area. Six classes are obtained by calculating the median of the ecosystem sample, and the tertiles of the physical exposure sample. Two specific cases cannot be included in this classification: - Areas prone to natural hazard and having ecosystem coverage, but no population observed. - Areas prone to natural hazard and having population exposed to it, but no presence of ecosystem coverage. Considering ecosystem enhancement to reduce disaster risk might still be relevant in the areas showing one of these specific cases.