To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.
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Updated
November 24, 2015
| Dataset date: Jan 1, 2015
This dataset updates: Every year
A comprehensive set of information on global volcanic hazard, historical events, population exposure, vulnerability, and impact has been provided to GAR15 by Global Volcano Model (GVM) and The International Association of Volcanology and Chemistry of the Earthโs Interior (IAVCEI). This work is the first of its kind in global coverage and level of contribution from a wide network of experts and institutions around the world.
Updated
November 24, 2015
| Dataset date: Jan 1, 2015
This dataset updates: Never
The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.
Updated
November 17, 2018
| Dataset date: Jan 1, 1980-Dec 31, 2001
This dataset updates: Never
This dataset includes an estimate of global drought annual repartition based on Standardized Precipitation Index. It is based on two sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. This product was designed by UNEP/GRID-Europe for the Project of Risk Evaluation, Vulnerability, Information & Early Warning (PreView). It was modeled using global data. Credit: UNEP/GRID-Europe.
[Preview this dataset] (http://preview.grid.unep.ch/index.php?preview=data&events=droughts&evcat=1&lang=eng)
Updated
November 24, 2015
| Dataset date: Jan 1, 2015
This dataset updates: Never
GAR 2015 Risk results are presented as a series of probabilistic risk metrics. One of them is the multi-hazard Average Annual Loss (AAL) which is the long-term expected loss per year, averaged over many years. While there may actually be little or no losses, over a short period of time, the AAL accounts much larger losses that may occur more infrequently. AAL is also an indication of the amount of savings a nation need to set aside each year to cover the cost of long term losses from that hazard. As GAR global risk assessment is performed at global scale, the AAL calculated should be read as order of magnitude for the potential recurrent extent of losses in a country.
Updated
January 26, 2016
| Dataset date: Jan 1, 2015
This dataset updates: Every year
The Global Exposure Database for GAR: A Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).
Updated
April 11, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
April 15, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
April 15, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
September 27, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
September 27, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
September 27, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
Updated
September 27, 2016
| Dataset date: Dec 31, 2015
This dataset updates: Never
The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
