he total economic value of the effects caused by Tropical Cyclone Pam was estimated to be approximately VT 48.6 billion (US$449.4 million). Of this, VT 29.3 billion (US$270.9 million) is attributable to damage, and VT 19.3 billion (US$178.5 million) is attributable to loss. This is equivalent to 64.1% of the gross domestic product (GDP) in Vanuatu, giving an indication of the scale of impact. Because of data limitations, however, it is likely that these figures underestimate the total impact.
*refer to pdf report for more information*
This table provides a summary of the estimated costs for recovery and reconstruction. Total recovery and reconstruction is estimated at VT 34.1 billion (US$316 million). Of this amount VT 10.3 billion (US$95 million) is focused over the short-term (12 months to four years).
*data extracted from the PDNA Cyclone Pam 2015*
For the purpose of this PDNA, the agricultural sector is defined to include the crops, livestock, fishery, and forestry subsectors
Commerce and industry comprises close to 40% of GDP and is therefore a key sector for Vanuatu. Tropical Cyclone Pam damaged the sector’s buildings and inputs for production, and it continued to affect the sector through increased costs of inputs and reduced activity in the economy. It is important to note that the figures reported are likely to be underestimated due to the amount of data available for inclusion within this PDNA, which was prepared in a short time frame.
Vanuatu has obligations under these international conventions and agreements
GEBCO’s gridded bathymetric data set, the GEBCO_2020 grid, is a global terrain model for ocean and land at 15 arc-second intervals. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2020 Grid is based.
If the data sets are used in a presentation or publication then we ask that you acknowledge the source.This should be of the form: GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)
Raster data representing the mean levels of chlorophyll in mg/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of dissolved oxygen in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of nitrate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of phosphate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of phytoplankton in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of salinity in practical salinity scale (PSS) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of silicate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
Raster data representing the mean levels of temperature in degrees Celsius (°C) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).
Marine data layers for present conditions were produced with climate data describing monthly averages for the period 2000–2014, obtained from pre-processed global ocean re-analyses combining satellite and in situ observations at regular two- and three-dimensional spatial grids.
The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells.
The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the year 2005. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells.
The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the year 2010. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells.
The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the year 2015. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells.