Global Flood Total Economic Loss Risk Deciles

The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

全球洪水总经济损失风险十位数数据集系由2.5分钟网格构成的全球洪水总经济损失风险图。本数据集采用Sachs等（2003年）提出的方法论，对国内生产总值（GDP）进行空间分配。首先，通过多种来源确定地方单位对其各自国家GDP的贡献比例。随后，将贡献率应用于已发布的国际复兴开发银行发展指标，以确定地方单位的GDP值。一旦国家GDP被空间分层至最小的行政单位，即使用世界人口网格分布数据（GPWv3）的人口分布数据，对网格单元的GDP值进行推导。在每个地方单位内，确定人均贡献值，并将其与每个网格单元的人口数相乘。在确定每个网格单元的GDP值之后，即使用从EM-DAT历史记录中推导出的区域变量损失率，以确定洪水风险对网格单元造成的总经济损失风险。最终表面并不呈现总经济损失的绝对值，而是基于计算的经济损失风险，对网格单元进行相对十位数（1-10，风险递增）的排名。该数据集是哥伦比亚大学灾害与风险研究（CHRR）、国际复兴开发银行/世界银行以及哥伦比亚大学国际地球科学信息网络（CIESIN）合作的结果。