DOI: 10.57760/sciencedb.11409
title: The global dataset on extreme precipitation exposure in industrial economic systems
title: 全球工业经济极端降水暴露度数据集制备

description: 1. Temporal Coverage of Data:The data collection periods are 2010, 2016-2035, and 2046-2065.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope:The data pertains to the fields of Earth Sciences and Geography.4. Data Volume:The total data volume is approximately 63.0 MB.5. Data Type:Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene):·7. Field (Feature) Name Explanation:Name Explanation:EXP: ExposureVUL: VulnerabilityUnit of Measurement:Exposure: days-USDVulnerability: dimensionless8. Data Source Description:Population and Industrial Value Added Projection Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.Meteorological Data:From the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC), primarily including the MIROC5, MPI-ESM-LR, and HadGEM2-ES3 climate models.Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agencies.9. Data Processing MethodsThe exposure of the industrial economy to extreme precipitation is defined as the product of the number of extreme precipitation days and the total industrial output value exposed in each grid cell.Where, INDEXPPR is the industrial economic exposure under extreme precipitation (unit: days-USD). R95p is the number of extreme precipitation days. IND_VALUE is the industrial output value.Extreme Precipitation Definition:Set 1961-1990 as the reference period and calculate the 95th percentile value of the annual wet-day (daily precipitation &gt; 1mm) precipitation sequence to define the extreme precipitation threshold. According to the WMO extreme precipitation index definition, the number of days with daily precipitation greater than 25mm is defined as heavy rain days. If the local 95th percentile threshold is less than 25mm, it is set to 25mm. Selected three climate models (MIROC5, MPI-ESM-LR, HadGEM2-ES) to predict precipitation data for 2010-2050 under different climate change scenarios, using a multi-model ensemble averaging method for climate multi-model coupling.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports, and Articles):Achievements: Published several academic articles that have enhanced the understanding of the vulnerability of global industrial economic systems under different Shared Socioeconomic Pathways (SSPs).
description: 1. Temporal Coverage of Data:The data collection periods are 2010, 2016-2035, and 2046-2065.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope:The data pertains to the fields of Earth Sciences and Geography.4. Data Volume:The total data volume is approximately 63.0 MB.5. Data Type:Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene):·7. Field (Feature) Name Explanation:Name Explanation:EXP: ExposureVUL: VulnerabilityUnit of Measurement:Exposure: days-USDVulnerability: dimensionless8. Data Source Description:Population and Industrial Value Added Projection Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.Meteorological Data:From the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC), primarily including the MIROC5, MPI-ESM-LR, and HadGEM2-ES3 climate models.Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agencies.9. Data Processing MethodsThe exposure of the industrial economy to extreme precipitation is defined as the product of the number of extreme precipitation days and the total industrial output value exposed in each grid cell.Where, INDEXPPR is the industrial economic exposure under extreme precipitation (unit: days-USD). R95p is the number of extreme precipitation days. IND_VALUE is the industrial output value.Extreme Precipitation Definition:Set 1961-1990 as the reference period and calculate the 95th percentile value of the annual wet-day (daily precipitation &gt; 1mm) precipitation sequence to define the extreme precipitation threshold. According to the WMO extreme precipitation index definition, the number of days with daily precipitation greater than 25mm is defined as heavy rain days. If the local 95th percentile threshold is less than 25mm, it is set to 25mm. Selected three climate models (MIROC5, MPI-ESM-LR, HadGEM2-ES) to predict precipitation data for 2010-2050 under different climate change scenarios, using a multi-model ensemble averaging method for climate multi-model coupling.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports, and Articles):Achievements: Published several academic articles that have enhanced the understanding of the vulnerability of global industrial economic systems under different Shared Socioeconomic Pathways (SSPs).