Table 2 Data sources of spatiotemporal downscaling covariates.
From: A High-Resolution Gridded Dataset for China’s Monthly Sectoral Water Use
Types | Data | Data sources | Spatial resolution | Temporal resolution | Usage |
|---|---|---|---|---|---|
Socio-Economic variables | Irrigated cropland map | CIrrMap250: annual maps of China’s irrigated cropland from 2000 to 202037 | 250 m | 2000–2020 | Spatial downscaling of irrigation water use |
Annual power generation of thermal power plant (GWh) | Global Power Plant Database (GPPD)36 | Point | 2017 | Spatial downscaling of thermal power cooling water use | |
Annual output value of company (Thousand yuan) | Chinese Industrial Enterprises Database (CIED) (https://www.lib.pku.edu.cn/portal/cn/news/0000001637,last access: 5 November 2024) | Point | 2007 | Spatial downscaling of manufacture water use | |
1 km population density (people per kilo square) | Population distribution dataset for China at a kilometer grid scale. (PopulationGrid_China)70 | 1 km | 2000 | Spatial downscaling of domestic water use | |
Monthly sales revenue of 31 manufacturing subsectors (Thousand yuan) | China Industry Database (https://www.epsnet.com.cn/index.html#/Index, last access: 1 April 2025) | Provincial | 2013–2022 Monthly | Temporal downsacling of manufacture water use | |
Meteorological variables | Temperature (K) | ERA5-Land (https://cds.climate.copernicus.eu/, last access: 5 November 2024) | 0.1° | 1965–2022 Daily | Temporal downsacling of domestic and thermal power cooling water use |
Potential evapotranspiration (mm) | ERA5-Land (https://cds.climate.copernicus.eu/, last access: 1st April 2025) | 0.1° | 1965–2022 Daily | Temporal downsacling of irrigation water use | |
Precipitation (mm) | ERA5-Land (https://cds.climate.copernicus.eu/, last access: 1st April 2025) | 0.1° | 1965–2022 Daily | Temporal downsacling of irrigation water use |
