High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020

High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Wa...

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Bibliographic Details
Published inEarth system science data Vol. 16; no. 5; pp. 2449 - 2464
Main Authors Hou, Chengcheng, Li, Yan, Sang, Shan, Zhao, Xu, Liu, Yanxu, Liu, Yinglu, Zhao, Fang
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 22.05.2024
Copernicus Publications
Subjects
China
Consumption
Datasets
Electricity
Energy industry
Hydrologic models
Hydrology
Industrial development
Industrial production
Industrial products
Industrial water
Management
Manufacturing
Monthly
Power plants
Regions
Resource management
Scaling
Seasonal variations
Statistics
Water
Water resources
Water resources management
Water supply
Water use
China
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Summary:High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074 (Hou and Li, 2023).
ISSN:1866-3516
1866-3508
1866-3516
DOI:10.5194/essd-16-2449-2024