15 May 2023
 | 15 May 2023
Status: this preprint is currently under review for the journal ESSD.

Water quality dataset in China

Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang

Abstract. Water data is a crucial asset for sustainable water resource management. However, the availability of China’s water datasets lags far behind modern expectations for open geoscientific data. This dataset is a part of the China Water Data Archive (CWDA), an upcoming national collection of water-related data covering all aspects of water data for boosting data sharing in China. The CWDA aims at providing free, clean, non-sensitive, coherent, and reliable water data within China for global researchers to support the national and global water resources management and the United Nations-Water Integrated Monitoring Initiative for Sustainable Development Goals 6 and 14. In this paper, we used Python and R language to collect, tidy, reorganize, and curate the publicly available inland and coastal/ocean surface water quality data in China, following a series of data quality dimensions (integrity, completeness, consistency, and accuracy). As the most comprehensive, publicly available, handy, and clean water quality dataset in China so far, it included water quality data for daily, weekly, and monthly in the period of 1980–2022, with 17 indicators for over 330,000 observations at 2384 sites from inland to coastal/ocean areas. This dataset will greatly support works relevant to the assessment, modelling, and projection of water quality, ocean biomass, and biodiversity in China.

Jingyu Lin et al.

Status: open (until 10 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2023-151', Rui Li, 19 May 2023 reply
    • CC2: 'Reply on CC1', Jingyu Lin, 20 May 2023 reply

Jingyu Lin et al.

Jingyu Lin et al.


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Short summary
Our paper provides a clean, editable, and sharable national water quality dataset across inland and coastal/ocean areas in China in the period of 1980–2022, with 17 indicators for over 330,000 observations at 2384 sites. We used Python and R language for collecting, cleaning, and statistical analysis. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, environmental management, and oceanography.