the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020
Chengcheng Hou
Shan Sang
Xu Zhao
Yanxu Liu
Yinglu Liu
Fang Zhao
Abstract. High-quality gridded data on industrial water use is 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, namely, the China industrial water withdrawal dataset (CIWW), which spans a 56-year period from 1965 to 2020 at a spatial resolution of 0.1° and 0.25°. We utilized >400,000 records of industrial enterprises, monthly industrial product output data, continuous statistical IWW records from 1965 to 2020, to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in the developing the dataset. The CIWW dataset presented significant improvement in characterizing the spatial and seasonal patterns of IWW dynamics in China, with a much higher accuracy at fine scale while ensuring consistency with statistical records. The CIWW dataset, together with its methodology, and auxiliary data, is useful for water resource management and for research in hydrology, geography, environment, and sustainability sciences. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074.v1 (Hou and Li, 2023).
Chengcheng Hou et al.
Status: open (until 21 Jun 2023)
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RC1: 'Comment on essd-2023-66', Anonymous Referee #1, 18 May 2023
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This manuscript developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, spanning a 56-year period from 1965 to 2020 at a spatial resolution of 0.1° and 0.25°. While the dataset covers a wide range of time, the spatial precision appears to be high. However, I have some concerns regarding the spatialization method used in the study. I think the method has too many uncertainties and strong assumptions, and the use of some definitions of industrial water is unclear. Therefore, I suggest that you make the following modifications to your manuscript to address these concerns:
Major suggestions:
1.The authors need to make the abstract more concise and focused. Instead of mentioning hydrology and geographical sustainability in a broad sense, the relevance of the dataset to specific research areas or applications should be emphasized.
2.line35-45. The author lists the spatialization methods of sectoral IWW , but does not demonstrate the shortcomings of the current methods. The low accuracy of dataset is mentioned, but how the author judges the low accuracy of these datasets is not clear.
3.line65-70. The rationale for the need for long-term and high-resolution IWW data in China requires further clarification. The reasons mentioned in the manuscript, such as water conflicts caused by increased water demand and water resource management are too broad and do not provide a specific explanation for the need of such data.
4.Why should this sentence be placed here alone.
5.In this manuscript, industrial water withdrawal and industrial water use are considered to have the same meaning. But in fact, the definitions of the two are different, industrial water use also includes industrial reuse water consumption.
6.I think the spatialization method used has a lot of uncertainties. The authors assume the industrial water use efficiency was the same for all industrial enterprises in the same province and the same subsector. A province contains large, medium and small enterprises, and their water use coefficients must be different. Also, the distribution coefficient of monthly water shortage regards the whole country as a whole, without considering the differences among provinces. Moreover, the manuscriptuse the water use efficiency of enterprises in 2008 for the spatialization of IWW from 1965 to 2020. Can the coefficient of 2008 represent the period from 1965 to 2020?
Citation: https://doi.org/10.5194/essd-2023-66-RC1
Chengcheng Hou et al.
Data sets
The China industrial water withdrawal dataset Chengcheng Hou and Yan Li https://doi.org/10.6084/m9.figshare.21901074.v1
Chengcheng Hou et al.
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