A data set of distributed global population and water withdrawal from 1960 to 2020

Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets,...

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Detalles Bibliográficos
Autores principales: Yan, Denghua, Zhang, Xin, Qin, Tianling, Li, Chenhao, Zhang, Jianyun, Wang, Hao, Weng, Baisha, Wang, Kun, Liu, Shanshan, Li, Xiangnan, Yang, Yuheng, Li, Weizhi, Lv, Zhenyu, Wang, Jianwei, Li, Meng, He, Shan, Liu, Fang, Bi, Wuxia, Xu, Ting, Shi, Xiaoqing, Man, Zihao, Sun, Congwu, Liu, Meiyu, Wang, Mengke, Huang, Yinghou, Long, Haoyu, Niu, Yongzhen, Dorjsuren, Batsuren, Gedefaw, Mohammed, Li, Yizhe, Tian, Zihao, Mu, Shizhou, Wang, Wenyu, Zhou, Xiaoxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587213/
https://www.ncbi.nlm.nih.gov/pubmed/36271026
http://dx.doi.org/10.1038/s41597-022-01760-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587213/
https://www.ncbi.nlm.nih.gov/pubmed/36271026
http://dx.doi.org/10.1038/s41597-022-01760-1

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