Abstract

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989–2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989–2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002–2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China.

The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

Details

Title
Gainers and losers of surface and terrestrial water resources in China during 1989–2016
Author
Wang, Xinxin 1 ; Xiao Xiangming 2   VIAFID ORCID Logo  ; Zou Zhenhua 3 ; Dong Jinwei 4   VIAFID ORCID Logo  ; Qin Yuanwei 2   VIAFID ORCID Logo  ; Doughty, Russell B 2   VIAFID ORCID Logo  ; Menarguez, Michael A 5 ; Chen Bangqian 6 ; Wang Junbang 7 ; Ye, Hui 7 ; Ma, Jun 8   VIAFID ORCID Logo  ; Zhong Qiaoyan 8 ; Zhao, Bin 8 ; Li, Bo 8   VIAFID ORCID Logo 

 Fudan University, Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, School of Life Sciences, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); University of Oklahoma, Department of Microbiology and Plant Biology, Center for Spatial Analysis, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018) 
 University of Oklahoma, Department of Microbiology and Plant Biology, Center for Spatial Analysis, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018) 
 University of Maryland, Department of Geographical Sciences, College Park, USA (GRID:grid.164295.d) (ISNI:0000 0001 0941 7177) 
 Chinese Academy of Sciences, Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 LinkedIn Corporation, Sunnyvale, USA (GRID:grid.266900.b) 
 Chinese Academy of Tropical Agricultural Sciences, Rubber Research Institute, Danzhou, China (GRID:grid.453499.6) (ISNI:0000 0000 9835 1415) 
 Chinese Academy of Sciences, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 Fudan University, Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, School of Life Sciences, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2422013048
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.