Arid
DOI10.1016/j.scib.2018.05.002
Long-term surface water changes and driving cause in Xiong’an, China: from dense Landsat time series images and synthetic analysis
Song, Chunqiao1,2; Ke, Linghong2,3; Pan, Hang2,4; Zhan, Shengan2; Li, Kai1; Ma, Ronghua1
通讯作者Song, Chunqiao
来源期刊SCIENCE BULLETIN
ISSN2095-9273
EISSN2095-9281
出版年2018
卷号63期号:11页码:708-716
英文摘要

China’s government statement recently reported the plan of constructing Xiong’an New Area, which aims to phase out some extra capital functions from Beijing and to explore an innovative urban development mode with the priority in eco-environmental protection. The New Area is located in the semi-arid North China Plain (NCP) and is home to NCP’s largest natural freshwater wetland, Baiyangdian Lake. A comprehensive realization of surface water dynamics would be crucial for policy-makers to outline a sustainable environment development strategy for New Area. In this study, we used a total of 245 time slices of cloud-free Landsat images to document the continuous changes of water bodies within Xiong’an City during 1984-2016 and to provide detailed evidence of water presence and persistency states and changes under the influences of climate change and human actions. Our results reveal that the New Area water body areas varied dramatically during the past 33 years, ranging from 0.44 km(2) in April 1988 to 317.85 km(2) in February 1989. The change of surface water area was not characterized by a monotonically decreasing tendency. The evolution processes can be divided into four sub-stages: the first extreme desiccation in mid-1980s, the wet stage with the most extensive inundation areas and strong inter-annual fluctuations from late-1988 to late 1999, another desiccation stage in early 2000s, and the overall recovering stage between 2007 and 2016. We also mapped the maximum water inundation extents and frequencies of all-season, pre-wet season (February-May) and post-wet season (September-December) for the 33 years and different sub-periods. Although there is good agreement between time series of surface water area evolution in the New Area and station-based precipitation and evaporation variations, multiple lines of evidences reviewed in previous research indicate that the degraded Baiyangdian Lake was also tightly associated with human activities from various aspects, including dam construction, groundwater extraction, agricultural irrigation, etc. We highlighted the current status of exploring the driving mechanism of surface water changes and existing problems, and then offer recommendations. (C) 2018 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.


英文关键词Xiong’an New Area Surface water Baiyangdian Lake Remote sensing Climate change Landsat
类型Article
语种英语
国家Peoples R China ; USA
收录类别SCI-E
WOS记录号WOS:000437397900009
WOS关键词BAIYANGDIAN LAKE ; NORTH CHINA ; WETLAND ; BASIN ; MOUNTAINS ; HIMALAYAS ; DEPLETION ; DYNAMICS ; STORAGE ; IMPACT
WOS类目Multidisciplinary Sciences
WOS研究方向Science & Technology - Other Topics
来源机构南京大学 ; 河海大学 ; University of California, Los Angeles
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/212888
作者单位1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;
2.Univ Calif Los Angeles, Dept Geog, Los Angeles, CA 90095 USA;
3.Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Jiangsu, Peoples R China;
4.Nanjing Univ, Dept Geog Informat Sci, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Song, Chunqiao,Ke, Linghong,Pan, Hang,等. Long-term surface water changes and driving cause in Xiong’an, China: from dense Landsat time series images and synthetic analysis[J]. 南京大学, 河海大学, University of California, Los Angeles,2018,63(11):708-716.
APA Song, Chunqiao,Ke, Linghong,Pan, Hang,Zhan, Shengan,Li, Kai,&Ma, Ronghua.(2018).Long-term surface water changes and driving cause in Xiong’an, China: from dense Landsat time series images and synthetic analysis.SCIENCE BULLETIN,63(11),708-716.
MLA Song, Chunqiao,et al."Long-term surface water changes and driving cause in Xiong’an, China: from dense Landsat time series images and synthetic analysis".SCIENCE BULLETIN 63.11(2018):708-716.
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