Climatic Change

, Volume 115, Issue 3–4, pp 537–558 | Cite as

Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China

  • Jun WangEmail author
  • Wei Gao
  • Shiyuan Xu
  • Lizhong Yu


Shanghai is a low-lying city (3–4 m elevation) surrounded on three sides by the East China Sea, the Yangtze River Estuary, and Hangzhou Bay. With a history of rapid changes in sea level and land subsidence, Shanghai is often plagued by extreme typhoon storm surges. The interaction of sea level rise, land subsidence, and storm surges may lead to more complex, variable, and abrupt disasters. In this paper, we used MIKE 21 models to simulate the combined effect of this disaster chain in Shanghai. Projections indicate that the sea level will rise 86.6 mm, 185.6 mm, and 433.1 mm by 2030, 2050, and 2100, respectively. Anthropogenic subsidence is a serious problem. The maximum annual subsidence rate is 24.12 mm/year. By 2100, half of Shanghai is projected to be flooded, and 46 % of the seawalls and levees are projected to be overtopped. The risk of flooding is closely related to the impact of land subsidence on the height of existing seawalls and levees. Land subsidence increases the need for flood control measures in Shanghai.


Digital Elevation Model Storm Surge Land Subsidence Yangtze River Estuary Tide Gauge Station 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper was financially supported by the National Basic Research Program of China (No. 2010CB951603), the Shanghai Youth Science and Technology Venus Program (No. 09QA1401800), the National Natural Science Foundation of China (No. 40730526, 40901010), the Fundamental Research Funds for the Central Universities, and the Humanities and social science projects of Education Ministry, (No. 12YJCZH257). The authors would like to thank Dr. Ye Mingwu, Ms. Huang Jing and Hou Yulan, for their assistance in model analysis. Finally, we have to extend our thanks to the professors who, in their busiest schedules, draw out enough time to review this paper.


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Key Laboratory of Geographic Information Science of Ministry of EducationEast China Normal UniversityShanghaiChina
  2. 2.USDA UV-B Monitoring and Research Program, Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA

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