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Water Res. 2019 Dec 1;166:115067. doi: 10.1016/j.watres.2019.115067. Epub 2019 Sep 7.

Synthesized trade-off analysis of flood control solutions under future deep uncertainty: An application to the central business district of Shanghai.

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Department of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, 200234, China.
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA; School of Finance and Management, SOAS University of London, London, WC1H 0XG, UK; International Institute for Applied Systems Analysis (IIASA), A-2361, Laxenburg, Austria. Electronic address:
Department of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, 200234, China. Electronic address:
Hangzhou Meteorological Services, Hangzhou, Zhejiang, China.
Shanghai Climate Center, Shanghai Meteorological Service, Shanghai, 200030, China.


Coastal mega-cities will face increasing flood risk under the current protection standard because of future climate change. Previous studies seldom evaluate the comparative effectiveness of alternative options in reducing flood risk under the uncertainty of future extreme rainfall. Long-term planning to manage flood risk is further challenged by uncertainty in socioeconomic factors and contested stakeholder priorities. In this study, we conducted a knowledge co-creation process together with infrastructure experts, policy makers, and other stakeholders to develop an integrated framework for flexible testing of multiple flood-risk mitigation strategies under the condition of deep uncertainties. We implemented this framework to the reoccurrence scenarios in the 2050s of a record-breaking extreme rainfall event in central Shanghai. Three uncertain factors, including precipitation, urban rain island effect and the decrease of urban drainage capacity caused by land subsidence and sea level rise, are selected to build future extreme inundation scenarios in the case study. The risk-reduction performance and cost-effectiveness of all possible solutions are examined across different scenarios. The results show that drainage capacity decrease caused by sea-level rise and land subsidence will contribute the most to the rise of future inundation risk in central Shanghai. The combination of increased green area, improved drainage system, and the deep tunnel with a runoff absorbing capacity of 30% comes out to be the most favorable and robust solution which can reduce the future inundation risk by 85% (±8%). This research indicates that to conduct a successful synthesized trade-off analysis of alternative flood control solutions under future deep uncertainty is bound to be a knowledge co-creation process of scientists, decision makers, field experts, and other stakeholders.


China; Climate change; Cost-effectiveness; Decision-making under deep uncertainty; Urban flood solutions

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