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Abstract
Decision-making for flood adaptation in coastal cities is complicated by deep uncertainty about sea level rise, subsidence, and socioeconomic trends, which increases the possibility of under- or over-investment. Using the megacity of Shanghai as a case study, we apply the dynamic adaptive policy pathways (DAPP) framework to demonstrate robust and flexible decision-making under uncertainty. The framework integrates compound flood risk modeling of flood risk, economic evaluation, and dynamic adaptation pathways. Our results show that without adaptation, annual damages and annual casualties could increase by 86–167%, and 45–97 times, respectively, by the year 2100. ‘Hard adaptation strategies’ such as levees can reduce projected damages by 58–94%. In contrast, local scale ‘soft adaptation’ (flood-proofing buildings) is only effective and economically efficient in combination with hard adaptation (‘hybrid strategy’). The best economic performance is a hybrid strategy that starts implementing a large storage tank adding a mix of measures around 2050 (coastal wetlands, dry-floodproofing, and land elevation). Depending on how the future plays out, a hybrid strategy of a combination of a storm-surge barrier and coastal wetlands would yield high economic benefits after ~2070.
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1 East China Normal University, Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); East China Normal University, Institute for National Safety and Emergency Management, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); East China Normal University, School of Geographic Sciences, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); Vrije Universiteit Amsterdam, Institute for Environmental Studies, Amsterdam, The Netherlands (GRID:grid.12380.38) (ISNI:0000 0004 1754 9227)
2 Vrije Universiteit Amsterdam, Institute for Environmental Studies, Amsterdam, The Netherlands (GRID:grid.12380.38) (ISNI:0000 0004 1754 9227)
3 East China Normal University, Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); East China Normal University, Institute for National Safety and Emergency Management, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); East China Normal University, School of Geographic Sciences, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365)
4 Princeton University, Department of Civil and Environmental Engineering, Princeton, USA (GRID:grid.16750.35) (ISNI:0000 0001 2097 5006)
5 University of Birmingham, School of Engineering, Birmingham, UK (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486)
6 East China Normal University, School of Public Management, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365)
7 East China Normal University, State Key Laboratory of Estuarine and Coastal Research, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365)
8 Shanghai Normal University, School of Environmental and Geographical Sciences, Shanghai, China (GRID:grid.412531.0) (ISNI:0000 0001 0701 1077)