Dynamic Adaptive Policy Pathways for Flood Adaptation in Shanghai under Deep Uncertainty

Abstract

Decisions on coastal cities flood adaptation are complicated by deep uncertainty about sea level rise, subsidence and socioeconomic trends, increasing the chance of under- or over-investment. Frameworks have been proposed to plan coastal adaptation in urban settings. In this study, we expand those frameworks to include elements critical to rational decision-making in coastal cities under deep uncertainty. Our framework, trained on the city of Shanghai, includes compound flood modeling, flood risk analysis, design and quantitative simulation of adaptation strategies, cost-benefit analysis, trade-off analysis and formulation of dynamic adaptive policy pathways (DAPP). We include land subsidence in modeling flood scenarios; we compute a diverse set of flood impacts on multiple sectors; we evaluate several techniques of cost-benefit analysis; and we include multiple adaptive strategies against compound flooding (i.e., pluvial, fluvial, coastal). We show that the hard adaptation strategies (e.g., storm-surge barriers and storage tank) can successfully reduce future increase in risk generated by sea level rise, land subsidence and socioeconomic development, by 58%~94%. In contrast, soft adaptation only generate considerable benefits when integrated with hard adaptation into hybrid strategies. A hybrid strategy that combines storm-surge barrier and wetland creation most effectively reduces flood damages and casualties, and yields promising co-benefits. We formulate DAPP for robust and flexible decision-making over time for the coming decades, which open up the decision-making space and help overcome policy paralysis due to deep uncertainty.