Authors: Laixiang Sun*, University of Maryland - College Park, Hengzhi HU, Shanghai Climate Center, Shanghai, China, Zhan Tian, South University of Science & Technology of China, Shenzhen, China
Topics: Urban and Regional Planning, China, Coupled Human and Natural Systems
Keywords: Robust decision making, climate change adaptation, urban flooding risk, Shanghai, China
Session Type: Paper
Start / End Time: 2:00 PM / 3:40 PM
Room: Marshall West, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Shanghai will have to face the adverse impact of extreme rainfall under future climate change, with increasing risk under the current protection standard. However, both historic records and climate models give no firm answer to the question how the climate and precipitation would change due to deep uncertainties. Long-term adaptation planning to manage flood risk is further challenged by uncertainty in socioeconomic factors and contested stakeholder priorities. In this study, we demonstrate a proof of concept for a combined robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) approach in flood risk management, using Shanghai as a case study. Three uncertain factors, including precipitation, rain island effect and decrease of urban drainage capacity, are selected to build the future extreme precipitation scenarios. Inundation depth and area are simulated; the direct physical loss is calculated based on the depth-damage curve. By implementing “scenario discovery”, we find that the decrease of drainage capacity is the most critical factor. Three Levers, including increase of public green area and improved standard of urban drainage system and construction of deep tunnel (and their combinations), are alternately implemented to examine their performance in 100 future possibilities. The risk reduction performances of each lever and their combinations are examined across different scenarios. The results show that the mid-terms robust plan is the combination of increase of green area, improved drainage system, and the deep tunnel with a runoff absorbing capacity of 30%, which can reduce the future flood risk by up to 98%.