An Index-Based Assessment of Perceived Climate Risk and Vulnerability for the Urban Cluster in the Yangtze River Delta Region of China

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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

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.