Low elevation coastal zone (LECZ) in China is densely populated and economically developed, which is exposed to increasing risks of hazards related to climate change and sea level rise. To mitigate risks and achieve sustainable development, we need to better understand LECZ. As the first step, in this paper we define the extent of the LECZ in China, and analyze the spatial distribution of LECZ and its population, using a geographic information system software (ArcGIS) to combine elevation models and population data sets. Our findings show that, overall, this zone covers 2.0 % of China’s land area but contains 12.3 % of the total population, which is the largest population living in LECZ in the world. There are large regional variations in the distribution of both LECZ and LECZ population, with half of the LECZ within 30 km from the coastline, and Jiangsu Province having the largest LECZ area and population. The LECZ is also concentrated in three major economic zones in China, which accounts for 54 % of LECZ and three quarters of all LECZ population in China. The impact of future climate change on China’s LECZ is exacerbated by rapid economic and population growth, urbanization and environmental degradation. Coordinating development in coastal and inland China, enhancing adaptive capacity and implementing integrated risk management for LECZ are needed to reduce the risks related to climate change and to achieve sustainable development.

Built-up land in floodplains (BLF) is a vital indicator of the socio-hydrological system, and its dynamics are key to understanding and managing flood risk. However, previous studies have neglected the impacts of BLF growth modes (e.g., patch sizes and expansion types) on flood vulnerability. This paper fills this gap by assessing the BLF's growth modes and revealing their divergent impacts on flood vulnerability using a case study in the Yangtze River Economic Belt (YREB), China. The results show that the BLF has nearly doubled in the YREB during 1990-2014. A considerable proportion (35.43%) of the BLF growth is scattered in small patches (≤1 km2), which have a much stronger correlation with flood occurrence than that of the other patch sizes. In terms of expansion types, the edge-expansion type dominates 57.52% of the BLF growth, followed by the leapfrogging and infilling expansions. Both the leapfrogging and the edge-expanding BLFs are significantly associated with flood occurrence, while the infilling type is not. The patch size and expansion type can thus influence the vulnerability of BLF patches, which is also supported by real-world cases. These findings enrich a general understanding of BLF growth and its impacts on flood vulnerability. The scientific community and policymakers should pay attention to not only the quantity of BLF growth, but also its spatial arrangement.

A valuable aid to assessing and managing flood risk lies in a reliable database of historical floods. In this study, a newspaper-based flood database for Shanghai (NFDS) for the period 1949–2009 was developed through a systematic scanning of newspapers. After calibration and validation of the database, Mann-Kendall tests and correlation analysis were applied to detect possible changes in flood frequencies. The analysis was carried out for three different flood types: overbank flood, agricultural waterlogging, and urban waterlogging. The compiled NFDS registered 146 floods and 92% of them occurred in the flood-prone season from June to September. The statistical analyses showed that both the annual flood and the floods in June–August increased significantly. Urban waterlogging showed a very strong increasing trend, probably because of insufficient capacity of urban drainage system and impacts of rapid urbanization. By contrast, the decrease in overbank flooding and the slight increase in agricultural waterlogging were likely because of the construction of river levees and seawalls and the upgrade of agricultural drainage systems, respectively. This study demonstrated the usefulness of local newspapers in building a historical flood database and in assessing flood characterization.

The timely and secure evacuation of an urban residential community is crucial to residents’ safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recognized for approximately 40 years. However, pre-evacuation time is rarely discussed in previous community-scale emergency evacuation studies. This paper proposes a new method that estimates the pre-evacuation time, which makes the evacuation simulation in urban residential communities more realistic. This method integrates the residents’ pre-evacuation behavior data obtained by surveys to explore the influencing factors of pre-evacuation time and builds a predictive model to forecast pre-evacuation times based on the Random Forest algorithm. A sensitivity analysis is also conducted to find the critical parameters in evacuation simulations. The results of evacuation simulations in different scenarios can be compared to identify potential evacuation problems. A case study in Luoshanqicun Community, Pudong New District, Shanghai, China, was conducted to demonstrate the feasibility of the proposed method. The simulation results showed that the pre-evacuation times have significant impacts on the simulation procedure, including the total evacuation time, the congestion time and the congestion degree. This study can help to gain a deeper understanding of residents’ behaviors under emergencies and improve emergency managements of urban communities.

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.