The intensity and frequency of extreme weather is increasing in major metropolitan areas around the world, which results in unprecedented urban floods. However, subway systems lack consideration of flood risk, and few studies have assessed risk during flood events from the view of subway travel. In this study, subway travel risk was evaluated by flood hazard, subway travel exposure and population vulnerability under three groups of rainfall scenarios. The degree of spatial exposure was calculated based on smart card data and a census, and the vulnerability of the population was assessed based on human stability in floodwaters. The results in the study area indicate that subway travel risk grows with an increase in the rainfall return period, and the highest subway travel risk occurs in the morning peak period. The return periods of rainfall, time-to-peak, and duration have an impact on the spatiotemporal trajectory of subway travel risk. Furthermore, the traditional census tends to underestimate subway transportation exposure due to subway travel. Subway travel risk increases significantly under the extreme rainfall scenario and requires further research. This study provides risk maps for making travel decisions before departures and support for subway operators to develop risk warnings.

As the risk of urban flooding increases worldwide, floods seriously endanger the safety of people’s lives and property. Understanding the protective coping behaviors of the public in flood disasters is crucial to the implementation of effective flood mitigation measures and flood risk management. In this study, influential factors affecting protective coping behaviors in the face of flood disasters were identified, and the effects of these factors were discussed as well. Shenzhen City in China was selected as the study area, in which a questionnaire survey of 339 respondents was carried out in three flood-prone districts. Correlation analysis was conducted to preselect potential influential factors. Then, two linear regression models were established to identify main influential factors and to explore the interaction effects of these factors. The results indicated that age, monthly income, flood experience, trust in government and insurance willingness were main influential factors of protective coping behaviors. Trust in government had the highest positive correlation coefficient, while monthly income and age were negatively associated with protective coping behaviors. The interaction between insurance willingness and monthly income jointly affected protective coping behaviors of the public. The findings of this study could help authorities better understand the public’s intention to cope with flood and design effective risk reduction measures, not only for Shenzhen, but also for many other similar cities that facing with the same situation.

As global warming exacerbates and urbanization accelerates, extreme climatic events occur frequently. Urban waterlogging is seriously spreading in China, resulting in a high level of vulnerability in urban societies and economies. It has been urgent for regional sustainable development to effectively identify and analyze the risk factors behind urban waterlogging. A novel model incorporating satellite image semantic segmentation into extreme gradient boosting (XGBoost) is employed for identifying and forecasting the urban waterlogging risk factors. Ground object features of waterlogging points are extracted by the satellite image semantic segmentation, and XGBoost is employed to predict waterlogging points and identify the primary factors affecting urban waterlogging. This paper selects the coastal cities of Haikou, Xiamen, Shanghai, and Qingdao as research areas, and obtains data from social media. According to the comprehensive performance evaluation of the semantic segmentation and XGBoost models, the semantic segmentation model could effectively identify and extract water bodies, roads, and green spaces in satellite images, and the XGBoost model is more accurate and reliable than other common machine learning methods in prediction performance and precision. Among all waterlogging risk factors, elevation is the main factor affecting waterlogging in the research areas. For Shanghai and Qingdao, the secondary factor affecting waterlogging is roads. Water bodies are the secondary factor affecting urban waterlogging in Haikou. For Xiamen, the four indicators other than the elevation are equally significant, which could all be regarded as secondary factors affecting urban waterlogging.

The high-quality development of the Yangtze River Economic Belt (YREB) plays a crucial role in economic transformation in China. Climate change, rapid population growth, and increased urbanization have contributed towards increased pressures on the water, energy, food (WEF) nexus system of YREB. Thus, there is an imperative need to improve the efficiency of WEF in YREB. However, few studies have conducted spatial-temporal heterogeneity exploration of YREB about the input-output efficiency of WEF (IOE-WEF). Using panel data from 2008–2017, a super slack based model (SSBM), combined with the spatial autocorrelation and spatial econometric method, were proposed to calculate the IOE-WEF of YREB’s 11 provinces, the results indicated that: (1) From the perspective of time, the IOE-WEF in YREB was relatively low and displayed a fluctuating downward pattern while considering the undesirable outputs. (2) From the perspective of space, the spatial distribution of IOE-WEF in YREB was uneven. The efficiency values of the three sub-regions of YREB were “the lower reaches > the middle reaches > the upper reaches”. The IOE-WEF of YREB had a prominent positive spatial correlation and also had a spatial spillover effect. (3) The spatial aggregation effect of IOE-WEF of YREB is gradually weakening. The spatial aggregation types of IOE-WEF in YREB were “high-high” cluster areas in lower reaches and “low-low” cluster areas in upper reaches. (4) From the perspective of driving forces, environmental regulation and technological innovation promoted the improvement of IOE-WEF of YREB, while the industrial structure and mechanization level inhibited the improvement of IOE-WEF of YREB. Furthermore, the role of government support of IOE-WEF of YREB was not obvious. The improvement of IOE-WEF in adjacent regions also had a notable positive spatial spillover effect on the region.

Urban roads are especially vulnerable to waterlogging during rainfall events. Assessments of urban road waterlogging risk are critical for disaster prevention and mitigation. This paper presents a rapid and practical approach for assessing the risk of urban road waterlogging based on a source–pathway–receptor concept and a digital elevation model (DEM). Waterlogging sources are identified by neighborhood analysis, overland flow pathways are extracted using a DEM, and the properties of cross‐nodes between pathways and roads are used to assess the waterlogging risk of roads. Futian District in Shenzhen, China, is selected as the study area. The risk results show that the waterlogging risk sources are clustered in residential land parcels and low‐lying road sections, while most road intersections and overpasses in the central area are at higher risks. The results also found that the waterlogging risk decreases as the road grade increases. Actual telephonic flooding records and reported waterlogging areas were used to validate the method, with the risk results displaying good agreement with the actual conditions. The prospects for generalizing this framework and its usage are discussed. The proposed approach provides support for pluvial flooding risk assessment and urban road waterlogging prevention.