Vulnerability assessment is an essential tool in mitigating the impact of urban flooding. To date, most flood vulnerability research has focused on one type of flood, such as a pluvial or fluvial flood. However, cities can suffer from urban flooding for several reasons, such as precipitation and river levee overtopping. Therefore, a vulnerability assessment considering different types of floods (pluvial floods, fluvial floods, and compound flooding induced by both rainfall and river overtopping) was conducted in this study. First, a coupled urban flood model, considering both overland and sewer network flow, was developed using the storm water management model (SWMM) and LISFLOOD-FP model to simulate the different types of flood and applied to Lishui, China. Then, the results of the flood modeling were combined with a vulnerability curve to obtain the potential impact of flooding on different land-use classes. The results indicated that different types of floods could have different influence areas and result in various degrees of flood vulnerability for different land-use classes. The results also suggest that urban flood vulnerability can be underestimated due to a lack of consideration of the full flood-induced factors.

Urban flooding is a severe and pervasive hazard caused by climate change, urbanization, and limitations of municipal drainage systems. Cities face risks from different types of floods, depending on various geographical, environmental, and hydrometeorological conditions. In response to the growing threat of urban flooding, a better understanding of urban flood vulnerability is needed. In this study, a comprehensive method was developed to evaluate the vulnerability of different types of urban floods. First, a coupled urban flood model was built to obtain the extent of influence of various flood scenarios caused by rainfall and river levee overtopping. Second, an assessment framework for urban flood vulnerability based on an indicator method was used to evaluate the vulnerability in different flood hazard scenarios. Finally, the method was applied to Lishui City, China, and the distribution and pattern of urban flood vulnerability were studied. The results highlight the spatial variability of flooding and the vulnerability distributions of different types of urban floods. Compound floods were identified to cause more severe effects in the urban areas.

Hydrological droughts were characterized using the run-length theory and the AIC (Akaike information criterion) techniques were accepted to evaluate the modeling performance of nine probability functions. In addition, the copula functions were used to describe joint probability behaviors of drought duration and drought severity for the major tributaries of the Huai River Basin (HRB) which is located in the transitional zone between humid and semi-humid climates. The results indicated that: (1) the frequency of hydrological droughts in the upper HRB is higher than that in the central HRB, while the duration of the hydrological drought is in reverse spatial pattern. The drought frequency across the Shiguan River along the south bank of the HRB is higher than the other two tributaries; (2) generalized Pareto distribution is the appropriate distribution function with the best performance in modelling the drought duration over the HRB; while the Generalized Extreme Value (GEV) distribution can effectively describe the probabilistic properties of the drought severity. Joe copula and Tawn copula functions are the best choices and were used in this study. Given return periods of droughts of <30 years, the droughts in the upper HRB are the longest, and the shortest are in the central HRB; (3) the frequency of droughts along the mainstream of the HRB is higher than tributaries of the HRB. However, concurrence probability of droughts along the mainstream of the HRB is lower than the tributaries of the HRB. The drought resistance capacity of HRB has been significantly improved, effectively reducing the impact of hydrological drought on crops after 2010.

Droughts are a frequent occurrence in Xinjiang, China, and therefore fundamental to determining their hydrologic characteristics is low flow analysis. To that end, 11 probability distribution functions and 26 copulas functions were employed to analyze the changing characteristics of low flow regime (defined as seven-day low flow) of the Tarim River Basin. Results indicated that: (1) The Wakeby distribution satisfactorily described the probabilistic behavior of the low flow regime. According to Akaike Information Criterion (AIC), Bayesian Information Criterions (BIC), maximum likelihood, and other residual-based metrics, Tawn copula, Farlie–Gumbel–Morgenstern copula and Frank copula were the best choice and used in this current study. (2) After 1987, hydrological droughts of longer return periods were prone to higher occurrence frequency. (3) The low flow volume has been increasing in recent years due to the temperature-induced increase of snowmelt and increasing precipitation. However, hydrological droughts can be expected to occur due to the massive increase in water demand from the development of irrigated agriculture, increasing arable land and livestock farming. As a result, the water shortage in the lower Tarim River Basin will be increasingly severe under the influence of climate change and human activities. To alleviate the shortage would call for the development of water-saving agricultural irrigation, water-saving technology, conservation of eco-environment and sustainable development of local socio-economy.

With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.