Na Li

Bend sections are ubiquitous in natural sandy river systems. This study employs Computational Fluid Dynamics–Discrete Phase Model (CFD-DPM) methodology to analyze particle transport dynamics in U-bend channel flows, focusing on the distinctions between partially vegetated (Case No.1) and non-vegetated (Case No.2) scenarios. The research aims to unravel the intricate relationships among bending channel-induced secondary flow, vegetation blockage, and particle aggregation, employing both quantitative and qualitative approaches. (I) The key findings reveal that vegetation near the inner walls of curved channels markedly diminishes the intensity of secondary circulation. This reduction in circulation intensity is observed not only within vegetated areas but also extends to adjacent non-vegetated zones. Additionally, the study identifies a close correlation between vertical vortices and particle distribution near the channel bed. While particle distribution generally aligns with the vortices’ margin, dynamic patch-scale eddies near vegetation patches induce deviations, creating wave-like patterns in particle distribution. (II) The application of the Probability Density Function (PDF) provides insights into the radius-wise particle distribution. In non-vegetated channels, particle distribution is primarily influenced by secondary flow and boundary layers. In contrast, the presence of vegetation leads to a complex mixing layer, altering the particle distribution pattern and maximizing PDF values in non-vegetated free flow subzones. (III) Furthermore, the research quantifies spatial–temporal sediment heterogeneity through PDF variance. The findings demonstrate that variance in non-vegetated channels increases towards the outer wall in bending regions. Vegetation-induced turbulence causes higher variance, particularly in the mixing layer subzone, underscoring the significance of eddy size in sediment redistribution. (IV) The study of vertical concentration profiles in vegetated U-bend channels offers additional insights, while secondary flow in non-vegetated channels facilitates upward sediment transport and vegetation presence, although increasing the Turbulent Kinetic Energy (TKE), restricts channel space, and impedes secondary flow, thereby reducing vertical particle suspension. Sediment concentrations are found to be higher in the lower layers of vegetated bends, contrary to the pattern in non-vegetated bends. These findings highlight the complex interplay between vegetation, secondary flow, and sediment transport, illustrating the reduced effectiveness of secondary flow in promoting vertical particle transportation in bending channels due to the vegetation obstruction.

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Sponge City Construction (SCC) can effectively solve urban areas’ water problems. Green infrastructure (GI), the core of SCC, exhibits a multifaceted capacity to deliver many co-benefits, while grey infrastructure primarily serves the single function of controlling rainfall runoff. However, existing assessment indicator systems fail to either consider or evaluate comprehensive impacts. In this work, a comprehensive indicator system has been established to assess the co-benefits of five different GI measures. The system includes twelve indicators from four aspects, i.e., disaster reduction, economic, environmental, and social benefits. This newly established assessment system is applied to Jinan as a case study. Six out of twelve indicators are evaluated using the self-developed Flood Risk Analysis Software and empirical equations. The results show that the inundation reduction ratios are 11.02%, 9.32%, and 8.02% under the 24-h design rainfall with a 5-year, 10-year, and 20-year return period, respectively. In addition, the corresponding direct flood loss reduction rates are 13.86%, 4.28%, and 4.09%. That is, as the rainfall return periods increase, the disaster reduction benefits become less pronounced. On the contrary, other benefits, e.g., groundwater recharge volumes, are more obvious. The corresponding groundwater recharge volumes are 2.23 million m³, 2.86 million m³, and 2.87 million m³, respectively. The proposed indicator system can be adopted to assess the performances of the different GI combinations, which provides effective support and reference to decision-makers in SCC at the planning and design stages.

Evaluating extreme flood disasters is a prerequisite for decision making in flood management. Existing extreme flood disaster assessments fail to either consider or evaluate comprehensive impacts from social, economic, and environmental aspects. This study first analyzes the causes of extreme flood disasters and subsequently the potential flood consequences in depth. On the basis of this comprehensive analysis, an extreme flood disaster indicator system is developed by taking into account social, economic, and environmental consequences. To assess the comprehensive impacts, we propose a refined social and economic impact evaluation method and a semi-quantitative environmental impact evaluation method, which are applied to Jingjiang Flood Diversion District (JFDD) located in the Yangtze River Basin, and analyze two extreme flood scenarios. The results show that almost all of the JFDD area is flooded with inundation areas of 901.36 km² and 879.49 km², respectively. The corresponding affected populations are 0.51 million and 0.5 million. The direct economic losses are 18.83 billion and 14.33 billion, respectively. Moreover, 5 potential pollutant sources and 11 protected areas are inundated under two scenarios. Extreme floods have relatively serious impacts on local ecology and the environment. The proposed methodology can provide effective support for decision makers.

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this study, we used Copulas to quantify the relationship between drought and water scarcity. Beijing–Tianjin–Hebei Metropolitan Areas (BTHMA) was chosen as the study area. Standard Precipitation and Evapotranspiration Index (SPEI) and water exploitation index plus (WEI+) was chosen to represent metrological drought and water scarcity. Inverse Distance Weighted method was used for spatial analysis of SPEI and WEI+, and Archimedean Copula was used to establish two-dimensional joint probability distribution of SPEI and WEI+. The results are as follows: (1) The southern part of the study area was wetter. The middle part was drier, with moderate drought happened for most times. (2) WEI+ of Beijing and Tianjin showed significant decreasing trends from 2000 to 2015, while WEI+ of Hebei Province did not, which indicated that Hebei Province is facing much severer water scarcity situation than Beijing and Tianjin. (3) Gumbel copula was the best-fitting model to establish the joint probability distribution of SPEI and WEI+. The condition probability provided a probability distribution of water scarcity under different drought conditions, which can provide technical support for government managers during policy making.

While the sponge city construction initiative is drawing attentions and fast spreading in China, some challenges and risks remain. This research surveyed some pilot cities and identified a broad array of challenges from technical, physical, regulatory, financial to community, and institutional. The survey results show that challenges may appear in various shapes, scales, and degrees depending on geographical locations, socio-economic context, and other conditions; however, certain common themes exhibit throughout the survey results. The most dominant challenges involve uncertainties and risks. To resolve the issues, this study also identified various gateways to improve China’s sponge city construction, and recommendations are proposed including urging local governments to adopt sponge city regulations and permits to alleviate water quality and urban pluvial flooding, fully measuring and accounting of economic and environmental benefits, embracing regional flexibility and results-oriented approaches, and focusing on wider range of funding resources for sponge city construction. Coordination among other government agencies is critical, and this is true at all levels of government. Only through greater coordination, education, and broader funding could sponge city construction be advanced meaningfully and sustainably.

Low impact development (LID), which aims at either infiltrating, evapotranspiring or storing water at the source, plays an important role in managing urban rainwater. This paper summarizes the effects of four individual LIDs (i.e., bioretention, green roof, porous pavement, and grass swales) and several combinations of those LID practices on rainfall-runoff management. The survey shows that both individual and combined LIDs are effective in controlling small and medium rainfalls, and the performances are less obvious with increases of the rainfall depths. Hence, the individual or combined LIDs applied with noticeable effects on low or moderate rainfalls might not be useful for heavy rain events which would probably cause urban floods in cities in China. Cities located in different regions show big differences in rainfall characteristics. Rainfall intensity is an even more important factor than rainfall depth that influences performances of LID practices. In the future, more studies should be directed to the effects of LID measures on large storm runoff managements under different rainfall intensity-duration-frequency (IDF), which would be helpful to select suitable LID practices for cities in China.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.