Spatiotemporal Variance Assessment of Urban Rainstorm Waterlogging Affected by Impervious Surface Expansion: A Case Study of Guangzhou, China

Spatio-temporal evolution characteristics and driving mechanisms of waterlogging in urban agglomeration from multi-scale perspective: A case study of the Guangdong-Hong Kong-Macao Greater Bay Area, China

Understanding the characteristics of waterlogging in urban agglomeration is essential for effective waterlogging prevention and management, as well as for promoting sustainable urban development. Previous studies have predominantly focused on the driving mechanisms of waterlogging in urban agglomeration at a single scale, but urban agglomeration space has greater spatio-temporal heterogeneity, it is often difficult to fully reveal such characteristics at a single scale. Consequently, this study endeavors to explore the spatio-temporal evolution characteristics and underlying mechanisms of waterlogging incidents within urban agglomerations by adopting a multi-scale analytical approach. The results indicate that: (1) The waterlogging degree and high-density zones increase in the GBA, and the waterlogging points are spatially polycentric. However, the waterlogging point in Hong Kong is decreasing. (2) The influence of ISP and AI on waterlogging is dominant at all scales, followed by RE and Slope. ISP∩Slope and ISP∩RE are the key interactions for waterlogging. (3) The aggregation of waterlogging decreases with grid scale, and the influence of land cover factors on waterlogging increases with grid scale. Moreover, the findings at the grid scale outperformed those at the watershed scale, indicating that the grid scale is more conducive to the investigation of waterlogging in urban agglomerations. This research broadens our comprehension of the mechanisms behind waterlogging in urban agglomeration and provide references for policy decisions on waterlogging prevention and mitigation within urban agglomerations.

Coupled and collaborative optimization model of impervious surfaces and drainage systems from the flooding mitigation perspective for urban renewal

Urban pluvial flooding mitigation is a significant challenge in city development. Many mature methods have been used to reduce the risk of flood. The optimal design of impervious surfaces (ODIS) is an adaptive solution to urban flooding from the perspective of urban renewal planning. However, existing ODIS models are limited because they do not consider the drainage systems. To address this issue, this study proposes an elastic and controllable optimization model based on assumptions about rainstorm and drainage capacity, nondominated sorting genetic algorithm-II (NSGA-II), multivariate linear programming (MLP) and soil conservation service curve number model (SCS-CN) in a case study of the old town of Guangzhou city, China. The model not only coupled the drainage systems, but also collaboratively optimized the impervious surfaces and the drainage systems. The results show that the proposed model achieved an optimized efficiency of 5.70 %, which is more than a tenfold improvement compared to existing ODIS models. The study emphasizes that the optimization of the drainage system should be the focus and the optimization of impervious surfaces should be supplementary, and different flood risk areas require different optimization strategies. Furthermore, transforming impervious surfaces into a "high-low-high" spatial distribution of impervious surface densities is the optimal design solution for impervious surfaces. In general, this study offers a novel perspective and approach to urban flooding mitigation, enabling comprehensive control of flooding from a global perspective.

Predicting and Improving the Waterlogging Resilience of Urban Communities in China—A Case Study of Nanjing

In recent years, urban communities in China have been continuously affected by extreme weather and emergencies, among which the rainstorm and waterlogging disasters pose a great threat to infrastructure and personnel safety. Chinese governments issue a series of waterlogging prevention and control policies, but the waterlogging prevention and mitigation of urban communities still needs to be optimized. The concept of “resilience” has unique advantages in the field of community disaster management, and building resilient communities can effectively make up for the limitations of the traditional top-down disaster management. Therefore, this paper focuses on the pre-disaster prevention and control of waterlogging in urban communities of China, following the idea of “concept analysis–influencing factor identification–evaluation indicators selection–impact mechanism analysis–resilience simulation prediction–empirical research–disaster adaptation strategy formulation”. The structural equation model and BP neural network are used by investigating the existing anti-waterlogging capitals of the target community to predict the future waterlogging resilience. Based on this simulation prediction model, and combined with the incentive and restraint mechanisms, suggestions on corrective measures can be put forward before the occurrence of waterlogging.

Conceptualizing Core Aspects on Circular Economy in Cities

Currently, there are many different interpretations in the literature of what a circular economy is and how it functions. As cities are still facing challenges to become fully sustainable, the need for a comprehensive analysis of how the circular economy can be implemented in urban areas is increasing. This article aims at outlining circular cities by their key characteristics and to further explore and provide a framework for fostering circularity at the city level. In order to achieve this goal, we performed a systematic review and analyzed key papers published in the field of circular economy to determine how circular economy practices form circular cities. We discovered that cities play a focal role in facilitating the transition towards circularity through the closing of the loops, recirculation, technical innovation, policy elaboration and citizens’ support. However, city policymakers are still uncertain about how a circular city looks like and what its purpose is, as views are ranging from a strategic ambition to a niche concept of a smart city. Such uncertainty brings challenges, especially in the transition phase that many cities are in at the moment. This further implies that circular economy applied at the urban level still needs effort and innovation to successfully pass the transition phase from the linear economy. Therefore, lastly, we developed a framework model that can be adapted in other cities to facilitate their transition to circular cities.

Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

Urban green infrastructures (UGI) can effectively reduce surface runoff, thereby alleviating the pressure of urban waterlogging. Due to the shortage of land resources in metropolitan areas, it is necessary to understand how to utilize the limited UGI area to maximize the waterlogging mitigation function. Less attention, however, has been paid to investigating the threshold level of waterlogging mitigation capacity. Additionally, various studies mainly focused on the individual effects of UGI factors on waterlogging but neglected the interactive effects between these factors. To overcome this limitation, two waterlogging high-risk coastal cities—Guangzhou and Shenzhen, are selected to examine the effectiveness and stability of UGI in alleviating urban waterlogging. The results indicate that the impact of green infrastructure on urban waterlogging largely depends on its area and biophysical parameter. Healthier or denser vegetation (superior ecological environment) can more effectively intercept and store rainwater runoff. This suggests that while increasing the area of UGI, more attention should be paid to the biophysical parameter of vegetation. Hence, the mitigation effect of green infrastructure would be improved from the “size” and “health”. The interaction of composition and spatial configuration greatly enhances their individual effects on waterlogging. This result underscores the importance of the interactive enhancement effect between UGI composition and spatial configuration. Therefore, it is particularly important to optimize the UGI composition and spatial pattern under limited land resource conditions. Lastly, the effect of green infrastructure on waterlogging presents a threshold phenomenon. The excessive area proportions of UGI within the watershed unit or an oversized UGI patch may lead to a waste of its mitigation effect. Therefore, the area proportion of UGI and its mitigation effect should be considered comprehensively when planning UGI. It is recommended to control the proportion of green infrastructure at the watershed scale (24.4% and 72.1% for Guangzhou and Shenzhen) as well as the area of green infrastructure patches (1.9 ha and 2.8 ha for Guangzhou and Shenzhen) within the threshold level to maximize its mitigation effect. Given the growing concerns of global warming and continued rapid urbanization, these findings provide practical urban waterlogging prevention strategies toward practical implementations.

Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm

Urban pluvial flooding has emerged as a serious threat to environmental conditions and human lives. Identifying its key drivers is crucial for the mitigation of flood risks. Although previous studies have demonstrated that pluvial flooding is caused by both natural (e.g., topography) and anthropogenic factors (e.g., land cover condition), much less effort has been devoted to investigating the potential influence of three-dimensional building configuration on pluvial flooding. To shed some light on this topic, we first analyzed the linear relationship between the density of flooding hotspots and different potential drivers in a highly-urbanized city using Pearson correlation analysis. Next, we designed two random forest-based models to quantify the importance of various building metrics. The first model considers only common drivers, while the second one also includes different types of building metrics. Results indicate that the density of buildings, building congestion degree, and building coverage ratio have exerted considerable influence on the occurrence of pluvial flooding. For example, the root relative squared error of our enhanced model (28.36%) is lower than that of the baseline model (32.58%). Our findings are expected to provide practical guidance for the mitigation of pluvial flood risks from the perspective of three-dimensional urban planning. Moreover, this methodological framework can be further applied to the analysis of flooding in many other regions.

Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China

Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas.

Pavement Overrides the Effects of Tree Species on Soil Bacterial Communities

Human disturbance and vegetation are known to affect soil microorganisms. However, the interacting effects of pavement and plant species on soil bacterial communities have received far less attention. In this study, we collected soil samples from pine (Pinus tabuliformis Carr.), ash (Fraxinus chinensis), and maple (Acer truncatum Bunge) stands that grew in impervious, pervious, and no pavement blocks to investigate the way pavement, tree species, and their interaction influence soil bacterial communities by modifying soil physicochemical properties. Soil bacterial community composition and diversity were evaluated by bacterial 16S amplicon sequencing. The results demonstrated that soil bacterial community composition and diversity did differ significantly across pavements, but not with tree species. The difference in soil bacterial community composition across pavements was greater in pine stands than ash and maple stands. Soil bacterial diversity and richness indices decreased beneath impervious pavement in pine stands, and only bacterial richness indices decreased markedly in ash stands, but neither showed a significant difference across pavements in maple stands. In addition, bacterial diversity did not differ dramatically between pervious pavement and no pavement soil. Taken together, these results suggest that pavement overwhelmed the effects of tree species on soil bacterial communities, and had a greater effect on soil bacterial communities in pine stands, followed by ash and maple stands. This study highlights the importance of anthropogenic disturbance, such as pavement, which affects soil microbial communities.

Identifying dominant factors of waterlogging events in metropolitan coastal cities: The case study of Guangzhou, China

Urban waterlogging disasters are affected by environmental conditions and human activities. Previous studies had explored the effect of land-use type on waterlogging in relatively small watersheds. Few, however, have comprehensively revealed the relative contributions of the environmental and anthropogenic factors to urban waterlogging concerning different scales of analysis. Indeed what is less known, are the dominant factors and the appropriate scale of analysis. To overcome this limitation, a novel method that integrates the stepwise regression model with hierarchical partitioning analysis is presented. The purpose is to investigate the complex mechanism of urban waterlogging by identifying the relative contribution of each environmental and anthropogenic factor and the stability linking waterlogging to influencing factors at multiple scales of analysis (i.e. 1 km, 2 km, 3 km, 4 km, and 5 km). We consider waterlogging events in the central urban districts of Guangzhou (PR China) from 2009 to 2015 as a case study. The results show that the spatial distribution of waterlogging events in the central urban area presents a strong agglomeration pattern. The waterlogging hot spots are mainly concentrated in the historical area of Guangzhou. Under all analysis scales, we find that the percent cover of urban green spaces (44.74%), percent cover of residential area (41.03%), and slope.std (36.85%) both have a dominant contribution to urban waterlogging, which suggests the importance of land cover composition in determining urban waterlogging. However, the relative contribution and dominant factors of waterlogging varied across different analysis scales, presenting a strong scale effect. Under a small analysis scale (1 km), the topography factors (slope.std and relative elevation) are confirmed as the dominant variables; however, with the increase of analysis scale, the influence of land cover composition (greenspace, residence area, grassland) and land cover spatial configuration (LPI, AI, Cohesion index) on waterlogging magnitude is greater than other factors. This finding provides additional insights that the urban waterlogging can be alleviated by balancing the relative composition of land cover features as well as by optimizing their spatial configuration. Since the optimal statistical scale for urban waterlogging studies only worked for specific influencing factors, the appropriate analysis scale for urban waterlogging study should be determined by the characteristics of study areas. This study has the capability to extend our scientific understanding of the complex mechanisms of waterlogging in the highly urbanized coastal city, providing useful support for the prevention and management of urban waterlogging.

IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data

Optical and Synthetic Aperture Radar (SAR) fusion is addressed in this paper. Intensity–Hue–Saturation (IHS) is an easily implemented fusion method and can separate Red–Green–Blue (RGB) images into three independent components; however, using this method directly for optical and SAR images fusion will cause spectral distortion. The Gradient Transfer Fusion (GTF) algorithm is proposed firstly for infrared and gray visible images fusion, which formulates image fusion as an optimization problem and keeps the radiation information and spatial details simultaneously. However, the algorithm assumes that the spatial details only come from one of the source images, which is inconsistent with the actual situation of optical and SAR images fusion. In this paper, a fusion algorithm named IHS-GTF for optical and SAR images is proposed, which combines the advantages of IHS and GTF and considers the spatial details from the both images based on pixel saliency. The proposed method was assessed by visual analysis and ten indices and was further tested by extracting impervious surface (IS) from the fused image with random forest classifier. The results show the good preservation of spatial details and spectral information by our proposed method, and the overall accuracy of IS extraction is 2% higher than that of using optical image alone. The results demonstrate the ability of the proposed method for fusing optical and SAR data effectively to generate useful data.

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China

With the rapid expansion of impervious surfaces, urban waterlogging has become a typical "urban disease" in China, seriously hindering the sustainable development of cities. Therefore, reducing the impact of impervious surfaces on surface runoff is an effective approach to alleviate urban waterlogging. Presently, the development mode of many cities in China has shifted from an increase in urban scale to the improvement of urban quality through urban renewal, which is the current and future development path for most cities. Optimizing the design of impervious surfaces in urban renewal planning to reduce its impact on surface runoff is an important way to prevent and control urban waterlogging. The aim of this research is to construct an optimization model of impervious surface space layout under the framework of a geographic simulation technology-integrated ant colony optimization (ACO) and Soil Conservation Service curve number (SCS-CN) model (ACO-SCS) in a case study of Guangzhou in China. Urban runoff plots in the study area are divided according to the area of the urban planning unit. With the goal of minimizing the runoff coefficient, the optimal space layout of the impervious surfaces is obtained, which provides a technical method and reference for urban waterlogging prevention and control through urban renewal planning. The results reveal that the optimization of impervious surface space layout through ACO-SCS achieves a satisfactory effect with an average optimization rate of 9.52%, and a maximum optimization rate of 33.16%. The research also shows that the initial impervious surface layout is the key influencing factor in ACO-SCS. In the urban renewal planning stage, the space layout of the impervious surfaces with a high-low-high density discontinuous connection can be constructed by transforming medium-density impervious surfaces into low-density impervious surfaces to achieve the flat and long-type agglomeration of the low-density and high-density impervious surfaces, which can effectively reduce the influence of urban development on surface runoff. There is spatial heterogeneity of the optimal results in different urban runoff plots. Therefore, the policy of urban renewal planning for urban waterlogging prevention and control should be different. The optimized results of impervious surface space layout provide useful reference information for urban renewal planning.

Assessment of the Impacts of Land Use/Cover Change and Rainfall Change on Surface Runoff in China

Assessment of the impacts of land use/cover change (LUCC) and rainfall change on surface runoff depth can help provide an understanding of the temporal trend of variation of surface runoff and assist in urban construction planning. This study evaluated the impacts of LUCC and rainfall change on surface runoff depth by adopting the well-known Soil Conservation Service-Curve Number (SCS-CN) method and the widely used Long-Term Hydrologic Impact Assessment (L-THIA) model. National hydrologic soil group map of China was generated based on a conversion from soil texture classification system. The CN values were adjusted based on the land use/cover types and soil properties in China. The L-THIA model was configured by using the adjusted CN values and then applied nationally in China. Results show that nationwide rainfall changes and LUCC from 2005 to 2010 had little impact on the distribution of surface runoff, and the high values of runoff depth were mainly located in the middle and lower reaches of the Yangtze River. Nationally, the average annual runoff depths in 2005, 2010 and 2015 were 78 mm, 83 mm and 90 mm, respectively. For the 2015 land use data, rainfall change caused the variation of surface runoff depth ranging from −203 mm to 476 mm in different regions. LUCC from 2005 to 2015 did not cause obvious change of surface runoff depth, but expansion of developed land led to runoff depth increases ranging from 0 mm to 570 mm and 0 mm to 742 mm from 2005 to 2010 and 2010 to 2015, respectively. Potential solutions to urban land use change and surface runoff control were also analyzed.

A Simple GIS-Based Model for Urban Rainstorm Inundation Simulation

With rapid urbanization, floods that occur are more frequently associated with non-riverine, urban flooding. Reliable and efficient simulation of rainstorm inundation in an urban environment is profound for risk analysis and sustainable development. Although sophisticated hydrodynamic models are now available to simulate the urban flooding processes with a high accuracy, the complexity and heavy computation requirement render these models difficult to apply. Moreover, a large number of input data describing the complex urban underlying surfaces is required to setup the models, which are typically unavailable in reality. In this paper, a simple and efficient urban rainstorm inundation simulation method, named URIS, was developed based on a geographic information system (GIS) with limited input data. The URIS method is a simplified distributed hydrological model, integrating three components of the soil conservation service (SCS) module, surface flow module, and drainage flow module. Cumulative rainfall-runoff, output from the SCS model, feeds the surface flow model, while the drainage flow module is an important waterlogging mitigation measure. The central urban area of Shanghai in China was selected as a study case to calibrate and verify the method. It was demonstrated that the URIS is capable of characterizing the spatiotemporal dynamic processes of urban inundation and drainage under a range of scenarios, such as different rainstorm patterns with varying return periods and different alterations of drainage diameters. URIS is therefore characterized with high efficiency, reasonable data input, and low hardware requirements and should be an alternative to hydrodynamic models. It is useful for urgent urban flood inundation estimation and is applicable for other cities in supporting emergency rescue and sustainable urban planning.