Impervious Surface Area Research Articles

Modeling the intensity of surface urban heat island based on the impervious surface area

The urban heat island (UHI) effect is anticipated to be intensified and cause severe damage to human society under future urbanization and climate change. Due to the complicated human-nature interactions in the urban domain, it is challenging to characterize the intensity of UHI quantitatively, especially at the global scale. Here, we proposed a framework to estimate the surface urban heat island intensity (SUHII) based on satellite-derived land surface temperature (LST) and the derived impervious surface area (ISA) data across 355 cities worldwide from 2003 to 2018. First, we employed linear regression models to estimate LST using ISA as the explanatory variable, given that the LST generally elevated with increasing urban intensity along the urban-rural gradient. Then, using the modeled LST, we estimated the spatiotemporal patterns of SUHII worldwide. The established linear regression models with ISA as the explanatory variable perform reasonably well in estimating LST for both daytime and nighttime, with average RMSEs of 1.40 and 0.80 °C, respectively. The resulting SUHII reasonably captures the spatial heterogeneity across cities in the world, with the highest value in the tropical region at daytime and in the arid region at nighttime. The estimated and observed SUHII show good agreement with average R2 values of 0.81 and 0.72 for daytime and nighttime, respectively. Using ISA as an indicator for LST estimation, this study first implemented a comprehensive estimation of SUHII at the global scale. The proposed methodology is of great potential to project future SUHII by expanding urban impervious surfaces under diverse scenarios jointly determined by shared socioeconomic pathways and climate change scenarios.

Survey of land cover datasets for updating the imperviousness field in urban parameterisation scheme TERRA_URB for climate and weather applications

In climate modelling, a critical issue is the accurate representation of urban areas in space and variation over time. Urban features may be parameterised through urban canopy parameters (UCPs), including the impervious surface area (ISA). One of the aims of the Consortium for Small-scale Modelling (COSMO) and the CLM-Community is to improve urban numerical weather prediction (NWP) and climate simulations by implementing the urban canopy scheme TERRA_URB in the ICOsahedral Nonhydrostatic (ICON) modelling framework. To harmonise and update the datasets adopted for the UCPs, the Consortium has launched the Priority Project “City Induced Temperature change Through A'dvanced modelling” (PP CITTÀ). Within this framework, the study explores recent and detailed land cover datasets to update ISA and harmonise it with datasets adopted for the UCPs. We identify and compare some relevant datasets at the pan-European level. The results show the benefits obtained by correlating the UCPs from urban Local Climate Zones taken from the ECOCLIMAP-Second Generation with the ISA depending on the imperviousness degree provided by the Copernicus Land Monitoring Service. These results will foster an updating of NWP and climate models with urban surface parameterisations to match urban features while avoiding inconsistencies consistently.

Effects of Glomalin-Related Soil Protein Driven by Root on Forest Soil Aggregate Stability and Carbon Sequestration during Urbanization in Nanchang, China.

Glomalin-related soil protein (GRSP) is a hydrophobic protein released by arbuscular mycorrhizal fungi. It is an important component of the soil carbon pool, and it improves the soil aggregate structure; however, it remains unclear whether GRSP can enhance soil carbon sequestration and improve soil quality during rapid urbanization. The built-up area in Nanchang, China was the study area, and the proportion of impervious surface area was the parameter of urbanization intensity. A total of 184 plots (400 m2) were set up to collect soil samples (0-20 cm) for analysis. Aggregates of five particle sizes were sieved, and the percentage amounts of soil organic carbon (SOC) and GRSP for them were determined. The results showed that the easily extractable GRSP (EE-GRSP) and total GRSP (T-GRSP) contents of the four aggregates of <2 mm were 22-46% higher in low urbanization areas than those in high urbanization areas (p < 0.05), indicating that the higher urbanization intensity was associated with the lower GRSP content of different aggregates. The GRSP was significantly positively correlated with SOC (p < 0.05). Moreover, the contribution of GRSP to the SOC pool in the <0.25 mm aggregate was significantly higher than that in other aggregates. In addition, the EE-GRSP content was significantly positively correlated with mean weight diameter (MWD) and geometric mean diameter (GMD) in the four aggregates of <2 mm, whereas it was negatively correlated with fractal dimension (D) in the >2 mm, 1-2 mm and <0.053 mm aggregates. The T-GRSP content showed significant correlations only with MWD, GMD, and D in the 1-2 mm aggregate. This study revealed that increasing urbanization intensity can significantly reduce the GRSP content of different sized aggregates. Moreover, the GRSP content significantly promoted SOC sequestration, and the EE-GRSP content more significantly promoted soil aggregate stability than that of the T-GRSP. These findings provide new ideas for exploring the improvement of soil quality during the process of urbanization.

Atmospheric wet deposition in urban and suburban sites across the United States

Compared to rural areas, there is a much poorer understanding of trends in wet atmospheric deposition across urban and suburban areas of the U.S. and how they compare to rural sites over time. The goal of this study was to investigate long-term trends in atmospheric wet deposition of nitrogen (ammonium and nitrate; NH4+ and NO3−), sulfate (SO42−), cations, and chloride (Cl−) for urban and suburban sites in the U.S. using data from the National Atmospheric Deposition Program (NADP) and to determine how long-term deposition trends relate to urbanization. For urban and suburban sites, we found that from the late 1970s to 2020, atmospheric deposition of NO3−, dissolved inorganic N (DIN), SO42−, hydrogen (H+), and magnesium (Mg2+) decreased over time, while deposition of NH4+ and potassium (K+) significantly increased over this period. When sites were categorized based on population density, we found no significant differences in rates of wet deposition between urban and background rural values, but the difference between urban and background rural values increased significantly over time for NO3− and DIN. When sites were categorized based on impervious surface area (ISA), we found that rates of NH4+, DIN, SO42, and calcium (Ca2+) deposition were significantly higher in urban sites (that had greater than 25% ISA) compared to background rural values. There were statistically significant positive relationships between population density and deposition of Mg2+, K+, sodium (Na+), and Cl− and significant positive relationships between % ISA and deposition of NO3− and Mg2+. Together, the results of this study demonstrate the urban enhancement of many forms of atmospheric deposition and the need for increased long-term monitoring of atmospheric deposition in cities and suburban sites throughout the U.S., especially in the western U.S.

Spatial change in glomalin-related soil protein and its relationships with soil enzyme activities and microbial community structures during urbanization in Nanchang, China

Rapid urbanization brings about drastic changes in land use and soil quality. Glomalin-related soil protein (GRSP), a sensitive indicator of soil quality, is possibly secreted by arbuscular mycorrhizal fungi (AMF). Besides, soil microorganisms and enzymatic activities play an irreplaceable role in improving soil quality and regulating soil nutrient circulation. However, little data are available on the interactions between GRSP, microorganisms, and enzymatic activities during urbanization. Here, we selected a built-up area (505 km2) in Nanchang, China, and collected soil samples from 184 sites from different urbanized areas. Soil microorganisms, soil physicochemical properties, soil enzymatic activities, vegetation characteristics, and land-use configurations were used to decouple the variations in total GRSP (TG) and easily extractable GRSP (EEG) during urbanization. Results showed that TG and EEG contents significantly decreased by 16.2 % and 19.4 % from low to heavy urbanization areas, respectively. Moreover, N-acetylglucosamine glycosidase (NAG), acid phosphatase (AP), and leucine aminopeptidase (LAP) activities, and the biomass of gram-positive bacteria (G+) and actinomycetes were significantly larger in lightly urbanized than in heavily urbanized areas (p < 0.05). EEG and TG positively correlated with β-1,4-glucosidase (BG), NAG, LAP, AP, G+, gram-negative bacteria, actinomycetes, and total soil microbial biomass (p < 0.05). Variance partitioning analysis showed that soil enzymatic activities are pivotal in GRSP production. However, no direct evidence shows that AMF secrete GRSP. Furthermore, structural equation modeling found that urbanization could diminish soil enzymatic activities by reducing vegetation cover and increasing the proportion of impervious surface area, indirectly causing a decrease in GRSP content. In conclusion, urbanization could indirectly reduce GRSP content by affecting land-use configurations and soil enzymatic activities. In the future, we shall consider improving soil quality by increasing GRSP content during urbanization.

Impervious surface Mapping and its spatial–temporal evolution analysis in the Yellow River Delta over the last three decades using Google Earth Engine

The unique geographical location of the land-sea transition makes the ecological environment of the Yellow River Delta very fragile and vulnerable to human activities. As one of the characteristics of anthropogenic activities, monitoring the spatiotemporal changes of impervious surface area (ISA) is of great significance to the protection of the ecological environment in the Yellow River Delta (YRD). Based on the Landsat historical images and computing resources provided by Google Earth Engine (GEE), an ISA mapping method was developed through combining spectral, texture features and random forest algorithm, and subsequently was applied to generate the spatiotemporal distribution data of ISA of the YRD for 1992, 1998, 2004, 2010, 2016 and 2021. The experimental results demonstrated that the proposed method achieved satisfactory accuracy, with an average overall accuracy of 92.23% and an average Kappa coefficient of 0.9090. Through further time-series analysis of ISA, it found that the area of ISA in the YRD increased from the initial 394.87 km2 to 1081.74 km2 during study periods, and the annual growth rate broke through new highs, ranging from the initial 1.01 km2/year to 67.87 km2/year. According to the research results, urban development activities in the region should be strictly restricted in order to protect the ecological environment of the YRD.

Monitoring Impervious Surface Area Dynamics in Urban Areas Using Sentinel-2 Data and Improved Deeplabv3+ Model: A Case Study of Jinan City, China

Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to accurate ISA dynamics monitoring. This research selected Jinan City as a case study to boost ISA mapping performance through integrating the dual-attention CBAM module, SE module and focal loss function into the Deeplabv3+ model using Sentinel-2 data, and subsequently examining ISA spatial-temporal evolution using the generated annual time-series ISA data from 2017 to 2021. The experimental results demonstrated that (a) the improved Deeplabv3+ model achieved satisfactory accuracy in ISA mapping, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. (b) In a comparison with traditional classification methods and other state-of-the-art deep learning semantic segmentation models, the proposed method performed well, qualitatively and quantitatively. (c) The time-series analysis on ISA distribution revealed that the ISA expansion in Jinan City had significant directionality from northeast to southwest from 2017 to 2021, with the number of patches as well as the degree of connectivity and aggregation increasing while the degree of fragmentation and the complexity of shape decreased. Overall, the proposed method shows great potential in generating reliable times-series ISA data and can be better served for fine urban research.

Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

Rapid urban growth causes the increase of impervious surface which was converted from vegetation land. This affects the rate of water penetration into soils, the rise of surface runoff, and the lowering of groundwater levels. The expansion of the impervious surface gives rise to a series of changes in the physical environment, affecting the quality of life of people due to a change in the thermal energy balance. Therefore, the purpose of this study is to analyse of changes of impervious surface area Ho Chi Minh City and Ba Ria - Vung Tau province which is the marine national economic hub in the period of 2009-2020 by using Landsat satellite image data. The main methods used are NDWI, PISI indices and ISODATA algorithm. The results show that the area of impervious surface area of Ho Chi Minh City is very high with 18.5% in 2009 and 35% increasing rate from 2009-2020. There are 75% districts in Ho Chi Minh city having over 50% impervious surface area. Ba Ria-Vung Tau has lower impervious surface area with 5.8% in 2009 but having the incredible increasing rate– over 100%.

Extraction of impervious surface with Landsat based on machine learning in Chengdu urban, China

In the process of urbanization, materials such as steel and concrete are widely used in urban construction such as road paving and house building, resulting in an increase in impervious surface area in cities. Rapid urbanization in China today has led to a huge increase in impervious surface area, which has increased the risk of urban waterlogging. Therefore, there are more reasons to conduct research on impervious surface and explore the relationship between impervious surface and urban waterlogging. This research has been done to obtain the optimal model for extracting impervious surface in Chengdu urban area and to explore the correlation between impervious surface changes and urban waterlogging in the same area. In order to obtain the optimal model, five different machine learning and deep learning algorithms such as Naive Bayes (NB), Convolutional Neural Network (CNN), Support Vector Machine (SVM), Capsules Network (CapsNet) and Random Forest (RF) have been used to extract the urban impervious surface in this study. The accuracy of the classification models is evaluated using the overall accuracy (OA), kappa coefficient (KC), and macro F1 (F1). It is found that CNN is the best option to extract the urban impervious surface of Chengdu with an OA of 0.7809. In addition, based on the optimal model to extract the impervious surface of Chengdu, a partial correlation analysis was conducted between urban waterlogging and urban impervious in the study area, it was concluded that there was a positive correlation between the two with a partial correlation coefficient of 0.979.

Application of Multi-Temporal Landsat Images to Analyze the Relationship Between the Land Surface Temperature (LST) and the Land Use Land Cover (LULC) in Ho Chi Minh City

Urban sprawl has changed the land use land cover (LULC) in Ho Chi Minh City (HCMC). It has caused the gradual decline of the natural vegetation and rapid increase of the impervious surface, which leads to the heat storage capacity of the urban surface being on the rise. There is a land surface temperature (LST) difference between the urban center and surrounding areas and it creates the urban heat island phenomenon (UHI). This study used Landsat 4, 5, and Landsat 8 satellite image data from 1990 to 2020 with the GIS algorithms on ENVI and ArcGIS to analyze and estimate the correlation between the LULC and the LST in HCMC. The Kappa coefficients of the LULC classifications were all over 0.8 in 1990 – 2020 period. The total area of impervious surface has increased by 57,338 hectares over the past 30 years; while the total area of natural green has decreased by 19,116 hectares. The LST has remarkably increased in the areas of construction lands, industrial zones, and impervious surfaces, with an average increase from 22°C to 27°C. While the LST in the others such as the vegetation surface and water surface were slower, from 20°C to 23°C. The solution of enhancing the urban green space was suggested to mitigate the increasing LST impact on HCMC.

Impacts of urbanization and climate change on surface runoff in Thu Duc City, Vietnam

Thu Duc City (TDC), a sub-city of Ho Chi Minh City (HCMC), Vietnam, has undergone rapid transformation in land use - land cover (LULC) over the past few decades. Flooding is one of the biggest challenges to the sustainable development of the city. Quantifying excess runoff induced by urbanization and climate change is essential for developing an efficient flood risk management strategy. This research aims to evaluate the effects of the impervious surface expansion (a key indicator of urbanization) and the rainfall increase caused by climate change on the excess runoff depth in TDC. The dataset derived from LANDSAT satellite images was used to explore the spatial-temporal dynamics of impervious surface areas from 1985 to 2020. The EPA-SWMM urban rainfall-runoff model was employed to investigate the effects of increased impervious surface and climate change on excess runoff depth. The satellite data showed that TDC has 24% of the impermeable surface area currently, much lower than 75.6% in areas located in the central districts such as Nhieu Loc – Thi Nghe (NL-TN) basin, and that impermeable areas have expanded rapidly in the north-south and west-east directions since 2010. The simulated results indicated that impervious surface development increased excess runoff depth by 14.0mm between 1985 and 2020. It also showed that increasing rainfall due to climate change could raise the average excess runoff depth by 18.2%, 64.0%, and 49.4%, respectively, at the beginning, the middle, and the end of the century compared to the current stage. It suggests mainstreaming stormwater retention and detention systems into urban development plans for adapting to the future uncertainties of urbanization and climate change. It also recommends that the impervious surface areas must be maintained within design thresholds to control runoff and mitigate floods in practice. The research findings could assist policymakers and stakeholders in formulating a long-term vision to propose an efficient urban stormwater management strategy in TDC and similar growing cities.

Analyzing the Urban Heat Island in Ho Chi Minh City in Relation to Urban Planning Processes from 2010 to 2020

This study aims to analyze the variation of land surface temperature (LST) and its effect on the urban heat island (UHI) in relation to the urban planning processes of Ho Chi Minh City (HCMC) from the years 2010 to 2020. These changes have impacted the urban environmental conditions and the city development. The study used the time series of Landsat 5 TM and Landsat 8 OLI acquired for the years 2010, 2015, and 2020, and used the algorithms for calculating the land surface temperature (LST), the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) to investigate the relationships between these phenomena in the city planning processes. The results highlight the increase in land surface temperature was related to the dense of impervious surface area and the decrease of vegetation area. The UHI zones occurred in the areas with high LST and NDBI values, and low NDVI values (concentrated in the central districts and the planned areas) compared to the surrounding areas with lower LST and NDBI and higher NDVI. The recommend planning solutions for the new urban development areas should focus on increasing the amount green areas to combat the UHI phenomenon and environmental problems against the development.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Emissions and fate of organophosphate esters in outdoor urban environments

Cities are drivers of the global economy, containing products and industries that emit many chemicals. Here, we use the Multimedia Urban Model (MUM) to estimate atmospheric emissions and fate of organophosphate esters (OPEs) from 19 global mega or major cities, finding that they collectively emitted ~81,000 kg yr−1 of ∑10OPEs in 2018. Typically, polar “mobile” compounds tend to partition to and be advected by water, while non-polar “bioaccumulative” chemicals do not. Depending on the built environment and climate of the city considered, the same compound behaves like either a mobile or a bioaccumulative chemical. Cities with large impervious surface areas, such as Kolkata, mobilize even bioaccumulative contaminants to aquatic ecosystems. By contrast, cities with large areas of vegetation fix and transform contaminants, reducing loadings to aquatic ecosystems. Our results therefore suggest that urban design choices could support policies aimed at reducing chemical releases to the broader environment without increasing exposure for urban residents.

Spatiotemporal Analysis of Urban Expansion in the Mountainous Hindu Kush Himalayas Region

As a major human activity, urbanization exerts a strong impact on the fragile ecosystem in the Hindu Kush Himalayas (HKH) region. To maintain sustainable development, reliable data on urban land change are required to assess the impact of urbanization. Here, the reliability evaluation of four global fine-resolution impervious surface area (ISA) products: global annual impervious area (GAIA), global annual urban dynamics (GAUD), global impervious surface area (GISA), and global urban expansion (GUE) was carried out. The characteristics of urban expansion for five representative cities including Kabul, Lhasa, Lijiang, Thimphu, and Xining were remarkably different. Based on the results of incremental analysis and the spatial difference of the ISA, it was found that the GAIA dataset at a 30-m spatial resolution could provide better ISA information than the others in characterizing urban expansion in the mountainous region. Subsequently, the changes in the urban area were analyzed using the GAIA dataset from 1993 to 2018. In general, human settlements had grown, with the transformation of small villages into larger towns and some towns into major cities. Urban expansion would continuously intensify the contradictions between human activity and sustainability and exert a more significant impact on the fragile ecosystem in the HKH region. More attention should be paid to the impact of urbanization on the fragile mountainous ecosystem.

The Relationship between Socioeconomic Factors at Different Administrative Levels and Forest Fire Occurrence Density Using a Multilevel Model

Wildfires are among the main factors that disturb terrestrial ecosystems, landscapes, and the environment. Understanding the differences that the socioeconomic factors of different administrative levels have on fire occurrence remains critical to inform the driving function of ignition sources. In this study, we collected socioeconomic and land use data for 21 cities and 81 counties in the study area from 2001 to 2019 and applied a multilevel model to explore the relationship between wildfire occurrence density and the driving factors. We estimated the fixed and random effect of the factors at different levels and built three hierarchical linear models (HLMs) to quantify the impacts of socioeconomic drivers on wildfires. The results showed that the variance among cities contributed to 14.01% of the unexplained variation of random effects at the county level. At the county level, the densities of middle school student populations, gross domestic product (GDP), and impervious surface areas were significantly positively correlated with fire occurrence density. At the city level, GDP and its interaction with county-level factors were significantly negatively correlated with fire occurrence density. This study provides a new method and findings for the research of wildfire occurrence and risk.

Automatic Extraction of Urban Impervious Surface Based on SAH-Unet

Increases in the area of impervious surfaces have occurred with urbanization. Such surfaces are an important indicator of urban expansion and the natural environment. The automatic extraction of impervious surface data can provide useful information for urban and regional management and planning and can contribute to the realization of the United Nations Sustainable Development Goal 11—Sustainable Cities and Communities. This paper uses Google Earth Engine (GEE) high-resolution remote sensing images and OpenStreetMap (OSM) data for Chengdu, a typical city in China, to establish an impervious surface dataset for deep learning. To improve the extraction accuracy, the Small Attention Hybrid Unet (SAH-Unet) model is proposed. It is based on the Unet architecture but with attention modules and a multi-scale feature fusion mechanism. Finally, depthwise-separable convolutions are used to reduce the number of model parameters. The results show that, compared with other classical semantic segmentation networks, the SAH-Unet network has superior precision and accuracy. The final scores on the test set were as follows: Accuracy = 0.9159, MIOU = 0.8467, F-score = 0.9117, Recall = 0.9199, Precision = 0.9042. This study provides support for urban sustainable development by improving the extraction of impervious surface information from remote sensing images.

Urban-rural disparities of carbon storage dynamics in China's human settlements driven by population and economic growth

China has experienced a rapid expansion of human settlement in both urban and rural areas over the past three decades. Regarding the impacts on carbon storage, previous studies that only focus on certain ecosystems cannot reflect urban-rural disparities, resulting in the carbon storage changes in human settlement remaining unknown. In this study, we aimed to explore China's urban-rural disparities in human settlement expansion and direct impacts on carbon storage by using the big Earth data technology. The results showed that from 1990 to 2018, the total amount of China's human settlement expansion reached 175,703.80 km2, and the inner-city, peri-urban, and rural components accounted for 21.00 %, 20.18 %, and 58.82 %, respectively. Along with the general tendency of impervious surface area (ISA) growth, there was more soil organic carbon (SOC) (1254.33 TgC) being sealed beneath ISA (0–100 cm depth), compared to a huge reduction in vegetation biomass carbon (VBC) (91.44 TgC) during the study period. The results further indicated that the change density of either VBC or SOC presented a slightly rising trend along the urban-rural gradient, due to the increasingly common encroachment on vegetation and soil types with higher carbon content. We also found that socioeconomic drivers had a greater influence in urban areas than rural areas, and the related correlation exhibited a descending trajectory in both urban and rural areas. There is thus an urgent need to preserve lands with abundant carbon storage and contain the waste of land resources in rural areas. All stakeholders should pay more attention to concerted and targeted regulation policies for well-planned and eco-friendly human settlement expansion such as enhancing rural land use efficiency and promoting large-scale afforestation and continuous urban greening, which will be critical not only for guiding sustainable urbanization all over China but also for mitigating climate change for the entire world.

Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis

The two prerequisites for monitoring SDG11.A “support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning” are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban–rural continuum for five typical urban agglomerations in China, including the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu–Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A.

Effectiveness of Design and Implementation Alternatives for Stormwater Control Measures Modeled at the Watershed Scale.

To evaluate the effectiveness of dispersed stormwater control measures (SCMs), it is important to consider groundwater-surface water interactions and their consequences for stream hydrologic responses relevant to channel geomorphic stability and ecology. This study aimed to evaluate the effectiveness of different SCM design scenarios and implementation alternatives on exceedance levels and volumes of streamflow at the watershed scale. For this purpose, a process-based block-connector model of Sligo Creek, an urban watershed (29 km2) in the suburbs of Washington, DC, was used to study the effects of SCM system design on the stream hydrograph. The watershed has 34% impervious area (IA), which was discretized into 14 similar-sized subwatersheds, each consisting of pervious and impervious surface areas. Each subwatershed was compartmentalized with the representative overland flow, unsaturated flow, groundwater blocks, and links to main channel segments. The model was calibrated and validated to existing conditions using a 3-year time series of observed flow data. Afterward, a predevelopment simulation was configured. Three SCM unit designs and IA diversions through the SCM retrofit system were simulated. The three unit design scenarios represented a simple pond with surface storage and overflow or SCMs that infiltrate with an engineered soil layer and with or without an underdrain pipe. Differences among the model simulations were evaluated using flow exceedance probability curves. The area of the SCM system was modeled as 5% of the IA retrofit. Three implementation levels, including 10%, 50%, and 90% of the IA diverted through SCMs, were considered for each SCM unit design. The results showed that at least a 50% retrofit of runoff from IA watershedwide would be needed to achieve similar predevelopment base flows and peak flows. Intermediate flows could not be matched but were closest for the infiltration with the underdrain pipe design scenario. It was also found that concentrating the SCMs in the lower portion of the watershed resulted in more effectively achieving the predeveloped exceedance curves than uniform SCM implementation. The results are relevant to planning-level decisions that depend on effectiveness predictions of different SCM unit designs and implementation alternatives in developed watersheds.