Urban Built-up Areas Research Articles

Analysis of the Spatiotemporal Evolution and Driving Mechanisms of Impervious Surfaces along the Jiaozhou Bay (China) Coast over the Past Four Decades

Impervious surfaces serve as critical indicators for monitoring urbanization processes and assessing urban ecological conditions. The precise extraction and analysis of the spatiotemporal variations in impervious surfaces are essential for informing urban planning strategies. The unique location advantage of Jiaozhou Bay makes it an important urban gathering area. Based on Landsat remote sensing image data, the extraction effect and accuracy of urban built-up area index, biophysical index, and random forest classification were compared and analyzed. Then, the optimal random forest method was used to extract impervious water information from 8 Landsat satellite images of the coastal area of Jiaozhou Bay from 1986 to 2022. Over the past four decades, the impervious surface area in the Jiaozhou Bay coastal region has expanded dramatically from 71.53 km2 in 1986 to 1049.16 km2 in 2022, with the most significant increase, nearly doubling, occurring between 2011 and 2017. Spatially, the distribution of impervious surfaces has expanded progressively from coastal to inland areas and from central to peripheral zones, particularly toward the southwest in Huangdao District and Jiaozhou City. The distribution of impervious surfaces in the Jiaozhou Bay coastal area is primarily confined to flat and gently sloping nearshore regions due to natural constraints like terrain slope. Concurrently, policy initiatives, along with population and economic growth, have catalyzed the rapid expansion of these surfaces. These insights are invaluable for comprehending the urban spatiotemporal dynamics and patterns along the Jiaozhou Bay coast and offer fresh perspectives for research into urban transformations and the sustainable development of ecological environments in other coastal regions.

EVALUATING THE TIME AND COST EFFICIENCY OF PUBLIC TRANSPORT SYSTEM IN DELHI

The National Capital Region of Delhi is ranked third as the largest built-up urban area in the world with a population density of about 14000 people per square kilometer. A complex system of public transit, paratransit and non-motorized transit system with last-mile connectivity is integral for such large areas to perform efficiently and promote sustainable transportation systems.To understand these urban processes and related space-syntax, it is crucial to understand which mode of public transport is preferred by the user and why. The user choices for a particular mode of transportation are primarily dependent on the time-cost relation of the mode. The present paper evaluates the efficiency of four modes of public and para-transit transport systems in the city of Delhi. The key modes identified are a combination of metro, bus, auto and taxi systems. The data on travel time and travel costs is collected and compared for the three selected routes. This shall be done by comparing four modes on three routes to generate 12 permutations. The selected routes have varying origin and same destination with route length of 20 kms, 12 kms and 7.1 kms respectively. The efficiency of the transportation system is evaluated on 9 parameters - velocity, Cost of vehicle & infrastructure, Travel Cost, Travel Time, Capacity, Flexibility, Aesthetics, Comfort and Maintenance. The research identifies the travel distance and settlement density to be critical elements in the user decision-making process and hence are key parameters to be considered in planning for public transportation in complex metropolitan cities.

Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

Improving street jogging environments can promote urban health and sustainability, especially within urban built-up areas. Public attention to the environment during jogging has not been sufficiently studied, and the recommended route data from the fitness app offers an opportunity for multiscale spatial exploration. Initially, by analyzing the recommendation reason text, the study identified 22 environmental factors and proposed the Environmental Factor Public Attention Intensity (EFPAI) for quantification, with subsequent comparisons to expert opinions. Further, by analyzing five types of data from the recommended routes, the relationships between EFPAI and jogging frequency, route shape, spatial distribution, and seasonal variations were uncovered. The study revealed: (1) discrepancies between expert opinions and public attention, with experts more likely to overlook factors such as slope, visual quality, width, and acoustic quality; (2) the public tends to notice factors like pedestrians, green spaces, waterfront spaces, landscapes, culture, sports facilities, and accessibility on routes with high jogging frequency; (3) EFPAI for traffic infrastructure, waterfront spaces, landscapes, residential areas, and campuses is associated with route shape; (4) EFPAI of certain environmental factors is influenced by urban geographical spatial factors, and substantial differences exist between different cities. Specific recommendations were provided from three different scales—site scale, street block scale, and urban scale—to guide the resolution of issues in planning and decision-making processes for urban renewal.

Urban biodiversity conservation: A framework for ecological network construction and priority areas identification considering habit differences within species

The construction of ecological networks within the context of urbanization is an effective approach to cope with the challenges of urban biodiversity decline, representing a crucial goal in urban planning and development. However, existing studies often overlook the richness and uniqueness within species communities by homogenizing traits of species in the same class. This study proposes a framework for constructing and optimizing ecological networks focused on differential conservation within the same class. By classifying birds into three groups (specialists of water, forest or urban areas) based on their ecological requirements and urbanization tolerance, we constructed an ecological network tailored to their distinct migratory dispersal patterns. We then identified strategic areas including pinch points, barriers, and breakpoints specific to each bird group. Our findings reveal notable variations in suitable habitat distribution among different bird groups in urban environments. Corridor layouts varied according to habitat preferences and migratory dispersal patterns. Despite these differences, urban built-up areas persist as central hubs for the distribution of suitable habitats for 75% of bird species, with peripheral mountain-plain transition areas constituting 63% of crucial dispersal corridors. This emphasizes the critical role of urban built-up areas in maintaining biodiversity and ecological connectivity. Prioritizing connectivity between central urban areas and distant natural spaces is imperative. Our approach innovatively classifies and constructs networks to identify strategic areas with diverse species-specific attributes, providing valuable spatial information for land planning and guiding solutions to enhance target species. While the primary focus is on bird conservation in Beijing, our framework is broadly applicable to global biodiversity management and green planning under urbanization challenges. Overall, this study offers innovative insights for urban planning development and serves as decision support for prioritizing urban actions.

Exploring the Differentiated Impact of Urban Spatial Form on Carbon Emissions: Evidence from Chinese Cities

The role of spatial factors in reducing carbon emissions has been receiving increasing attention from researchers; however, these impacts may involve spatial heterogeneity. In this study, 337 prefecture-level cities in China were taken as the research object. Based on national-level urban data, the global impact of urban spatial form on carbon emissions was then investigated using ordinary least squares regression, the spatial error model, and the spatial lag model. The local effects of urban spatial form on carbon emissions in different cities were then investigated using geographically weighted regression. The findings are as follows. Overall, the larger the urban built-up area and the more fragmented and decentralized the urban land use, the greater the carbon emissions. Conversely, the more centralized the urban center of a city, the lower its carbon emissions. Locally, for some Chinese cities, the total area, landscape shape index, and mean Euclidean nearest-neighbor distance were found to have significant positive effects on carbon emissions, while the largest-patch index had a significant negative impact. For all Chinese cities, the patch density was found to have no significant effect on carbon emissions. In 29% of the cities in which the landscape division index was found to significantly affect carbon emissions, this effect was positive, while it was negative in the remaining 71%. The policy implications emerging from this study lie in the need for decision-makers and urban planners to guide the shaping of low-carbon urban spatial forms.

The Synergistic Effect of Urban Economic, Social and Space Factors on Residential Carbon Emissions: A Case Study on Provincial Capitals in China

Within the context of China’s commitment to carbon reduction goals, particularly in urban areas, addressing carbon emissions stemming from residents’ travel activities assumes paramount significance. Drawing upon established theoretical frameworks, this study advances several hypotheses delineating the determinants of low-carbon behaviors among urban residents. It analyzes panel data from 30 provincial capitals in China using a time–individual dual fixed effects model. This study empirically scrutinizes the posited theoretical model, aiming to elucidate the factors shaping urban residents’ low-carbon behavioral patterns and provide a decision-making basis for low-carbon construction and management of urban space. The findings underscore several notable associations. The disposable income, population density, and urban built-up areas exhibit significant positive correlations with carbon emissions among residents. Conversely, the urban gross domestic product (GDP) displays a significant negative correlation with carbon emissions. Furthermore, a positive correlation is discerned between the expanse of green spaces and the per capita carbon emissions intensity, while the availability of subway systems exhibits a negative correlation with both the per capita public green space area and the carbon emissions intensity. Notably, the configuration intensity of urban bus systems manifests an inverted U-shaped relationship with residents’ carbon emissions intensity. Specifically, within a certain threshold, an escalation in the bus equipment intensity coincides with heightened carbon emission intensity; however, beyond this threshold, a notable reduction in the per capita carbon emissions intensity ensues. Additionally, a U-shaped relationship is observed between the number of urban parks and residents’ carbon emissions intensity, indicating that an increase in parks may not necessarily contribute to carbon reduction efforts. Moreover, a discernible synergy is observed among various factors influencing carbon reduction efforts. These factors encompass residents’ education levels and disposable incomes, the presence of subway and regular public transportation systems, urban land utilization scales, economic development levels, green space provisions, public transportation infrastructure, population densities, and land equilibrium. This interplay underscores the interconnectedness and interdependence of diverse variables in shaping strategies for mitigating carbon emissions within urban contexts.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Potential and Influencing Factors of Urban Spatial Development under Natural Constraints: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA.

Land Use/Cover Dynamics and its Implication on the Sustainability of Urban Agriculture in Selected Urban Centers of Ethiopia

Abstract Analysis of urban Land Use/Land Cover (LULC) dynamics in light of urban agriculture (UA) helps to understand its implication for UA practice and in turn making the necessary interventions. Therefore, the main objective of this study was to examine the LULC dynamics in selected urban centers of Ethiopia (Addis Ababa, Bahir Dar, Adama, and Hawassa cities) for the last seventeen years (2006–2022). SPOT 5 satellite imagery for the year 2006 and 2016 and Sentinel image for the year 2022 were analyzed. In addition, data from key informant interviews, focus group discussions, and field observations were used to triangulate LULC analyses information and to identify the main causes of LULC dynamics in the studied urban centers. The findings reveled that there were rapid expansion of urban built-up areas at the expense of other urban LULC types mainly of peri urban horticultural lands for the last 17 years (2006‒2022) in Addis Ababa, Hawassa, Bahir Dar, and Adama cities of Ethiopia. This has adverse impact on the sustainability of UA. Rapid urban population growth mainly because of high rural urban migration, expansion of squatter settlement, and increment of investment were the main driving forces of LULC dynamics. Based on the findings of this study (changes in LULC and driving factors) the studied cities administrations or authorities need to develop sustainable development plans by considering UA.

Spatial heterogeneity of the effects of river network patterns on water quality in highly urbanized city

River water quality deterioration is a serious problem in urban water environments. River network patterns affect water quality by influencing the flow, mixing, and other processes of water bodies. However, the effects of urban river network patterns on water quality remain poorly understood, thereby hindering the urban planning and management decision-making process. In this study, the geographically weighted regression (GWR) model was used to explore the spatial heterogeneity of the relationship between river network pattern and water quality. The results showed that the river network has a complex structure, high connectivity, and relatively even distribution and morphology. Important river structure indicators affecting water quality included the water surface ratio (Wp) and multifractal features (∆α, ∆f) while important river connectivity indicators included circuitry (α) and network connectivity (γ). River structure has a more complex effect on water quality than connectivity. This study recommends that the Wp should be increased in agricultural areas and appropriately reduced in urban built-up areas, and the number of river segments and nodes should be controlled within a rational configuration. Our study provides key insights for evaluating and optimizing the river network patterns to improve water quality of urban rivers. In the future, the land use intensity, hydrological processes, and human activities should be coupled with the river network pattern to deepen our understanding of urban river environment.

A Multi-Temporal Analysis on the Dynamics of the Impact of Land Use and Land Cover on NO2 and CO Emissions in Argentina for Sustainable Environmental Management

This study presents an analysis of NO2 and CO emissions in Argentina, utilizing remote sensing data. This research aims to determine the spatiotemporal distribution of NO2 and CO emissions from 2019 to 2021. It examines the influence of land use and cover on NO2 and CO emissions using various climatic, anthropic, and natural indicators. The year with the highest CO and NO2 concentration was 2020. NO2 exhibited the highest concentrations in built-up urban areas and croplands, notably impacting the capital city and the northern region of Buenos Aires province. Also, CO concentration was influenced by anthropic variable distances to national route, mining extraction, power plants, airports, and urban index (UI). They were also influenced by climatic and natural variables (Palmer drought index, vapor pressure, maximum environment temperature, wind speed, DEM, humidity, and normalized difference vegetation index (NDVI)) for the different uses and land covers. NO2 concentrations were influenced by anthropic (distance to airports, service stations, open dumpsites, power plants, and factories), climatic, and natural variables (Palmer drought index, vapor pressure, wind speed, and DEM) for the different uses and land cover. This research supports sustainable environmental management by guiding the development of effective emission mitigation strategies for improved community health and well-being.

Assessment and Monitoring of Local Climate Regulation in Cities by Green Infrastructure—A National Ecosystem Service Indicator for Germany

In densely built-up urban areas, green spaces such as gardens, parks, forests and water bodies can greatly enhance the quality of life for local residents and promote human health. These areas mitigate heat stress and the urban heat island effect to create a balanced local climate. To quantify the ecosystem service of “urban climate regulation” provided by urban green infrastructure, we developed a national indicator for specific measurement and monitoring. This indicator captures both the supply of climate-regulating services by urban green spaces and the demand for this service from the residential population. Using nationwide geodata, a cooling capacity value can be calculated that reflects the tree canopy, soil cover, sizes of green area and site characteristics. This cooling capacity value is then related to the affected residential population in the neighbourhood. Our analysis indicates that 76% of the population in the 165 case cities in Germany enjoy high or very high cooling capacities in their immediate living environment. In 37 cities, over 85% of the population benefits from good or very good cooling capacity provided by green space. The proposed indicator enables a comparison of the cooling service of urban green infrastructure and offers a sound basis for spatial planning and decision-making in urban areas.

Local and landscape factors shape alpha and beta trophic interaction diversity in urban gardens.

Promoting urban green spaces is an effective strategy to increase biodiversity in cities. However, our understanding of how local and landscape factors influence trophic interactions in these urban contexts remains limited. Here, we sampled cavity-nesting bees and wasps and their natural enemies within 85 urban gardens in Zurich (Switzerland) to identify factors associated with the diversity and dissimilarity of antagonistic interactions in these communities. The proportions of built-up area and urban green area at small landscape scales (50 m radius), as well as the management intensity, sun exposure, plant richness and proportion of agricultural land at the landscape scale (250 m radius), were key drivers of interaction diversity. This increased interaction diversity resulted not only from the higher richness of host and natural enemy species, but also from species participating in more interactions. Furthermore, dissimilarity in community structure and interactions across gardens (beta-diversity) were primarily influenced by differences in built-up areas and urban green areas at the landscape scale, as well as by management intensity. Our study offers crucial insights for urban planning and conservation strategies, supporting sustainability goals by helping to understand the factors that shape insect communities and their trophic interactions in urban gardens.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Assessing the Lifecycle Environmental Resilience of Urban Green Infrastructures Coping with Acute Disturbances and Chronic Stresses

Urban green infrastructure (UGI), a key component of nature-based solutions (NbSs), plays a vital role in enhancing urban resilience. Nonetheless, the absence of a thorough resilience evaluation for UGI has hindered the efficacy of its design and implementation. This article proposes an innovative urban environmental resilience index (ERI) framework designed to evaluate the lifecycle performance of UGI. First, a coupled environmental resilience evaluation system is proposed that encompasses indicators for the adaptation to acute disturbances and the mitigation of chronic pressures. Second, the inventive formulas for calculating the environmental resilience index are presented, which establish the weighting of indicators through Delphi-analytic hierarchy process (AHP) analysis, and the Storm Water Management Model (SWMM), GaBi, and i-Tree models are employed for the quantitative assessment. Third, four representative UGI scenarios in urban built-up areas have been selected for comparative analysis and in-depth discussion by calculating the resilience index. This research presents UGI solutions as adaptive measures for “Black Swan” events and “Gray Rhino” phenomena, offering significant case studies and methodological frameworks which will inform future endeavours in green and sustainable urban development.

A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced.

Evolution of urban land and population system coupling micro–dynamics and macro-stability: Trends and paths

Cities are constantly dynamics, but emerge the stability of urban systems as evidenced by robust urban scaling. Both properties serve as necessary perspectives for understanding the evolution of cities and the complexity of urban systems. However, the evolution trends and mechanisms behind the above relationships, particularly concerning geographic processes, remain to be explored in depth. In this study, the dynamic fluctuations within individual cities and the macro-stability of the evolution of urban systems over the past two decades are systematically demonstrated. Subsequently, associations between urban systems and individual cities are established on the basis of micro- and macro-dynamic mechanisms. The trajectory of individual cities can be divided into scaled–driven growth (SG) and urban excess growth (EG). The former is the result of scaling effects within the system, while the latter is affected by global external effects. Taking as examples urban built-up area and social productions, the evolution pathways that vary from different attributes are analyzed. The results indicate that urban built-up area is more affected by scaling effects whereas social productions is driven more by global external effects. Moreover, we examine the driving forces and mechanism of EG from the aspects of economic development level, technological and economic progress, industrial structure and regional policy through an empirical analysis. This study provides a nuanced understanding of the trends and pathways of urban land–population system from the perspective of complex urban systems and holism to deepen the understanding for general properties of cities and co-evolution for urban population size and land expansion.

GDP Spatialization in City of Zhengzhou Based on NPP/VIIRS Night-time Light and Socioeconomic Statistical Data Using Machine Learning

This article introduces a comprehensive methodology for mapping and assessing the urban built-up areas and establishing a spatial gross domestic product (GDP) model for Zhengzhou using night-time light (NTL) data, alongside socioeconomic statistical data from 2012 to 2017. Two supervised sorting algorithms, namely the support vector machine (SVM) algorithm and the deep learning (DL) algorithm, which includes the U-Net and fully convolutional neural (FCN) network models, are proposed for urban built-up area identification and image classification. Comparisons with Municipal Bureau of Statistics data highlight the U-Net neural network model exhibits superior accuracy, especially in areas with diverse characteristics. For each year from 2012 to 2017, a spatial GDP model was developed based on Zhengzhou's urban GDP and U-Net sorted images. This research provides valuable insights into urban development and economic assessment for the city.

Evaluation Model of Distributed Photovoltaic Utilization in Urban Built-Up Area

Photovoltaic (PV) power generation is emerging as a key aspect of the global shift towards a more sustainable energy mix. Nevertheless, existing assessment models predominantly concentrate on predicting the overall capacity of PV power generation, often neglecting temporal dynamics. Drawing upon the urban energy substitution rate, utilization rate, and power supply stability, this study has devised a comprehensive evaluation model for the utilization of distributed photovoltaic systems (SUS). This model integrates the quantification of spatio-temporal features inherent in urban settings and buildings. Using Hohhot city as a case study, this study conducted simulations to analyze how the installation of PV systems affects the electricity consumption patterns across different land plots within the urban core. The study additionally examines how urban planning influences the adoption of PV power, taking into account both the timing of PV power usage and the stage of PV technology development. The evaluation model surpasses the constraints of current urban PV assessments, which primarily emphasize enhancing power generation potential without adequately quantifying supply–demand dynamics or spatial and temporal variations. This breakthrough significantly improves the precision and reliability of assessing the efficiency of distributed PV systems. Its implications extend widely to subsequent comprehensive evaluations of urban PV applications.

Fast urban inundation simulation with RIM2D for flood risk assessment and forecasting

IntroductionUrban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge.MethodsThis study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas.Results and discussionWe show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.