Urban Change Research Articles

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of China traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people’s awareness of the importance of local culture, awakens people’s sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in China, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Urban green space availability and change patterns (a case study of Tbilisi, Georgia)

Aim: This research aimed to determine the distribution of different types of Urban green spaces (UGS) in Tbilisi districts, the distribution of access zones, the number of people who have access to UGSs, how current active housing construction affects UGSs and where reduction hotspots are located. Methodology: A 500-m buffer was set around UGSs. UGS changes were detected based on the NDVI from the 2017 and 2023 (June-August) PlanetScope imagery. The NDVI classification thresholds were -1 to 0.19 (nonvegetation), 0.19 to 0.5 (shrubs), and 0.5 or greater (trees). Thereafter, UGS patterns and reduction hotspots were determined in the district. Results: The percentage of access zones was lowest for Didube (5.4%) and highest for Nadzaladevi (14.7%). Chughureti (8.3%), Mtatsminda (8.8%), Isani (9.3%), and Gldani (9.9%) had similar numbers. The worst situation was observed in Samgori (8.9%) whereas the best situation was in Didube (69.8%). UGS degradation was high in Saburtalo (35.3%), and only a 0.6% decrease was found in Chughureti. Interpretation: UGSs are better distributed spatially and available to more people in "old" districts (Mtatsminda, Chughureti) and territories that were developed in the 1970s. The urban green spaces were neglected by contemporary urban sprawl. Though legislation has helped to preserve urban green spaces with status, but locations without status are victims of active development. Key words: Hotspots, Image analysis, Land use and land cover, Normalized difference vegetation index, Tbilisi, Urban green spaces

Examination of Urban Transformation Implementation Legislation: Elazığ Example

In this study, it was aimed to examine and reveal the general results of the models applied in urban transformation works in Elazığ province after the 6.8 Mw Sivrice earthquake on January 24, 2020. The population of the research, Elazığ province sample, is Karşıyaka, Cumhuriyet, Abdullahpaşa, Mustafapaşa, Rüstempaşa, Sürsürü, Kızılay and Sivrice Country Gölbaşı Neighborhoods. The study is qualitative research in the form of a field study in terms of revealing the results of urban transformation implementation at the sample points. In this context, observation technique, interview technique, and documentary scanning technique were used in the descriptive analysis. General physical-structural characteristics of buildings in the observation technique; in the interview technique, 100 building owners and users' opinions about the buildings; In the documentary scanning technique, the reflections of literature information in practice are interpreted as a whole. In comparing the literature information, project technical documents obtained from the Ministry of Environment, Urbanization and Climate Change and Elazığ Municipality were used. As a result, the model to be chosen in the urban transformation process is an important parameter in the success of the application and reaching the desired goal. More qualified urban transformation applications will be achieved by reducing the problems that arise in public transformation projects, especially in determining the legislation, and by choosing the appropriate model for comprehensive transformation.

Effective Storm Water Management in The Urban Area – A Case Study

Urbanization and climate change have negative effects on the changes of natural precipitation and runoff regime, which results with more frequent floods and landslides. Besides of the increased quantity of the stormwater that drainage channels have to collect, neg- ative consequences of urbanization are also evident through increased pollution of runoff stormwater. Stormwater has immense impacts on urban flooding and water quality, leaving the marginalized and the impoverished disproportionately impacted by and vulnerable to stormwater hazards. However, the environmental health concerns of socially and econom- ically marginalized individuals are largely underestimated. Therefore stormwater manage- ment should be adapted to these changes so that the negative effects of new hydrological conditions of precipitation and runoff and stormwater pollution in the urban areas are mitigated. By 2050 the world population will grow to about 9 billion contributing to deep changes in urban areas structure. This would increase the effect of water deficiency and along with projected climate changes the impact of urban flooding, urban heat islands or drought. Stormwater threats can be mitigated with the application of stormwater management meth- ods named differently such as ‘Sustainable (Urban) Drainage Systems’ (SUDS), ‘Low Impact Development’ (LID) or ‘Best Management Practices’ (BMPs). This method includes differ- ent techniques such as Green roof, Rain barrels and cisterns, Infiltration basin, Detention Ponds, Retention Ponds, Green walls, Porous pavements, Bioretention basins, Bioswales. The aim of the research includes a study of the effectiveness of urban stormwater manage- ment techniques for the case study area. The concept of urban stormwater management techniques refers to the ability to provide and manage this primary resource in quantitative and qualitative terms in order to satisfy the future needs of population. The storm wa- ter management techniques provides several benefits: mitigation of the urban heat island effects, reduction of air pollution, additionally, It induces important hydraulic advantages acting as an effective tool for reducing flood risk in urban area with runoff reduction, attenuation and delay of the peak flow. The results confirm that bioretention basin, Permeable pavement, Grassed swale, Infiltration basin are effective techniques for the reduction of total runoff volume of rainwater falling on their area.

Spatiotemporal dynamic relationships and simulation of urban spatial form changes and land surface temperature: a case study in Chengdu, China.

Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space-Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

Automated Geospatial Approach for Assessing SDG Indicator 11.3.1: A Multi-Level Evaluation of Urban Land Use Expansion across Africa

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries.

Unraveling Dengue Virus Diversity in Asia: An Epidemiological Study through Genetic Sequences and Phylogenetic Analysis.

Dengue virus (DENV) is the causative agent of dengue. Although most infected individuals are asymptomatic or present with only mild symptoms, severe manifestations could potentially devastate human populations in tropical and subtropical regions. In hyperendemic regions such as South Asia and Southeast Asia (SEA), all four DENV serotypes (DENV-1, DENV-2, DENV-3, and DENV-4) have been prevalent for several decades. Each DENV serotype is further divided into multiple genotypes, reflecting the extensive diversity of DENV. Historically, specific DENV genotypes were associated with particular geographical distributions within endemic regions. However, this epidemiological pattern has changed due to urbanization, globalization, and climate change. This review comprehensively traces the historical and recent genetic epidemiology of DENV in Asia from the first time DENV was identified in the 1950s to the present. We analyzed envelope sequences from a database covering 16 endemic countries across three distinct geographic regions in Asia. These countries included Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka from South Asia; Cambodia, Laos, Myanmar, Thailand, and Vietnam from Mainland SEA; and Indonesia, the Philippines, Malaysia, and Singapore from Maritime SEA. Additionally, we describe the phylogenetic relationships among DENV genotypes within each serotype, along with their geographic distribution, to enhance the understanding of DENV dynamics.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73°C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50°C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Fuzzy evaluation and obstacle factors of urban ecological health changes in the Wei River Basin, northwest China

BackgroundUrban ecological health is crucial for the long-term sustainable development of watershed. Accurately evaluating the health level of the ecological environment helps to develop reasonable strategies for ecological environment restoration and resource management. This paper constructed a comprehensive evaluation index system based on the Pressure-State-Response (PSR) framework and evaluated the ecological health of eleven administrative regions in the Wei River Basin (WRB), northwest China in 1980, 2000, and 2020 using an evaluation model established by fuzzy mathematics. Further, obstacle degrees were used to quantify the contribution of pressure, state, and response modules, as well as individual indicators to ecological health.ResultsThe comprehensive evaluation system constructed based on the PSR framework could effectively reflect the ecological health conditions of different regions in the WRB. During the study period, the ecological health went through a process of first deterioration and then improvement. By 2020, the ecological health of seven administrative regions reached healthy levels. The state module was the main obstacle module of the PSR framework to the ecological health of the most regions. The population density (P1), patch density of construction land (S5), comprehensive elasticity index (S8), soil erosion index (R1), and per capital GDP (R3) were the most crucial individual indicators affecting the ecological health. For different cities, the main obstacle factors varied. In economically developed cities, the limiting effect of P1 was more significant, while in economically underdeveloped cities, the limiting effect of R3 was stronger.ConclusionsIn response to the special natural environment and socio-economic conditions of arid and semi-arid areas in the WRB, an ecological health evaluation index system suitable for the characteristics of the basin was constructed. The results indicated that, to improve the levels of urban ecological health, it is necessary to restore the natural ecological environment and control population size while accelerating economic construction. Our results can provide scientific support for the ecological health evaluation and protection of the WRB and even the arid and semi-arid areas in northwest China.

A thermosurvey dataset: Older adults’ experiences and adaptation to urban heat and climate change

We introduce the thermosurvey dataset, a comprehensive collection focusing on the thermal comfort, heat-related experiences, health, socioeconomic status, and perceptions of older adults (aged 65 and over) in Warsaw and Madrid. The two cities differ greatly in their heat experiences, but due to climate change, both face increasing temperatures. The study aimed to understand how heat affects cities’ older adult population and how we can better adapt to rising temperatures. We call the study a thermosurvey because it connects traditional survey data with temperature and humidity measurements done before, after and during the interview, offering a holistic view of the participants’ thermal environments. The dataset can be used to better understand thermal comfort, the interplay of health and heat experiences, and the relationship between experiences and climate change views. We hope our data will enable scholars to analyse the impact of climate change on older adults and to develop strategies to help them adapt to a warming climate.

HashGAT-VCA: A vector cellular automata model with hash function and graph attention network for urban land-use change simulation

Vector cellular automata (VCA) models, which excel at representing spatiotemporal dynamics of irregularly shaped land parcels, have been widely employed in land use change simulations. However, current research faces the following issues: (1) most VCA models neglect the spatial heterogeneity of driving factors within each land parcel when evaluating the environmental driving effects; (2) when calculating the neighborhood effects, simple statistics of land use types in neighboring parcels are often used, overlooking the influence of driving factors within neighboring parcels; (3) the ability to explore the interactions between land parcels is often limited. To address the aforementioned issues, this study proposes a HashGAT-VCA model for investigating urban land use changes. The model utilizes a Hash function to encode the non-uniform distribution of each driving factor within each irregularly shaped land parcel into a fixed-length vector, and constructs a graph structure between land parcels based on their spatial topological relationships. By employing a Graph Attention Network (GAT), the model explores the mechanisms of environmental driving effects and inter-parcel interactions to calculate the probability of land use change for each parcel. The proposed HashGAT-VCA model was applied to simulate urban land use changes in Shenzhen, China, from 2009 to 2012. Compared to other VCA models, the HashGAT-VCA demonstrated higher simulation accuracy. The results indicated that HashGAT-VCA can effectively capture the impacts of the heterogeneously distributed driving factors and the interactions between land parcels on land use changes. Additionally, this study simulated land use patterns for the years 2025 and 2030 under ecological control strategies, providing decision support for urban planning.

Forecasting urban temperatures through crowdsourced data from Citizen Weather Stations

This work explores the potential of using crowdsourced data from Citizen Weather Stations (CWS) to forecast urban temperatures. Five case studies were selected (Madrid, London, Rome, Paris and Berlin), using data from the manufacturer Netatmo to gather the hourly temperatures of the years 2021 and 2022 for 776 CWS. A quality-control process was implemented to enhance data accuracy, and heat maps were generated. Multiple linear regression models were created for each CWS and month using only reference weather data as input, resulting in the development of accurate models (RMSE lower than 1.5 °C) for an average of 374 CWS. Then, reference weather predictions were used on the 2nd of April 2023 to forecast the hourly temperatures of those CWS for the period from the 3rd to the 9th of April (168 h). The outcomes showed that accurate CWS data and precise prediction models for the reference weather are crucial to improve the accuracy of the forecasts using crowdsourced data. The study demonstrates the potential benefits of using CWS data and simple models to forecast urban temperatures in a cost-efficient way even 168 h ahead, with implications for various sectors such as urban planning, energy consumptions, health impacts, or climate change adaptation strategies.

Dam Construction, Company Towns and Planned Urban Development: The Example of Salto del Esla, Spain

ABSTRACT Company towns are integral to the urban and territorial changes wrought by the Industrial Revolution, especially in remote, resource-rich areas. Among these, villages constructed alongside dams stand out due to their profound territorial impact. This study focuses on one such unique settlement near the Ricobayo dam in Zamora, Spain, exploring its construction and environmental integration. At its inception, Ricobayo was Europe’s largest dam, its creation marked by notable feats like relocating a church, and pioneering hydraulic engineering and bridge construction, including a record-breaking design by Torroja. The adjacent company town set a precedent for creating liveable spaces for workers in isolated locales, influencing later developments. Research included on-site investigations, archival work at Iberdrola’s Historical Archive, and review of historical texts, underscoring the cultural and landscape significance of 20th-century dam settlements and the distinctive features of Ricobayo’s surroundings.

Spatial analysis of socio-economic and demographic factors influencing urban flood vulnerability

Rapid urbanization and climate change require a thorough understanding of flood vulnerability in order to assure urban safety and resilience. Understanding the factors that contribute to flood vulnerability, allows us to develop effective initiatives that could mitigate the destructive consequences of flooding, while also protecting communities. The objective of this research is to identify and model the socio-economic and demographic factors that significantly influence flood vulnerability in the floodplains of Jackson, Mississippi, and Birmingham, Alabama, USA. First we analyzed the correlation between socio-economic and demographic factors then employed Principal Component Analysis (PCA) to address multicollinearity, a common challenge in multivariate statistical modeling. Subsequently, PCs-based global regression (PCR) and geographically weighted regression (PCGWR) analysis are used to identify key drivers of flood vulnerability. The findings demonstrate that a significant proportion of the variance (>80%) of these factors can be captured by first two to three Principal Components (PCs). Consistent with existing research, African American, poverty, seniors, and the number of less educated people positively correlate with flood vulnerability, while income and housing prices exhibit a negative correlation. Additionally, PCGWR outperformed the Principal Component Regression (PCR) in most cases, highlighting the spatial heterogeneity of flood vulnerability. This study focuses on two U.S. cities, and the methodology is applicable to other cities with similar characteristics. The identified factors align with global research on flood vulnerability, making the proposed research and findings valuable worldwide. The findings of this research are useful for local governments, policymakers, and urban developers to make detailed location specific flood vulnerability plan to reduce impact of flood and improve urban resilience.

Whither the post-industrial city?

This commentary provides a sympathetic critique to Luger and Schwartz's contention that the concept of ‘post-industrial’ lacks continued utility in a world of urban (and regional) change. Drawing on the authors’ use of stigma and narrative, I argue the post-industrial concept remains relevant, though scholars must attend to who is mobilizing the concept, and to what ends.

Climate change-induced urban flooding trend analysis and land use change: a case study of flood-prone Pathumthani Province, Thailand

Climate change-induced urban flooding trend analysis and land use change: a case study of flood-prone Pathumthani Province, Thailand

Evaluating the Impact of Controlled Ultraviolet Light Intensities on the Growth of Kale Using IoT-Based Systems

Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation.

How urban spatial characteristics impact surface urban heat island in subtropical high-density cities based on LCZs: A case study of Macau

Although numerous studies have explored the influence of urban spatial characteristics (USC) on surface urban heat island intensity (SUHII) from the perspective of local climate zone (LCZ), the spatio-temporal heterogeneity within and between LCZs has not been demonstrated. In this study, a total of 1540 grid units in Macau across 7 years were screened as the research samples. USC, SUHII and LCZ were calculated and derived from multi-source data. GWR and GTWR models were applied to detect the spatio-temporal non-stationarity between USC and SUHII, and Geodetector was used for ranking the interpretation strength of driving factors in each LCZ class. Results show that SUHII in LCZ 7 and LCZ 8 is the highest. Besides, the GWR model had the best fitting degree in accounting for thermal variations due to urban form changes, and the regression performance was generally different among LCZ classes, especially between the compact-types and the open-types. Furthermore, strategies related to urban form optimization were finally proposed in each LCZ class based on the contribution of driving factors. This study sheds new light on the spatio-temporal variations of “USC-SUHII-LCZ” linkage and provides specific planning recommendations for thermal environment improvement, especially in subtropical highdensity cities.

Numerical simulation study on the effect of underground drainage pipe network in typical urban flood

Rapid urbanization and climate change have heightened urban flood risks. An in-depth examination of the underground drainage pipe network’s effect on urban flood inundation can enhance urban stormwater management and mitigate disaster risks. This study presents a full hydrodynamic urban flood model (FHUM) coupling the pipe transport module of the Storm Water Management Model (SWMM) with a shallow water model. Based on unstructured grid cells, runoff yield and confluence calculations are carried out to effectively separate the underground drainage pipe network from other factors. The model is applied to Minzhi Area and Haidian Island to quantitatively assess the influence of the underground drainage system on urban flooding. Results indicate that FHUM performs well, accurately reflecting the process from rainfall to inundation. The underground drainage pipe network reduces surface inundation in the study area and improves the city’s stormwater drainage capacity. With increasing rainstorm intensity, the impact of the pipe network does not exhibit a consistent trend. During heavy rainstorms, the pipe network consistently plays a significant and stable role, particularly in reducing high inundation depths. However, attention should be paid to the potential flood risk transfer associated with using pipe networks in urban flood management. This work provides a scientific basis for urban stormwater management, holding significant importance in improving flood control and disaster reduction capabilities.

Measuring Urban and Landscape Change Due to Sea Level Rise: Case Studies in Southeastern USA

As a consequence of global climate change, sea level rise (SLR) presents notable risks to both urban and natural areas located near coastlines. For developing effective strategies to mitigate and adapt to these risks, it is essential to evaluate the potential impacts of SLR in coastal areas. While substantial research has been conducted on mapping the broad-scale impacts of SLR based on scenarios of Global Mean Sea Level (GMSL), consideration of regional scenarios, systematic classification, and distinct stages of SLR have been largely overlooked. This gap is significant because SLR impacts vary by region and by the level of SLR, so adaptations, planning, and decision-making must be adapted to local conditions. This paper aims to precisely identify the landscape and urban morphology changes caused by the impact of SLR for each foot of elevation increase based on remote sensing technologies, focusing on St. Johns County, Florida, and Chatham County, Georgia. These two counties are both situated along the southeastern coastline of the United States but with completely different urban forms due to distinct historical and cultural developments. Regional forecasting SLR scenarios covering the period from 2020 to 2100 were utilized to assess the landscape transformation and urban changes, incorporating selected landscape and urban metrics to calculate quantitative data for facilitating comparative analyses. This study investigated gradual alterations in urban morphology and green infrastructure both individually and in combination with the effect on wetlands due to SLR. The mapping outcomes of this research were generated by employing comprehensive remote sensing data. The findings of this research indicated that, when the sea level rose to 3 feet, the wetlands would experience notable alterations, and the level of fragmentation in urban built areas would progressively increase, causing most of the metric data to exhibit a pronounced decline or increase.