Urban Climate Change Research Articles

Assessment of shift in GWPZs in Kashmir Valley of Northwestern Himalayas

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

Separation of the Urbanization Effects on Thermal Environment

Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

Differential Evaluation of Ecological Resilience in 45 Cities along the Yangtze River in China: A New Multidimensional Analysis Framework

The rapid pace of urbanization and global climate change necessitates a thorough assessment of urban ecological resilience to cultivate sustainable regional ecosystem development. Cities along the Yangtze River face an intensifying conflict between ecological preservation and socio-economic growth. Analyzing the ecological resilience of these urban centers is essential for achieving equilibrium in regional urban ecosystems. This study proposes a “system process space” attribute analysis framework, taking into account urban development processes, ecosystem structure, and resilience evolution stages. Utilizing data from 45 Yangtze River cities, we establish a “Driver, Pressure, State, Impact, and Response” (DPSIR) evaluation index system to evaluate changes in ecological resilience levels and evolution trends from 2011 to 2022. Our findings indicate that: (1) The ecological resilience index of Yangtze River cities increased from 0.177 to 0.307 between 2011 and 2022, progressing through three phases: ecological resilience construction, rapid development, and stable development. (2) At the city level, ecological resilience along the Yangtze River exhibits uneven development characteristics. Upstream cities display a significant “stepped” pattern, midstream cities exhibit a significant “Matthew effect”, and downstream cities present a pyramid-shaped pattern. While regional differences in ecological resilience persist, overall polarization is gradually decreasing, intercity connections are strengthening, and there is a growing focus on coordinated regional development. (3) The spatial distribution of ecological resilience in Yangtze River cities demonstrates both continuity and evolution, generally forming a “core-edge” clustered pattern. Based on these findings, we recommend enhancing inter-city cooperation and connectivity, addressing imbalances in urban ecological resilience, and promoting high-quality ecological resilience development along the Yangtze River through tailored development strategies for each city.

LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

Examining the suitability of the local climate zones (LCZ) framework in informal urban settlements: Insights from Kabul, Afghanistan

To better investigate the Urban Heat Island (UHI) effect, a standardized framework known as Local Climate Zones (LCZ) has been developed and widely applied to numerous cities. However, cities from least-developed countries with heterogeneous typologies are underrepresented in the LCZ literature. This study assesses the applicability of the LCZ framework in the slum-dominant built-up environment of Kabul. Using a combined method involving GIS and remote sensing, we classified natural and built-type LCZs and analyzed LCZ-LST fluctuations. The analysis revealed that four new subclasses cover 23 % of the built type LCZs: LCZ 35 (mid/high-rise buildings within compact lowrise layouts) and LCZ 65 (midrise buildings among open lowrise areas) have the lowest LSTs at 34.36 °C and 34.42 °C in July, respectively. In contrast, LCZ 73 (two/three-story buildings in lightweight configurations), and LCZ 9F (sparse buildings on bare soil or sand) have higher LSTs at 37.2 °C and 38.6 °C in July, respectively. These subclasses showed distinct zone parameter thresholds compared to standard LCZs. In most built-type LCZs, Average Building Height (ABH) and Pervious Surface Fraction (PSF) negatively influenced LST, while impervious surfaces and Sky View Factor contributed to higher LST. Based on the findings, LCZ-specified strategies (Vegetation, urban form, and using high-albedo materials) for LST mitigation are proposed. Furthermore, we provide planning, design, and policy recommendations aimed at mitigating urban heat, with potential applicability to other cities facing rapid urbanization and growth of informal settlements. The findings can inform action toward urban climate change adaptation.

Sustainable Urban Planning: A Comparative Study of Green City Initiatives Around the Worldqq

Sustainable urban planning has become increasingly imperative in addressing environmental challenges and promoting resilient, livable cities globally. a comparative study of green city initiatives across different regions, examining strategies, successes, and challenges in integrating sustainability principles into urban development. Key focus areas include sustainable infrastructure, green spaces, energy efficiency, waste management, and community engagement. By analyzing case studies and current practices, this study aims to identify best practices and lessons learned from leading green cities, highlighting innovative approaches that enhance urban sustainability and quality of life for residents. Ultimately, the research underscores the importance of holistic planning approaches and collaborative governance in achieving sustainable urban futures amidst urbanization pressures and climate change impacts.

Institutional Designs for Procedural Justice and Inclusion in Urban Climate Change Adaptation

Although there is broad consensus that more inclusive approaches are needed in climate adaptation planning, it is unclear how cities should redesign rules, institutions, and decision-making processes to produce more equitable forms of participation and engagement. This paper evaluates different planning procedures and institutional arrangements across twenty-five U.S. cities. Although arrangements are context-specific, institutional designs fall into three categories: consultative partnerships, strategic collaborations, and expansive co-governance arrangements. Each institutional design leads to different kinds of inclusion outcomes. Our results empiricize how cities can pursue more inclusive climate adaptation planning and highlight opportunities to advance and implement broader procedural equity goals.

Climate change adaptation at the margins. The case of Cairo, Egypt

The duration, frequency and intensity of climate change are increasing. Adaptation to climate change is oftentimes perceived as a local concern. Vulnerable groups at the margins try to find nature-based ways to cope with extreme urban climate change events with diverse direct and indirect adaptive strategies in absence of government interventions. From this study's investigative approach in Cairo, Egypt we draw results preliminary from interviews with locals and observational walks in three vulnerable areas in the same urban zone. Research revealed that adaptation can be political, demanding that locals sometimes make hard choices, especially during the absence of government intervention. Nevertheless, some of the adaptive strategies observed can be promoted as mainstream sustainable nature-based adaptation. The discussion reflects some social and economic dimensions that affect local adaptation synergies. This study hopefully contributes to the understanding of how the capacity of individual homeowners can help mitigate climate change risk through the implementation of their own physical and psychological adaptive strategies. In addition, it bears relevance not only for countries sharing similar climates, economic conditions or similar social norms, but also for general adaptation strategies that might support ecosystem-based or nature-based adaptation or tailor-made scenarios for locally applicable adaptation to climate change.

Built environment professionals’ perceptions of vertical greenery systems: a case of Delhi, India

ABSTRACT This study examines built environment professionals’ perceptions of vertical greenery systems in Delhi, a city confronting rapid urban growth and climate change challenges. Through a detailed mixed-methods approach using an online survey, this study evaluates the perceived benefits and challenges of such systems. The results indicate cautious optimism, with nearly 39% of respondents having prior experience and acknowledging their aesthetic and environmental benefits, including reducing urban heat and improving air quality. However, concerns regarding technical implementation and regular maintenance of vertical greenery systems emerge as significant barriers. This study highlights the critical need for inclusive urban planning strategies that embrace nature-based solutions, such as vertical greenery systems, to combat urban sustainability challenges considering Delhi’s distinctive climate. Furthermore, it underscores the importance of addressing the uncertainty among building owners and policymakers towards adopting these systems, despite their recognized potential for climate change mitigation. The findings advocate for further research and targeted policies to facilitate the broader application of vertical greenery systems, not only in Delhi, but also in urban settings globally facing similar environmental pressures, thereby contributing to the discourse on urban sustainability and climate resilience.

A cradle-to-customer life cycle assessment case study of UK vertical farming

Global population rise, increased rural to urban migration, climate change and conflict have placed strain on global trade and food supply chains. Vertical farming (VF) is a relatively novel method for cultivating many fresh fruits and vegetables, and often is claimed to have lower environmental impacts than field cultivation. However, to date there are few studies utilising primary data which have evaluated the environmental impact VF may have. This study utilised Life Cycle Assessment (LCA) to evaluate the environmental impact of lettuce production in a commercial vertical farm in the UK. This was compared with data from the literature on the impacts of lettuce in field cultivation. Scenarios were also examined to evaluate the impact of VF systems with varying electricity sources, and waste, water and nutrient management options. The VF was found to have a similar or lower Climate Change impact than field cultivation, depending on energy source used. VF had a slightly higher environmental impact than field cultivation in some other environmental impact categories such as freshwater eutrophication potential and acidification potential. Electricity demand and the medium used for ‘plugs’ in the VF were found to be the main hotspots of the system. The results of this study provide insights on the environmental impact and resource efficiency of VF, feasibility for larger scale deployment, and provide further empirical evidence to support the claims made previously in the literature.

Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities.

Scenario construction and vulnerability assessment of natural hazards-triggered power grid accidents

In light of escalating urbanization trends and climate change impacts worldwide, the susceptibility of urban power grids to natural disasters has become an overarching global concern. Prior research has predominantly concentrated on singular calamities while often disregarding cumulative repercussions from multiple concurrent events affecting power grid resilience. This investigation presents an exhaustive framework for assessing grid vulnerabilities by quantifying diverse impacts from potential natural disaster scenarios and delineating adaptive pathways for evaluating inadvertent occurrences. The framework amalgamates an extensive array of metrics— including probability assessments, system state evaluations, trigger threshold analyses, responsiveness measurements, and adaptability adjustments— within a dynamic scenario-oriented model. The inquiry progresses through distinct stages: formulating an all-encompassing methodology for assessing vulnerabilities; assessing varied impacts stemming from different environmental perils; mapping out post-disaster evolutions; and executing a case analysis focusing on an urban power grid.Concentrating specifically on rainfall, snowfall, and freezing incidents, the case analysis uses locale-specific data to appraise grid susceptibilities while employing multi-criteria decision analysis (MCDA) to facilitate decision-making. During this deliberative process, optimal strategies are derived, and mitigative actions are recommended with the aim of diminishing power-grid vulnerabilities. This investigation underscores intricate risk dynamics within urban power grids while presenting a feasible framework for sustainable planning and effective emergency responses in confronting natural hazards.

Not population density, but city size and per capita income influence the urban forest carbon sequestration: A case of growing cities in Arkansas, USA

While urban trees provides significant ecosystem services, the impact of increasing urbanization and population on their carbon sequestration potential remains relatively underexplored in interior sprawling cities, highlighting a critical research gap amid growing urban environmental challenges. Although existing studies suggest a strongly correlation between urban forest cover and carbon sequestration, this study aims to investigate how the urban demography factors influence land cover and carbon sequestration across cities with low, medium, and high population densities. Using the i-Tree Canopy tool, we sampled 18,814 random points to estimate city land cover and carbon sequestration in Arkansas, interior state of the US. Results from the principal component analysis revealed that 74% of the variation in a city's land cover among population density classes is attributed to tree and soil cover percentages. The mean annual (1.40 ± 0.08 T ha-1) and total carbon sequestration rates (35.30 ± 5.57 T ha-1) were significantly higher in the areas with low population density due to a higher average tree cover percentage (46%). Carbon sequestration rate was negatively associated with land cover types such as impervious surfaces like buildings (r = -0.44), roads (r = -0.28), grass cover (r = -0.46), and impervious other (r = -0.53). The log-linear model with scaled variables suggested that factors such as city's size and tree cover has positive influence urban trees' annual and total carbon sequestration, while spatial distribution and per capita income negatively affect these ecosystem befits in growing cities, regardless of population density. Travel time within city may have negative impact, while decadal population change may positively impact on carbon sequestration alongside management effort. Valuing this potential urban forest carbon sequestration for emerging carbon market could provide monetary benefits to the urban communities, city managers, and policymakers, enabling the development of effective urban forest management strategies in the context of urban climate change.

Urban Climate Change in Populated Kathmandu Valley, Nepal: A Case Study

Kathmandu Valley, the capital and commercial hub of Nepal, is experiencing rapid and unmanaged urbanization, leading to poor environmental conditions. The population, a key indicator of urbanization, has been growing at an average rate of 2.08% per year. This population growth from 1990 to 2021 has significantly altered land use, resulting in increased temperatures and transforming the valley into an Urban Heat Island (UHI). This study examines the effects of UHI by comparing normalized Land Surface Temperature (LST) in Kathmandu Valley and assessing the influence of land use changes on UHI intensity between 1990 and 2021. The result shows that built-up area has expanded by 30.7% of the total area, while the agricultural area has decreased by 33.59%. These changes in land use and land cover have caused a significant temperature increase at a rate of 0.038 °C per year. Green space density, measured by the Normalized Difference Vegetation Index (NDVI), has declined from 35.59% to 29.17%, being replaced by built-up areas. LST is inversely related to the presence of surface vegetation. The UHI phenomenon is observed to be stronger in urban centers such as Tribhuvan International Airport, Patan industrial area, Kritipur, and Kalanki, while it is weaker in semi-urban regions like Suryabinayak and Godawari. As climate change poses significant health and environmental challenges, adaptation has become a major issue for developing countries. Therefore, it is essential to implement change control and mitigation strategies specifically tailored to each urban region.

Urban Fire Risk Dynamics and Mitigation Strategies in Shanghai: Integrating Spatial Analysis and Game Theory

In recent decades, the increasing frequency of urban fires, driven by urban functional enhancements and climate change, has posed a growing threat to metropolitan sustainability. This study investigates the temporal and spatial characteristics of fire incidents in Shanghai from 2019 to 2023. Using satellite fire point data and official government records, kernel density analysis and wavelet analysis were employed to analyze the time series and spatial distribution of fire data. Subsequently, eleven primary factors influencing urban fire occurrence were identified, encompassing probability, regional characteristics, and hazard sources. A combined methodology of subjective and objective weights with game theory was used to generate a fire risk assessment at a 1 × 1 km2 grid scale. Furthermore, the spatial distribution characteristics of the assessments were analyzed. The results reveal that the downtown area exhibits the highest intensity of urban fires in terms of spatial domain, with a decreasing intensity towards the suburbs. Temporally, fire frequency demonstrates significant periodicity at an 18a time scale, while clear seasonal fluctuations and periodicity are observed at a 16-22a time scale, with higher occurrences in spring and winter. The study identifies typical aggregation patterns of urban fires, with high-risk centers in downtown Shanghai. Considering the impact of climate change and human activities, high-risk areas may gradually expand to adjacent urban suburbs, presenting a concerning future scenario. By examining the dual attributes of “combustibles and fireproof space” within urban greening systems, this research offers recommendations for the future strategies of disaster prevention and mitigation of green systems in Shanghai.

Urban Climate Change Resilience and Adaptation Assessment in Semi-arid Region Based on TOPSIS Analysis Method

Urban Climate Change Resilience and Adaptation Assessment in Semi-arid Region Based on TOPSIS Analysis Method

Comparison of Urban Climate Change Adaptation Plans in Selected European Cities from a Legal and Spatial Perspective

The aim of this paper is to identify and compare the key institutional features of urban climate change adaptation plans in three geographically, systemically, and climatically distinct European countries (Greece, Spain, and Poland). The paper concentrates on the tool indicated and confirms the circumstances and potential outcomes of its usage in the selected countries. A case study of a particular city was chosen in each country and the applicability of the climate change adaptation plan there was confirmed. Analysis was also performed on the plans’ legal aspect, connection to national-level strategic planning, and spatial planning. The research questions formulated and addressed are as follows: how do urban climate change adaptation plans in the selected countries define key climate challenges? Is the content of the municipal climate change adaptation plans consistent with the content of the diagnosis of climate challenges at the supra-local level and in the scientific discussion? How are climate change adaptation plans translated into the implementation sphere? Τhe example of Spain and Greece confirms that plans can combine general climate change adaptation objectives with specific (evasive) guidelines for urban policies, while the example of Poland shows that the content of climate change adaptation plans can often be too vague and difficult to further integrate into urban policies. The research results obtained are relevant from the perspective of comparing institutional responses to climate challenges. The research proposes possible methods for making such comparisons.

Effects of landscape changes on urban climate change: A case study in the city of São Paulo

Given the vulnerability of Brazilian cities to climate change, it is imperative to monitor urban areas’ susceptibility to temperature fluctuations. In this article, we employ thermal remote sensing and digital image processing techniques to illustrate a substantial rise in surface temperatures across the Northwest Region of São Paulo City over the past three decades. This surge in surface temperature is closely linked to alterations in the urban landscape. Our findings emphasize that one significant environmental consequence of São Paulo City’s rapid urbanization is the pronounced increase in surface temperatures. These results also underscore the significance of assessing landscape features, such as vegetation cover, to inform the prudent, sustainable, and resilient management of urban centers, thereby mitigating climate change effects in metropolitan areas.

Current flood risk management: gaps and perspectives in Stung Sen Catchment, Cambodia

ABSTRACT People who live in Cambodia's flood-prone areas are at a heightened risk of experiencing a flood disaster due to climate change and urbanization. Annual flood disasters have an impact on the Stung Sen River Basin, the largest tributary river of Tonle Sap Lake. Through interviews with pertinent stakeholders, we investigate the current gaps in flood control risk management at the Stung Sen Catchment, the national to local levels. At a common level, the Kampong Kor, Kampong Svay, and Meanchey were selected for the interview and field survey. The findings highlight that a top-down approach is used to alert locals about potential flood danger. The results emphasized the difficulties in addressing cross-border flood warning issues, the effects of incorrect urbanization development and climate change on vulnerable populations, and the inadequate readiness for the safety of children in flood-prone locations. Consequently, we suggest the integrated flood risk management framework as a very effective way to lessen the impact of flood risk for both present and future flood risk harm.