This session invites studies focused on modelling and monitoring the dynamics of multiple sectors and city-biosphere interactions. Topics of interest include, but are not limited to:
• Demography
• Urban transport networks
• Energy consumption
• Anthropogenic emissions and Pollution
• Urban climate
• Urban hydrology
• Urban ecology
Our aim is to elucidate the complex dynamics within urban environments and explore how urban form and function can be optimised to enhance the liveability and well-being of their citizens.
Orals: Tue, 5 May, 08:30–12:25 | Room D3
Compounding with global warming induced by carbon emissions, urbanization is exerting an additional warming effect, further threatening local urban residents. However, due to the heterogeneity within urban areas, the risk among residents in the same city varies significantly. Researchers have explored various methods to describe urban surfaces, among which the Local Climate Zone (LCZ) framework has proven to be an effective tool for characterizing diverse urban morphologies globally. Although global LCZ mapping products are available, inter-annually comparable LCZ time series remain scarce in the current literature. Additionally, the evaluation of thermal characteristics of urban morphology often relies on comparing median or average Land Surface Temperature (LST) between LCZ types, which can introduce substantial bias due to spatial autocorrelation caused by terrain differences, uneven human activities, and other factors. Thus, there is an urgent need for a dynamic monitoring and impact evaluation paradigm for urban morphology.
In this study, we present an annual LCZ time-series mapping framework and generate time series from 2000 to 2020 for three major Chinese urban agglomeration: Jingjinji, the Yangtze River Delta, and the Greater Bay Area. Comparing with baseline (supervised classification directly), LCZ time-series generated by our framework ensured the consistency between years. Furthermore, by integrating the LCZ time series proposed in this study with MODIS Land Surface Temperature (LST) datasets, we developed a time-series analysis method to quantify LST changes induced by different urban morphology transformations. The mapping results reveal that high-rise buildings are the primary distinguishing feature between urban areas of different sizes. Over the past two decades, the composition of urban morphology has been converging between urban areas of varying sizes but diverging within intra-city land use zones. Moreover, urban morphology patterns differ significantly between urban expansion areas and urban renewal areas. Our findings indicate that the impact of urban morphology changes varies significantly. Specifically, urban renewal, predominantly characterized by vertical development, exerts an asymmetric effect on urban temperatures: it mitigates urban warming during the day but intensifies it at night. In contrast, the effect of urban expansion on urban warming is more pronounced during the day than at night. At the city scale, changes in urban morphology generally contribute to a warming effect, both diurnally and nocturnally. Urban expansion is identified as the primary driver of rising city temperatures. However, the divergent impacts of vertical development, which is likely to dominate future urbanization, must not be underestimated.
How to cite: Zhao, J.: Mapping the Thermal Footprint of Urbanization: A Long-Term Perspective based on Local Climate Zone Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13029, https://doi.org/10.5194/egusphere-egu26-13029, 2026.
Urban heat is a growing concern, especially under global climate change and continuous urbanization. However, the understanding of its spatiotemporal propagation behaviors remains limited. In this study, we leverage a data-driven modelling framework that integrates causal inference, network topology analysis, and dynamic synchronization to investigate the structure and evolution of temperature-based causal networks across the continental United States. We perform the first systematic comparison of causal networks constructed using warm-season daytime and nighttime air temperature anomalies in urban and surrounding rural areas. Results suggest strong spatial coherence of network links, especially during nighttime, and small-world properties across all cases. In addition, urban heat dynamics becomes increasingly synchronized across cities over time, particularly for maximum air temperature. Different network centrality measures consistently identify the Great Lakes region as a key mediator for spreading and mediating heat perturbations. This system-level analysis provides new insights into the spatial organization and dynamic behaviors of urban heat in a changing climate.
How to cite: Wang, C., Wang, Y., Wang, Z.-H., and Yang, X.: Urban thermal environments as interconnected systems: Emergent causal networks and dynamic synchronization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15824, https://doi.org/10.5194/egusphere-egu26-15824, 2026.
Understanding local-scale surface energy and water fluxes is essential for assessing the impact of urbanization on local climate. The Surface Energy and Water Balance Scheme (SUEWS) is a widely used urban land surface model for simulating these fluxes at the neighbourhood scale. However, its application in rapidly urbanizing data-scarce regions such as Indian cities is challenging due to limited flux observations and urban surface data. With the aim to assess the performance of SUEWS in capturing the surface energy and water balance dynamics, we use satellite-derived inputs with the nearest neighbour image processing technique to represent land use land cover, urban morphology and surface characteristics. The study area comprises two locations in Delhi, each delineated into nine grids centered on an existing meteorological observation station. The model has been run for a decade, and its performance is evaluated with remote sensing proxies for surface energy fluxes and observations of temperature and relative humidity. Results show that SUEWS can reasonably capture the seasonal variation and magnitude of urban energy components despite using derived data products. The study highlights practical strategies for urban flux modelling in data-scarce regions and is crucial for providing insights that support evidence-based urban planning to mitigate the urban heat island effect and sustainable water management policies.
How to cite: Thakur, D., Dawson, R., and Dhanya, C. T.: Modelling Urban Energy and Water Fluxes with SUEWS under Data-Scarce Conditions in Delhi, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1136, https://doi.org/10.5194/egusphere-egu26-1136, 2026.
Urban ventilation under light-wind conditions is a critical factor of thermal comfort and air quality in high-density cities, particularly during extreme heat events when synoptic forcing is weak. This study presents an integrated framework that combines city-scale wind mapping, large-eddy simulation (LES), neighborhood-scale pollutant dispersion analysis, and model parameterization to advance understanding and representation of urban ventilation processes in weak-wind regimes.
First, a city-scale wind mapping tool is developed for Guangzhou based on urban morphological parameters. The results reveal extensive low-wind-speed zones at pedestrian level, especially in high-density districts, indicating suppressed wind-driven ventilation and an increased reliance on buoyancy-induced airflow.
Second, high-resolution three-dimensional LES is conducted to investigate buoyancy-driven thermal plume dynamics under weak ambient winds. Validation against laboratory experiments demonstrates that the model accurately captures plume bending, vertical transport, and plume merging. The simulations show that surface-heating-induced thermal plumes generate strong near-ground horizontal inflow and coherent plume-merging structures, producing pedestrian-level convergence velocities of approximately 1–2 m/s, which is comparable to ventilation induced by moderate background winds.
Third, the CFD framework is applied to assess traffic-related pollutant dispersion at the neighborhood scale. Results indicate that buoyancy-driven ventilation substantially enhances pollutant removal under calm and light-wind conditions. Interactions between weak background winds and rising thermal plumes induce oscillatory flow structures and enhanced turbulence, effectively reducing near-surface pollutant accumulation.
Finally, drawing on a large ensemble of LES results, a parameterization scheme for urban ventilation under light-wind conditions is developed and incorporated into the UT&C model. By explicitly accounting for buoyancy intensity and urban morphology, the new scheme improves the representation of air exchange velocity.
This improvement directly enhances the assessment of heat and air quality risks in dense urban areas under light-wind and extreme-heat conditions, thereby providing a more robust scientific basis for urban design, planning, and climate-resilience strategies.
How to cite: Mei, S., Hu, S., and Sun, T.: Urban Ventilation in Light-Wind Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1768, https://doi.org/10.5194/egusphere-egu26-1768, 2026.
Urban areas are increasingly vulnerable to extreme thermal conditions due to rapid urbanization and climate change. The accurate prediction of ambient air temperature (AT) at fine temporal scales is essential for mitigating the impacts of urban heat waves, heat pockets, and heat islands. Despite ongoing research, limited interpretability of traditional AI models has constrained their utility in decision-making. This study aims to improve real-time temperature forecasting in the Central National Capital Region (Central-NCR) of India through explainable machine learning techniques. Hourly AT was modeled using four advanced machine learning algorithms—Random Forest (RF), Gradient Boosting (GB), XGBoost, and LightGBM (LGBM). A structured workflow was followed involving data preprocessing, hyperparameter tuning, cross-validation, model training, and evaluation. Model performance was compared using residual plots, validation curves, and statistical metrics including RMSE, MAE, MSE, R², MAPE, and Explained Variance Score (EVS). A Taylor dia-gram was used for holistic model comparison. Among all tested models, RF demonstrated the highest predictive accuracy, achieving an R² of 0.81 and the lowest RMSE of 3.36 during the test phase. Relative humidity (RH) and barometric pressure (BP) emerged as the most influential predictors. SHAP analysis further confirmed RH, BP, and solar radiation (SR) as key drivers of AT variability. Seasonal patterns indicated that increased RH during monsoon months reduced AT, while elevated SR levels during summer contributed to higher temperatures. Dependence and partial dependence plots revealed non-linear interactions: RH exhibited a strong inverse relationship with AT, SR drove exponential increases, and BP displayed oscillatory patterns reflective of atmospheric fluctuations. The integration of explainable AI techniques with meteorological data enables more accurate and interpretable urban temperature forecasting. These insights can support policymakers and urban planners in developing informed strategies for heat mitigation, regulatory compliance, and climate adaptation.
How to cite: Mahato, S.: Towards Climate-Resilient Cities: Exploring Meteorological Drivers of Urban Heat Variability with Explainable Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13542, https://doi.org/10.5194/egusphere-egu26-13542, 2026.
The urbanized Weather Research and Forecasting (uWRF) model is widely used for high-resolution urban climate modeling, yet its excessive computational cost restricts real-time forecasting and long-term climate assessment. To overcome this bottleneck, we propose AI-uWRF, a novel physics-informed generative emulator designed to bypass the computational demands of dynamical downscaling. The core architecture is a Hybrid Spatiotemporal Conditional Diffusion Model that integrates a Spatial Transformer within a U-Net backbone. A key innovation is the dual-stream condition encoder, which effectively fuses static urban surface heterogeneity (e.g., land use, topography) with dynamic large-scale atmospheric forcing. Unlike purely data-driven approaches, AI-uWRF incorporates physical constraints, including hydrostatic balance and continuity equations, into the training process to ensure thermodynamically consistent outputs. Validated against high-resolution (333 m) uWRF simulations in Wuhan, China, our emulator accelerates the generation of key meteorological fields (e.g., 2m temperature, 10m wind, surface pressure) by three orders of magnitude. The results demonstrate that AI-uWRF captures complex urban land-atmosphere interactions with high fidelity, offering a transformative tool for time-sensitive applications such as building energy optimization and probabilistic heatwave risk management.
How to cite: Song, J. and Zhang, Q.: AI-uWRF: A Physics-Informed Spatiotemporal Diffusion Transformer for High-Resolution Urban Weather Emulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2266, https://doi.org/10.5194/egusphere-egu26-2266, 2026.
Cities are complex, multi-scale systems where the built environment, urban microclimate, and human behavior interact dynamically. Among these interactions, the response of building energy demand to extreme heat is a critical feedback loop that impacts urban functional stability and energy security. However, quantifying these cross-sectoral feedbacks—specifically how outdoor thermal variations translate into indoor cooling behavior and energy demand—remains a significant modeling challenge. To address this, we propose a hybrid modeling framework that integrates machine learning with a physics-based building energy balance model to bridge the gap between urban microclimate and building energy consumption. Our approach estimates the power consumption of air conditioning (AC) systems by distinguishing operational states based on the coupling and decoupling of indoor and outdoor climate variations. The framework employs an XGBoost model to identify AC operation within optimal time windows, followed by the Pelt algorithm to detect state transition points and pinpoint exact operational periods. Subsequently, a Resistance-Capacitance (R-C) model is parametrized using coupled indoor-outdoor climate data during AC-off periods, which is then utilized to estimate real-time AC power.
The model was validated against data from a residential building in Beijing, demonstrating good accuracy in both predicting AC operating status and estimating power loads. The hybrid model was then applied to real-world urban scenarios to quantify the impact of extreme heat on cooling demand using only monitored climate variations, independent of direct energy metering data. This research provides a robust quantitative tool for climate-adaptive planning, advancing our ability to model the complex dependencies between urban energy systems and a changing climate.
How to cite: Cao, Z. and Kai, W.: Impact of extreme heat on building cooling energy demand: a hybrid model based on the coupling of indoor and outdoor climate variations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5301, https://doi.org/10.5194/egusphere-egu26-5301, 2026.
Urban trees are widely promoted to mitigate urban heat and reduce cooling demand through shading and evapotranspiration. However, added moisture can increase dehumidification energy loads, making the net impact of greening on building energy demand climate-dependent and poorly quantified. Here, we use the Urban Tethys-Chloris model coupled with a Building Energy Model (UT&C-BEM) to quantify vegetation-driven impacts on summer air-conditioning energy consumption (ECAC,summer) in 100 globally significant cities spanning diverse climates, urban forms, and vegetation patterns. Under present-day vegetation cover, urban trees reduce mean daily summer cooling energy demand in all 100 cities, but with a clear trade-off between absolute energy savings and relative sensitivity of savings to green area. By systematically increasing tree fraction from each city’s baseline up to 100% cover, we found that greening efficiency is highest in hot arid cities and markedly weaker in hot humid climates, where enhanced dehumidification demand offsets sensible cooling benefits. Cities in hot arid climates, where greening efficiency is highest, should prioritize tree-based cooling as a cost-effective energy mitigation strategy.
How to cite: He, X., Meili, N., and Fatichi, S.: Impacts of Urban Vegetation on Cooling Energy Demand Across 100 Global Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9395, https://doi.org/10.5194/egusphere-egu26-9395, 2026.
Accurate weather data are critical for simulating building energy use and assessing power-grid demand. Typical meteorological year (TMY3) datasets are widely used for this purpose but represent long-term average conditions assembled from different years, limiting their ability to capture interannual variability and extreme events that often drive peak loads. Actual meteorological year (AMY) data provide continuous, year-specific weather records and thus offer a more realistic depiction of variability and extremes. However, their application has been constrained by limited duration, spatial coverage, and the coarse resolution of many long-term products. In this study, we compare residential building energy consumption across more than 500 U.S. urban locations using TMY3 data and 23 years of AMY data enabled by the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H v1.1). AMY-based simulations reveal substantial year-to-year variability and consistently higher peak loads than TMY3-based results. Relative to the 23-year AMY simulations, TMY3 underestimates cooling energy demand by 11.7 ± 7.5% and overestimates heating demand by 13.6 ± 16.5% on average. These findings demonstrate that reliance on TMY3 can systematically misrepresent both energy demand magnitude and extremes, and underscore the necessity of long-term, urban-resolved AMY datasets for robust building energy assessments and climate-resilient power-system planning.
How to cite: Leffel, J., Wang, C., and Horsey, H.: Reliance on typical weather data misrepresents cooling and heating energy use: Insights from 23 years of building energy simulations across the U.S., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16025, https://doi.org/10.5194/egusphere-egu26-16025, 2026.
Residential heating is a large source of greenhouse gas emissions and a priority for urban climate change mitigation efforts. However, effective planning of decarbonization policies is hampered by the lack of fine-resolution emission estimates at sub-city scales. Such spatially disaggregated data are essential for analyzing how emission patterns co-vary with important social, economic, and demographic characteristics within cities, which is needed for designing targeted and equitable policy interventions.
We use high-resolution population and building data from the 2022 German census to estimate carbon dioxide emissions from residential buildings across Germany. We then explore how emission patterns covary with socioeconomic and demographic variables relevant for policy design.
Our analysis reveals significant spatial heterogeneity in per capita emissions within cities. We find that areas with higher rates of home ownership exhibit elevated per capita emissions, suggesting these neighborhoods represent prime targets for building renovation incentives directed at homeowners. Additionally, we observe higher per capita emissions in areas with larger proportions of senior residents (>66 years old), who typically consume more energy for heating. This pattern indicates that high-emitting buildings (larger, older buildings heated with carbon-intensive energy carriers) tend to spatially overlap with populations likely to have intensive heating behaviors, potentially compounding resulting emissions.
These findings underscore the importance of analyzing urban carbon dioxide emission patterns at fine spatial scales and examining their spatial correlation with relevant socioeconomic and demographic characteristics. Our analysis reveals sub-city emission patterns with clear implications for policy design. Effective decarbonization strategies must account for these spatial patterns to plan interventions that account both for building infrastructure and occupant characteristics, ensuring efficient resource allocation and equitable climate action across diverse urban settings.
How to cite: Block, S., Ulrich, V., Kong, G., Martin, M., and von Elverfeldt, K.: Fine-scale covariation of residential heating emissions and socioeconomic variables across Germany: implications for urban climate policy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18358, https://doi.org/10.5194/egusphere-egu26-18358, 2026.
Nighttime light (NTL) data at daily scales presents an innovative foundation for monitoring human activities, offering vast potential across various research domains such as urban planning and management, disaster monitoring, and energy consumption. The daily moonlight-adjusted nighttime lights product (VNP46A2), sourced from Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), has been providing globally corrected daily NTL data since 2012. However, persistent challenges, such as fluctuations in the daily NTL series due to spatial mismatch and angular effects, as well as data holes, have significantly impacted the accuracy and comprehensiveness of extracting daily NTL changes. To address these challenges, a dataset production framework focusing on error correction, interpolation, and validation was developed. This framework led to the creation of a high-quality daily NTL (HDNTL) dataset from 2012 to 2024, which specifically targets 653 cities with populations predictably exceeding one million in 2025. A comparative analysis with the VNP46A2 dataset revealed promising results in spatial mismatch correction for two sample areas – the airport and highway (angular effect can be ignored). These areas exhibited reduced fluctuations in HDNTL time series and enhanced spatial consistency among pixels with homogeneous light sources. Furthermore, the correction of angular effects across various urban building landscapes demonstrated sound improvements, mitigating angular effects in different directions and reducing periodicity from the angular impacts. The spatiotemporal interpolation of data holes shows high similarity with reference data, as indicated by a Pearson correlation coefficient (r) of 0.99, and it increased the valid pixels of all cities by about 2 %. The HDNTL dataset exhibited enhanced consistency with high-resolution Sustainable Development Science Satellite 1 (SDGSAT-1) NTL data regarding the NTL change rate. Also, it showed high alignment with ground truth data of power outages, showcasing superior performance in short-event detection. Overall, the HDNTL dataset effectively mitigates instability in daily series caused by spatial mismatch and angular effects observed in VNP46A2, improving data comparability across both time and space. This dataset enhances the ability of the NTL to reflect the ground events, providing a more accurate reference for daily-scale nighttime light research. Additionally, the dataset production framework facilitates easy updates from future VNP46A2 products to HDNTL. The HDNTL is openly available at https://doi.org/10.5281/zenodo.17079409 (Pei et al., 2025). This study was supported by the National Natural Science Foundation of China (no. 42401474).
How to cite: Pei, Z., Zhu, X., Hu, Y., Chen, J., and Tan, X.: A High-Quality Daily Nighttime Light Dataset for Dynamic Urban Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7380, https://doi.org/10.5194/egusphere-egu26-7380, 2026.
Nighttime light (NTL) data, a remote-sensing record of surface brightness, offer unique observations of cities. With the advantage of high spatiotemporal coverage, NTL data have been widely used to extract urban extents, monitor human activities, quantify socio-economic resources, and estimate energy consumption. Recent products with a higher spatiotemporal resolution have further expanded applications, enabling daily 500-m-resolution monitoring of festivals, wars, and fishing vessels. Most existing studies, however, use only the radiance or spatial extent of NTL and ignore the angular information, which limits their application in observing the internal spatial structure of cities.
Beyond brightness features, the angular information of NTL data characterizes the urban spatial structure. As the viewing zenith angle (VZA) of daily satellites varies, recorded radiance differs because buildings increasingly mask the light, creating an angular effect. Existing studies have modelled angular effects with linear, quadratic, or polynomial models, revealing divergent angular signatures between urban centres and suburbs. However, two gaps persist. First, no unified angular-effect model exists. Although linear and quadratic regressions can depict positive, negative, or U-shaped angular effects, the angular effects they quantify are not directly comparable. Second, explanatory insight into the drivers of the angular effects remains unclear. Although correlations with building height and density have been reported, interpretability is lacking. These knowledge gaps hinder the translation of angular effect research from theory into practice.
Here, we quantify and explain the angular effects across five U.S. cities—Baltimore, Boston, Dallas, Washington D.C., and New York—from 2013 to 2024. We first construct a novel model that captures the relationship between VZA and NTL intensity, introducing a new metric for quantifying angular effects. The model performs well overall, with R2 > 0.6 for more than 70% of pixels. We then develop a series of indicators and apply an interpretable machine-learning framework. We found that pixels with high angular-effect values are characterized by high building-light blockage, high building density and significant variation in building height. All ten indicators collectively explain the angular effect. This study bridges the gap between the angular effects and urban structure, enabling large-scale and high-frequency monitoring of urban structure in data-deficient regions (such as Africa) in the future.
How to cite: Li, S., Chen, W., and Zhou, Y.: Modelling Multi-angle Nighttime Light Observations to Investigate the Impact of Urban Structure on Angular Effect, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9121, https://doi.org/10.5194/egusphere-egu26-9121, 2026.
Cities are complex multi-scale systems in which transport networks interact dynamically with the built environment, such as population distribution, land use, and transport network structure, leading to traffic congestion patterns that vary across space and time. Considering the complex and dynamic characteristics of traffic congestion, it is essential to explore the spatiotemporal heterogeneity and dynamics in the relationships between built environment factors and urban traffic congestion to develop effective policies that enhances urban livability. Hence, this study employs a geographically weighted machine learning framework that integrates random forest (RF) with geographic weighted regression (GWR), referred to as the geographically weighted random forest (GWRF). Additionally, the SHapley Additive exPlanations (SHAP) method is applied to identify dominant associated factors, interpret nonlinear relationships, and reveal local feature differences between explanatory variables and traffic congestion across different time periods. An empirical case study is conducted in Chongqing, China, a mountainous megacity characterized by complex transport dynamics and strong spatial constraints. The case study utilizes multi-source datasets collected over five months, selects 25 candidate variables that represent built environment characteristics, including land-use diversity, road network design, public transit service, and destination accessibility, as well as demographic and socioeconomic attributes, such as population density and economic indicators. Traffic congestion patterns are examined during morning and evening peak hours on both weekdays and weekends to capture temporal dynamics. Compared with traditional spatial regression models and global machine learning approaches, the geographically weighted machine learning framework achieves about 15-20% higher predictive accuracy. Moreover, the framework exhibits improved stability and adaptability by explicitly incorporating a spatial weighting matrix. From a global perspective, betweenness centrality, office density, bus stop coverage, and shopping density are identified as the dominant factors associated with traffic congestion across the four peak periods. The above results further reveal the nonlinear associations, and threshold effects between key explanatory variables and congestion levels. From a local perspective, the impacts of dominant factors display strong spatial clustering, with the pattern, magnitude, and direction of these associations varying significantly across different spatial regions and time periods. Overall, these findings enhance the understanding of urban transport dynamics, and provide valuable insights for urban planners and operators in developing the planning and management strategies to alleviate traffic congestion and improve urban livability.
How to cite: Chen, S., Qin, S., and Wang, W.: Exploring spatiotemporal heterogeneity and dynamics of the built environment impacts on urban traffic congestion with geographically weighted machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7329, https://doi.org/10.5194/egusphere-egu26-7329, 2026.
Global climate change driven by carbon dioxide (CO2) emissions has brought great challenges to countries around the world. Road transport takes an important part in CO2 emissions from transport sector. Although many studies have explored road transport CO2 emissions, few studies focused on CO2 emissions from both highway transport and urban road transport at city level. To address this gap, this study examines road passenger transport CO2 emissions across 31 provincial-level divisions and 325 cities in China using a bottom-up method, and analyzes their spatial-temporal characteristics and driving factors. The results show that road passenger transport CO2 emissions in China increased steadily from 2010 to 2023, with a compound annual growth rate of 9.20%. The overall spatial pattern is characterized by higher emissions in the eastern regions and lower emissions in the western regions. In addition, the city level CO2 emissions have significant spatial-temporal heterogeneity and disparities in CO2 emissions have narrowed over time. Globally driving factors analysis indicates the population size and economic development are key factors of CO2 emissions. At the city level, the effects of population, economy, road infrastructure, and land use on CO2 emissions exhibit spatial-temporal non-stationarity among cities, indicating dynamic changes in CO2 emission driving mechanisms across different periods and spaces. This study provides critical insights for policymakers aiming to reduce road transport CO2 emissions and achieve the objectives of carbon peaking and carbon neutrality.
How to cite: Qin, S., Chen, S., and Wang, W.: Spatial-temporal characteristics and driving factors of city level CO2 emissions from road passenger transport in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12565, https://doi.org/10.5194/egusphere-egu26-12565, 2026.
The share of carbon emissions in the transport sector has been increasing year by year, and how to optimise the urban environment in order to promote green and low-carbon travel for residents has become a research hotspot. Much of the existing research has focused on the environmental characteristics of trip origins and destinations, with less attention paid to the impacts of the built environment in the travel path. This study takes Shenzhen, a megacity in China, as a case study to investigate the impact of street environment characteristics on residents' green travel behavior to urban parks. First, we conducted a questionnaire survey among visitors to urban parks between March and May 2023, finally collecting a total of 3,970 questionnaires. Then, by extracting and analyzing 137,000 street view images, we extracted street characteristics such as the green view index, sky openness index, sidewalk proportion, and wall coverage on respondent’s traveling road to urban parks. These street features were combined with visitor’s socio-demographic data, urban park’s characteristics, and other urban built environment to construct a generalized ordered logistic regression model. The results indicate that street greenery and sky openness are key factors contributing to low-carbon travel (Std=-2.886, p=0.000***; Std=-2.249, p=0.004***). In 2023, the total visitor volume to 176 urban parks in Shenzhen reached approximately 492 million visits, generating a total travel-related carbon emission of about 41,300 tons. The carbon emissions exhibited significant spatial variations, with higher emissions observed in coastal areas such as Nanshan District and Futian District. Additionally, there was a decreasing trend in carbon emission intensity from west to east. Based on the findings from travel mechanism studies, we proposed three different scenarios of low carbon development, including scenario of transportation system optimization with Shenzhen's public transport modal share reaching 65%, scenario of energy efficiency improvement with new energy vehicles accounting for 40% of the fleet, and scenario of street environment enhancement with the green visibility increased to 0.18. It found that these three scenarios would contribute to carbon emission reduction by 27%, 17%, and 4%, respectively. Even if the improvement of the street built environment does not provide the highest carbon emission reduction, it still has high potential for low-carbon development in high-density populated cities. This study reveals the critical role of the built street environment in promoting low-carbon travel and provides new methods and empirical support for low-carbon urban planning. Additionally, through future scenario analysis, this research offers scientific evidence for developing adaptive policies aimed at reducing carbon emissions.
How to cite: Zhang, W. and Guan, H.: How urban street-scape visual features influence carbon emissions from residents visiting urban parks: A case study of Shenzhen, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2060, https://doi.org/10.5194/egusphere-egu26-2060, 2026.
Understanding how configurations of urban land-use features influence the spatial concentration of property crimes is vital for developing evidence-based safety and planning strategies. This study evaluates the geospatial associations between eighteen distinct land-use features and three types of property crimes: robbery, burglary, and theft in Nashik, India. Using property crime records from 2022 and locations of land-use features extracted through the Google Maps API, the research integrates Kernel Density Estimation (KDE), Location Quotient (LQ), and Multiple Linear Regression (MLR) modelling to uncover spatial patterns and statistically significant relationships.
Using KDE, property crime subzones were identified to capture local-scale variations of property crimes and land-use features. LQ analyses revealed uneven geographic concentrations of property crimes and land-use features across subzones. MLR models revealed that several land-use features, including ATMs, banks, hospitals, police stations, recreational places, shops, and transit places, significantly influence property crime occurrences. However, the magnitude of influence varies across different types of property crimes. One-way ANOVA test results confirmed that the MLR models were statistically significant, validating the geospatial associations between property crimes and land-use features.
The findings underscore the importance of integrating spatial analytics with urban planning to enhance safety. By demonstrating how geospatial patterns of land-use features influence property crimes, this study contributes to urban geoscience research and provides actionable insights for urban planners, urban designers, and policymakers. Future research could extend the analysis to spatiotemporal patterns or apply the methodology to cities with different built environment morphologies.
How to cite: Kumbhre, A. and Bharule, S.: Geospatial Associations between Property Crimes and Land-use Features: A Case of Nashik, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-706, https://doi.org/10.5194/egusphere-egu26-706, 2026.
Understanding the interactions between competing land policies is crucial for identifying governance challenges and assisting urban planners and policy analysts in make informed decisions. However, a methodology for incorporating land use patterns and the policy implementation processes within the framework of hierarchical land management remains underexplored. Here, we employ an agent-based model (ABM) to investigate how land use change occurs as policies intersect across different hierarchical levels and branches of government in Wuhan, China. Changes in land use arise from the interplay between five agents—the central level, the local level that incorporates three departments, and the village collective level—in the decisions on land acquisition, conversion, and reclamation. Four parameters characterize the enforcement levels of relevant policies, and multi-objective optimization with genetic algorithms was applied to calibrate them. The results show that: (1) Our ABM exhibits a figure of merit value of 0.3 at the city level and 0.58 in the larger urban area, indicating its capability to simulate real land use dynamics. (2) Policy implementation gaps led to high land conversion and low farmland reclamation. (3) The dynamic enforcement scenarios provide a viable pathway for negotiated governance, enabling demand-responsive rate attenuation and conflict mitigation, which is distinct from the exacerbated land use conflicts observed under the other scenarios. (4) Policy should incorporate adaptive mechanisms to maintain a buffer between competing land demands rather than binary constraints. This ABM introduces a novel hierarchical framework to decode policy interplay and implementation tensions, advancing sustainable land governance and urban planning insights.
How to cite: Gao, J.: How Do Interacting Policies Reshape Land Use Patterns? A Hierarchical, Cross-Departmental Agent-Based Exploration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5353, https://doi.org/10.5194/egusphere-egu26-5353, 2026.
Focusing on ports as important infrastructure units within complex urban systems, this study addresses the challenges of highly coupled multifunctional land use, complex spatial structures, and scale-sensitive modelling in port and port-adjacent areas by developing an integrated framework for multi-scale spatial representation and function–structure joint modelling. First, considering the relatively limited spatial extent of port areas and the high requirements for spatial information accuracy, land-use functional characteristics are described from the perspectives of composition, morphology, and spatial pattern. A two-level spatial scale selection model is proposed, in which landscape indices combined with mean change-point analysis are used to determine appropriate spatial representation scales for port land-use functions. On this basis, a joint optimisation model of land-use functions and spatial structure in port and port-adjacent areas is further developed. The model explicitly accounts for interactions among different land-use functions and spatial heterogeneity, and quantitatively optimises functional configurations and spatial structures by maximising a weighted objective of economic, ecological, and social benefits. Model validation based on case studies demonstrates that the proposed framework effectively captures the impacts of multifunctional coupling on overall system performance and reveals the pathways through which port subsystems influence urban spatial structure and environmental responses. The study provides a quantitative modelling approach to support the analysis and optimisation of key infrastructure subsystems within complex urban systems.
How to cite: Sun, J., Xu, Y., Wang, W., and Guo, Z.: Multi-Scale Spatial Representation and Function–Structure Joint Optimization in Ports and Adjacent Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4852, https://doi.org/10.5194/egusphere-egu26-4852, 2026.
In high-density built-up areas, a key sustainability challenge is the spatial mismatch between ecosystem service (ES) supply and residents’ daily activity demand. Focusing on Longgang District (Shenzhen, China), we develop a reproducible workflow to track the structural evolution of ecosystem service value (ESV) and diagnose ES supply–demand mismatch from 2010 to 2023. We estimate ESV for five benchmark years (2010/2013/2016/2019/2023) and derive a residents’ activity intensity (RAI) index from multi-source digital proxies, including POI density, road-network node density, public-transport accessibility, and night-time light brightness. All indicators are standardised and integrated using a weighted overlay approach.
To characterise mismatch patterns, we apply an ESV–RAI two-dimensional coupling framework that uses district-wide means as thresholds and classifies spatial units into four coupled types (high/low ESV × high/low RAI). Results indicate a pronounced structural transition characterised by “low-value expansion and mid-value collapse”: the share of low-ESV areas increases from ~52% (2010) to nearly 59% (2023), while the mid-value layer—functioning as an intermediate transmission layer for ES delivery—contracts sharply, with an inflection around 2019. Low-ESV–high-RAI areas become the dominant coupled type, suggesting persistent structural barriers to translating ecological value into residents’ everyday living spaces.
Finally, we interpret corridor-based mechanisms through a hierarchical “urban–landscape coexistence” corridor system comprising three levels: regional ecological skeleton corridors, built-up transition corridors, and fine-grained embedded corridors supporting daily mobility and recreation. We argue that coordinated optimisation across this corridor hierarchy is critical to rebuild the intermediate transmission layer and mitigate ES–activity spatial mismatch in complex high-density cities.
How to cite: Cui, T.: Rebuilding the “intermediate transmission layer” of ecosystem services in a high-density city: ESV–RAI coupling and hierarchical urban–landscape coexisting corridors in Longgang (Shenzhen), 2010–2023 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2188, https://doi.org/10.5194/egusphere-egu26-2188, 2026.
India’s rapid urbanization in major metropolitan cities has triggered significant shifts in land-use patterns, exerting far-reaching effects on regional environmental balance and the future resilience of local ecosystems. Bangalore, as a major metropolitan, has expanded its paved surface, placing its ecological systems under significant stress. Standard methods for modelling land use often struggle with complex spatial and temporal connections. These approaches also tend to lack strength when it comes to creating forecasts based on data for extended scenarios. This research presents an innovative hybrid transformer-based framework designed to predict fine-scale urban land-use dynamics within the city of Bengaluru. The multi-temporal Land use data of Bangalore were derived from satellite image classification, alongside static and dynamic geospatial predictor variables, which were considered necessary for land use forecasting based on the literature review. The proposed model architecture is a hybrid that integrates the Convolutional Neural Networks (CNNs) for spatial feature extraction with a Transformer-based encoder, leveraging self-attention mechanisms to effectively capture complex spatio-temporal dependencies from the data. A baseline model, using CNN encoders, has been successfully implemented and trained on the 2012-2023 dataset. Preliminary results yield a high overall accuracy and a Kappa score. The framework is designed to achieve state-of-the-art prediction accuracy by uniquely capturing both spatial and temporal dependencies. The evaluation focuses on key spatial metrics, where we project superior 'quantity' and 'allocation' agreement and a more accurate capture of the heterogeneous patterns of both 'infill' and 'expansion' growth. The validated framework will be used to simulate two critical future scenarios for Bangalore's development: a 'Business as Usual' (BAU) scenario based on historical trends and a policy-driven 'Sustainable Development' (SD) scenario. By providing geospatial forecasts of these radiating paths, this research will offer a dynamic decision-support tool, empowering planners to visualize and assess the long-term environmental and ecological impacts of future growth and to guide policy towards a more sustainable urban future.
Keywords: Deep Learning, Transformer, Convolutional Neural Networks, Classification
How to cite: Sah, D., Gautam, A., and Haridas Aithal, B.: Hybrid Deep Learning Framework for Urban Land-Use Prediction and Scenario Modelling of Bangalore, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-160, https://doi.org/10.5194/egusphere-egu26-160, 2026.
Abstract
This research explores the implementation of a Cut-Cell Method (CCM) within a Cartesian-grid CFD framework to mitigate the geometric distortions of building boundaries not aligned with the grid. By avoiding excessive grid refinement, CCM offers an efficient alternative for high-fidelity urban wind modeling. Performance was validated against AIJ wind-tunnel experimental data for Niigata, covering 80 points across 16 inflow directions. Comparisons with the conventional Stair-Step Method (SSM) demonstrate that CCM significantly enhances prediction accuracy. Quantitatively, the domain-averaged Index of Agreement increased by 18%, while RMSE and Mean Bias decreased by 18% and 55%, respectively. Detailed analysis reveals that while SSM creates artificial eddies and constricts street canyons, CCM more realistically captures building corners and spacing. However, in convergence and reattachment zones near tall and mid-rise buildings, CCM tends to overpredict reattachment lengths and enlarge secondary vortices, leading to localized wind speed underestimation. Despite these specific deviations, the method successfully brings all evaluated statistical indicators within recommended ranges. Overall, CCM provides a superior representation of complex urban morphology, proving essential for urban ventilation assessment, wind-corridor planning, and pollutant dispersion analysis. Future research should further evaluate this method under thermally stratified conditions to broaden its practical applicability.
Acknowledgments
This study was carried out with the support of 'R&D Program for Forest Science Technology '(Project No. "RS-2025-25404070")' provided by Korea Forest Service(Korea Forestry Promotion Institute).
Key words: CFD model, AIJ wind tunnel validation, cut-cell method, stair-step method
How to cite: Lee, H.-J., Kim, J.-J., and Cha, H.-W.: Improving urban wind-flow prediction in complex built environments using a Cut-cell Cartesian grid CFD model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3673, https://doi.org/10.5194/egusphere-egu26-3673, 2026.
Urban heat stress is governed by apparent temperature, yet many building-resolving CFD studies oversimplify humidity or impose it through external forcing. We develop a building-resolving CFD system with an online warm-phase microphysics coupling to simulate meter-scale wind–temperature–moisture variability in dense urban form. The model is applied to Jungnang-gu, Seoul (17–28 March 2024) and evaluated against hourly AWS 409 observations using MAE, RMSE, R², and 3D diagnostics. Relative to LDAPS, it better captures the temporal evolution of near-surface thermodynamic conditions, with RMSE = 1.17 °C for air temperature and 7.4% for relative humidity, and improves wind performance. Precipitation timing and variability are reproduced, though some hours show intensity bias, consistent with point-to-grid representativeness gaps and sensitivity to terminal-velocity assumptions. During rainfall, surface rain rate follows rainwater mass flux set by rain mixing ratio and net downward motion, and weak rain exhibits strong sub-kilometer intermittency. Urban ventilation structures shape coupled heat–moisture contrasts, producing hot–dry pockets under stagnation and cooler, moister conditions along ventilated corridors. These contrasts yield ~2–5 °C apparent-temperature differences over short distances, underscoring that heat-stress assessment should consider ventilation and humidity variability in addition to temperature.
Acknowledgments
This study was carried out with the support of 'R&D Program for Forest Science Technology '(Project No. "RS-2025-25404070")' provided by Korea Forest Service (Korea Forestry Promotion Institute).
Key words: Urban microclimate; CFD model; Warm-phase cloud microphysics; Humidity variability; Apparent temperature
How to cite: Lee, H., Wang, J.-W., Shin, J., Do, Y., and Kim, J.: A coupled CFD–Microphysics parameterization framework for urban-scale microclimate simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3687, https://doi.org/10.5194/egusphere-egu26-3687, 2026.
Industrial activity is among the principal sources of atmospheric emissions in urban areas. The ceramic industry, for example, emits high quantities of atmospheric contaminants, including particulate matter produced during clay extraction and production. The Santa Gertrudes ceramic industrial hub (SGCIH), located in the state of São Paulo, Brazil, is the sixth largest exporter of ceramic products in the world. The aim of this research is to present preliminary results concerning the effects of SGCIH ceramic industry agglomeration on the spatial distribution of particulate matter (PM10) in cities located in surrounding areas. In addition to the Santa Gertrudes (23,192 inhabitants; 48 ceramic industries in 2025) and Rio Claro (210,323 inhabitants; 18 industries), which are cities located in the SGCIH, we also analyzed PM10 data from four cities located far from the SGCIH, which have smaller numbers of ceramic industries: Piracicaba (440,835 inhabitants; 4 industries), Limeira (301,292 inhabitants; 2 industries), Americana (246,665 inhabitants; 1 industry), and Paulinia (116,674 inhabitants; 1 industry). Daily PM10 data from August 1, 2025, to September 30, 2025, obtained from six air quality monitoring stations located in the aforementioned cities, were used. PM10 medians were calculated and subsequently spatially interpolated using the inverse distance weighting algorithm. A first-degree trend surface map and a spatial autocorrelation pollutant map generated by the Getis‒Ord Gi statistical method were produced. The results revealed that the median PM10 was significantly greater in Santa Gertrudes (86 µg/m3) (p-value<0.001) than in Rio Claro (57 µg/m3), Limeira (47 µg/m3), Piracicaba (42 µg/m3), Americana (41 µg/m3) and Paulinia (32 µg/m3). Municipalities with a greater number of ceramic industries presented the highest concentration of PM10 (r=0.928; p-value=0.004). No significant association was observed between city population quantity and PM10 concentration (r=-0.257; p-value=0.718). These results may indicate that the effect of the number of ceramic industries on PM10 may be more important than city size is. The PM10 regional trend surface showed a slope toward the south-southeast, with the highest positive residual values of PM10 in the cities of the SGCIH and negative residual values in Americana and Paulinia, which were 34 km and 52 km from Santa Gertrudes, respectively. The spatial autocorrelation map revealed that PM10 presented a significant spatial autocorrelation index (z=1.874; p=0.039), with high PM10 values clustered up to a 20-km radius around the SGCIH. We concluded that particulate matter (PM10) in the atmosphere of the studied area presented a strong and positive spatial autocorrelation, which was influenced by the SGCIH location. We also reported that the PM10 concentration increases significantly with increasing proximity to the SGCIH. Moreover, compared with smaller cities, such as Santa Gertrudes and Rio Claro, which are located within SGCIH, populous cities located farther from the SGCIH presented lower PM10 concentrations. In the next step of this research, we will apply this spatial analysis methodology to evaluate the possible regional dispersion of MP10 and MP2.5 pollution using longer historical data series and a greater number of cities.
How to cite: Ferreira, M.: Spatial Analysis of Particulate Matter (PM10) Air Pollution in Cities Surrounding a Ceramic Industrial Hub in the State of São Paulo, Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4583, https://doi.org/10.5194/egusphere-egu26-4583, 2026.
This study examines how a high-rise building complex (HB) in the Haeundae District of Busan, South Korea influences nearby airflow structures and gust generation using the Parallelized Large-Eddy Simulation Model (PALM) Version 6.0. The simulations were validated by comparing modeled wind speeds with observations collected during the landfall of Typhoon Hinnamnor that impacted the Busan area. To examine the influence of building height, we conducted a set of scenario experiments in which HB height was modified from 0% to 75% of the actual height in 25% increments. The results indicate that increasing HB height strengthens downdrafts and enhances flow separation, which markedly elevates pedestrian-level mean wind speeds and turbulent wind speeds. Meanwhile, the gust factor decreases as HB height increases, implying that gust factor alone is insufficient for representing gust intensity under strong-wind conditions. To compensate for this limitation, we conducted an additional analysis centered on turbulent gusts, showing that gust intensity rises in densely built low-rise areas adjacent to HB as HB height increases. Gust probability analysis further suggests that extreme gust events were very rare during typhoon landfall; however, with greater HB height, the occurrence of moderate and strong gusts increases in regions where flow separation is intensified. Overall, these results advance the understanding of airflow structures around high-rise buildings and demonstrate that high-resolution Large-Eddy Simulation under extreme weather conditions can improve wind hazard assessment accuracy and support evidence-based decisions for pedestrian safety and urban resilience.
Keywords: Large-eddy simulation; CFD model; High-rise building; Gust; Typhoon landfall
How to cite: Kim, D.-H., Rho, J.-H., Lee, C.-H., and Kim, J.: Large-eddy simulations of typhoon-landfall gust generation in areas surrounding super high-rise buildings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5196, https://doi.org/10.5194/egusphere-egu26-5196, 2026.
Urban agglomerations function as integrated spatial systems in which infrastructure provision and public services jointly shape urban livability outcomes. While existing studies largely focus on individual cities, empirical evidence at the urban agglomeration scale remains limited. Addressing this gap, this study examines disparities in livability across major Chinese urban agglomerations from an integrated infrastructure and public service perspective.
Using multi-source statistical data covering multiple urban agglomerations in China, a comprehensive indicator system is constructed to capture transportation infrastructure, public green spaces, cultural facilities, and public service provision. Principal component analysis (PCA) and factor analysis are employed to extract key dimensions influencing livability, followed by cluster analysis to classify urban agglomerations based on their infrastructure structure and livability performance.
The results reveal pronounced heterogeneity in the capacity of infrastructure to support livability across urban agglomerations. Transportation infrastructure, measured by road network length, exerts a stronger influence on livability in rapidly expanding and spatially dispersed agglomerations, whereas public green spaces, cultural facilities, and public service facilities play a more prominent role in relatively mature and compact agglomerations. Based on these differentiated effects, urban agglomerations can be broadly categorized into two dominant types: transportation-oriented agglomerations, where mobility-oriented infrastructure constitutes the primary livability foundation, and ecology-oriented agglomerations, where green spaces, environmental quality, and public service provision contribute more substantially to livability enhancement.
By integrating infrastructure and public services into a unified analytical framework at the urban agglomeration scale, this study extends the empirical understanding of livability formation mechanisms beyond the city level. The findings offer policy-relevant insights for differentiated infrastructure and public service strategies, emphasizing the importance of aligning development priorities with the structural characteristics and developmental stages of urban agglomerations.
How to cite: jing, S. and zhao, H.: Spatial Disparities in Livability across Chinese Urban Agglomerations:An Infrastructure and Public Service Perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6127, https://doi.org/10.5194/egusphere-egu26-6127, 2026.
This study focuses on bridge risk assessment and the analysis of alternative road network scenarios by using traffic simulation methods. The primary objective of the research is to assess the risk associated with the Neochori–Katochi Bridge and to analyze alternative road network scenarios in the event of partial or total loss of its serviceability, particularly due to seismic events. The bridge is located at the Municipality of the Sacred City of Messolonghi, in the Regional Unit of Aetolia-Acarnania, western Greece, and constitutes a critical link in the local and regional road network. The study area corresponds to the expanded Municipality, with approximately 32,000 inhabitants, comprising the municipal units of Messolonghi,
Aitoliko and Oiniades. The Oiniades unit, with about 9,000 inhabitants, hosts the Neochori–Katochi Bridge, which lies along a provincial road and serves as a key connection for daily commuting, freight transport and regional accessibility. The bridge has an approximate length of 190 m and is located at coordinates 38.4094° N, 21.2605° E. Given its strategic importance, any disruption would have significant social and economic impacts to the wider area. To address these issues, an integrated methodological framework was applied, combining seismic, traffic and behavioural analyses. Data collection included traffic flows, speeds and geometric characteristics of the road network, as well as structural and seismic information related to the
bridge. In parallel, a questionnaire-based survey was conducted among residents and drivers of the surrounding areas to capture travel behaviour and route choice preferences under both normal and disrupted traffic conditions. Based on seismic hazard maps previously developed for the study area, our analysis evaluates the seismic risk and functional importance of the bridge. Secondly, multiple traffic management and network reconfiguration scenarios are developed to represent potential conditions after loss of bridge serviceability. These scenarios include alternative routing strategies designed to accommodate displaced traffic flows while minimizing traffic congestion and travel delays. Traffic microsimulation models are
used to analyse and compare the proposed scenarios. The evaluation focuses on key performance indicators such as volume to capacity ratios (v/c) and travel delays. Specific attention is given to the ability of the surrounding road network to absorb rerouted traffic without severe degradation of operational conditions. Moreover, the questionnaire survey data are analysed using discrete choice models, in order to identify the factors that influence drivers’ selection of alternative routes. Variables such as travel time, perceived safety, reliability and road characteristics are examined
to understand how users adapt their behaviour in response to local network disruptions. Overall, the current research offers a comprehensive approach to bridge risk assessment that goes beyond structural considerations by focusing on traffic performance and road users’ behaviour. The findings support the development of more resilient road network planning strategies and could assist in practical guidance for emergency traffic management and long-term infrastructure improvement in seismically active regions.
How to cite: Lyros, E., Barkas, S., and Theofilatos, A.: Risk Assessment of Bridges and Analysis of Road Traffic Scenarios through Traffic Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9182, https://doi.org/10.5194/egusphere-egu26-9182, 2026.
This study aims to develop a high-resolution forecast–data assimilation cycling system to support Urban Air Mobility (UAM) operations. We implemented and evaluated a WRF-based 3DVAR–IAU (Incremental Analysis Update) cycling framework. Although 3DVAR is computationally efficient and suitable for high-frequency assimilation, directly incorporating the analysis into model integration can cause initial forecast discontinuities and spin-up issues. IAU mitigates these problems by gradually applying the analysis increment over the assimilation window.
The coupled WRF–WRFDA cycling procedure was automated to repeatedly perform 3DVAR analyses and subsequent forecasts using IAU. A preprocessing workflow was also established to process surface and vertical-profile observations, including LiDAR measurements, for data assimilation. To evaluate the performance of the system, we conducted two experiments: a CYCLE experiment (applying 3DVAR–IAU cycling) and a NOCYCLE experiment (a WRF-only free forecast without data assimilation). Forecast performance was assessed against observations using bias, root-mean-square error (RMSE), and correlation coefficients.
The results indicate that applying IAU reduces initial forecast discontinuities and leads to more stable early forecast behavior compared to NOCYCLE. Time–height cross-sections of wind speed error show that the CYCLE experiment generally produces smaller errors than NOCYCLE throughout the evaluation period. Consistently, the CYCLE experiment tends to yield lower RMSE and higher correlations relative to NOCYCLE for most vertical levels, indicating improved agreement with observations. Overall, these findings suggest that the proposed 3DVAR–IAU cycling approach can enhance the quality of assimilated initial conditions and contribute to continuous performance improvements in UAM-specific high-resolution prediction systems.
Key words: WRF, WRFDA, 3DVAR, IAU(Incremental Analysis Update), Cycling data assimilation
How to cite: Cho, S.-I. and Shin, J.: Performance Evaluation of a UAM-Specific High-Resolution Forecast-Data Assimilation Cycling System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16621, https://doi.org/10.5194/egusphere-egu26-16621, 2026.
Despite being a key determinant of urban energy demand, anthropogenic heat emissions, and indirect carbon dioxide emissions, precise observational data for entire cities remains scarce. This study develops a data-driven framework that reconstructs monthly electricity consumption for individual parcels across Seoul by integrating building characteristics, microclimate, and human activity. We collected millions of monthly observation records from 2020 to 2024 and converted billed electricity quantities into electricity use intensity (EUI, kWh m⁻²) using building floor areas. These records were linked with parcel-level attributes (e.g., land use, land price, gross floor area, construction year), local climate zones, socioeconomic indicators, high-density Smart Seoul City Data of Things (S-DoT) meteorological observations, and hourly living population data. Random Forest and LightGBM models were trained and evaluated using 5-fold cross-validation. LightGBM demonstrated the best performance across all parcels, achieving a Mean Absolute Error (MAE) of 6,712 kWh, a Weighted Mean Absolute Percentage Error of 39.6%, and an R² of 0.709. SHAP (SHapley Additive exPlanations) analysis revealed urban land price, building size, construction year, and income as key determinants of EUI. Concurrently, the living population and microclimate variables exerted nonlinear additional effects, particularly in high-activity commercial districts. High-density, high-rise business centers exhibited high power intensity despite relatively mild outdoor maximum temperatures, suggesting a decoupling between indoor cooling demand and the surrounding thermal environment. The estimated dataset for building-specific electricity consumption across the entire city provides essential data for artificial heat estimation, energy planning, and future urban climate and emissions modeling.
How to cite: Lee, Y. and Im, J.: Citywide Parcel-Level Electricity Use Estimation from Building GIS, Microclimate, and Human Activity Data in Seoul, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17064, https://doi.org/10.5194/egusphere-egu26-17064, 2026.
Cities are complex systems and its many components strongly interrelated. Still, urban scaling studies have observed regularities in urban output across multiple national urban systems. Urban scaling studies examine how urban characteristics change systematically with population size. Previous research has shown that socio-economic outputs, such as GDP and patents, typically scale superlinearly, meaning that they increase more than proportionally with population size. In contrast, infrastructural quantities, such as road length, tend to scale sublinearly. Beyond average trends, scaling residuals identify cities that over- or underperform relative to their size, offering insights into additional drivers of urban outcomes and a tool for monitoring policy impacts.
While urban scaling research has largely focused on socio-economic and infrastructural features, studies have shown that health indicators such as obesity, smoking, diabetes and influenza also exhibit scaling relationships with city size. Moreover, recent work has found non-linear scaling relationships for well-being indicators in Dutch cities. However, urban well-being scaling has not yet been examined systematically across different national contexts. It therefore remains unknown whether the observed relationships between city size and well-being are the same across different national contexts. Furthermore, the potential of scaling residuals analysis for well-being policy remains to be explored.
This study uses a unique dataset provided by Gallup to study urban scaling for well-being for 18 countries, with varying geographical contexts and economic development stages. The dataset covers a range of topics related to well-being. The same questions and methodology are used for all countries, enabling country comparisons. We show that some well-being indicators exhibit scaling relationships and that scaling relationships depend on the country context. In addition, we explore whether out- or underperforming cities share common urban environmental characteristics.
With current rapid urbanisation it is important to increase our understanding of urban – well-being interactions. Urban scaling studies of well-being can increase our understanding of well-being patterns and outliers in a system of cities.
How to cite: van Hemmen, M., Ligtenberg, A., de Bruin, S., Sabel, C., Gort, G., Vreugdenhil, C., Frome, H., Foy, D., and de Beurs, K. M.: Urban scaling of well-being, a cross-country comparison, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19378, https://doi.org/10.5194/egusphere-egu26-19378, 2026.
Weather and climate simulations continue to evolve towards higher resolutions. This allows them to resolve small-scale processes more explicitly, but the added value is constrained by the availability of accurate localized data, particularly in urban areas where there is a large variety in urban structures and surface properties. Currently, mesoscale models like WRF rely on typological classifications such as the Local Climate Zones. Despite their proven effectiveness, they bundle multiple properties into a single urban class, which means individual parameters cannot be represented independently. Recent studies have introduced fine-scale explicit datasets on various urban properties such as building heights and vegetation fraction. But to the best of our knowledge, local datasets of albedo and emissivity of urban surfaces are not available at scale.
In the “Urban-M4” project, we are exploring whether street view imagery can provide these missing radiative properties for use in urban weather models. Such imagery is widely available nowadays, either as proprietary data (e.g. Google Streetview), but also increasingly as open data from municipalities or through crowdsourcing platforms such as Mapillary and Kartaview. Simultaneously, computer vision methods have become much more powerful. State of the art models can now perform advanced tasks including detection of a wide range of objects and materials based on free prompts. This allows us to extract individual buildings or building parts from street view images and analyse their characteristics. As a proxy for albedo, we have been experimenting with various brightness metrics of building pixels, resulting in a first preliminary map of façade albedo for Amsterdam based on 100k images. We are currently setting up an observational campaign to validate and refine this method. To eventually estimate emissivity as well, we are investigating the capability of existing computer vision models to recognize (urban) materials.
We are developing this openly on GitHub, and to facilitate adoption the functionality is bundled in a Python package called ‘streetscapes’. It includes tools for retrieving images from various sources and running a number of computer vision models. While it is possible to automatically segments millions of images, the quality of the results is still affected by the heterogeneity of images and the varying accuracy of the models. Therefore, we aim to further develop the package to accommodate a ‘human-in-the-loop’ workflow, so it becomes manageable to inspect images and their metadata in a spatial context, and filter or modify images and metadata from a graphical interface. We have modified WRF to enable ingestion of 2D maps of urban albedo and emissivity and are preparing the first tests.
How to cite: Kalverla, P., Schilperoort, B., Hadjiivanov, A., Steeneveld, G.-J., Timmermans, W., Sandvik, B. E., Milosevic, D., Gadde, S., and Hafkamp, V.: Estimating urban albedo and emissivity from street view imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19442, https://doi.org/10.5194/egusphere-egu26-19442, 2026.
Large language models (LLMs) offer the potential to make complex scientific software accessible through natural language interfaces. However, LLMs hallucinate by design—generating plausible but incorrect physics, inventing parameter values, and confidently explaining non-existent model features. For scientific computing, this poses unacceptable risks to research integrity.
We present a solution: a Model Context Protocol (MCP) server for the Surface Urban Energy and Water balance Scheme (SUEWS). MCP, introduced by Anthropic in 2024 and now adopted by major AI providers, enables a fundamental architectural shift from AI-generated code to AI-orchestrated validated operations. Rather than prompting an LLM to write Python code and hoping it implements correct physics, we provide 15 typed tools with validated inputs and outputs. The AI can orchestrate these tools but cannot bypass validation or invent new operations.
The SUEWS-MCP server implements tools across five categories: configuration (create, update, validate, inspect), knowledge (list models, access schema, retrieve physics implementations), simulation (run SUEWS), utilities (calibrate OHM coefficients, document variables), and analysis (load and export results). Each tool enforces physical constraints—albedo must lie between 0 and 1, temperatures must exceed 0 K—rejecting invalid configurations before computation.
A key innovation addresses hallucination at the knowledge level. When explaining how SUEWS calculates storage heat flux, the AI retrieves and interprets actual Fortran source code rather than generating explanations from training data. If the implementation changes, the explanation changes. This direct coupling between AI responses and model code ensures trustworthy scientific communication.
We evaluated the system using 50 test questions across difficulty levels, comparing four configurations: baseline (no tools), reference (full repository access), and two MCP-enabled models. MCP improved answer accuracy by 18–20% over baseline, with largest gains on physics questions requiring equations and implementation details. The smaller model with MCP tools outperformed the larger model, demonstrating that tool access matters more than model size for domain-specific applications.
This work demonstrates that AI can make scientific software accessible without sacrificing rigour. Natural language interfaces become viable for urban climate modelling when AI orchestrates validated operations rather than generating unchecked code. The approach generalises: any computational tool with well-defined operations can expose an MCP interface, enabling trustworthy AI assistance across scientific domains.
How to cite: Sun, T.: Talking to Cities: A Model Context Protocol Server for SUEWS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20672, https://doi.org/10.5194/egusphere-egu26-20672, 2026.
City-scale 3D building modelling is essential for understanding complex cities, but it remains difficult due to heterogeneous data sources. The process is particularly challenging with cross-sourced point clouds and processing them in tiles. Differences in density and noise between LiDAR and photogrammetry, combined with tile boundaries that cut through buildings, often lead to incomplete or inconsistent models.
In this study, we extend the plane-based reconstruction method originally designed for single buildings to work at a city scale. We propose a workflow that handles tiles intelligently. By using buffered processing and clustering, we ensure that buildings spanning multiple tiles are reconstructed completely. We also introduce a strategy to assign each building to a single tile, which avoids duplicates and keeps the process scalable. We evaluated this approach in dense urban areas with diverse building types. The results show that the method generates consistent models across tile boundaries while maintaining high geometric accuracy. This framework supports automated modelling of large areas and provides a solid foundation for analyzing complex built environments.
How to cite: Parvaz, S. and Teferle, F. N.: From single buildings to cities: accurate LOD modelling from tiled airborne cross-source point clouds., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21394, https://doi.org/10.5194/egusphere-egu26-21394, 2026.
Rapid urban expansion in transitional regions intensifies interactions between human activities and ecological systems, yet quantitatively capturing the spatial balance between anthropogenic pressure and ecological capacity remains a major scientific challenge. Existing approaches often conceptualize human–environment interactions or focus on land-change outcomes without explicitly measuring coupling strength, direction, and spatial dynamics. This study introduces a quantitative geospatial analytics framework to assess human–environment coupling across space and time, using Prishtina (Kosovo) as a representative transitional urban system.
The framework conceptualizes the urban system through two spatially explicit and normalized gradients: an anthropogenic forcing gradient, representing cumulative human pressure, and a geo-ecological capacity gradient, capturing the environment’s ability to regulate and respond to that pressure. These gradients are derived from harmonized multi-source geospatial indicators, including satellite-based environmental proxies and complementary spatial datasets, and are integrated through standardized preprocessing, normalization, and data-driven weighting procedures to ensure spatial comparability and analytical robustness.
Human–environment coupling is quantified using a normalized spatial index ranging from 0 to 1, where higher values indicate balanced interactions and lower values signal increasing imbalance between anthropogenic forcing and ecological capacity. Spatial statistical techniques, including spatial autocorrelation and hotspot analysis, are applied to examine clustering patterns, transition zones, and emerging disequilibrium, while temporal analysis supports the exploration of coupling trajectories under rapid urban transformation.
The proposed framework enables spatially explicit investigation of human–environment coupling heterogeneity within Prishtina and supports the identification of zones characterized by balance, dominance, or transition. By integrating multi-source geospatial data with advanced geospatial analytics, the study offers a transferable quantitative approach for diagnosing human–environment interactions and informing sustainability-oriented spatial planning in transitional urban regions.
How to cite: Berila, A., Chassin, T., and Sulzer, W.: Spatial quantification of human–environment coupling using multi-source geospatial data and geospatial analytics: evidence from Prishtina, Kosovo, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15419, https://doi.org/10.5194/egusphere-egu26-15419, 2026.
How to cite: Liu, X. and Ma, S.: Spatial identification of "production living ecological" spaces in urban-rural regional system by integrating multiple source data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3340, https://doi.org/10.5194/egusphere-egu26-3340, 2026.
Background
Geographic access to healthcare is considered in urban environments to aid strategic planning and evaluation of healthcare interventions. Evidence has shown that, in addition to geographic access, multiple factors influence the travel behavior of people seeking health services. Yet most studies have largely framed the issue from the perspective of how far people should travel, while few have explained how far people actually travel, thereby overlooking potential insights into travel behaviors.
Methods
In this research, we used in-person interviews, telecom mobility data, and spatial analysis to estimate people’s accessibility to TB service points in Pakistan. Characteristics of TB service points, including urbanicity, socio-economic class, and administrative province, were further analysed for their association with TB service accessibility. We compared the accessibility rates obtained from in-person interviews among people visiting TB service points with telecom mobility data to assess whether measured population movements to TB service points differ by data source.
Results
Our results show that significant variations in TB service accessibility are associated with administrative provinces, and the density of TB service points. We found a significant difference between the geographic accessibility measured by the two data sources across distance bands. We also found that relative, and not absolute, TB service accessibility is similar across both data sources, with a steeper decay curve in the interview data.
Conclusions
While telecom mobility data and survey data capture different population movement patterns, both provide insights which may help to align service availability with population needs and improve the well-being of the population.
How to cite: Ifejube, O. J., Mergenthaler, C., Stephens, P., Saif, U., Ng, Y. T., Butt, B., Syed, R., Cobelens, F., and Rood, E.: Evaluating tuberculosis service accessibility in Pakistan by analyzing population movement patterns from telecom mobility and survey data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10686, https://doi.org/10.5194/egusphere-egu26-10686, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 4
EGU26-18030 | Posters virtual | VPS32
Urban Geo-climate Footprint (UGF) for Classifying Italian Cities by Geological and Climatic FeaturesWed, 06 May, 15:18–15:21 (CEST) vPoster spot 4
Urban areas are increasingly exposed to complex interactions of geological, climatic, and anthropogenic pressures. The UGF methodology (Lentini et al., 2024), already applied to more than 40 European cities, provides a structured approach to assess these multi-dimensional conditions and support urban planning and risk management. In this study, UGF was applied to 21 Italian regional capitals, selected to capture the geographic, climatic, and structural diversity of the country, from alpine regions to coastal plains and southern volcanic districts. Italy thus represents an ideal natural laboratory to test the methodology, offering a wide range of geological and climatic settings within a single country.
The methodology integrates multiple drivers: deep geological processes (DEE, e.g., seismicity and volcanism, gas emissions), superficial processes (SUP, e.g., landslides, subsidence, floods, coastal erosion), exogenous processes (EXO, e.g. heavy rains, droughts, sea level change), geological complexity (GEO, e.g., stratigraphy, groundwater, slope), and anthropogenic pressures (SAP, e.g., land use change, soil sealing, pollution). For each city, the UGF Index quantifies the intensity of these drivers, allowing classification into four UGF classes that reflect the spectrum of urban geo-climatic conditions.
Results from Italy highlight a wide range of situations: Trento and Campobasso fall into UGF-1, indicating minimal geologic-climatic pressures, while Napoli and Genova are classified as UGF-4 due to the combined influence of high-intensity drivers, including active volcanism, high seismicity, subsidence, and strong anthropogenic pressures. Intermediate classes (UGF-2 and UGF-3) include cities such as Milano, Firenze, Bari, and Venezia, where moderate interactions of these drivers prevail.
Geographical patterns emerge from the analysis of drivers. UGF index generally increases southward, reflecting higher exposure to Mediterranean climatic extremes, active seismicity along the Apennines, and southern volcanic districts. Coastal cities show high SUP and EXO contributions due to erosion, storm surges, and sea-level rise, while SAP is prominent in large urban centers, reflecting land consumption, groundwater contamination, and subsurface instability. The GEO driver is relatively consistent across the country, emphasizing Italy’s intrinsic geodiversity.
It is important to note that UGF classes do not rank cities by “risk” or “misfortune,” but rather identify the prevailing geological, climatic, and anthropogenic pressures to support planning and mitigation. A semi-qualitative assessment of geo-benefits further highlights positive contributions to urban systems, with cities such as Milano, Napoli, Palermo, Roma, Trento, Trieste, and Venezia showing higher scores.
Overall, the UGF approach provides an explicit and concise understanding of urban geo-climatic conditions, also integrating natural hazards, climatic pressures, and human impacts. It highlights local differences often masked by traditional indicators and offers a valuable tool for evidence-based urban planning, climate adaptation, risk reduction, and sustainable urban regeneration. The methodology emphasizes the recognition of the subsurface as a primary urban infrastructure, essential for resilient city development.
Lentini, A., Galve, J. P., Benjumea, B., Bricker, S., Devleeschouwer, X., Guarino, P. M., Kearsey, T., Leoni, G., Puzzilli, L. M., Romeo, S., Venvik, G., & La Vigna, F. (2024). The Urban Geo-climate Footprint approach: Enhancing urban resilience through improved geological conceptualisation. Cities, 145, 105287. https://doi.org/10.1016/j.cities.2024.105287
How to cite: Romeo, S., Bonasera, M., Campolunghi, M. P., Di Paola, G., Guarino, P. M., Leoni, G., Proietti, R., and La Vigna, F.: Urban Geo-climate Footprint (UGF) for Classifying Italian Cities by Geological and Climatic Features, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18030, https://doi.org/10.5194/egusphere-egu26-18030, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot 1b
EGU26-18190 | ECS | Posters virtual | VPS23
A Multi-Criteria Spatial Modelling Framework for Port-Urban Growth in a Coastal City System: The Vizhinjam-Trivandrum Corridor, IndiaThu, 07 May, 15:12–15:15 (CEST) vPoster spot 1b
Coastal port-city regions operate as intricate urban systems, where transport infrastructure, land-use change, environmental limits, and socio-economic forces interact across multiple spatial and temporal scales. In rapidly evolving coastal cities, port-led development may bring economic opportunities, but it also tends to introduce new environmental risks and social tensions. This duality is especially visible in cities where growth is unfolding faster than planning frameworks can adapt, which suggests a need for analytical approaches that are both integrated and spatially grounded. This study develops a multi-criteria spatial framework to assess land suitability and identify potential growth nodes along the Vizhinjam-Trivandrum corridor in southern India shaped by the development of the Vizhinjam International Seaport.
The framework integrates multi-temporal remote sensing data, geospatial indicators, and expert-derived weights using the Analytic Hierarchy Process (AHP) within a GIS environment. Land-use and land-cover dynamics from 2005 to 2025 are analysed alongside transport connectivity, environmental sensitivity, geo-hazard exposure, economic feasibility, and socio-regulatory constraints. These factors are represented as interconnected components of the urban system. To balance analytical rigour with practical applicability, literature-based indicators are consolidated into a concise hierarchical structure. This structure encompasses physical environmental, infrastructural, economic, and socio-community dimensions. Expert judgement is incorporated through structured pairwise comparisons, producing a transparent and reproducible weighting scheme.
The resulting analysis produces a spatial suitability surface that highlights development potential and constraints across the corridor. Early findings indicate that proximity to port infrastructure and transport connectivity strongly influence emerging growth patterns. At the same time, this advantage is often offset by environmental sensitivity and hazard exposure. These overlaps point to some of the core trade-offs that define port-city development, particularly in ecologically fragile coastal settings. By combining urban change monitoring with spatial decision-support analysis, the proposed framework demonstrates the value of integrated approaches for supporting sustainable and resilient development in complex coastal urban environments.
How to cite: Bala, D., Paul, S. K., and Yadav, A.: A Multi-Criteria Spatial Modelling Framework for Port-Urban Growth in a Coastal City System: The Vizhinjam-Trivandrum Corridor, India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18190, https://doi.org/10.5194/egusphere-egu26-18190, 2026.
