Urban geometry plays a key role in determining the urban climate through its complex shading and trapping effects on solar radiation. As a result, urban albedo is typically lower than rural albedo, suggesting a larger solar heat gain in urban areas. As cities grow larger and more heterogeneous in geometry, quantifying the impact of this variation on albedo at the city scale requires computationally efficient models that can also resolve the 3D geometry of real cities. To this end, we developed a simplified 3D urban radiation model and used it to examine the variations in albedo due to heterogeneous geometry in the city of Shanghai. The model reduces computational complexity from O(n²) to O(n) while maintaining an accuracy within 5% compared to traditional 3D models. The case study in Shanghai shows that albedo has a linear relationship with building height but varies nonlinearly with changes in building density. The lowest albedo occurs when the building density (λp) is around 0.2 and the building height-to-length (H/L) ratio is 6, while occurs at λp > 0.3 with H/L = 1. This suggests that optimizing building geometry could improve the urban climate and potentially being used to increase the utilization of solar energy.
How to cite: Zhou, H. and Wang, K.: Development of simplified 3D urban radiation model to examine the variations of albedo due to heterogenous geometry in the city of Shanghai, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4805, https://doi.org/10.5194/egusphere-egu25-4805, 2025.
Urban heat storage (Qs) is an essential component of urban surface energy balance. Urban with 3D structure has larger surface area than rural and urban would absorb and release more energy than rural. Qs is the main factor for urban heat island (UHI) at nighttime. The quantitative contribution of Qs to UHI is still unclear, due to the lack of a spatio-temporal continuous Qs dataset. In this study, firstly, we developed an urban surface thermal inertia model considering diurnal variation of surface temperature (LST) using hourly LST of Himawari-8. Secondly, the hourly Qs at 2-km resolution in three urban agglomerations in China was simulated by a half-order time derivative method which derived from combining the one-dimensional heat diffusion equation and Fourier’s law for heat conduction, using the urban thermal inertia model and hourly Himawari-8 LST. Thirdly, the relationship between Qs and air temperature (Ta) was studied at different time scales (day and nighttime, four seasons) and different LCZs (local climate zones). The Ta was derived from the interpolation of dense automatic weather stations with more than 10000 sites in China. Finally, some urban heat mitigation measures were provided based on the above analysis. Based on the in-situ observation, the accuracy of urban thermal inertial in this study was higher than other model, RMSE, MAE, R2 were improved from 4.65 K, 3.58 K and 0.88 to 1.86 K, 1.53 K and 0.97. In addition, the simulated Qs were validated by the observed Qs (the minus of net radiation, sensible and latent heat flux from in-situ flux tower, and anthropogenic heat flux simulation) in Beijing, Shanghai and Guangzhou, R2 could be up to 0.92. The results showed that, Qs was more consistent with Ta at nighttime than daytime, with R2 of 0.96 and 0.1, respectively. That showed that Qs is the main factor for nighttime UHI in this study area. During nighttime, the high-rise building has higher Ta than low-rise building, due to higher Qs and release more energy than low-rise. In natural surfaces, water has larger Qs and higher Ta than dense trees. The loop (between hourly Qs and hourly Ta) shape were different at different LCZs, with different loop area and loop slope. Based on the loop area and slope, we found that high-rise building had higher UHI but varied quickly, however, low-rise UHI is lower but would last longer. The water surface in nighttime is also heat source and has a longer time UHI. Therefore, the high-rise building and water surface are not conductive to alleviating the nighttime UHI.
How to cite: Li, N., Guo, F., Dou, J., Ma, Y., and Miao, S.: Remote sensing-driven analysis of hourly urban heat storage and its effects on urban heat islands in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5462, https://doi.org/10.5194/egusphere-egu25-5462, 2025.
The frequent occurrences of heat wave events and air pollution episodes have become pressing global concerns. Concurrent heat and ozone pollution events, in particular, have been widely documented across various regions and often result in more severe impacts compared to isolated stressors, leading to increased mortality and morbidity rates. However, our understanding of these compound events in urban environments, particularly their dynamics under different background climates and urban settings, remains limited. In this study, we systematically characterized the frequency, intensity, and duration of compound heat and ozone pollution events during warm seasons across all urban areas in the continental U.S. using long-term, high-resolution daily air pollution and air temperature datasets. Results suggest that urban heat waves, defined by daily maximum temperature, were more frequent, more intense, and longer lasting than their rural counterparts, primarily due to the urban heat island effect. In contrast, over half of the U.S. cities experienced fewer, less intense, and shorter ozone pollution episodes than surrounding rural environments. The spatially heterogeneous disparities in ozone pollution episodes among cities are mainly attributed to whether ozone production is limited by VOC or NOx, as revealed by time series analyses. Despite the overall decreasing trend of surface ozone concentrations during the last two decades, 89% of U.S. cities experienced more frequent compound heat and ozone pollution episodes than rural areas. Additionally, the cumulative heat and ozone intensities were higher in 91% and 88% of U.S. cities, respectively, than in their rural backgrounds. The duration of compound events tends to be shorter in urban areas. These findings highlight the dependence of such compound events on local and background conditions, emphasizing the need for locally tailored mitigation plans to reduce their impacts. This study also calls for detailed regional numerical simulations to elucidate the mechanisms driving these events.
How to cite: Wang, C., Hu, X.-M., and Leffel, J.: Compound heat and ozone pollution in urban areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13711, https://doi.org/10.5194/egusphere-egu25-13711, 2025.
Urban thermal environment is known to be strongly affected by the composition of urban land cover, with densely built-up areas characterised with distinctly higher temperatures than densely vegetated ones. These observations come from the analysis of relatively coarse land surface temperature (LST) satellite data and conclusions are typically derived for city districts or other variedly defined mapping units. Whilst these analyses provide useful insights to excess heat mitigation at city scales, these do not describe the nuance of the thermal response of the heterogenous urban form at local levels. This study investigated the relationship between LST of variedly configured immediate neighbourhoods of single patches of different land cover types (buildings, paved, grass, trees) extending from 0 to 100m away to determine the shape and type of urban features including water that contribute to the formation of cold and hot urban spaces. The study area comprised three English towns: Milton Keynes, Bedford, and Luton, collectively comprising a wide range of urban forms that are representative for England and other European towns located in the temperate climate zone. The analysis was carried out for two summer days a month apart, capturing the different thermal responses as temperatures rise over summer. The microscale of the analysis was enabled by downscaled LST obtained from Landsat 8 thermal bands acquired at 100m resolution down to 2m, supported by high resolution spectral indices derived from very high resolution hyperspectral aerial imagery. Patch-level landscape metrics were used to describe the shape of the different patches of urban land cover derived from land cover map at 2m resolution. K-means analysis was used to determine groups of land cover patches of a given type with common thermal and spatial properties. Random forest regression algorithm was used to identify the important descriptors of LST for these groups and ANOVA analysis to determine statistically significant effects for various spatial configuration metrics. The findings suggested that the coldest patches of buildings, grass and paved were associated with highly aggregated patches of trees in the immediate neighbourhood, with PLADJ greater than 73 to 85% and COHESION greater than 93 to 97%, and buildings requiring somewhat lower aggregation levels than grass or paved. Hottest patches of these land cover classes were associated with PLADJ smaller than 63–69% and COHESION smaller than 83–87%, with elevation and distance to water being the most important factors, whose importance increased as the summer progressed. Overall, this study provided further insights into the spatial characteristics of patches of common land cover types in urban areas that contribute to the formation of particularly hot or cold urban spaces, which can facilitate the design of climate resilient cities.
How to cite: Zawadzka, J., Harris, J., and Corstanje, R.: The importance of spatial configuration of urban form in local temperature regulation investigated from very high resolution LST and land cover data and landscape metrics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19759, https://doi.org/10.5194/egusphere-egu25-19759, 2025.
The Vulcan Project version 4.0 emissions data product has generated all fossil fuel CO2 emissions across the US landscape, every hour, from 2010-2022 down to the scale of neighborhoods. From this complex landscape, we have extracted FFCO2 emissions for every urban area, following multiple commonly used urban definitions. The information extracted includes both Scope 1 and Scope 2 emissions with a wide array of “functional” attributes such as sector, fuel, vehicle class, building class, road class, and industrial sub-sector. Here, we analyze ~4000 US cities in terms of their size scaling properties. In particular, urban scaling properties provide novel insight into emergent properties such as the relationship between urban metabolism and urban size properties. This relationship varies by region and is indicative of the relationship between urban form and economies of scale including implications for infrastructural development and urban sprawl.
How to cite: Gurney, K., Dass, P., Lobo, J., and Shutters, S.: Scaling Properties of Carbon Emissions in US Cities: Bigger is Better, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3332, https://doi.org/10.5194/egusphere-egu25-3332, 2025.
The fast-evolving nature of urbanization and its complex patterns require precise and interpretable machine learning models to effectively predict urban growth. To address this challenge, this study introduces a novel framework combining Hybrid Intelligence and Explainable AI (XAI), specifically Shapley Additive Explanations (SHAP) to improve model performance, robustness, and transparency. Using a weighted ensemble technique, the proposed method systemically integrates linear, tree-based, and neural network models to propose a hybrid of Elastic Net, XGBoost, and Wide & Deep Neural Network (EN-XGB-WDN) frameworks for urban growth prediction. The methodology follows a multistep approach and includes the development of the hybrid model, its evaluation for binary classification, integration of SHAP-based feature analysis to identify key drivers of urban growth and improve model interpretability, retraining of the hybrid model to increase accuracy and reduce overfitting, and validation of the proposed framework using standard evaluation metrics including accuracy, precision, recall, F1 score, and AUC. The hybrid model achieves an overall accuracy of 87.34%, a weighted F1-score of 87.18%, and an AUC of 0.9442. The SHAP analysis revealed that Drive Time (DT), Distance from Roads (DfR), and Elevation are the most impactful features to understand the dynamics of urban growth. The findings revealed how variations in specific features, such as higher DT and lower DfR, significantly affect urban growth probabilities. The hybrid model also categorized urban growth probabilities into five classes: very low (40.62%), low (23.27%), moderate (15.38%), high (12.10%), and very high (8.63%), revealing spatial patterns of urban expansion. The framework combines hybrid ensemble methods with SHAP-based explanations to significantly enhance the predictive and explanatory power of urban growth models compared to the limitations of traditional approaches. This study highlights the efficiency of integrating hybrid machine learning and Explainable AI to understand and predict complex urbanization dynamics. The outcomes offer actionable insights for policymakers and urban planners, facilitating data-driven strategies for sustainable urban development. This research demonstrates the effectiveness of hybrid intelligence coupled with Explainable AI, offering a scalable and interpretable framework to better understand and predict urbanization patterns.
How to cite: Khan, D. and Khan, N.: Hybrid Intelligence and Explainable AI for Urban Growth Prediction Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4759, https://doi.org/10.5194/egusphere-egu25-4759, 2025.
Anthropogenic heat generated by building energy use contributes to the urban island and climate change. Quantifying high spatiotemporal resolution city scale building energy use (BEU) and anthropogenic heat emission (AHE) is necessary for understanding urban microclimate and sustainable development. However, the current shortage of such data is insufficient to support urban energy management and climate decision-making. We estimated BEU and AHE from buildings in Hong Kong using a GIS-based city-scale building energy model (GIS-CBEM) and investigated their spatiotemporal variations. First, all buildings were categorized into 11 types, and a prototype was developed for each type. These prototypes were then calibrated using annual building energy consumption data from surveys. We studied the energy use profile for each building prototypes under the Typical Meteorological Year (TMY) weather data. Then, we estimated hourly BEU and AHE for all buildings in Hong Kong at the individual building level. The study results unveiled the spatiotemporal variation of buildings in Hong Kong at high resolution and detected divergent structure of building end-use and fuel use for different building prototypes. We found that the total BEU of all buildings in Hong Kong peaked at 5.1 × 109 kWh in August, with 36.7% from HAVC system, while the lowest BEU was found in February at 3.5 ×109kWh, with 14.1% from HAVC system. Total AHE from all buildings reached a maximum of 8.1 × 109 kWh in July and minimum of 4.1 × 109 kWh in February. Our findings have critical significance in enhancing energy efficiency, reducing environmental impact, and promoting sustainable development.
How to cite: Liu, Q. and Zhou, Y.: Unveiling Spatial and Temporal Variations of Building Energy Use and Anthropogenic Heat Emissions in Hong Kong, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9314, https://doi.org/10.5194/egusphere-egu25-9314, 2025.
Evaluating human interaction with environmental health determinants in space and time is fundamental to estimate personal environmental exposures. The increasing demand in an exposure assessment of entire populations requires to combine environmental variables at high resolution on large spatial extent, e.g. at nationwide or continental scale, with the space-time activity pattern of each individual in a study population.
Modelling population health and citizens' exposures is a complex process involving multiple procedural steps. One major step is to generate spatio-temporal information on environmental factors, either considered as beneficial for human wellbeing, for example, accessibility to green space or blue space, or considered as having negative health impacts such as the existence of air pollution, noise or heat. To capture the spatial variability these datasets need to be generated at high resolution. To allow for studies comparing cities, regions or countries, a geographical extent of subnational or larger size is required. In addition, data can be temporal to cover diurnal or seasonal variation of an environmental variable. Another major step is to use the environmental factors to as input to models calculating exposures for entire study populations, ranging from a few hundred participants up to millions of citizen. Here, socio-economic variables, mobility, different travel modes, and other daily activities with accompanying location changes need to be considered to mimic the space-time paths of each participant of a study population. These tasks require sufficient flexibility in both constructing environmental models as well as executing those eventually on HPC systems to break computational barriers of common workstations.
We present a computational framework for implementing both procedural steps and show the development of two European scale raster maps on a 25m grid and their subsequent usage to estimate human exposures to greenness visibility and noise. The maps were created with LUE (https://lue.computationalgeography.org/), an open-source modelling framework providing a Python package with currently 115 general-purpose operations for the construction of spatio-temporal simulation models. We implemented two custom focal operations that make use of the LUE framework. The first focal operation calculates for each raster cell the visible green area within a particular buffer size (c.f. Labib 2021, https://doi.org/10.1016/j.scitotenv.2020.143050). The second focal operation aggregates traffic-related noise within a particular buffer size, considering attenuation due to geometric divergence, atmospheric absorption, ground effects and diffraction.
We calculated visible green within a radius of 800m and noise within 1500m radius using 768 CPUs on eight HPC cluster nodes, and then used Campo (https://campo.computationalgeography.org/) for activity-based exposure assessment. The obtained exposure estimates can show considerable differences for different typical human activity patterns, such as homemaker or commuter, as well as a high spatial variability.
How to cite: Schmitz, O., de Jong, K., Shen, Y., and Karssenberg, D.: Assessing human exposures to environmental risk factors at continental-scale: accounting for short range variation in environmental factors and human activity patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8801, https://doi.org/10.5194/egusphere-egu25-8801, 2025.
With rapid urbanization, cities face critical sustainability challenges, including poverty, resource shortages, pollution, and climate impacts. The EU Cities Mission supports 112 cities in developing Climate City Contracts to achieve climate neutrality by 2030 [1], emphasizing strategic, cross-sectoral approaches and stakeholder collaboration. This study introduces a systematic and indicator-based assessment of urban resilience, utilizing EU-sourced environmental, OpenStreetMap, and a few nationally sourced data. The methodology incorporates 12 key indicators, mapped at high resolution for 83 Italian cities using open-source GIS software [3], ensuring full reproducibility and applicability to other European cities. The indicators are categorized into five classes:
(i) nature and biodiversity, including forest canopy coverage, native habitat areas, biodiversity, geodiversity [4], ecological corridors, and heat island effects [5];
(ii) natural hazards, including susceptibility to flooding, earthquakes, wildfires, and landslides [6];
(iii) air pollution, including concentration of PM2.5 and NO2;
(iv) transport, including availability of sustainable and affordable transport systems;
(v) social indicators, including population living in close proximity to green spaces or water sources, and public services.
This study evaluates the current state of Italian cities [7], identifies regional differences, and highlights the strengths and weaknesses of each city individually, based on results provided by the urban indicators.
The software developed for this study is flexible, as the input data exists for the whole of Europe and it is easily extensible with modular scripts, to include additional indicators. The scripts processes data to produce spatially distributed results (raster maps) for each indicator in each class listed above and then summarize each indicator with a numerical figure.
Preliminary findings suggest significant regional variation in factors contributing to climate resilience and citizen well-being [8]. Cities in Northern Italy exhibit larger green space coverage but also higher air pollution levels. In contrast, Central Italy stands out for its high species biodiversity and geodiversity. Moreover, results uncover regional spatial patterns, offering actionable insights for policymakers to design locally informed and effective strategies. The findings contribute to advancing sustainability goals, supporting urban transformations toward enhanced resilience and reduced environmental impact. A comprehensive set of urban indicators, including those derived in this study and summarized into a single numerical output for each category, allows ranking of cities and promoting the adoption of data-driven strategies for sustainable development.
References
[1] United Nations (2023) https://sdgs.un.org/goals/goal11
[2] Sarretta et al., Int. Arch. Ph. Rem. Sens. Spat. Inf. Sci. (2021) https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-159-2021.
[3] Neteler et al., Env. Mod. Softw. 31 (2012) https://doi.org/10.1016/j.envsoft.2011.11.014
[4] Burnelli et al., Geomorphology 471 (2024) https://doi.org/10.1016/j.geomorph.2024.109532
[5] Morabito et al., Sci. Tot. Env. (2021) https://doi.org/10.1016/j.scitotenv.2020.142334
[6] Loche et al., Earth-Science Reviews 232 (2022) https://doi.org/10.1016/j.earscirev.2022.104125
[7] Alvioli, Land. Urb. Plan. 204 (2020) https://doi.org/10.1016/j.landurbplan.2020.103906
[8] Boeing et al., Lancet Global Health 10 (2022) https://doi.org/10.1016/S2214-109X(22)00072-9
How to cite: Tarasova, E. and Alvioli, M.: Measuring resilience of urban areas using public data and a reproducible approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11618, https://doi.org/10.5194/egusphere-egu25-11618, 2025.
Cities are highly interconnected networks of networks (referred to as the Urban Multiplex) that include the power grid and transportation networks, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams – all intertwined with the natural environment and socioeconomic and public health sectors. While the Urban Multiplex is physically and functionally connected, the data produced within its individual sectors are not. This prevents us from fully understanding how the Urban Multiplex is connected, and how failures triggered by external stressors like floods cascade.
Knowledge Networks are an AI technology that (i) integrates Urban Multiplex data, (ii) produces real-time flood forecasts across the continental U.S., (iii) serves as the foundation to evaluate the total impact of floods on cities, and (iv) supports queries at the nexus of water and energy. This talk will describe the development of the Urban Flooding and Water-Energy Nexus Open Knowledge Networks that aim to provide actionable answers to questions such as:
- Real-time flood mitigation and response: Will my neighborhood flood? Will I have access to water and power? Will this storm disrupt the power grid, drinking water treatment plant, or a bridge?
- Long-term design, planning and research: What is the total socioeconomic impact of this flood? Which critical urban infrastructure will likely fail in a future flood? Which failures will affect the most people or the most vulnerable people? Are there vulnerable communities downstream of this coal mine?
The interdisciplinary team behind this project has brought together academic researchers, industry, federal government, U.S. National labs and local stakeholders. It is funded by the U.S. National Science Foundation’s Convergence Accelerator Program that is structured to enable rapid advancement in highly complex problems of critical societal importance.
How to cite: Yeghiazarian, L. and the Knowledge Networks Team: Knowledge Networks help address urban flooding and water-energy challenges , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13868, https://doi.org/10.5194/egusphere-egu25-13868, 2025.
Posters on site: Thu, 1 May, 10:45–12:30 | Hall X4
Climate change may influence energy demand, with shifts in energy needs not only altering the energy structure but also posing challenges to the sustainability and resilience of energy systems. These impacts could further complicate the feasibility of achieving decarbonization goals. Residential energy sector is a critical component of global energy consumption. As temperature fluctuates and weather variability intensifies, households will adapt energy use to maintain comfortable living conditions. Energy consumption may increase due to climate change, but the magnitude remains uncertain. Considering various income groups around the world, residents may react to climate change heterogeneously.
Traditionally, some models use Heating Degree Days (HDD) and Cooling Degree Days (CDD) to serve as index of temperature change, which are often calculated by formulas below, where i means gridded cell, j means region, 
means daily temperature, and 
represents comfortable temperature, 
means population. First, calculate gridded HDD/CDDs as the difference between daily temperature and comfortable temperature. Then aggregate the gridded daily HDD/CDDs to region.
(1)
(2)
(3)
However, calculation for HDD/CDDs still have several aspects that could be further improved. First, most temperature data used are predicted on SRES, and HDD/CDDs are assumed to be constant, so HDD/CDDs need to be updated to better reflect future climate change. Second, previous calculation always neglects the impact of crucial factors such as GDP when aggregating gridded temperature difference to regional level, only considering population distributional effects. Third, the difference resulted from income and climate also should be considered, for rich residents can afford more energy consumption, and long-term climate also impact response of people when faced with climate change.
Considering potential shortcomings mentioned above, we update the global HDD/CDDs of 32 regions in Global Change Analysis Model (GCAM). First, we use daily temperature data predicted by four climate models under different Shared Socioeconomic Pathways (SSPs) combining Representative Concentration Pathways (RCPs) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6), thus bridging the gap between climate model and GCAM. For in GCAM climate input module, HDD/CDDs are calculated based on historical climate data and are lack of fine-scale calculation. Second, our calculation adopts two weighting methods considering influence of population and GDP distribution on residential energy demand respectively. Third, beyond global-scale calculation, we refine calculation for China to the provincial level.
Fig. 1 Research Framework
Based on the updated HDD/CDDs, we use GCAM to analyze how climate change impact residential energy demand, aiming to provide scientific support for formulating policies that address the challenges posed by climate change to energy system. Our analysis offers comprehensive insights into residential energy demand change under SSPs and RCPs scenarios, accounting for income heterogeneity. These findings are informative to design effective mitigation policies in the context of climate change.
How to cite: Zhu, M., Zhao, M., Zhu, R., Mei, F., and Ou, Y.: Effects of Climate Change on Residential Energy Structure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-503, https://doi.org/10.5194/egusphere-egu25-503, 2025.
Adequate sunlight exposure is crucial for human wellbeing, yet its accessibility in cities is significantly compromised by both could cover and complex three-dimensional (3D) urban structure. Here we adopted an analytical framework that integrated natural day length variations, cloud cover effects, and 3D urban structure to quantify actual sunlight duration in urban areas. By using high-resolution satellite products, fine-scale canopy height data, and detailed 3D building footprints, we mapped the spatiotemporal patterns of sunlight availability and quantified the relative contributions of cloud cover and urban structures on the loss of sunlight for Chinese cities. Our analysis reveals pronounced spatial disparities and trends in urban sunlight resources in China, underscoring the urgent need for evidence-based urban planning strategies that optimize natural light accessibility for sustainable urban development.
How to cite: Wu, X. and Chen, B.: Quantifying urban sunlight accessibility across Chinese cities: Impacts from cloud cover and three-dimensional (3D) urban structure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7638, https://doi.org/10.5194/egusphere-egu25-7638, 2025.
Exploring the effect of building morphology on Land Surface Temperature (LST) has received surging attention. In this process, a fundamental precondition is selecting an appropriate spatial statistical unit to calculate building morphological indices and corresponding LST. However, different units lead to divergent results, indicating they inevitably suffer from the Modifiable Areal Unit Problem (MAUP), which brings large uncertainties. This study places special emphasis on proposing a new spatial unit, the Homogenous Unit of Building Morphology (HUBM), to re-describe building morphology and re-analyze its effect on LST with less uncertainty. Results show: (1) building morphology portrayed by HUBM maintains more spatial characteristics and remains relatively stable across scales, which is more consistent with the realistic building environment. (2) The relationship identified by HUBM shows building morphology is not strongly correlated with LST in essence and is regarded as more authentic due to the more objective portrayal of building morphology, while this relationship may be overestimated by previous common units. (3) The effect of building morphology on LST explored by HUBM also remains relatively stable across different scales (R2 fluctuation amplitude of 0.08, 0.12, and 0.08 in the spring, summer, and winter, respectively) compared to regular grids (R2 fluctuation amplitude of 0.18, 0.2, and 0.2), effectively alleviating the uncertainty associated with the MAUP. These findings provide new insights into re-examining the authentic effect of building morphology on LST, assisting in addressing urban heat island effects and promoting sustainable urban development. Moreover, HUBM can be applicable to other urban issues for mitigating MAUP.
How to cite: Yang, L., Yang, X., and Li, S.: Is 3D building morphology really related to land surface temperature? Insights from a new homogeneous unit, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10149, https://doi.org/10.5194/egusphere-egu25-10149, 2025.
Due to climate change, new challenges arise in drinking water infrastructure planning and in the re-assessment of well-established urban drinking water utilities. We observed temperatures exceeding 25 °C in drinking water supply pipes, which pose a health threat and water quality problem, as these temperatures are favorable for microbial growth.
We set out to predict temperatures in drinking water supply networks. The key step to achieve this goal is to monitor and model soil temperatures and soil moisture derived from meteorological forcing functions. With meteorological observations and soil material properties, we describe the heat transport and water flow from the ground surface into the subsurface and from there into the pipes and with the water in the pipes.
In order to achieve this goal, we solved the heat and water balances jointly at the atmosphere-subsurface interface, using the open-source numerical simulation framework DuMuX. We were able to do this because of the available meteorological observations (e.g., radiation balance, precipitation intensity) next to the newly installed pipes. These balances provide a novel interface condition for heat transport and water flow modelling. We coupled the heat transport through the drinking water pipe walls to the drinking water in the pipes and to the subsurface transport processes.
At a pilot site, we installed typical drinking water pipes (PE and cast iron), backfilled with known material (typical gravelly conditions below roads and naturally existing sandy clay), and applied land-cover (asphalt and natural vegetation). We were able to reproduce the joint measurements of temperatures and soil moisture under various conditions (well-draining gravel vs. less-draining clayey material; vegetation vs. asphalt).
In this presentation, we demonstrate results of the multi-year measurement campaign, the results of 1D and 2D subsurface heat transport models coupled to dynamic hydraulic conditions in the drinking water pipes, and an innovative surrogate-based Bayesian active learning-assisted model calibration methodology.
This work presents an important first step towards predicting temperatures in drinking water supply pipes and will be directly relevant for chemical and biological processes that occur in non-isothermal conditions (e.g., due to climate change), for example, in relation to contaminant remediation. Our results are of relevance for drinking water supply companies, shallow geothermal design, and urban planning.
How to cite: Haslauer, C., Kroeker, I., Nißler, E., Oladyshkin, S., Nowak, W., Class, H., and Osmancevic, E.: Large Temperatures in Water Distribution Pipes as a Water Quality Threat: Measurements and Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13131, https://doi.org/10.5194/egusphere-egu25-13131, 2025.
Equitable access to daily necessities and services is crucial for enhancing human quality of life and is integral to achieving the United Nations’ Sustainable Development Goals. However, knowledge about global access to these essential resources remains limited and fragmented, primarily due to the absence of a comprehensive infrastructure inventory and scalable measures of accessibility. Here we compiled the most extensive global database of points of interest (POI) to represent six essential infrastructure categories—living, healthcare, education, entertainment, public transit, and work. We used refined 30-meter resolution friction surface data to map travel times to these critical infrastructures as a proxy for accessibility across the urban-rural continuum and assessed disparities across geographic, urbanization, and socio-economic contexts. Our results reveal that access to daily necessities and services is unevenly distributed in terms of total infrastructure, per capita availability, and travel time. Globally, only 38.7% (2.6 billion people) and 50.7 % (3.4 billion people) of the population resides within a 15-minute and 30-minute walking distance of essential daily necessities and services, respectively. These results highlight the urgent need to optimize strategies for planning, allocation, and management of critical infrastructure to promote inclusive and sustainable development.
How to cite: Chen, B., Wu, S., Nelson, A., and Gong, P.: Measuring global human access to essential daily necessities and services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8416, https://doi.org/10.5194/egusphere-egu25-8416, 2025.
Urban geo-scenes (UGS) are an abstraction of the basic units of cities. Understanding and functional recognition of UGS is crucial to balancing and optimizing urban spatial layout, rationally allocating urban resources, and enhancing urban resilience and vitality. To construct UGS, urban geo-objects (UGO) e.g., derived from remote sensing must be combined with semantic information, which has seldom be done so far. Consequently, this study designed a UGS recognition framework based on multimodal deep learning. First, we use very high-resolution satellite data to derive UGOs. Second, the self-built SE-DenseNet branch is used to mine deep physical visual features and social semantics from satellite image data and auxiliary data (POI, building footprints from UGOs). Finally, we build an urban fabric graph model to mine spatial semantics between UGOs. In addition, a spatial semantic fusion module is introduced for the collaboration and interaction of multi-modal and multi-scale features. We evaluate the effectiveness of the proposed framework in the complex Beijing and Shenzhen regions of China. The overall accuracy is 91.35% and 90.24% respectively, which is higher than the state-of-the-art multimodal methods. In addition, our study also emphasizes the key role of spatial relationships and distribution patterns of UGO in UGS recognition, and the addition of POIs and building heights improves the recognition accuracy. The multimodal UGS recognition framework based on urban fabric can more effectively understand urban functions, thereby achieving urban planning and management.
How to cite: Bao, H., Zhou, L., and Lehnert, L.: Understanding and recognition of geo-scenes based on multimodal spatial semantics to monitor complex urban systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4066, https://doi.org/10.5194/egusphere-egu25-4066, 2025.
China has undergone rapid urbanization in terms of population and land use in recent years. However, there are notable lags in "people-oriented" dimensions of urbanization, including urban social services, environmental sustainability, and equity. Here, considering the complex interactions of sub-components of urbanizations, we examined 16 "people-oriented" urbanization indicators across four dimensions - economic, social, environmental, and equity dimensions - from 2005 to 2020. Using methods such as paired t-tests and the evenness measurement, we analyzed and identified the dynamic relationships between these 16 indicators with population/land urbanization at multiple scales, including national, regional, urban agglomeration, and different city sizes. We found that between 2005 and 2020, China's urbanization indicators showed an overall upward trend, with changes ranging from 1.09 to 53.95 times. Among "people-oriented" urbanization indicators, economic and social indicators lagged behind land and population urbanization, while environmental indicators took the lead. The evenness index among indicators showed a "U-shaped" change pattern. Particularly since the implementation of China's New-type Urbanization Plan in 2014, the evenness index among indicators gradually increased from 35.43 to 37.39 in 2020, representing a 6.9% improvement. Looking forward, it is necessary to strengthen investment in social service systems and implement placed-based coordination strategies to promote further development and balanced growth of "people-oriented" urbanization.
How to cite: Gu, T., Huang, Q., and Hou, Y.: Do people-oriented urbanization catch up with land and population urbanization in China？, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6392, https://doi.org/10.5194/egusphere-egu25-6392, 2025.
In recent decades, urban expansion across Europe has accelerated, driving the rapid growth of rural-urban interfaces. The increasingly complex and dynamic nature of territorial transitions calls for the timely development of classification systems designed to systematically organize areas along a spectrum, from distinctly urban to distinctly rural. Current classifications often rely on predefined criteria, such as population size and density, which may not fully capture the nuanced and evolving nature of transitions driven by the complex interplay of socioeconomic processes, demographic shifts, environmental factors, and dynamic geographic forces.
This research addresses existing gaps by employing modern data-driven approaches, including machine learning and clustering techniques, to develop adaptive typologies that integrate diverse demographic, socioeconomic, and environmental variables. Using Switzerland as a case study, the proposed methodology offers a dynamic and scalable framework for territorial classification, supporting the effective management of territorial transitions and landscape conservation in Alpine regions. The analysis leverages a multidimensional dataset derived from the 2020 official census, incorporating 18 variables that encompass demographic profiles, socio-economic, and the physical space characteristics.
We used Self-Organizing Map (SOM) combined with hierarchical clustering. SOM, a type of competitive learning neural network, reduces the complexity of high-dimensional data by mapping it onto a two-dimensional grid of neurons. Visual outputs, such as heatmaps, enhance the interpretation of trends and patterns, providing a clearer understanding of variables distributions and interrelationships. Afterward, the SOM output grid of neurons was aggregated into six distinct clusters, which were mapped onto the geographical space. This produced a visual representation of the spatial organization of territorial typologies along the rural-urban continuum in Switzerland at a detailed municipal level.
The data-driven clustering approach developed in this study proved effective in capturing the complex and diverse nature of Swiss territorial typologies. The key findings reveal a landscape marked by a complex rural-urban interface, extensive intermediate zones, and significant spatial fragmentation. These final six territorial typologies could be characterized as follows: urban centres, representing the main hubs at the highest level of the Swiss urban hierarchy; suburban areas, located near and well-connected to urban centres; two peri-urban areas, distinguished into aging-rural areas and rural-urban edge; rural-forest areas, situated at medium to high elevations, featuring a forested landscape and rural settings; unproductive areas, encompassing high-altitude regions and including critical Alpine glaciers.
How to cite: Tonini, M., Yu, J., and Hagen-Zanker, A.: Exploring Switzerland's Rural-Urban Continuum Through Unsupervised Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6870, https://doi.org/10.5194/egusphere-egu25-6870, 2025.
The rapid expansion of urban areas has led to significant environmental changes, most notably the Urban Heat Island (UHI) phenomenon, characterized by higher temperatures in urban areas compared to their rural counterparts. Addressing and mitigating UHIs is vital for public health, energy demand management, and enhancing urban livability, especially amidst global climate change. This study focuses on classifying Local Climate Zones (LCZs) in Taipei, Taiwan, using digital building data, satellite imagery, and urban morphological indices. LCZs offer a standardized framework to analyze urban morphology and its influence on local climates. By applying unsupervised clustering methods, we achieved a detailed classification of urban areas, enabling a data-driven exploration of their climatic and morphological characteristics.
To downscale and refine the analysis at the community level, Principal Component Analysis (PCA) was employed to reduce data dimensionality and extract key features such as building coverage, vegetation index, and sky view factor. K-means clustering was then used to categorize urban morphological types, resulting in distinct LCZs across Taipei. Our findings reveal significant differences in environmental variables among clusters. These results highlight how urban morphology, including building density and vegetation cover, impacts local climate conditions. The study also emphasizes the role of thermal comfort, underscoring the complex interplay between urban form and environmental factors.
This research demonstrates the effectiveness of unsupervised classification methods in identifying urban climate zones and provides a practical framework for urban planning and climate adaptation. By enabling targeted interventions, such as greening strategies or ventilation optimization, the study contributes to enhancing urban sustainability and resilience. The findings underscore the importance of interdisciplinary approaches to address the multifaceted challenges of urbanization and climate change.
How to cite: Chen, W.-J., Juang, J.-Y., and Chien, S.-S.: Self-Organizing Local Climate Zones by Using Integrated information in Urban Community – a case study in DaXue Village, Taipei, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17835, https://doi.org/10.5194/egusphere-egu25-17835, 2025.
Traffic congestion continues to challenge urban development, yet most research emphasizes large-scale factors such as road layouts and land use, overlooking localized visual aspects encountered by drivers. This study employs geographically weighted random forest, a non-linear and spatially explicit method, to explore how localized visual features—such as vehicle density, building structures, greenery, and road conditions—impact traffic congestion in Chicago. By integrating transport network dynamics with visual streetscape characteristics, the geographically weighted random forest approach captures spatial heterogeneity and complex interactions more effectively than traditional models. Results demonstrate that incorporating these multi-scale features improves model fit, revealing that greenery mitigates congestion, while dense urban structures and vehicle clusters exacerbate delays. These results highlight the potential of integrating visual characteristics of streetscapes into urban strategies to address congestion more effectively.
How to cite: Xu, M. and Weng, Q.: Modeling the Spatial Dynamics of Traffic Congestion Through Street-Level Visual Features: Evidence from Street View Images in Chicago, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9394, https://doi.org/10.5194/egusphere-egu25-9394, 2025.
Over the past few decades, the rapid growth of cities has evolved into a significant social, demographic, and architectural phenomenon, highlighting the vital importance of urban planning in fostering sustainable development. In this context, machine learning has emerged as a game-changing discipline, utilizing advanced algorithms to reshape traditional approaches to urban data management and analysis.This study combines Geographic Information Systems (GIS), Deep Learning techniques, and verified data from the General Directorate of the Spanish Cadastre to perform a comprehensive analysis of the urban environment through façade images in Murcia, one of Spain’s most dynamic metropolitan areas.Leveraging the clustering analysis of the studied variables, an automated binary classification model for façade images was developed using the pretrained EfficientNetB0 architecture in Python. To enhance interpretability, heat maps were generated to visualize the regions the model focuses on during classification. These heat maps reveal the critical features of the facades that guide the model’s decisions, providing valuable insights into the key factor influencing the classification process.The results were integrated into ArcGIS PRO, using the cadastral reference of the properties as a key attribute for a detailed spatial analysis. This approach revealed two significant areas linked to the metropolitan growth of Murcia, laying a strong foundation for future urban studies in the region.
Funding: Twin-ER: Earthquake Risk Pilot Digital Twin. Grant PID2023-149468NB-I00, funded by MCIU/AEI/10.13039/501100011033 and FEDER/EU
How to cite: De La Cruz Luis, M. I., Martinez Cuevas, S., Garcia Aranda, C., Morillo Balsera, M. C., and Poveda Lorente, E.-M.: Advancing Urban Environment Studies in Murcia, Spain through an Automated Façade Image Classification Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7834, https://doi.org/10.5194/egusphere-egu25-7834, 2025.
Urban climate modelling tools like the Surface Urban Energy and Water balance Scheme (SUEWS) are indispensable for investigating complex surface–atmosphere interactions and guiding urban adaptation strategies. However, these models often present substantial barriers to use: they require extensive technical know-how, involve intricate input datasets, and can be time-consuming to set up and interpret. Recent advancements in Large Language Models (LLMs) hold promise for bridging this gap by transforming complex domain-specific tasks—such as data validation, simulation setup, and error diagnosis—into user-friendly interactive experiences.
In this study, we propose a novel workflow that leverages LLM capabilities—such as generative text, code suggestion, and context-driven troubleshooting—to streamline SUEWS usage and improve accessibility for researchers and practitioners:
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Automated Model Configuration
We explore the use of LLM-guided prompts to generate properly formatted SUEWS input files, such as specifying hourly meteorological forcing data (e.g., temperature, wind speed, and humidity) or land cover fractions required for accurate simulations. By conversing with the model about location, time range, and data availability, users can rapidly produce consistent and error-checked setup files, reducing manual edits that often lead to inconsistencies. -
Interactive Error Diagnosis
LLMs can parse error logs and suggest potential solutions in real time. For example, if SUEWS outputs an error related to missing albedo values for a specific land cover type, the LLM can pinpoint the source of the issue and suggest default values or a method for calculation based on site-specific conditions. For example, if a runtime error indicates a mismatch in the date format of meteorological input data, the LLM can identify the exact line causing the error, recommend the correct format, and provide a command or script snippet to rectify the issue. Through iterative dialogue, the model clarifies the root causes of typical setup or runtime issues, explaining how to fix them without requiring the user to trawl through detailed documentation. -
Model Output Interpretation
Interpreting large volumes of SUEWS output, such as energy balance components (net radiation, latent heat flux, and sensible heat flux) or water budget terms (runoff and evapotranspiration), can be daunting, especially for newcomers. LLMs can summarise key metrics—like energy flux partitioning and surface runoff patterns—and highlight discrepancies in data, thereby assisting in rapid analysis and scenario comparison.
Our findings indicate that an LLM-enabled approach substantially lowers the learning curve and operational overhead associated with SUEWS, while still maintaining scientific rigour. We piloted trial deployments in teaching and professional contexts, reporting improvements in both setup speed and user confidence. Future work includes refining the LLM’s domain-specific training to ensure physically consistent responses—such as maintaining energy balance across flux computations or ensuring water budget closure—and incorporating advanced visualisation plugins for immediate data interpretation.
By harnessing the dialogic strengths of LLMs, we aim to remove barriers to the complexity of urban climate modelling, ultimately broadening participation and fostering more informed decision-making in cities worldwide.
How to cite: Sun, T.: Remove Barriers to Accessible Urban Climate Modelling with Large Language Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13462, https://doi.org/10.5194/egusphere-egu25-13462, 2025.
In recent decades, inequalities in economic, health, and education sectors have intensified spatial clustering of populations and resources, further reinforcing disparities within urban environment. Identifying these geographic boundaries is crucial for developing targeted policies to address inequality effectively. While traditional approaches to studying urban segregation rely primarily on socioeconomic indicators, this research introduces a novel methodology that combines subjective perceptions of the urban environment and objective characteristics of urban areas—such as land use and infrastructure—to identify distinct spatial clusters within Glasgow, a city with a varied socioeconomic landscape. Using MIT Place Pulse dataset of crowd-sourced streetscape perceptions, we developed a deep learning model to predict perception scores for new areas. These perception scores, along with image embeddings and land use information, enabled the geographic clustering of areas based on perceived and functional similarities. Our analysis reveals that perception-based boundaries often diverge from traditional census dissemination areas, suggesting that administrative boundaries may not fully capture the lived experiences of urban space. This research advances our understanding of urban inequality by demonstrating how perceived environmental qualities interact with physical infrastructure to shape distinct urban zones, providing policymakers with new tools for targeted intervention strategies.
How to cite: Pawar, S., Robertson, T., Marino, A., and Anderson, C.: Redefining Urban Clusters: Combining Subjective Perceptions and Objective Data to Map Inequality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16365, https://doi.org/10.5194/egusphere-egu25-16365, 2025.
The building stock accounts for 34% of global energy demand and 37% of CO₂ emissions related to energy and industrial processes. Additionally, the current increase in urbanization rates poses significant environmental challenges. Policy makers are becoming increasingly aware of these impacts, developing strategies aimed at improving energy efficiency and obtaining decarbonization of the built environment. Achieving these goals requires modeling actual building stock energy consumption patterns, future energy developing trends as well as the impact of energy retrofitting measures on CO₂ emissions. Urban Building Energy Models (UBEMs) and bottom-up engineering models have proven to be valuable tools. However, these models require detailed and accurate building attributes related to physical properties (building geometry, height, building type, thermal transmittances, etc.), local climate (air temperature, humidity, solar radiation, etc.) and data related to occupants' energy behavior (occupants’ schedule, heating and cooling energy demand, efficiency of the system etc.). Among others, building construction year is one of the most relevant parameters since it is a key proxy for essential characteristics such as morphology, facade design, building materials, and energy efficiency. However, obtaining building construction year is particularly challenging as it is rarely available in public databases and, when available, the data are often incomplete or inconsistent. In this regard, remote sensing techniques can play a crucial role in the study and monitoring of the building stock. In particular, satellite images represent an excellent tool for the estimation of building age at local or regional scale given their extensive temporal and spatial coverage, as well as and the continuous updates of collections. The study focuses on the city of Parma, for which seven images covering the year range between 1985 and 2011 were selected. After a literature review, five built-up area extraction indices suitable for TM sensor were selected: Normalized Difference Built-up Area Index (NDBI), New Built-up Index (NBI), Band Ratio for Built-up Area (BRBA), Normalized Built-up Area Index (NBAI), and Vegetation Index Built-up Index (VIBI). In addition, Normalized Difference Vegetation Index (NDVI) was also considered, leading to a total of six indices. To improve the ability of these indexes to discriminate urban surfaces from areas with similar spectral signature (bare soil, sand, rock, etc.) annual greenest pixel composite images were generated using Google Earth Engine. Indexes performance was then compared on each image evaluating Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, as well as performance metrics such as F1-score and Area Under the Curve (AUC). The results indicate that the NDVI is the best- Finally, temporal series were derived from the classification of images from different years, enabling the assessment of urbanization growth over time and, consequently, the estimation of building ages.
This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) – SPOKE TS 1
How to cite: Vigato, T., Dalle Vedove, L., Dalla Vecchia, C., Zandonella Callegher, C., and Zilio, S.: Comparing the effectiveness of Landsat-derived spectral indices for building age prediction in urban energy modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20244, https://doi.org/10.5194/egusphere-egu25-20244, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 2
EGU25-3305 | ECS | Posters virtual | VPS30
Assessment and Development of a Sustainable Development Strategy for Yasooj City Using the SWOT Model and SPACE MatrixFri, 02 May, 14:00–15:45 (CEST) | vP2.7
Abstract
Yasooj, with its rich cultural, historical, and musical heritage, stunning natural landscapes, and a young, educated workforce, is one of Iran's cities with significant potential to become a leading example of sustainable development. However, challenges such as weak urban management, lack of economic development, inadequate infrastructure, and natural constraints hinder its progress. In this context, assessing the current situation and providing solutions to enhance urban management and formulate sustainable development strategies are of great importance.
The objective of this study is to identify the capabilities and challenges of Yasooj's urban management and to develop a strategic vision for sustainable development, based on the SWOT model and the SPACE matrix. This research is applied-developmental in nature and employs a descriptive-analytical research method. The statistical population consists of 40 urban experts, and the required data were collected through field observations, reviews of comprehensive and detailed plans, and questionnaires.
Data analysis was conducted using the SWOT model, which identified the strengths, weaknesses, opportunities, and threats of Yasooj City. The findings indicate that the final score of the IFE matrix (internal factors of strengths and weaknesses) is 2.10, and the score of the EFE matrix (external factors of opportunities and threats) is 2.37, both of which are significantly below the average score of 2.5. The internal-external (IE) matrix analysis revealed that Yasooj is in a defensive (WT) position, requiring a review of management structures and the formulation of new operational plans.
Additionally, Yasooj's potential in tourism, agriculture, industry, and services has been identified as a key opportunity for sustainable development. To achieve sustainable development in Yasooj, urban management must revisit its structures and plans, focusing on enhancing inter-institutional cooperation and fostering greater citizen participation in decision-making processes.
Utilizing the city's natural, cultural, and social capacities, strengthening academic tourism through Yasooj University, hosting cultural festivals; and supporting agro-tourism are among the proposed solutions. Furthermore, Yasooj should aim to establish itself as a successful model of sustainable urban development by improving infrastructure, enhancing good urban governance, and fostering partnerships with the private sector and civil society.
Step-by-step and participatory planning, along with a thorough review of past strategies and the definition of clear visions, will contribute to achieving this goal. Establishing closer links between academic institutions and urban management will also play a key role in the successful implementation of development strategies.
Keywords: Sustainable Development, Yasooj City, SWOT Model, Urban Management, Tourism.
How to cite: salamatnia, A., balist, J., and Nahavandchi, M.: Assessment and Development of a Sustainable Development Strategy for Yasooj City Using the SWOT Model and SPACE Matrix, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3305, https://doi.org/10.5194/egusphere-egu25-3305, 2025.
EGU25-13665 | ECS | Posters virtual | VPS30
Monitoring the Impact of Urban Growth Scenarios on No Net Land Take of Wallonia, Belgium Using a Cellular Automata ModelFri, 02 May, 14:00–15:45 (CEST) | vP2.6
The pressing demand for urbanised land due to the growing global population has led to increased land consumption, posing significant challenges to sustainable urban development. The European Union's No Net Land Take (NNLT) 2050 initiative aims to mitigate this issue by curbing urban expansion through urban densification or circular construction. Therefore, it is imperative to study the existing demand trajectory and their effects on the current development situation.
Wallonia, the southern region of Belgium, characterised by urban and peri-urban development, predominantly experience urban expansion. As a solution to that, government implemented various plans which focuses on revitalizing urban cores, addressing vacant buildings, and promoting the regeneration of central areas to prevent further urban sprawl. These ongoing urban pressures, makes it an ideal study area for conducting research on strategic planning.
In this study, we develop a Multinomial Logistic based Cellular Automata (MNL-CA) model calibrated using geophysical, accessibility, socioeconomic and spatial zoning data. Hereto, the model simulate futuristic urban growth until 2050 under two distinct scenarios:
- Business-As-Usual where urban growth continues following the historical demand trends within existing policies.
- Growth-As-Usual represents a scenario of latest observed built up demand trend along a constant rate .
The BAU scenario demonstrates a marked decline in urban expansion rates, stabilizing at 0 hectares per day by 2040. This trajectory reflects a shift toward densification and more spatially cohesive urban development. Meanwhile, the GAU scenario forecasts a sustained expansion rate of 2.51 hectares per day, resulting in a projected 49.20% increase in urban land by 2050. Together, these scenarios provide complementary insights: BAU serves as a valuable reference point for understanding controlled growth dynamics, while GAU offers a perspective for exploring the implications of constant expansion, thereby enhancing the robustness of future urban planning strategies.
While BAU offers a pathway aligned with policy goals, incorporating elements from GAU scenarios allows policymakers to "stress-test" urban strategies. This dual approach can enhance resilience and flexibility in urban planning, enabling better accommodation of future growth challenges while adhering to sustainability principles.
How to cite: Chakraborty, A., Mustafa, A., and Teller, J.: Monitoring the Impact of Urban Growth Scenarios on No Net Land Take of Wallonia, Belgium Using a Cellular Automata Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13665, https://doi.org/10.5194/egusphere-egu25-13665, 2025.
