This session brings together research that advances the characterization, modeling, and mitigation of urban heat and flood risks through quantitative, data-driven, and geospatial approaches. We welcome contributions leveraging remote sensing, in-situ observations, numerical and process-based models, spatial statistics, machine learning, as well as the integration of multi-source datasets (satellite, airborne, ground-based, and socio-economic). Particular emphasis is placed on understanding heat–flood interactions, evaluating green adaptation strategies across scales, and assessing their impacts on microclimate, hydrology, energy demand, biodiversity, and human well-being.
Topics of interest include geospatial and AI-based methods to monitor, model, and predict urban heat dynamics, quantitative assessments of urban heat island mitigation and cooling demand, modeling of flood attenuation and runoff reduction through green infrastructure, integrated analyses linking heat with air quality, health, energy use, and social vulnerability, and strategies to optimize the costs and benefits of urban ecosystems under climate stress. By bridging geospatial analyses, modelling frameworks, and urban environmental science, this session aims to deepen understanding of urban climate processes and support the design of resilient, sustainable, and climate-adaptive cities.
Urban climate research relies on multispectral (MS) satellite imagery because of its global coverage and relatively high spatial and temporal resolution. However, its coarse spectral detail limits the analysis of complex urban surfaces. New hyperspectral (HS) satellite missions provide much finer spectral information, supporting detailed analysis of urban microclimates. Here, we present an overview of current HS satellite products and examine the potential of PRISMA (PRecursore IperSpettrale della Missione Applicativa) and DESIS (German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer) missions for two key applications in urban environments: material abundance estimation and local climate zone (LCZ) mapping. To estimate material abundances, we apply constrained spectral unmixing to HS imagery over Milan, Italy. Results are compared with near-simultaneous MS data and validated against the local geotopographic database. The derived abundances are also linked to high-resolution air temperature maps predicted using a machine learning-based regression approach. Secondly, LCZs are mapped using a combined RS and GIS-based method, integrating spectral and spatial information for improved classification of urban areas.
Our results show that HS imagery supports sub-pixel material estimation, opening the possibility for a transition from single land-cover labels to multi-material representations within each pixel. Thermal assessment further validated these estimates, with natural materials reducing heat and artificial surfaces increasing it. Finally, LCZ mapping resulted in higher accuracy with HS imagery compared to MS products.
HS imagery provides a promising path for applications in urban climate research and other urban studies. Thanks to its technical advantages over MS imagery, HS data enable the generation of data suitable for microclimate modelling, heat mitigation assessment or urban management. Although HS imagery is not yet as widely available as MS, upcoming missions are steadily expanding access for scientific use in urban monitoring.
How to cite: Žgela, M., Vavassori, A., and Brovelli, M. A.: Hyperspectral satellite imagery for urban climate applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-372, https://doi.org/10.5194/egusphere-egu26-372, 2026.
Understanding the impacts of urbanization and environmental transitions in rapidly developing regions such as Aligarh District, Uttar Pradesh, requires a comprehensive assessment of land use/land cover (LULC) and land surface temperature (LST). For this study, a semi-automated hybrid classification approach, integrating maximum likelihood classification with object-based image analysis, was applied to Landsat-8 OLI imagery from 30 May 2022 to map LULC. LST was derived from thermal band 10 using a four-step procedure that converted the satellite-recorded digital numbers (DNs) into accurate land surface temperature values. Accuracy assessment using 250 reference sites yielded an overall accuracy of 94.4% and a Kappa coefficient of 0.93, confirming high reliability. LST analysis revealed considerable spatial and thermal variability, with summer temperatures ranging from 26.48°C to 46.40°C (mean: 36.32°C). Pearson’s correlation results indicated consistent relationships between LST and key remote sensing indices. NDVI and SAVI showed moderately negative correlations with LST, demonstrating the cooling influence of vegetation, while NDBI exhibited a strong positive correlation, highlighting the urban heat island effect. NDWI showed a negative relationship with LST, and NDBaI displayed a weaker positive correlation, underscoring the moderating effect of water bodies on surface temperature. The Ordinary Least Squares (OLS) regression model explained 70.06% of LST variance, with an Akaike Information Criterion (AICc) value of 3792.15. Coefficient patterns indicated that NDBI contributed to LST intensification, whereas NDVI, NDWI, and SAVI significantly reduced surface temperatures. The Geographically Weighted Regression (GWR) model substantially improved explanatory power, achieving an R² of 0.9405 and reducing residual spatial autocorrelation, as reflected by the decline in Moran’s I from 0.30 (OLS) to 0.02 (GWR). Overall, the findings demonstrate that LULC dynamics drive surface temperature fluctuations in the Aligarh district, and GWR's ability to capture geographical variations makes it highly effective for environmental modelling.
Keywords: Land surface temperature (LST), Geographically weighted regression (GWR), Spatial regression, Ordinary least squares (OLS), Geospatial analysis.
How to cite: Khan, M. A. and said, S.: Geospatial and Regression-Based Modelling of Land Surface Temperature in Aligarh: A Comparative Study of OLS and GWR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-699, https://doi.org/10.5194/egusphere-egu26-699, 2026.
Vehicular traffic is a major contributor to anthropogenic heat flux (AHF) in urban areas, amplifying urban heat island effects. However, few Earth system models explicitly represent traffic conditions and their associated heat emissions. This study introduces a new urban traffic module into the Community Earth System Model (CESM), enabling interactive simulation of traffic-related heat in urban areas. The module adopts a bottom-up approach to estimate traffic heat flux (Qtraffic) based on time-varying traffic volume and vehicle type distributions, while dynamically responding to meteorological conditions such as snow, rain, and low temperatures. Model validation was performed using observational data from two urban sites: Capitole of Toulouse, France (FR-Capitole), and Manchester, UK (UK-Manchester). At the FR-Capitole site, an annual mean Qtraffic of 22.23 W/m2 in 2004 resulted in a simulated annual mean canopy air temperature increase of 0.4K, improving the simulated turbulent heat flux compared to observations. At the UK-Manchester site, the simulation with a yearly mean Qtraffic of 16.27 W/m2 showed a 0.25K air temperature increase in 2022. These traffic-induced canopy warming also influenced the indoor environment, contributing to increased air conditioning use in summer and reduced building space heating demand in winter. This new functionality offers potential applications such as simulating traffic-induced AHF and its impacts on the climate system under future climate changes and transport transition scenarios.
How to cite: Sun, Y., Oleson, K., and Zheng, Z.: Modeling urban traffic heat flux in the Community Earth System Model: Formulation and validation for two sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2014, https://doi.org/10.5194/egusphere-egu26-2014, 2026.
Fine-resolution urban weather nowcasting is crucial for urban resilience, yet it is fundamentally limited by the sparse and irregular distribution of monitoring stations. To overcome this, we introduce an inductive, physics-informed spatio-temporal graph network that transforms discrete sensor data into a continuous, on-demand forecast field. Our framework uniquely synergizes multi-source data: point-scale station observations, grid-scale numerical weather predictions, and high-resolution urban morphological features. The model core is a novel encoder-decoder architecture designed for deep feature extraction. A hybrid temporal encoder captures complex weather dynamics, while a multi-graph attention mechanism learns heterogeneous spatial interactions based on physical similarity (e.g., thermal or wind-driven connections), moving beyond simple geographic proximity. These multi-faceted features are then fused via a subsequent attention layer. Critically, we enforce physical consistency by integrating a thermodynamics-aware loss function, which ensures physics-informed predictions of key variables like temperature and humidity. Evaluated on a comprehensive dataset from Wuhan, China, our model demonstrated high accuracy and strong correlation with observational data for 6-hour ahead nowcasting. Its inductive design is a key advantage, enabling reliable predictions for arbitrary, unmonitored locations by leveraging their local morphological context. This work presents a scalable and robust framework for generating physically plausible, high-resolution urban weather intelligence, essential for proactive applications in energy systems, public safety, and climate-adaptive urban planning.
How to cite: Zhang, Q. and Song, J.: An Inductive Spatio-Temporal Graph Network for Fine-Resolution Urban Weather Nowcasting Integrating Multi-Source Data and Physical Constraints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2288, https://doi.org/10.5194/egusphere-egu26-2288, 2026.
With climate change on the rise, increasing the frequency and intensity of drought stress, we aim to define drought tolerance by assessing the plasticity of several physiological parameters in urban trees. Urban trees face unique sets of challenges compared to trees in the forest, leading them to be more exposed to extreme conditions, as they are restricted to tree pits surrounded by impervious structures (e.g., size, depth, morphology, surface cover and connectedness to other trees). Understanding the impacts of tree pit surface cover can help gain better insights for planting a more resilient urban forest.
To advance our understanding of urban European tree species, we investigate the plasticity of the turgor loss point (TLP), xylem potential when 50% conductivity is lost (P50), specific leaf area (SLA) and Huber Values (HV) for four widespread urban tree species, Acer platanoides (L., Sapindaceae), Ginkgo biloba (L.), Platanus x hispanica (Münichh.) and Tilia cordata (Mill., Malvaceae), growing in different tree pit surface cover conditions (e.g., concrete, exposed soil, grass or vegetation) in the city of Munich, Germany. We used different growing periods of the 2025 growing season for 70 individuals; TLP was measured in July and September, while P50, SLA and HV were measured in August.
Our results indicate that the TLP did not change between early and late season sampling for any of the species or tree pit surface cover types. Moreover, TLP varied between species, while tree pit surface cover influenced TLP only within species. P50, however, was strongly related to species identity and was also affected by tree pit surface cover within species. In particular, A.platanoides P50 was less negative in the concrete tree pit surface cover type, while the other tree pit surface covers have more negative P50’s, suggesting that A.platanoides growing in a concrete tree pit surface cover is less drought tolerant, than when A.platanoides is growing in a tree pit with exposed soil, grass or vegetation surface cover.
When assessing drought tolerance of urban trees, the TLP and P50 provides insights on how different tree species respond to different tree pit surface cover growing conditions. To strengthen urban forests’ resilience to drought stress, our work suggests that tree pit surface covers should be taken into consideration when designing urban forests for specific tree species.
How to cite: McKinney, S. Q. and Peters, R. L.: Assessing the plasticity of drought tolerance in urban trees growing in different tree pit surface cover conditions in Munich, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-970, https://doi.org/10.5194/egusphere-egu26-970, 2026.
With the acceleration of urbanization, the urban heat island effect has become a critical issue in the context of global climate change. As a representative city in southwestern China, Chengdu has experienced pronounced changes in land surface temperature (LST) and near-surface air temperature (T2) as a result of urbanization. To investigate the spatiotemporal characteristics of the discrepancies between LST and T2 in Chengdu under an urbanization background and to elucidate the underlying physical mechanisms, this study integrates meteorological station observations, the MODIS land surface temperature/emissivity monthly product (MYD11C2), and the ERA5-Land reanalysis dataset with numerical simulations from the Weather Research and Forecasting (WRF) model. The variations in land surface temperature and their associated surface energy balance processes are examined across multiple temporal scales and spatial resolutions.
First, this study compares daytime and nighttime land surface temperatures in Chengdu for the years 2003 and 2023. The results indicate that daytime LST derived from MYD11C2 is generally higher than that from ERA5-Land and exhibits a larger range of variability. Secondly, the performance of the WSM6 and Thompson microphysics schemes in simulating air temperature and precipitation over Chengdu was evaluated. By comparing the root mean square errors (RMSEs) against meteorological station observations, the results show that the WSM6 scheme performs slightly better than the Thompson scheme in air temperature simulations, whereas the Thompson scheme exhibits a clear advantage over WSM6 in precipitation simulations. These findings indicate that the choice of microphysics scheme exerts a significant influence on model performance for different meteorological variables, and that an appropriate scheme should be selected according to the specific research objectives.
To further elucidate the mechanisms underlying the divergence between land surface temperature and air temperature, this study integrates a surface energy balance analysis based on the WRF model to investigate the primary drivers of the LST–T2 differences. The results demonstrate that variations in surface energy partitioning—particularly changes in net radiation, sensible heat flux, and latent heat flux—are key factors governing the formation of discrepancies between LST and T2. In addition, urban surface characteristics, such as the proportion of impervious surfaces and building density, play an important role in modulating the differences between land surface temperature and near-surface air temperature.
How to cite: Zhang, Y.: Investigating the Spatiotemporal Characteristics and Energy Balance Physical Mechanisms of the Difference between Land Surface Temperature and Air Temperature in Chengdu Based on the WRF Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6406, https://doi.org/10.5194/egusphere-egu26-6406, 2026.
Rapid urbanization and extreme heat pose growing energy and thermal comfort challenges for citizens living in arid cities such as Riyadh. Rooftop-based heat mitigation strategies are being deployed across cities as potential mitigation solutions to extreme heat. However, the city-wide temperature reduction achieved by rooftop strategies under arid conditions remains inadequately quantified across different urban morphologies. This study employs Weather Research and Forecasting (WRF) with the urban canopy model (UCM) to evaluate the cooling potential of cool roofs, green roofs, and rooftop photovoltaic (PV) systems during a six-day heatwave event in Riyadh. The Local Climate Zone (LCZ) framework is used to differentiate rooftop mitigation performance across urban morphologies. The post-processing analysis evaluates air temperature, surface temperature, and surface energy fluxes across the different scenarios. Results indicate that daytime surface temperatures are reduced by up to 1.23 °C for green roofs and up to 4.62 °C for super cool roofs relative to the base case, with the strongest cooling observed over compact low-rise LCZs. Cool roofs also produce substantially lower sensible heat fluxes than green roofs across all urban LCZ Categories. Green roofs provide localized evaporative cooling benefits but are less effective than cool roofs at reducing city-wide temperatures under arid conditions. The results also show that cooling benefits vary across LCZs, with compact low-rise neighborhoods showing the greatest temperature reductions. Overall, the findings demonstrate that modeling frameworks that integrate LCZs and WRF simulations can inform evidence-based rooftop mitigation strategies to enhance heat resilience in arid climates.
How to cite: Azmeer, A., Vinodhkumar, B., Tahir, F., and Al-Ghamdi, S.: Modeling of rooftop mitigation strategies in arid climates based on local climate zones using WRF , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7382, https://doi.org/10.5194/egusphere-egu26-7382, 2026.
Urban heat represents a growing environmental and societal challenge, particularly during heatwave events, when the interaction between climate extremes and urban form amplifies thermal exposure. Advancing the characterization of urban heat therefore requires geospatial approaches capable of capturing both temporal dynamics and fine-scale spatial heterogeneity. This study investigates Surface Urban Heat Island (SUHI) intensity and intra-urban land surface temperature (LST) patterns during heatwaves in three climatically contrasting Argentine cities: Posadas (humid subtropical city), Buenos Aires (temperate coastal megacity), and Neuquén (semi-arid Patagonian city).
We apply an integrated geospatial framework combining high-temporal-resolution MODIS LST data with high-spatial-resolution Landsat 8 and Sentinel-2 imagery. Heatwave periods are analysed to quantify daytime and nighttime SUHI across urban, peri-urban, and rural zones, while Local Climate Zones (LCZs) are mapped to assess how urban morphology, land cover, and vegetation modulate thermal patterns at the intra-urban scale. Statistical analyses are used to evaluate significant temperature differences among zones and urban typologies under extreme heat conditions.
Results reveal strong inter-city contrasts and complex spatial responses. Posadas and Buenos Aires exhibit pronounced nocturnal SUHI, reflecting urban heat retention during heatwaves, whereas daytime patterns differ substantially depending on regional context. In Neuquén, a heterogeneous thermal response emerges, including a negative daytime SUHI relative to the surrounding semi-arid plateau, highlighting the influence of soil moisture, vegetation scarcity, and topography. Across all cities, compact and densely built LCZs consistently show higher LST, while vegetated areas, river corridors, and water bodies act as persistent cooling zones during heat extremes.
By integrating multi-source geospatial data within an LCZ-based analytical framework, this study advances the characterization of urban heat under extreme conditions and provides transferable insights for climate-resilient urban planning, heat risk mitigation, and spatially targeted adaptation strategies.
How to cite: Cimolai, C. and Aguilar, E.: Characterizing heatwave-driven urban heat patterns using multi-source geospatial data: SUHI dynamics and intra-urban thermal variability in Argentine cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12717, https://doi.org/10.5194/egusphere-egu26-12717, 2026.
Surface Heat Island (SUHI) is a result of complex and non-linear interactions between atmospheric processes and urban surface features. These interactions operate at different spatiotemporal scales. Research shows that surface coverage and urban morphology affect the urban thermal environment; however, most SUHI modeling approaches still rely on surface features and mostly ignore important parameters such as atmospheric humidity and precipitation. This problem limits the ability of existing SUHI models to accurately represent interactions between the surface and atmosphere and thermal variability.
This research presents a deep learning-based framework for SUHI modeling which is developed based on integrating urban, atmospheric, and environmental features. The proposed framework integrates Landsat-derived land surface indicators, including Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), which represents vegetation cover, the Normalized Difference Built-up Index (NDBI) and Night Time Light (NTL), which characterize built-up areas, and the Normalized Difference Water Index (NDWI), which represents surface water bodies, with GNSS-derived Precipitable Water Vapor (PWV) as a measure of atmospheric humidity and Global Precipitation Measurement (GPM) data. Other effective parameters include topography from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model and population density in different part of the city.
A Convolutional Neural Network (CNN) architecture is developed to capture spatial dependencies in urban areas and to understand the non-linear interactions between surface, atmospheric, social, and environmental features. This model is composed of many stacked convolutional layers with regularization and pooling algorithms to maintain generalization and preserving spatial structure. SUHI intensity, defined as the contrast between LST in urban and rural areas, is used as the target for prediction. Model training and validation are based on cross-validation to assess robustness and transferability across different temporal subsets.
The case study is Wrocław, Poland that has been experiencing rapid urban development and has undergone substantial land-use and structural transformation over the past decade. Comparisons between results from models that include and exclude humidity and precipitation demonstrate that GNSS-derived PWV and precipitation play a significant role in SUHI modeling.
The results highlight the importance of accounting for urban–atmosphere interactions in SUHI modeling. This deep learning framework provides a practical basis for subsequent eXplainable Artificial Intelligence (XAI) analyses. XAI analysis can identify SUHI drivers and support climate-resilient urban planning and heat mitigation strategies.
How to cite: Tasan, M. and Dąbrowska, J.: Deep Learning–Based Modeling of Surface Urban Heat Island Integrating GNSS-Derived Atmospheric Humidity and Multi-Source Urban Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12749, https://doi.org/10.5194/egusphere-egu26-12749, 2026.
Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. Effective action to address this challenge requires reliable metrics and data, which are often not readily available. Consequently, the science directing urban heat adaptation is strongly influenced by studies that use satellite-based land surface temperature (LST), which is globally available and address data gaps in cities, particularly in the Global South. Hence, LST now often serves as the default lens through which many cities view their heat realities. Yet this lens is fundamentally misfocused. LST, is a poor surrogate for near-surface air temperature, physiologically relevant human thermal comfort, or direct human heat exposure. This flawed practice leads to issues for several downstream use cases by inflating adaptation benefits, distorting the magnitude and variability of urban heat signals across scales, and thus misguiding urban adaptation policy. Drawing on remote sensing, climate science, and governance theory, we clarify what LST does and does not represent and expose where its use drifts most dangerously across disciplines. We argue that satellite-based LST must be treated as a distinct indicator of surface climate, which, though relevant to the urban surface energy budget, is frequently decoupled from human-relevant thermal impacts. We then advance practical guardrails and principles for using LST wisely, alongside a Surface-to-Society framework to re-align urban heat governance with metrics grounded in human heat exposure. We argue that the global community must urgently pivot from cooling pixels to cooling people.
How to cite: Bechtel, B., Kotthaus, S., Zhan, W., Du, H., Nazarian, N., Chakraborty, T., Krayenhoff, S., Martilli, A., Naserikia, M., Roth, M., Sismanidis, P., Stewart, I., and Voogt, J.: Satellite-derived Land Surface Temperatures Strongly Mischaracterise Urban Heat Hazard, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12756, https://doi.org/10.5194/egusphere-egu26-12756, 2026.
Urban areas are increasingly affected by environmental and public health challenges driven by rising temperatures. Due to ongoing climate change and the increased presence of greenhouse gases in the atmosphere, the frequency and duration of heatwaves are expanding. These phenomena have serious implications for human health, particularly among vulnerable populations such as the elderly, individuals with pre-existing cardiovascular or respiratory conditions, and disadvantaged socio-economic communities.
Air temperature at 2 meters above the surface is a critical variable for assessing climate change impacts and thermal stress, especially in densely populated urban environments. However, ground-based observations of air temperature are often sparse, mostly concentrated in developed regions, and frequently suffer from temporal gaps. This spatial and temporal inconsistency limits our ability to monitor urban thermal conditions effectively. On the other hand, satellite data provide continuous and global measurements of land surface temperature (LST), but do not directly measure air temperature. Since LST and air temperature are not equivalent, translating satellite-based LST into reliable air temperature estimates remains challenging.
In this work, we developed a statistical approach that leverages MODIS satellite observations and ERA5-Land model data across the 70 largest and most populous cities worldwide, geographically distributed with a maximum of three cities per country to prevent national over-representation and ensure global balance. The dataset spans from 2012 to 2023 and is categorized by three latitudinal zones equatorial (0 to ±15°), tropical-temperate (±15° to ±45°), and temperate-subpolar (±45° to ±75°) and by month, distinguishing between day and night observations.
For each geographical and temporal class, we fit the parameters of the equation:
Tair = a * LSTday + b * LSTnight + c [1]
This resulted in parameter triplets (a, b, c) specific to each month and latitude band. These parameters were then applied to MODIS and VIIRS data, for the year 2024, to assess the inter-sensor scalability. The resulting air temperature estimates are obtained at the native spatial resolution of the input datasets (1 kilometer). The method operates on a daily basis, leveraging both daytime and nighttime satellite acquisitions to ensure consistent and temporally detailed air temperature estimates, an essential feature for capturing urban thermal dynamics and short-term variability, such as the urban heat island effect.
The model was validated using data from the year 2024 for the same cities, with the ERA5-Land dataset (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview) serving as a reference. Pearson correlation coefficients ranged from 84% to 93% for daytime temperatures and from 77% to 93% for nighttime temperatures.
The approach is also adaptable to ongoing and future satellite missions with improved spatial resolution (e.g. ECOSTRESS). Looking toward future developments, the integration of Artificial Intelligence could further enhance this methodology by incorporating additional weather variables, improving the representation of complex ambient conditions. This work represents a promising advancement in the field of high-resolution, daily thermal comfort assessments across urban areas, offering a scalable and flexible tool for heat-related stress monitoring.
[1] Hooker, J. et al., A. A global dataset of air temperature derived from satellite remote sensing and weather stations. (2018). https://doi.org/10.1038/sdata.2018.246
How to cite: Settembre, D., De Santis, D., Cappelli, D., and Del Frate, F.: Daily Air Temperature Mapping in Urban Areas from Satellite Land Surface Temperature and ERA5 Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12814, https://doi.org/10.5194/egusphere-egu26-12814, 2026.
Tree shade is widely recognized as an effective nature-based cooling solution to mitigate thermal exposure under a warming climate. However, factors modulating the intensity of shade-related cooling remain poorly understood, particularly beyond urban settings where most prior studies have focused on individual tree traits and local land use. This study examines whether shading effect varies across different landscapes and identifies key temporal and spatial drivers of shade-induced cooling across three survey sites: urban, post-mining, and lakeside environments in Lusatia, Germany. More than 100 trees were assessed for their shading effects. Surface temperature of shaded and adjacent non-shaded surfaces were measured using a handheld thermal camera during heat events in August 2023 and 2024, when daily maximum temperatures exceeded 30°C. Land-use information was derived from field-collected RGB imagery. Additional variables including distance to water and forest, vegetation index and canopy height were extracted from remote-sensing datasets. Shading effects were quantified using paired statistical tests, and an XGBoost regression model combined with post-hoc interpretability analyses was applied to identify key predictors and their influence on cooling intensity. Across all survey sites, shaded surfaces were significantly cooler than non-shaded surfaces, with non-urban areas exhibiting larger cooling effect. The predictive model achieved moderate performance (R2 = 0.34). Temporal factors, particularly year and time of day, emerged as the most influential predictors, indicating substantial temporal variability in shade-induced cooling. Spatial configuration also played a critical role: shade-induced cooling increased with distance from forested areas and decreased with distance to water bodies. The relative importance of spatial variables varied by landscape type. Canopy height showed a negative relationship with cooling magnitude, suggesting that areas dominated by shorter trees may enhance shading effectiveness. Vegetation greenness and land-use categories had comparatively minor effects, while landscape type itself exerted no substantial influence. These findings demonstrate that shade-related cooling is governed not only by local tree or land-use characteristics but also by broader environmental context, including surrounding vegetation and landscape configuration. Incorporating multiscale geospatial predictors into microclimate assessments can therefore improve the design of climate-resilient landscapes and heat-mitigation strategies.
How to cite: Aryal, A., Mengsuwan, K., and Ryo, M.: Tree shade as a nature-based strategy for mitigating heat exposure but effectiveness varies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1562, https://doi.org/10.5194/egusphere-egu26-1562, 2026.
Climate change is driving a sustained increase in global temperatures and an intensification of extreme events, including heatwaves. Urban areas are particularly vulnerable, as they are characterized by higher temperatures compared to surrounding suburban and rural environments, a phenomenon known as the Urban Heat Island (UHI). This effect is primarily associated with the presence of buildings and inhomogeneous surfaces, which modify surface energy and water exchanges as well as local wind circulation. Additional factors, such as land-use changes, reduction of vegetated areas, local anthropogenic heat emissions (e.g. traffic and air conditioning), and air pollution, further alter the urban heat balance.
The UHI strongly affects urban climate, ecosystems, air quality, and human thermal comfort, and its impact is exacerbated during heatwaves, posing significant risks to human health. Therefore, monitoring and characterizing UHI is crucial for climate mitigation and adaptation strategies.
Satellite-derived Land Surface Temperature (LST) provides an effective means to investigate the Surface Urban Heat Island (SUHI), enabling comprehensive spatial and temporal analyses of urban thermal patterns, identification of hot- and cold-spots, and assessment of mitigation measures such as high-albedo materials and urban green areas.
In this study, thermal infrared satellite observations are used to analyze LST over selected urban areas in Central and Southern Italy, with the aim of characterizing SUHI dynamics. Long-term variations (>10 years) are investigated using Landsat 8 data (100 m spatial resolution, 16-day revisit time, available since 2013). For recent years, Landsat 8 and 9 observations (8-days revisit time when used in combination) are combined with ECOSTRESS data (70 m spatial resolution, variable overpass times, 1–2 day revisit), significantly enhancing temporal sampling. This multi-sensor approach enables an improved assessment of urban temperature evolution and its response to climate change.
This work was supported by the ASI SpaceItUp contract N. 2024-5-E.0, CUP
I53D24000060005., SPOKE 5 and SPOKE 7 activities.
How to cite: Pecci, M., Scalabrini, A., Buongiorno, M. F., Musacchio, M., Silvestri, M., and Rabuffi, F.: Satellite high-resolution thermal infrared imagery for UHI monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13195, https://doi.org/10.5194/egusphere-egu26-13195, 2026.
Tree shading is one of the most effective mechanisms to improve daytime human thermal comfort, specifically in urban contexts where exposure to direct shortwave radiation dominates heat stress. The quality and quantity of tree shading are heavily controlled by canopy closure and crown architecture. However, with limited data on the link between tree structure in different tree species and their shade quality, these relationships are frequently overlooked both in practice and in urban microclimate modelling.
In this study, we present a new framework to quantify tree shading potential using Terrestrial Laser Scanning (TLS). The TLS scan of a tree is used to derive its canopy gap fraction, which represents a proxy for direct shortwave radiation transmissivity. We evaluate different processing methods (laser pulse-based and point-based) and perform a digital validation of the different approaches. After validating and selecting the most appropriate method, the transmissivity values are linked back to the tree’s structural characteristics which can be derived from the TLS point cloud information. By including indices such as tree height, crown volume, and leaf area density, we investigate the link between tree structure and shading behavior.
The proposed framework is applied to a database of over 50 individually scanned urban trees, all measured in summer across multiple cities in Belgium. This allows for a comparison of shading capabilities between different tree species and morphologies. In addition to the expected differences in overall transmissivity between trees, preliminary results reveal strong variations in gap fraction across different zenith angles, with transmissivity values ranging from 3 to 28% at low and high zenith angles, respectively. This indicates not only a variation in shading intensity between individual trees, but also potential differences throughout the day.
These results are informative for comparing urban tree species and their management strategies when the goal is to optimize pedestrian shading and thermal comfort. Additionally, they provide empirically derived parameters to improve the representation of tree structure and shading effects in urban microclimate models.
How to cite: Daelman, T., Verbeeck, H., Demuzere, M., and Vancoillie, F.: Understanding urban tree shade: characterizing direct shortwave transmissivity through urban tree canopies using Terrestrial LiDAR Scanning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13264, https://doi.org/10.5194/egusphere-egu26-13264, 2026.
Historic variations in building regulations and construction practices have shaped the thermal properties of today’s urban fabric, yet these differences are often neglected or oversimplified in urban weather and climate models. Building materials, insulation levels, and construction techniques vary strongly across construction periods, leading to spatial differences in heat storage and release, energy demand, and vulnerability to temperature extremes. Most current urban mesoscale models rely on generic classifications, such as Local Climate Zones (LCZs), which limits their ability to capture this heterogeneity and reduces the accuracy and reliability of weather and climate forecasts.
Using Amsterdam (The Netherlands) as a case study, we present a novel geospatial modeling framework that explicitly incorporates historical building characteristics into numerical weather and climate simulations. Based on detailed cadastral data and an extensive review of historical building regulations and practices in The Netherlands, we define ten “heritage building classes” representing distinct construction periods and their typical thermal properties. These classes are mapped across the city using GIS techniques, producing high-resolution heritage building maps. For each class, representative thermal parameters are derived and implemented into the Weather Research and Forecasting (WRF) model.
We assess the sensitivity of simulated urban temperatures to these period-specific building properties and evaluate model performance against in-situ meteorological observations from the Amsterdam Atmospheric Monitoring Supersite (AAMS). This approach provides a scalable pathway for integrating historically informed building characteristics into urban climate models, creating a foundation for future improvements in urban climate simulation.
How to cite: Sandvik, B. E., Milošević, D., Kalverla, P., Donnelly, C., and Steeneveld, G.-J.: Developing a Historical Building Classification for Mapping Urban Thermal and Morphology Parameters in Urban Climate Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13567, https://doi.org/10.5194/egusphere-egu26-13567, 2026.
Cities around the world, including those located in predominantly cold climates that were once thought to be relatively immune to warming, are experiencing rapid temperature increases and more frequent heatwave events, with substantial impacts on people’s well-being and critical urban infrastructure. Green infrastructure (GI) can help mitigate these impacts by cooling through shade and evapotranspiration, but expanding vegetation cover is increasingly difficult because of land competition driven by urbanization. This makes it critical to understand how to maximize the cooling effect of a given amount of vegetation. Key considerations include the fact that different types of vegetation can confer varying levels of cooling and that the spatial distribution of vegetation can influence its cooling impact.
In this presentation, we report preliminary findings from a large ongoing study comparing 12 cities in Southern Ontario, Canada. We mapped two types of vegetation (i.e., trees and shrubs/grass) using Sentinel satellite imagery, and examined how different aspects of their spatial patterns, quantified using landscape metrics, affect land surface temperature (LST) derived from Landsat imagery averaged over summer months. We evaluate and compare these relationships across cities of different sizes, from small cities with fewer than 500,000 residents to large metropolitan areas such as Toronto. We also investigate how the relationship between GI spatial patterns and LST varies across spatial scales, and we evaluate multiple modelling approaches, including spatial regression models, as well as advanced machine learning (ML) and deep learning (DL) models, including random forest and convolutional neural networks.
Our findings to date yield several insights:
- In all cities, the spatial pattern of GI exerts a significant influence on LST even after controlling for the total amount of vegetation. However, the relative importance of specific spatial pattern characteristics (e.g., connectivity, geometric complexity of patches) varies across cities, with distinct differences between larger and smaller urban areas.
- Consistent with existing literature, trees provide substantially greater cooling effects than shrubs/grass, although the magnitude of cooling varies meaningfully across cities.
- The influence of spatial pattern on LST is strongly scale-dependent, with relationships generally strengthening from finer to intermediate spatial scales, and also varying with the shape of analytical units.
- Spatial regression models prove essential for accurately characterizing vegetation–temperature relationships, as non-spatial models tend to overestimate effect sizes and increase the likelihood of falsely identifying significant relationships.
- While machine learning and deep learning models excel at prediction, spatial regression models continue to offer interpretative insights not captured by ML and DL models. We provide recommendations on the appropriate use of each model.
We believe these findings help fill an important knowledge gap on cold-climate cities, particularly in the Canadian context, where urban morphology may differ from that of other cold-region cities. Our results provide a more nuanced understanding of how vegetation type, spatial configuration, and scale interact to shape cooling, and they offer practical guidance for policymakers and practitioners on strategically deploying GI to maximize cooling benefits.
How to cite: Masoudi, M. and Mahdianpari, M.: Understanding Green Infrastructure-Temperature Relationships in Cold-Climate Cities: Evidence from 12 Canadian Urban Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15300, https://doi.org/10.5194/egusphere-egu26-15300, 2026.
Urban green infrastructure (e.g., lawns, trees, green roofs) is a critical nature-based solution for mitigating urban heat island effect and reducing building cooling energy demand. While its biophysical processes, such as evapotranspiration, shading, and photosynthesis, are known to modify local microclimate and surface-atmosphere exchanges, most existing assessments rely on simplified, static representations of vegetation. This overlooks essential dynamic processes such as seasonal growth, phenological changes, greening-browning shifts due to heat and moisture stress responses, leading to uncertainties in quantifying its full cooling and energy-saving potential. To address this gap, we develop and apply an enhanced Urban Canopy Model (UCM) that integrates a dynamic ecohydrological module for vegetation with a building energy model capable of simulating outdoor thermal conditions and anthropogenic heat emissions. We first conducted comprehensive field campaigns in Wuhan, China, using a newly established urban eddy covariance tower and a green roof monitoring system, coupled with data on irrigation and other anthropogenic activities within the flux footprint. The model was rigorously validated against measurements of air/soil temperature, moisture, and turbulent heat fluxes. We then performed sensitivity analyses to evaluate how dynamic vegetation parameters (e.g., soil moisture, vegetation greenness, irrigation regimes) and building properties interactively affect outdoor microclimate and indoor energy demand. Our findings demonstrate that accounting for vegetation dynamics significantly improves the accuracy of microclimate and energy simulations, providing actionable insights for the planning and optimization of green infrastructure towards energy-efficient and climate-resilient cities.
How to cite: Zhang, J. and Song, J.: Modeling the Impact of Vegetation Dynamics on Urban Microclimate and Building Energy Demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2289, https://doi.org/10.5194/egusphere-egu26-2289, 2026.
Severe PM2.5 pollution (particles with an aerodynamic diameter ≤ 2.5 μm) and the urban heat island (UHI) effect pose serious threats to human health and living environments in densely populated cities. However, the specific role of aerosols in shortwave and longwave radiation transfer, as well as the mechanisms through which radiation processes affect urban heat island intensity (UHII), remain insufficiently understood. In this study, the WRF-Chem model was employed to simulate several typical pollution episodes during 2016–2017. We quantitatively assessed aerosol radiative forcing and further distinguished the contributions of different aerosol components to shortwave and longwave radiation, systematically analyzing their impacts on surface UHI, canopy-layer UHI, and boundary-layer UHI. The results show that, overall, boundary-layer UHI increases with worsening pollution, while the peak intensities of surface and canopy-layer UHIs are significantly weakened under polluted conditions. However, during sustained pollution episodes, as pollution intensifies, the maximum UHI intensities of both tend to increase. To exclude the influence of indirect aerosol radiative effects, periods with high pollution but low cloud cover were selected for further analysis. Comparative sensitivity experiments reveal that absorbing aerosols enhance UHIs at all levels, particularly daytime canopy-layer UHI (by 14.65%) and nighttime boundary-layer UHI (by 20.04%). In contrast, scattering aerosols weaken boundary-layer UHI and daytime surface UHI, while strengthening canopy-layer UHI and nighttime surface UHI. By comparing radiative heating profiles in urban and rural areas, we found that absorbing aerosols absorb more radiation in urban areas during the day, resulting in a markedly higher heating rate than in rural areas; at night, urban areas also exhibit slightly stronger heat retention. Decomposing the radiative heating profiles into shortwave and longwave components further indicates that absorbing aerosols strongly absorb shortwave radiation during the day and subsequently heat the near-surface layer via longwave radiation at night. Scattering aerosols reduce radiation received by the surface and boundary layer during the day, while at night they intercept longwave radiation in the upper boundary layer, leading to warming above and cooling below the boundary layer. In summary, absorbing aerosols enhance UHIs at all levels by absorbing shortwave radiation during the day and continue to intensify UHI through longwave radiation release at night. Scattering aerosols, by scattering solar radiation, weaken boundary-layer UHI and reduce daytime surface heating, while scattering radiation toward the canopy enhances canopy-layer UHI. This study distinguishes between the radiative effects of absorbing and scattering aerosols, revealing their differential impacts on multi-layer urban heat islands and providing new insights into pollution-climate interactions. The findings offer relevant implications for urban climate adaptation planning and synergistic air quality management.
How to cite: Qian, C. and Yang, Y.: Mechanisms of Aerosol Composition Effects on Multi‑Layer Urban Heat Islands: A Case Study of Beijing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15720, https://doi.org/10.5194/egusphere-egu26-15720, 2026.
Rapid urbanization and global climate change have intensified the urban heat island (UHI) effect, posing significant risks to public health and urban livability. Street-level shading is a vital passive cooling strategy to mitigate heat stress and enhance pedestrian thermal comfort. However, traditional methods often struggle to achieve large-scale, high-precision identification of diverse shade facilities—such as building overhangs, street trees, and specific structures like arcades (Qilou)—across the urban pedestrian network.
This research proposes a comprehensive framework leveraging Street View Imagery (SVI) and advanced AI analytics to bridge this gap. Initially, the study employs the YOLOv11 (You Only Look Once) deep learning architecture to automatically detect and quantify heterogeneous shading elements. By training on high-resolution SVI datasets, the model identifies multi-type shade facilities including building facades, arcades, street trees, and artificial awnings in complex urban environments.
Subsequently, the research evaluates the synergistic effects of these facilities on the pedestrian thermal environment. The extracted geospatial shade data are integrated with microclimate simulation tools to quantify their impact on thermal comfort indicators. Key parameters, such as the Sky View Factor (SVF), are derived from the pedestrian perspective to evaluate shading performance and its role in reducing heat exposure.
The findings are visualized through high-resolution thematic maps depicting shade coverage density and thermal comfort assessment results. This research provides urban planners and managers with scientific decision-making evidence to identify shade-deficient areas and optimize street designs for heat-risk reduction. By combining YOLOv11-based object detection with geospatial analytics, this study offers a scalable approach to enhance urban climate resilience and support sustainable, walkable urban development.
How to cite: Gu, M., Yin, S., Zhong, L., Zhou, J., and Xia, D.: Deep Learning-based Identification of Urban Pedestrian Shade Facilities and Thermal Environment Assessment using Street View Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8982, https://doi.org/10.5194/egusphere-egu26-8982, 2026.
How to cite: Wang, L., Li, D., and Yuan, X.: Impacts of VPD Stress on the Interaction Between Urban Heat Islands and Heat Waves Over CONUS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16094, https://doi.org/10.5194/egusphere-egu26-16094, 2026.
Urban greening is a primary strategy for mitigating the Urban Heat Island (UHI) effect, yet quantifying its impact on building energy consumption remains challenging due to the complex reciprocal feedbacks between the urban microclimate and building systems. This study investigates the influence of urban trees on air-conditioning (AC) energy demand across seven climatically diverse cities (Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo) during the hot season. We employ a coupled urban ecohydrological and building energy model (Urban Tethys-Chloris - BEM) to simulate varying urban densities, tree cover scenarios, and plant physiological properties. Our analysis isolates the relative contributions of shading, temperature reduction, and humidity alterations on AC loads. Results indicate that well-watered trees yield the highest average summer AC reduction (-17%) in hot-dry climates, driven predominantly by shading. In humid climates, AC demand decreased by 6% to 9%; however, vegetation-induced humidity increased dehumidification loads, particularly under high ventilation rates. In these regions, optimal energy savings were achieved at 40% tree cover. These findings provide critical insights for tailoring urban greening strategies aimed at minimizing AC energy demand to specific regional climates.
How to cite: Fatichi, S. and Meili, N.: On the role of urban trees in reducing building energy consumption , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16717, https://doi.org/10.5194/egusphere-egu26-16717, 2026.
As world mean temperature arises, the Urban Heat Island (UHI) phenomenon is intensifying in most cities worldwide and is frequently exacerbated by heatwave events. It directly affects citizens’ health, thermal comfort and daily activities. A detailed understanding of the spatial and temporal variability of the UHI is therefore increasingly necessary for urban planning and the development of heat mitigation strategies. UHI intensity is commonly evaluated using localized air temperature observations, which often provide limited spatial coverage of the urban area in the absence of a sufficiently extensive sensor network. In this context, thermal infrared (TIR) remote sensing data with high spatial and temporal resolution are considered as a valuable tool for assessing the urban thermal environment. Several studies have used satellite data to investigate Surface Urban Heat Island (SUHI) and to analyse urban thermal behaviour. However, the accuracy of satellite-derived measurements depends on observation geometry and the properties of surface endmembers within the satellite field of view, and these measurements can differ substantially from near-surface air temperature observations. Since the relationship between surface and near-surface temperatures remains insufficiently determined, it is essential to investigate the link between both variables to enhance the use of satellite data in urban climate studies.
In this work, the thermal variability of land surface temperature (LST) derived from Landsat 8 and 9 (30 m resampled spatial resolution) over the city of Valencia (Spain) is compared with near-surface air temperature observations from the VITUclim thermal sensor network. The VITUclim network comprises more than 80 thermohydrometer sensors distributed across the urban area of Valencia and represents one of the densest urban thermal monitoring networks in Europe. Sensors are installed at 3 m above ground level following a systematic deployment strategy to ensure observational consistency. This study represents the first application of VITUclim data for urban climate studies. Results reveal notable discrepancies between LST and air temperature, reaching up to 10 K during daytime conditions. Further analyses will be conducted to improve the understanding of the relationship between these two variables in the study area.
How to cite: Pérez-Planells, L., Gavilà-Lloret, S., Valiente, J. A., and Khodayar, S.: Urban thermal variability from satellite and ground observations: a case study over Valencia (Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16832, https://doi.org/10.5194/egusphere-egu26-16832, 2026.
Under the impact of ongoing climate change, urban environments exhibit warming rates significantly exceeding those of surrounding rural areas. Assessing the resulting impacts on human health requires high-resolution data that current Earth Observation (EO) products struggle to provide. We address the inherent trade-off between temporal frequency and spatial detail by integrating a multisensor data suite, comprising SEVIRI (high temporal), MODIS (daily), and Landsat LST (high spatial) EO data, weather reanalysis and dense urban station network data, to train a machine learning-based downscaling framework.
Our methodology generates city-wide temperature maps at 100m resolution every hour. To capture the complex physical drivers of the urban canopy layer, the model incorporates a diverse array of covariates, including land cover, spectral indices, sky view factor, and building morphology. The model also accounts for energy balance variables such as anthropogenic heat emissions and utilizes a precipitation proxy to simulate the effects of evaporative cooling on surface temperatures.
We validate this framework across the city of Newcastle (UK), utilizing the high-density Urban Observatory sensor network to train and benchmark model accuracy against ground-truth data. We further evaluate the model’s transferability through case studies in the cities of London (high-density metropolitan landscape) and Southampton (coastal-urban interactions), representing environments with sparse ground networks.
A key aspect is the generation of design heatwaves and cold snaps. Rather than relying solely on historical records, we employ stochastic modeling to produce extreme weather series that allow for the high-resolution quantification of the full spectrum of thermal risk, including unprecedented events. These outputs serve as the primary forcing data for indoor thermal simulations and population exposure models within the ETHOS project. Ultimately, this framework provides clinicians and local authorities with the precise spatial risk information needed to protect vulnerable populations during thermal extremes.
How to cite: Berendsen, S., Li, X., Tanathitikorn, C., Blenkinsop, S., James, P., Namdeo, A., and Sheffield, J.: High-resolution multi-sensor fusion for urban temperature downscaling: integrating physical covariates and stochastic extremes for health impact assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17781, https://doi.org/10.5194/egusphere-egu26-17781, 2026.
The prevalence of built structures in urban areas has led to the emergence of heat islands, intensifying the impact of climate change. Hence, introducing vegetation in cities is key to regulate urban micro-climates, reduce heat-risks, and improve the physical and mental well-being of urban residents. Green infrastructures can help mitigate urban overheating via two main mechanisms: (1) direct shading, which reduces the amount of solar radiation absorbed by the urban surfaces, and (2) evapotranspiration, which regulates the partitioning of latent and sensible heat fluxes within the urban fabric. Yet, planting and managing vegetation in cities face several challenges, from limited space, to plant damages, and conflicts with other infrastructure (e.g., pipes, cables).
To address these problems, we investigate the potential cooling effect of an innovative lightweight structure (bamX) made of bamboo canes weaved together and vegetated with hops. We use the PALM model to run high resolution (0.5 m) computational fluid dynamics simulations in idealized conditions and for different stages of plant maturity. Preliminary results show that the greatest cooling potential of these lightweight vegetated structures is associated with shading and depends on the leaf area density of vegetation. Evapotranspiration processes, despite slightly increasing relative and specific humidity locally, do not significantly alter human thermal comfort within and around the structure.
How to cite: Manoli, G., Brandi, A., Varupenne, J., Suzuki, S., Vonlanthen, M., and Pauly, M.: Simulation of the cooling effect of a lightweight vegetated structure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18535, https://doi.org/10.5194/egusphere-egu26-18535, 2026.
Lucknow's urban footprint has expanded significantly in a relatively short time. The city's built-up area essentially doubled, leaping from approximately 521 km² in 2014 to over 1,041 km² by 2019. This surge was fueled by both urban sprawl and a rising population. From 2002 to 2014, the city's urban area experienced a 39% increase, while the average temperature rose by 0.75 °C. This swift transformation has altered land use patterns, reduced green and permeable spaces, and intensified the Urban Heat Island (UHI) effect across the city. Heat stress has extended outside the downtown area. Increased surface temperatures are recorded at multiple locations along the Gomti River, with industrial areas routinely displaying the highest measurements.
This study presents a planning-centric methodology that utilizes spatial analysis, focused modelling, and localized observations to tackle urban heat in Lucknow. Multi-temporal satellite data were employed to derive land surface temperature and relevant indices of land-use change, vegetation cover, and built-up area density. A model based on U-Net architecture was developed to refine the identification of built-up and surface material patterns, while key morphological parameters including building height, density, shape complexity, and contiguity were incorporated to capture the influence of urban form on thermal behavior. These inputs enabled the delineation of priority heat zones across the city.
Site investigations and preliminary surveys revealed unique thermal profiles associated with pavement materials, surface treatments, and urban patterns, especially in the historic regions of Old Lucknow. Industrial regions that encounter heightened solar radiation and substantial exposure require mitigation strategies. These initiatives should concentrate on materials and energy-positive solutions while simultaneously reducing adverse environmental effects. This study presents a zonal heat mitigation framework, which classifies urban areas based on land use, material characteristics, and morphological attributes, thus enabling context-specific planning and design approaches.
By linking urban climate assessment with statutory planning instruments, the proposed framework demonstrates a transferable approach for integrating heat resilience into urban planning in rapidly urbanising cities of the Global South.
Keywords: Urban Heat Island (UHI); Urban Resilience; Land Surface Temperature; Microclimate Simulation; Urban Greening; Solar Potential; Heat Mitigation Policy Framework
How to cite: Yadav, A., Paul, S. K., and Bala, D.: Urban Resilience: Framework and Policy for Heat Mitigation by addressing UHI Effects, Lucknow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18851, https://doi.org/10.5194/egusphere-egu26-18851, 2026.
Urban greening plays an important role as a tool for climate change mitigation, adaptation and environmental protection. An extensive green roof is one the most favorite methods of urban greening as it does not require additional area and intensive maintenance and is also efficient as a rain water retention during extreme events and for the deposition of particulate matter. However, it is not very efficient for cooling at pedestrian level, for CO2 sequestration and biodiversity. The suggested solution is to convert the extensive green roofs into semi-intensive using automatic solar-powered irrigation system with collected rainwater. The model green roof was built and experiment was carried out starting in 2020. The roof was irrigated during the summer months with 2 mm day-1. The green roof effects on microclimate, WBGT index, water balance, particulate matter binding, greenhouse gas fluxes and biodiversity are measured continuously and compared to a reference area of a parking lot. In addition albedo and surface temperature measurements were carried out using manual instruments and different drone-borne thermal cameras. The results show higher albedo of irrigated roof than parking lot. The microclimatic effects of semi-intensive roof on the microclimate are quite variable. The surface temperatures differences within the roof are more extreme than on the parking lot. However, the air temperature extremes are lower. The air temperature reduction comparing to parking lot is more pronounced during the night time and under calm conditions, with minimal external influences. During the daytime the warming effects of the roof are well-expressed depending on the weather conditions. The effects of different plant species and substrate to total cooling or warming of green roof were also quantified.
How to cite: Panferov, O., Hietel, E., Rössner, U., Alef, J., and Newton, J.: Rainwater-irrigated greenroofs as climate change adaptation in urban areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18880, https://doi.org/10.5194/egusphere-egu26-18880, 2026.
Tree planting is a leading strategy for mitigating the Urban Heat Island (UHI) effect. However, current urban climate models often conflate the physiological transpiration cooling of trees with their physical shading effects. To isolate and quantify this specific "biological bonus," we introduced the “Living Control” framework.
In a controlled field experiment on mature trees in a Mediterranean climate, we applied chemical anti-transpirants to inhibit stomatal conductance (gs) and transpiration (E), while maintaining identical canopy geometry and aerodynamic properties. This effectively decoupled latent heat flux from radiative shading.
Our results demonstrate the efficacy of the manipulation: the application of auxinic herbicides significantly reduced gs by 32% and E by 23% compared to untreated controls. This suppression of physiological activity led to a statistically significant rise in leaf surface temperature. However, this distinct physiological warming revealed an intriguing contrast: it did not translate into significant differences in human thermal comfort metrics, specifically Mean Radiant Temperature (MRT) and the Universal Thermal Climate Index (UTCI).
These findings challenge the prevailing assumption that canopy cooling linearly improves pedestrian comfort. They suggest that in water-limited environments, the primary contribution of trees to thermal comfort is static shading rather than active transpiration. This study highlights the complexity of microclimatic interactions and provides a vital baseline for future experiments evaluating Nature-Based Solutions (NBS) and water-wise strategies for urban heat mitigation.
How to cite: Linder, R., Preisler, Y., and Orenstein, D.: Is shade all there is to it? Quantifying the contribution of tree transpiration to cooling cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20874, https://doi.org/10.5194/egusphere-egu26-20874, 2026.
Urban air quality and heat island effect are major concerns for human health and thermal comfort, particularly under the increasing trend of extreme heat events. But traditional networks of stationary monitoring stations provide limited spatial representativeness of intra-urban microclimate variability at the scales where citizens experience thermal stress and are exposed to air pollution.
To address this observational gap, we have developed a mobile monitoring system integrating a number of compact sensors mounted on an electrical bicycle platform. This setup enables high-density geolocated sampling along pedestrian areas, walkways and bike lanes -where citizens are truly exposed to the urban environment- while minimizing perturbations to the environmental conditions. It also presents advantages over other mobile platforms, such as UAVs or electric vehicles, in terms of operation permissions, accessibility to pedestrian areas, and time endurance.
The system simultaneously records: (i) meteorological variables (air temperature, relative humidity, wind speed and direction) (ii) radiative components including, sun/shade discrimination, shortwave hemispherical irradiance (sunlight), directional radiometric temperature (from six directions), enabling estimation of the standard Wet Bulb Globe Temperature (WBGT) for outdoor thermal comfort assessment; and (iii) environmental pollution indicators (suspended black carbon, PM2.5, PM10 and ambient noise). The data is geolocated by GNSS and recorded by a datalogger every second, providing approximately 3m spatial resolution at 10km/h cycling speed.
The mobile monitoring system has been tested during a summer heat wave in Valencia, Spain, performing mobile transects at solar noon and after sunset to capture differential cooling dynamics across urban morphologies. The 20 km routes were designed to pass through different types of neighbourhoods and densities of green-blue spaces.
The geolocated measurements are integrated within a Geographic Information System (GIS) framework together with several layers of geospatial city information, like distribution of buildings, pavement types, urban green and blue spaces, individual tree inventory, vegetation density indices from satellite imagery, and building height (DEM). This set of multisource data enables advanced geospatial analytics combining spatial statistical and deep-learning to: (i) generate informative maps of thermal comfort and air quality at high spatial resolution; (ii) identify urban hot and cold spots; and (iii) quantitatively evaluate the effectiveness of different nature-based solutions (NbS) for UHI mitigation.
This novel mobile monitoring approach, delivering unprecedented spatial density of CUHI observations combined with multi-source geospatial data, provides a scalable methodology for microscale air quality mapping and evaluating urban planning strategies and nature-based cooling interventions.
How to cite: Alonso-Chorda, L., Torrenti-Salom, F., and Calatayud-Lorente, V.: Microscale Urban Heat Island and Air Quality Assessment Through Multi-sensor E-Bike Monitoring: Integrating High-Density Observations with Geospatial Analytics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19719, https://doi.org/10.5194/egusphere-egu26-19719, 2026.
Over the last few years, climate change has intensified heat effects across broad swaths globally. In 2024, for the first time, global temperatures remained at least 1.5°C above the pre-industrial average for one year. In 2025, persistent heat caused record-setting European heatwaves, which have been recurrent since 2022. The spatial distribution of such effects clearly includes areas where human life is concentrated: cities, megacities, and urban agglomerations at large, rendering cities dangerously hotter and necessitating urgent, specific adaptation measures. We examine the increasing trend in summer temperatures in cities, a key driver of environmental and health issues, to identify the major risks posed by extreme heat, particularly for vulnerable communities. We also evaluate how well current measures across cities worldwide address this growing, ubiquitous issue, with a focus on European cities.
We analyse and compare specific measures and strategies used across cities worldwide to address rising urban heat. We review real-world examples from 2023 and 2024 to examine how cities (such as those in the C40 alliance) are coping with extreme temperatures, employing solutions ranging from urban greening to early warning systems, from water management strategies to population sheltering. We find that, while some cities have made considerable progress in enhancing their heat resilience, a pressing need remains for more refined measures to address urban heat effectively and strategically protect human health. Metropolitan areas across Europe and expanding megacities worldwide thus need comprehensive strategies and shared best practices to manage summer heatwaves and adapt to the impacts of a changing climate that poses novel, compounded hazards to human health.
We argue that public urban spaces are central to climate adaptation in cities because they are highly vulnerable to extreme heat. However, those very spaces can also be pivotal for implementing innovative solutions to improve citizens' well-being. We thus underscore the urgency for cities to adopt adaptive strategies to cope with rising temperatures, given the foreseeable trajectory of heatwaves through time. In analysing the pressing global urban heat challenge, we urge policymakers and urban planners to prioritise sustainable and effective interventions demanded by populations across the complex spectrum of contemporary societies and the compounded hazards that these face.
How to cite: Fracassi, U., Leal Filho, W., Dinis, M. A. P., and Nagy, G. J.: Heat and the City: How urban agglomerations devise adaptation measures to protect human health, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20703, https://doi.org/10.5194/egusphere-egu26-20703, 2026.
The successful implementation of marine spatial planning (MSP) and mitigation against coastal hazards needs to have access to a variety of quality data. Critical marine information, such as bathymetry, fisheries, biodiversity, aquaculture, coastal infrastructure, and oceanographic models, in the Sultanate of Oman is usually divided among ministries and institutions, is in a wide range of formats, and is not standardised in terms of metadata. This type of insulation would add to the absence of interoperability, discoverability, and reuse, which has a direct effect on evidence-based policy and sustainable development of a blue economy. This study fills this gap by designing and testing a conceptual model of a national Marine Spatial Data Infrastructure (MSDI), which is clearly designed to be founded on the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles. Going beyond a generic SDI model, the framework offers a customised way of implementation in the Omani context. The methodology will integrate an in-depth examination of the best practices of international MSDI, as well as a stakeholder requirements analysis of the main Omani government and research institutions. The suggested framework explains architectural elements, metadata profiles, semantic interoperability protocols, and a governance model in order to achieve long-term sustainability. This framework, as applied to the case study, can revolutionise the marine data situation in the Sultanate of Oman. Some major products are the prototype metadata catalogue, the semantic ontology of alignment between national data and international vocabularies, and a policy roadmap. The study also provides a generalisable template to other coastal countries and illustrates that FAIR-based MSDIs are not the technical systems and structures but the basic support systems of transdisciplinary ocean science, climate resilience and efficient maritime spatial governance.
How to cite: Al-Subhi, N., Al-Suqri, M., and Hamad, F.: Bridging the Data Gulf: Designing a FAIR-Compliant Marine Spatial Data Infrastructure for Sustainable Coastal Governance in The Sultanate of Oman, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2356, https://doi.org/10.5194/egusphere-egu26-2356, 2026.
Xitou is a mid-elevation mountain forest region (1000–1200 m a.s.l.) in central Taiwan where intensive tourism infrastructure is embedded within an otherwise continuous forest ecosystem. This setting provides a rare opportunity to apply a spatially explicit monitoring framework to characterize urban heat island (UHI) effects using an ecologically meaningful forest reference state, rather than conventional urban–rural comparisons.
This study establishes a forest temperature gradient baseline based on year-long in-situ thermal observations from elevation-differentiated forest sites. The derived forest lapse rate exhibits pronounced diurnal asymmetry, with weaker daytime cooling and stronger nocturnal cooling, reflecting the combined influence of evapotranspiration, radiative processes, and boundary-layer stability. Expected forest temperatures at hotel elevations were then reconstructed from this baseline and compared with observed temperatures to isolate tourism-driven UHI intensity.
Results show that hotel developments generate a persistent warming of approximately 0.9–1.3 °C relative to the forest baseline. UHI intensity exhibits strong diurnal contrasts: one hotel shows pronounced nocturnal dominance, with nighttime warming nearly 1.8 times daytime values, indicating the importance of building heat storage and nighttime heating, while another shows comparable daytime and nighttime warming, suggesting substantial daytime anthropogenic heat emissions from tourism activities. Seasonally, UHI intensity is significantly stronger in winter and weaker in summer, contrary to typical urban patterns, highlighting seasonal modulation by forest evapotranspiration, which partially offsets anthropogenic heat during the growing season.
Despite the forest’s strong thermal buffering capacity, the results demonstrate that conversion of forest land to tourism facilities measurably intensifies local UHI, altering near-surface atmospheric stability and potentially affecting fog formation, boundary-layer processes, and forest microclimates. These thermal changes imply broader ecological impacts, including increased nighttime heat stress and disruption of forest–atmosphere energy exchanges.
How to cite: Lai, Y.-J. and Lin, P.-H.: Quantifying Urban Heat Island Effects in a Mountain Forest Tourism Area Using a Forest Reference Baseline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2402, https://doi.org/10.5194/egusphere-egu26-2402, 2026.
Urban greening with trees and other vegetation is gaining popularity as a means to benefit urban populations, particularly by mitigating heat exposure. At the same time, concerns about equity in the distribution of urban greenspaces and their benefits have become central to urban climate planning and research. A persistent challenge in the field is the trade-off between data accuracy and scale. Detailed, empirical ground data is typically only available for individual cities, whereas broader comparative or global analyses must rely on satellite data and extrapolated or heavily aggregated socioeconomic data. Here, we compare these approaches in the context of the socioeconomic distribution of urban tree-related cooling benefits. We derive multiple metrics using ground-based temperature measurements, satellite data, and census-based socioeconomic indicators. By benchmarking analyses based on broadly available datasets against those using detailed local measurements, we assess the extent to which large-scale, data-sparse approaches reproduce patterns observed in high-resolution ground data. Using several US and European cities as case studies, we then assess the spatial distribution of cooling effects in relation to socio-economic conditions of the neighbourhood. Our results provide guidance on the reliability and limitations of commonly used data sources for assessing equity in urban heat mitigation benefits across cities and regions.
How to cite: Reek, J., Zohner, C., Jonsson, V., and Pelissier, L.: Scaling urban tree cooling and its socioeconomic distribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7250, https://doi.org/10.5194/egusphere-egu26-7250, 2026.
Urban heat islands (UHIs) are areas within urban environments where air temperatures are consistently higher than those in surrounding rural areas. UHIs arise from urban morphology, anthropogenic heat emissions, and altered radiative and evapotranspiration balances.
The local effects of urban heat islands during extreme events (e.g., heat waves) are difficult to predict, in great part due to the mismatch between large-scale atmospheric processes and small-scale urban physics, and the mechanisms involved in the overall energy transfer during the formation, persistence, and decay of UHIs under these circumstances still remain unclear. The objective of this study is to determine the triggering factors that influence these mechanisms and to better understand the onset of this phenomenon.
We use experimental observations from a network of meteorological stations with a 10-minute sampling rate, deployed between March and December 2025 in six cities near the Rouen metropolitan area in Normandy (France), along the Seine River. The dataset obtained by these stations is complemented by publicly available data from local operational stations.
We calculate urban–rural temperature differences and their temporal variability under different general and local conditions of paired urban–rural sites, using a combination of physical and statistical analyses, such as moment analysis, auto- and cross-correlation analysis, and temporal evolution of the probability density function for temperature measurements. Preliminary results indicate that urban temperatures are on average about 1°C higher than those in neighboring rural areas, with peaks reaching up to 5°C in four cities along the Seine Valley, near Rouen, between June 19 and July 4 and between August 8 and 18, 2025, periods during which heat waves were reported in France. During these periods, we found that most stations reached these peak values at night, consistent with the normal UHI behavior reported in the literature.
Future work will focus on reproducing observed UHI patterns through high-temporal- and spatial-resolution numerical simulations with the Weather Research and Forecasting (WRF) model. Subsequently, Markov processes will be explored to develop a UHI prediction model based on experimental data from the stations and results from numerical simulations.
How to cite: Francisco Alcantara, E., Barbu, V. S., and Danaila, L.: Regional downscaling for extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10235, https://doi.org/10.5194/egusphere-egu26-10235, 2026.
Accelerating climate change has intensified urban heat risks, particularly in coastal cities where urban heat island effects interact with maritime climatic influences, yet spatially explicit frameworks that diagnose heterogeneous vulnerability mechanisms remain limited. Busan, a representative coastal metropolis in South Korea, faces high heatwave vulnerability due to dense urban development, rapid population aging, and limited green space.
This study develops a spatial heat vulnerability assessment framework for Busan that integrates physical thermal conditions and social vulnerability to classify high-risk areas into distinct typologies representing different vulnerability pathways, and to inform tailored resilience strategies.
Satellite-derived thermal indicators, demographic characteristics, infrastructure accessibility, and building conditions were combined to construct composite indices of exposure, sensitivity, and adaptive capacity. These indices were then jointly analyzed to derive typology-based vulnerability patterns, and spatial clustering analysis using Local Indicators of Spatial Association was applied to identify statistically significant spatial concentrations.
The results highlight Jung-gu and Sasang-gu as representative high-vulnerability districts characterized by structurally distinct vulnerability mechanisms. Jung-gu exhibits high exposure and sensitivity driven by dense commercial development and limited vegetation, whereas Sasang-gu shows low adaptive capacity due to aging buildings and insufficient green infrastructure, illustrating different pathways through which heatwave vulnerability is produced.
These findings demonstrate that heatwave vulnerability emerges from coupled social and environmental structures, and indicate that typology-specific interventions provide an evidence-based foundation for climate adaptation planning and urban resilience in coastal metropolitan cities facing intensifying heatwave risks.
†This research was supported by "Development of living shoreline technology based on blue carbon science toward climate change adaptation" of Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (KIMST-20220526)
How to cite: Tae, M. and Chon, J.: Spatial Assessment of Urban Heat Vulnerability and Typology-Based Diagnostic Framework for Resilience Strategies in Busan, South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10502, https://doi.org/10.5194/egusphere-egu26-10502, 2026.
Abstract: Global warming and rapid urbanization are intensifying the complexity of urban thermal environments, exacerbating heat exposure disparities across diverse scales and demographics. Within the IPCC framework, urban heat risk emerges from the dynamic interaction of hazard, exposure, and vulnerability. While significant progress has been made in quantifying these components—leveraging remote sensing for hazards, human mobility for exposure, and socioeconomic indices for vulnerability—a large-scale synthesis evaluating the global evolution and integration of these research paradigms remains absent.
To bridge this gap, this study conducted a comprehensive search on the Web of Science using a Boolean strategy encompassing three core dimensions: urban thermal hazards, population exposure, and social vulnerability. This process yielded a corpus of over 7,000 peer-reviewed papers published between 2000 and 2025. Leveraging a Large Language Model (LLM), we autonomously extracted geographical metadata and thematic focus from the abstracts. Furthermore, this study analyzed the research dynamics and epistemological shifts in urban heat risk research based on searching results. Our findings reveal: (1) An unprecedented explosion in academic interest, with annual publications surging 150-fold, reflecting the urgency of heat adaptation. (2) A clear paradigm shift from a historical preoccupation with physical hazards toward holistic, multidimensional risk frameworks, particularly over the last five years. (3) A persistent thematic imbalance; hazard assessments still dominate (accounting for over 85% of literature), while human exposure and social vulnerability remain significantly underrepresented. (4) A pronounced "digital divide" in knowledge production, with research heavily concentrated in China and the United States. This leaves critical data voids in highly vulnerable regions of the Global South, including parts of Africa and Southeast Asia. This study underscores the necessity of bridging thematic and geographic divides to foster equitable global urban heat resilience.
Keywords: Urban heat risk, Hazard-exposure-vulnerability, Spatial inequality, Research evolution
How to cite: Tong, M.: From Hazards to Integrated Risks: Decoding Disparities in Global Urban Heat Research, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12805, https://doi.org/10.5194/egusphere-egu26-12805, 2026.
Under the combined influence of global warming and rapid urbanization, extreme heat has become a major challenge for urban resilience and public health. Urban green infrastructure provides important cooling benefits through evapotranspiration and shading, yet spatially explicit assessment of these services remains challenging. Existing approaches rely heavily on computationally expensive physical models and dense input data, which limits their applicability beyond well-studied regions. In this study, we present a scalable approach for mapping urban green cooling services by combining Earth observation foundation models with insights from process-based modeling. We use the Green Cooling Services Index (GCoS) as the core metric, which is derived from simulations of the Soil-Canopy-Observation of Photosynthesis and Energy Fluxes (SCOPE) model. To enable large-scale applications, we build a surrogate model that maps annual multimodal satellite embedding vectors from AlphaEarth Foundations to GCoS reference data. These embeddings integrate multisource Earth observation information across the full year, capturing key vegetation phenology and climate dynamics. The analysis covers 14 Functional Urban Areas across Europe and surrounding regions. Model performance is evaluated through three complementary experiments: a continent-scale assessment, a leave-one-city-out test, and stratified error analyses in representative cities. Results show that the surrogate approach can reproduce vegetation cooling effects with high accuracy while requiring substantially fewer data and computational resources than conventional physical models. Importantly, the model maintains stable performance when applied to cities not included in training. This framework addresses a key scalability gap in urban heat assessments and enables consistent mapping of green cooling services in data limited regions.
How to cite: Li, H., Duarte Rocha, A., and Wallis, C.: Scalable Mapping of Urban Green Cooling Services Using AlphaEarth Foundations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14736, https://doi.org/10.5194/egusphere-egu26-14736, 2026.
The rapid expansion of cities, combined with the increased frequency and intensity of droughts and heatwaves in the Mediterranean region, have made Urban Heat Islands (UHI) widespread. Here, focusing on the city of Patras, the third largest city of Greece, we analyzed the spatiotemporal patterns of urban land surface temperature during summertime, and we quantified the relationships between temperature and geomorphological (e.g., elevation, slope) and urban (e.g., building height, road network density) features. The delineation of UHI was maded using Land Surface Temperature (LST) data from 2018 to 2025 based on the NASA’s ECOSTRESS mission. This dataset provides high-resolution (70 x 70m) thermal infrared imagery with diurnal coverage, thanks to its irregular temporal sampling. In-situ air temperature data available from a network of urban meteorological stations were also used to verify the spatiotemporal patterns of temperature variability. At the daily time scale, no clear links were found between daily summer-time temperature and urban and topographic features. However, when data were analyzed at the diurnal time scale, clear dependences between hourly temperature variability and urban features were revealed. More specifically, building and road density, as well building height, exerted low correlation with temperature during morning hours, with this cross-correlation becoming positive during late afternoon and evening hours, i.e., areas with denser urban fabric showed higher evening temperature values. Regarding green spaces, as quantified with the values of the NDVI index, the correlation with hourly temperature was low during daytime, yet, this cross-correlation became significantly negative during nighttime, i.e., areas with higher values of NDVI showed a rapid decrease in nighttime temperature values. The obtained results link the spatiotemporal variability of land surface temperature over the city of Patras with key urban and topographic features and provide valuable insights towards targeted interventions enhancing the overall resilience of the city to future climatic stressors.
How to cite: Vafeiadis, I., Pantazis, C., Nastos, P., and Pappas, C.: Urban Form and Climate Resilience: Understanding the Heat Island of Patras, Greece , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21145, https://doi.org/10.5194/egusphere-egu26-21145, 2026.
Urban morphology exerts a fundamental control on land surface temperature (LST), governing the spatial and temporal variability of urban thermal environments. In South Asia, rapid urban expansion coupled with the increasing frequency and intensity of extreme heat events has amplified surface urban heat island (SUHI) effects, posing serious challenges to urban livability and thermal comfort. This study investigates the diurnal and spatial variability of LST across local climate zones (LCZs) in three major South Asian cities, Delhi, Bangalore, and Lahore, using multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. Summer-specific urban LST data were analyzed to characterize grid-based diurnal thermal contrasts and intra-LCZ thermal heterogeneity under daytime and nighttime conditions. The results reveal robust diurnal thermal differentiation among LCZs, with distinct surface thermal responses across the three cities. Water-dominated LCZs consistently exhibited the lowest surface temperatures due to high thermal inertia and evaporative cooling, whereas sparsely vegetated and bare surface zones emerged as the primary contributors to elevated SUHI intensity, driven by enhanced solar absorption and limited moisture availability. Vegetated and built-up LCZs displayed intermediate thermal behavior, reflecting the combined influences of surface materials, vegetation cover, and urban forms. Spatial analyses further identified persistent thermal hotspots and coldspots, strongly regulated by underlying LCZ characteristics. The geographically weighted random forest approach revealed spatially varying controls on urban thermal extremes. Overall, the findings highlight the critical role of urban surface composition and structural configuration in modulating SUHI dynamics across South Asian cities. Our analysis provide new insights into the spatiotemporal behavior of urban thermal patterns under extreme heat conditions and offers a robust scientific basis for climate-responsive urban planning and heat-mitigation strategies.
How to cite: Pandey, A. and Banerjee, T.: Diurnal Surface Urban Heat Island Variability Across Local Climate Zones in South Asian Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16140, https://doi.org/10.5194/egusphere-egu26-16140, 2026.
Rapid urbanization in East Africa profoundly transforms landscapes, yet a critical understanding of the causal mechanisms behind associated land surface warming remains limited. This study quantifies the spatiotemporal dynamics and causal drivers of urban expansion and its thermal impacts in three East African cities; Wakiso-Kampala (Uganda), Nairobi (Kenya), and Bujumbura (Burundi) from 1995 to 2024. Using Landsat imagery, we derived land use/land cover (LULC), Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Built-up Index (NDBI). A Land Cover Thermal Impact (LCTI) metric was introduced to quantify per-unit-area warming contributions, while advanced computational methods Convergent Cross Mapping (CCM, with causal strength measured by ρ) and its spatial extension, Geographical CCM (GCCM) were applied to distinguish causal links from mere correlation, moving beyond traditional statistical approaches. Results show built-up areas tripled in Wakiso-Kampala and Nairobi and quadrupled in Bujumbura, displacing 35-80% of natural vegetation and croplands. This expansion drove a mean LST increase of 5.1°C, 3.3°C, and 2.7°C, respectively. The LCTI revealed that water bodies provided the most efficient per-unit-area cooling in Wakiso-Kampala (LCTI = -1.49 °C km⁻²) and Bujumbura, while forest gains and bare-land conversion were the primary cooling mechanisms in Nairobi. Crucially, causal analysis revealed an asymmetric relationship: NDBI consistently acted as a driver of LST (ρ up to 0.77), while NDVI exerted a cooling causal influence (ρ down to -0.57). These findings confirm that urban expansion and vegetation loss are fundamental, causal drivers of rising surface temperatures. Our geospatial analytics framework demonstrates how data-driven causal inference can inform climate adaptation strategies in rapidly urbanizing regions. The spatial mapping of causal relationships provides actionable insights for urban planners to prioritize locations for blue-green infrastructure expansion, optimize nature-based cooling interventions, and develop targeted heat mitigation strategies that address the specific thermal dynamics of East African cities.
Keywords: Urban Heat Island, Geospatial Analytics, Causal Analysis, Nature-based Solutions, Climate Adaptation, East Africa
How to cite: Ebaju, G. K. and Zhang, F.: Quantifying Causal Drivers of Urban Heat through Geospatial Analytics: Evidence from Three East African Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16879, https://doi.org/10.5194/egusphere-egu26-16879, 2026.
This study presents the results of numerical modeling of the urban heat island (UHI) of Almaty using the Weather Research and Forecasting (WRF) model adapted to local climate zones (LCZ). The simulations were performed for a period characterized by pronounced anticyclonic conditions and, consequently, unfavorable atmospheric ventilation.
To assess model performance, verification was carried out using data from ground-based meteorological stations and satellite observations. The results demonstrate that WRF adequately reproduces the diurnal temperature variation and weak wind regime within the urban agglomeration. The model successfully simulated the dynamics of inversion layers and local circulations, which play a key role in the development of stagnation conditions.
The UHI was analyzed using the “virtual rural landscape” approach, in which the thermal field was calculated as the difference between scenarios with and without urban development. The modeling results show that the maximum UHI intensity occurs under nighttime conditions in the flat northwestern part of the city. Daytime UHI is characterized by lower intensity but a larger spatial extent compared to nighttime conditions. This pattern is explained by nocturnal katabatic flows of cold air from the mountains in the southern part of the city, which reduce the UHI intensity.
A joint analysis of the UHI and wind fields at different times indicates that wind speeds exceeding 2 m/s lead to the transport of thermal pollution. The configuration of the heat island reveals that at night, katabatic flows displace warm air from the southern part of the city, while in the northern part it is captured by a zonal wind that bypasses the mountain range and blows in a northeasterly direction. During daytime, heat transport occurs toward the southeast, resulting in the advection of heat emitted by surrounding settlements into the urban area and toward the mountainous regions.
The performed simulations demonstrate that the formation and evolution of the urban heat island in Almaty strongly depend on the time of day and the wind regime. The WRF model has proven to be an effective tool for analyzing urban microclimatic conditions and can be used in the development of adaptation strategies and air quality management, taking into account regional and local meteorological conditions.
How to cite: Dedova, T. and Bostanbekov, K.: Numerical Modeling of the Urban Heat Island of Almaty Using the WRF Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16909, https://doi.org/10.5194/egusphere-egu26-16909, 2026.
The urban heat island (UHI) effect represents a significant environmental challenge for cities located in regions with complex topography and a continental climate, such as Almaty. This study analyzes the spatiotemporal variability of the urban heat island of Almaty using satellite observations and geographic information system (GIS) analysis methods.
Land surface temperature fields were derived from VIIRS (daytime and nighttime observations), MODIS, and Landsat satellite data in the thermal infrared range. Satellite data processing was performed using cloud-based technologies within the Google Earth Engine platform, enabling a consistent analysis of monthly, seasonal, and diurnal characteristics, as well as a long-term analysis based on MODIS data. To enhance the robustness of the results, temperature fields were normalized relative to two reference cold areas located within urban green zones, allowing the identification of relative thermal anomalies across the urban area.
The results indicate pronounced spatial heterogeneity of the urban heat island, characterized by persistent high-temperature zones associated with dense urban development, industrial areas, and extensive impervious surfaces. Park areas, river corridors, and mountainous regions form stable low-temperature zones. Despite its limited temporal resolution, Landsat data enable detailed identification of local thermal anomalies, while VIIRS and MODIS data provide reliable representation of the overall UHI structure and its seasonal evolution.
The identified spatiotemporal patterns provide a basis for further analysis of the relationship between the urban heat island, synoptic conditions, and urban morphology, and may also be used as input data for numerical modeling and the development of adaptation strategies aimed at improving the urban climate of Almaty.
How to cite: Balakay, L. and Kuznetsova, O.: Spatiotemporal Variability of the Urban Heat Island of Almaty Based on Satellite Monitoring Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17541, https://doi.org/10.5194/egusphere-egu26-17541, 2026.
Urban structures are essential for human habitation, yet they profoundly alter surface energy balances, with significant environmental and health implications. Although urban heat island phenomenon is well recognized, the thermal influence of surrounding urban structures and its interaction with climate remain insufficiently understood. This study presents a global analytical framework that quantifies the thermal impact of surrounding structures, analyzes the combined effects of climate and urban morphology, and projects future thermal trajectories across 967 cities worldwide. By integrating climate variables and local climate zones with machine learning, we assess the thermal impacts of surrounding urban structures. Through a comprehensive weighting of the constituent thermal influences, we introduce the city-scale thermal impact of surrounding urban structures (TBE), revealing that climate and morphology jointly contribute to global urban heat. Across climate zones, cities exhibiting high daytime TBE are characterized by low- and mid-rise built form, whereas sparsely built types prevail in cities with low TBE. These patterns persist at night. Future projections show that the spatial patterns of TBE will be distinct from current conditions, with combined climate-structure effects will dominate urban thermal environment in almost half of global cities. These projections also reveal significant regional disparities between the Global South and the Global North. Our findings highlight the role of combined effects in shaping present and future urban heat, informing the need for adaptation strategies tailored to individual cities.
How to cite: Lee, S., Yoo, C., Son, B., Cho, D., Im, J., and Chakraborty, T.: Combined effects of climate and urban morphology on global urban heat, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19642, https://doi.org/10.5194/egusphere-egu26-19642, 2026.
With climate change, the frequency of extreme heat days is projected to increase, highlighting the need for climate-resilient urban design. Nature-based solutions, such as urban forestry, can help mitigate heat-related stress in cities.
Over the past few years, studies have investigated the role that vegetation complexity plays in various climatic contexts, including urban forests. However, uncertainties remain regarding the effect of vegetation complexity on the microclimate of urban public squares. Thus, we investigate the relationship between the diurnal microclimate of urban public squares and the complexity of vegetation. We define the latter as the vertical arrangement of different vegetation layers, such as shrubs and trees.
We captured the vegetation structure of six public squares in Munich using terrestrial laser scanning (TLS) during the summer of 2025. The squares were selected based on qualitative criteria indicating differences in vertical structure, specifically whether they contained a small hedge or tall understorey plants. We then used the resulting point clouds to understand how the complexity of vegetation in public squares can be quantified. We further measured several weather parameters on-site during July and August for the six squares, as well as on two non-vegetated public squares as controls.
The preliminary analysis of the weather data shows differences between the control group and the squares with more complex vegetation structure. The latter group shows lower maximum temperatures, higher humidity, and lower wind speed. Further, the first metrics calculated from the point cloud indicate quantifiable differences between the squares.
The final results will provide insights into the potential benefits and drawbacks of complex vegetation structures in creating climate-resilient public squares.
How to cite: Hostlowsky, A., Islam, A., Pattnaik, N., Hanzl, A., and Pauleit, S.: Diurnal Microclimate Patterns in Munich’s Public Squares: The Role of Vegetation Complexity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19722, https://doi.org/10.5194/egusphere-egu26-19722, 2026.
Urban areas experience higher temperatures than their rural surroundings due to the Urban Heat Island (UHI) effect, primarily caused by heat-absorbing surfaces and human activities. Urban forests help mitigate UHI through shading and transpiration. However, street trees in cities face high abiotic stress, such as compacted soil and increased pollution exposure. Their effectiveness depends on their suitability to the specific geoclimatic and microhabitat conditions where they are planted.
In this study we examine the rate of adaptability of four local urban tree species as expressed by their anatomical and physiological adjustments. We ask how stressed are street trees and if there are certain tree species that exhibit lower stress and greater suitability for urban street environments.
Surprisingly, our initial results suggest that despite the more challenging conditions of street trees, their stress levels are not as expected as expressed by key physiological and anatomical traits - A promising path for further research.
How to cite: Dekel, A., Shemesh-Mayer, E., and Preisler, Y.: Urban trees as heat mitigator-Investigating physiological and anatomical traits across street and park microhabitats, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20485, https://doi.org/10.5194/egusphere-egu26-20485, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 2
EGU26-15799 | ECS | Posters virtual | VPS5
Peri-urban heat amplification of monsoon drought impacts on grasslands in the Kathmandu ValleyTue, 05 May, 15:15–15:18 (CEST) vPoster spot 2
Kathmandu has undergone significant urbanization over the past decade, resulting in consistently warmer peri-urban regions compared to nearby rural landscapes due to the urban heat island effect. This study examines how baseline warming interacts with monsoon droughts to affect grassland ecosystems.
Using the Kathmandu Valley as a case study, we analyzed monsoon-season (June–October) data from 2000 to 2022, comparing land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS, volumetric soil moisture from ERA5-Land, and reference evapotranspiration (ET₀) from Terra-Climate between peri-urban and rural grasslands. Grasslands were considered instead of agricultural regions to avoid the effects from irrigation. The premises of Tribhuvan University were chosen as the peri-urban location for their closeness to the core-city region and Changunarayan (Bhaktapur) was chosen as a rural location. The Standardized Precipitation Index (SPI-3) was derived from historical precipitation records (1980–2020) and drought years were identified by negative monsoon-mean SPI values.
The results reveal a persistent peri-urban heat penalty throughout the study period. On average, peri-urban grasslands were 0.94°C warmer than their rural counterparts. This contrast increased to 1.15°C during non-drought years but narrowed to 0.5°C during drought years, as rural grasslands experienced sharper warming related to soil moisture depletion and reduced evaporative cooling. Despite the partial thermal convergence, the peri-urban zone experienced greater ecological stress during droughts, with NDVI declining by approximately 4% relative to rural areas as soil in peri-urban region are 1.12% drier during droughts compared to rural grasslands. An average potential evapotranspiration difference of 23.6 mm exists between the region, and during droughts, the evapotranspiration is 2.66% higher in peri-urban region.
These findings demonstrate that monsoon drought reduces spatial thermal contrasts but does not eliminate peri-urban vulnerability. Persistent background heating in peri-urban landscapes results in elevated vegetation stress even when meteorological drought conditions are similar. These results highlight the importance of peri-urban land management and thermal mitigation strategies in reducing ecological stress under increasing climate variability in rapidly growing cities.
How to cite: Dahal, P. and Lamichhane, S.: Peri-urban heat amplification of monsoon drought impacts on grasslands in the Kathmandu Valley, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15799, https://doi.org/10.5194/egusphere-egu26-15799, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot 2
EGU26-4510 | Posters virtual | VPS6
Assessing Long-Term Flood Risk and Elevation Ordinance Comparisons Using a Web-Based Geospatial Decision ToolThu, 07 May, 14:27–14:30 (CEST) vPoster spot 2
To support resilient community planning and informed hazard mitigation decisions, having an effective flood risk evaluation is very important especially in coastal and flood prone areas. This presentation is focused on the development of an interactive web-based decision-making platform designed to analyze future flood risk and elevation ordinance impacts across five parishes in Louisiana, USA. The website allows users to explore long-term flood risk projections and ordinance related costs over multiple future decades from 2030 to 2100. The platform integrates various geospatial datasets including multi-return-period flood depth projections, decadal population forecasts, and building inventories. Flood depth raster datasets are converted from raster to point data using python and then assigned to building data obtained from Coastal Protection and Restoration Authority (CPRA) using spatial join. Then the obtained datasets are used to calculate Average Annual Loss (AAL) for different elevation ordinances. This framework incorporates a range of flood elevation ordinances, including ASCE 24-14, ASCE 24-24, and freeboard-based standards (BFE +1’, +2’, and +3’), with ordinance costs and risk outcomes by decade. ArcGIS Pro is used for spatial analysis and 3D geospatial visualization, while interactive webpages and different elevation ordinance scenario comparisons are implemented with react vita app. To improve accessibility for non-technical users, the website integrates AI-driven features that assist users in navigating the tool, interpreting results, and comparing ordinance scenarios. The platform supports hotspot analysis, side-by-side visualization of present and future flood risks, and iterative refinement through user feedback sessions. Overall, this tool provides planners, homeowners, and policymakers with a forward-looking environment to assess flood mitigation strategies, ordinance performance, and population-driven risk changes over time by combining advanced spatial analytics with interactive and user-centered design.
How to cite: Kunku, L. P., Friedland, C. J., and Mostafiz, R. B.: Assessing Long-Term Flood Risk and Elevation Ordinance Comparisons Using a Web-Based Geospatial Decision Tool, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4510, https://doi.org/10.5194/egusphere-egu26-4510, 2026.
EGU26-488 | ECS | Posters virtual | VPS6
Utilising geospatial data to understand urban heat island and its effect on urban thermal comfort in selected Indian cities using Remote Sensing and GISThu, 07 May, 14:30–14:33 (CEST) vPoster spot 2
Urbanization, together with increasing global population pressure and climate variability, has introduced heat-related challenges across urban areas, severely impacting humans and the Earth. The rapid population growth has placed India at the top of the global population ranking. Demographic surges concentrate stress on existing urban systems, making Indian metropolises-both inland hubs and rapidly transforming coastal centers-critical laboratories for studying UHI dynamics.
The understanding of patterns and possible causes of the UHI effect due to urbanization-induced anthropogenic activities is a vital area in urban climate research. This study presents an overall multi-decadal day/night spatiotemporal seasonal analysis and trends in LST and UHI for six Indian cities: Ahmedabad, Mumbai, Panjim, Mangalore, Kochi, and Thiruvananthapuram, spanning the last three decades.
MODIS LST and AOD data are used to explain the possible reasons for the change in LST and UHI, focusing on the seasonal thermal behavior of cities under prevailing atmospheric, meteorological, and anthropogenic conditions. The Landsat series datasets are used to develop LULC maps and delineate high-resolution UHI zones, to explain shared trajectories and city-specific patterns that expose complex vulnerabilities within urban ecosystems in India. The findings, which integrate multi-decadal 30-year satellite-derived LST, LULC, and AOD data, demonstrate that greater increases in nighttime LST are associated with a decrease in the diurnal temperature range across all cities. Mumbai consistently showed lower mean LST values compared to Ahmedabad, which exhibited substantially higher values and extreme seasonal amplitudes ranging from 17.23 °C to 50.05 °C. Goa and Mangalore depicted a 1-4 °C increase in seasonal mean LST between 1993 and 2023. Corresponding to a rise in built-up area and a decline in vegetation, Kochi too exhibited a rise in LST. Thiruvananthapuram showed a strong warming, with a mean LST increase of about 3°C. AOD patterns also demonstrated similar spatial and temporal gradients across cities, helping to reinforce land-use change, urban expansion, and inland-coastal climatic contrasts as the significant causes of LST trends.
Collectively, these findings reveal how land-use transition, and climatic variability, significantly alters the thermal environment of Indian cities; making such studies important for climate-responsive planning and better urban management to enhance resilience and thermal comfort.
How to cite: Rai, R. and Murali R, M.: Utilising geospatial data to understand urban heat island and its effect on urban thermal comfort in selected Indian cities using Remote Sensing and GIS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-488, https://doi.org/10.5194/egusphere-egu26-488, 2026.
EGU26-15541 | ECS | Posters virtual | VPS6
Constructing a Multi-Scale Urban Cooling Island Ventilation Network to Mitigate the Urban Heat Island Effect: A Case Study of Changsha, ChinaThu, 07 May, 14:33–14:36 (CEST) vPoster spot 2
To address the intensifying urban heat island (UHI) effect driving by rapid urbanization, current research reveals a significant scale discontinuity between macro-level strategies, such as regional cooling network design, and micro-level studies that focus on localized cooling mechanisms of individual green patches. Macro-scale approaches often overlook small cooling islands embedded within dense urban fabrics, while micro-scale investigations lack systematic understanding of inter-patch connectivity. This study proposes a multi-scale cooling island ventilation network to synergistically mitigate UHI impacts across spatial hierarchies. Using the core area of Changsha City as a case study, the research introduces an innovative three-tier scale classification framework incorporating building density. By integrating relative land surface temperature and morphological spatial pattern analysis, the study identifies core cold island sources. Further, a cold island ventilation resistance surface is constructed using the CRITIC objective weighting method, enabling the identification of key nodes and corridors for establishing a comprehensive multi-scale ventilation network. Findings reveal that, amidst urban expansion and increasing building/road densities, landscape fragmentation has led to a “shrinking-in-size, growing-in-number” trend for both primary and secondary cold island sources. From 2009 to 2016, the total area of primary-scale cold sources declined sharply from 45 km² to 19.8 km², while their number rose from 130 to 151. The average patch size fell from 0.35 km² to 0.07 km², and the minimum temperature increased from 28.7 °C to 35.3 °C-signaling a depletion risk. Similarly, secondary cold sources shrank from 215.38 km² to 144.83 km², as their number increased from 123 to 169, with average patch size dropping from 1.75 km² to 0.86 km²-weakening their thermal buffering capacity. Despite this, ventilation corridors peaked in 2020, totaling 371 in number and 528.5 km in length, continuing to act as "relay stations" transmitting peripheral cooling effects to the urban core. Notably, tertiary cold sources rebounded after 2016 due to strengthened ecological conservation efforts, expanding by 237.5 km² by 2020. Their temperatures stabilized between 35–38 °C—significantly cooler than the urban core—demonstrating sustained cooling potential. Policy recommendations are proposed across three spatial scales: 1) primary scale, remove obstructions at cold source points to broaden cooling supply channels; 2) secondary scale: prioritize the protection of key corridors and junctions to preserve inter-patch connectivity and maintain dynamic cold air flow; 3) tertiary scale: safeguard and enhance core ecological areas to ensure stable and continuous cooling output. By identifying cold island sources and constructing a multi-scale ventilation network, this study offers a science-based framework for optimizing thermal environments in high-density urban areas.
Graphical Abstract
How to cite: Zhong, X., Wei, B., Liu, L., and Huang, Y.: Constructing a Multi-Scale Urban Cooling Island Ventilation Network to Mitigate the Urban Heat Island Effect: A Case Study of Changsha, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15541, https://doi.org/10.5194/egusphere-egu26-15541, 2026.
EGU26-16835 | ECS | Posters virtual | VPS6
Assessing the Impacts of Water Bodies Encroachment on Urban Land Surface TemperatureThu, 07 May, 14:51–14:54 (CEST) vPoster spot 2
Water bodies play a crucial role in controlling urban heat by acting as a sink and enhancing evaporative cooling. However, rapid urbanisation in India has led to the progressive encroachment and shrinkage of water bodies, which threatens the urban thermal environment. In this study, we investigate the impact of urban water body encroachment on surrounding temperature using multidecadal Landsat-derived land surface temperature (LST) data at 30 m spatial resolution and water body datasets. The LST of Water bodies and surrounding urban areas within their vicinity are estimated to assess spatiotemporal changes in LST. Our results reveal a substantial increase in LST in urban regions surrounding water bodies in recent decades, indicating a decline in their local cooling effectiveness. Furthermore, encroached water bodies exhibit a pronounced rise in surface water temperature than non-encroached water bodies. The warming of both water surfaces and adjacent urban areas highlights the compound thermal impacts of water body encroachment. The findings indicate that the loss of urban water bodies due to encroachments contributes to the warming of urban areas. The study underscores the importance of protecting and restoring urban water bodies as effective nature-based solutions for mitigating rising urban temperatures and enhancing climate resilience in rapidly growing urban cities.
How to cite: Naik, M. N. and Mishra, V.: Assessing the Impacts of Water Bodies Encroachment on Urban Land Surface Temperature, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16835, https://doi.org/10.5194/egusphere-egu26-16835, 2026.
