RESUMEN

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban-rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban-rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level.

Asunto(s)

RESUMEN

Given their multifold benefits, green roofs are often considered to mitigate the urban heat island (UHI) effect. Most mesoscale studies consider 100 % green roof fraction or the same green roof fraction in each urban land use category while analysing the influence of green roofs on the UHI effect, which can overestimate their impact on UHI. Consequently, the impact of green roofs evaluated in these studies may not be suitable for informing policy decisions. Furthermore, the effect of morphologies on temperature reduction due to green roofs has not been previously studied. To address this gap, in this paper, we evaluate the impact of a realistic fraction of green roofs specific to the respective local climate zones (LCZ) on the UHI effect during a heatwave in Liège, Belgium, employing a high-resolution WRF study using the BEP-BEM parameterisation with LCZ land use classification. The realistic fraction is estimated for every LCZ class based on the average percentage of flat roofs observed in each LCZ class in Liège. Accordingly, distinct realistic fractions of green roofs are assigned to each LCZ class in WRF. We run the WRF simulation for the base scenario (without green roofs), extreme scenario (100 % green roof fraction), and realistic scenario. The results indicate a limited reduction in near-surface air and surface temperature in a realistic scenario, with a nighttime increase in temperature. Additionally, in the extreme scenario, the temperature reduction largely depends on the morphology. However, in a realistic scenario, it depends on the green roof fraction. Other indicators like heat index and UHI intensity also are not reduced considerably with realistic greening. Therefore, realistic roof greening alone will not be sufficient to achieve an impact on a city-scale.

RESUMEN

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

Asunto(s)

RESUMEN

Due to the differences in land cover and natural surroundings within cities, residents in various regions face different thermal risks. Therefore, this study combined multi-source data to analyze the relationship between urban heat risk and local climate zones (LCZ). We found that in downtown Shenyang, the building-type LCZ was mainly found in urban centers, while the natural- type LCZ was mainly found in suburbs. Heat risk was highest in urban centers, gradually decreasing along the suburban direction. The thermal risk indices of the building-type LCZs were significantly higher than those of the natural types. Among the building types of LCZs, LCZ 8 (open middle high-rise) had the highest average thermal risk index (0.48), followed by LCZ 3 (0.46). Among the natural types of LCZs, LCZ E (bare rock and paved) and LCZ F (bare soil and sand) had the highest thermal risk indices, reaching 0.31 and 0.29, respectively. This study evaluated the thermal risk of the Shenyang central urban area from the perspective of LCZs and combined it with high-resolution remote sensing data to provide a reference for thermal risk mitigation in future urban planning.

Asunto(s)

RESUMEN

Greece was affected by a prolonged and extreme heat wave (HW) event (July 28-August 05) during the abnormally hot summer of 2021, with the maximum temperature in Athens, the capital of the country, reaching up to 43.9 °C in the city center. This observation corresponds to the second highest maximum temperature recorded since 1900, based on the historical temperature time series of the National Observatory of Athens weather station at Thissio. In the present study, a multi-scale numerical modeling system is used to analyze the urban climate and thermal bioclimate in the Athens urban area (AUA) in the course of the HW event, as well as during 3 days prior to the heat wave and 3 days after the episode. The system consists of the Weather Research and Forecasting model, the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model) and the human-biometeorological model RayMan Pro, and incorporates the local climate zone (LCZ) classification scheme. The system's validation results demonstrated a robust modeling set-up, characterized by high capability in capturing the observed magnitude and diurnal variation of the urban meteorological and heat stress conditions. The analysis of two- and three-dimensional fields of near-surface air temperature, humidity and wind unraveled the interplay of geographical factors (surface relief and proximity to the sea), background atmospheric circulations (Etesians and sea breeze) and HW-related synoptic forcing with the AUA's urban form. These interactions had a significant impact on the LCZs heat stress responsiveness, expressed using the modified physiologically equivalent temperature (mPET), between different regions of the study area, as well as at inter- and intra-LCZ level (statistically significant differences at 95 % confidence interval), providing thus, urban design and health-related implications that can be exploited in human thermal discomfort mitigation strategies in AUA.

Asunto(s)

RESUMEN

The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi'an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.

Asunto(s)

RESUMEN

Urban ventilation corridors introduce fresh air into urban interiors and improve urban livability, while mitigating the urban heat island (UHI) effect. However, few studies have assessed the impact of urban ventilation corridors on UHI intensity (UHII) from the perspective of the local climates of different cities. Therefore, this study integrated multisource data to construct ventilation corridors from the perspective of local climate zone (LCZ) and analyzed its impact on UHII. The results showed the following: (1) the average UHII of constructed LCZs was higher than that of natural LCZs, among which the building type LCZ10 (heavy industry) had the highest intensity (5.77 °C); (2) in extracted ventilation corridors, the pixel number of natural LCZs was substantially larger than that of constructed LCZs, among which LCZE (bare soil/paved) was the largest; and (3) for natural LCZs, the average UHII of each LCZ was lower within the ventilated corridors than within the non-ventilated corridors (except for LCZG [water]), with the UHII of LCZB (scattered trees) exhibiting the greatest mitigation effect. Quantitative research on the composition and function of ventilation corridors can not only assess the ability of ventilation corridors to mitigate UHIs, but also provide a reference for urban ventilation corridor planning.

Asunto(s)

RESUMEN

Surface urban heat islands (SUHIs) are an important socio-environmental problem associated with large cities, such as the Santiago Metropolitan Area (SMA), in Chile. Here, we analyze daytime and nighttime variations of SUHIs for each season of the year during the period 2000-2020. To evaluate socioeconomic inequities in the distribution of SUHIs, we establish statistical relationships with socioeconomic status, land price, and urban vegetation. We use the MODIS satellite images to obtain the land surface temperatures and the normalized difference vegetation index (NDVI) through the Google Earth Engine platform. The results indicate more intense SUHIs during the nighttime in the eastern sector, coinciding with higher socioeconomic status and larger green areas. This area during the day is cooler than the rest of the city. The areas with lower and middle socioeconomic status suffer more intense SUHIs (daytime and nighttime) and match poor environmental and urban qualities. These results show the high segregation of SMA. Urban planning is subordinated to land prices with a structure maintained over the study period. The lack of social-climate justice is unsustainable, and such inequalities may be exacerbated in the context of climate change. Thus, these results can contribute to the planning of the SMA.

Asunto(s)

RESUMEN

Urbanization is the second largest mega-trend right after climate change. Accurate measurements of urban morphological and demographic figures are at the core of many international endeavors to address issues of urbanization, such as the United Nations' call for "Sustainable Cities and Communities". In many countries - particularly developing countries -, however, this database does not yet exist. Here, we demonstrate a novel deep learning and big data analytics approach to fuse freely available global radar and multi-spectral satellite data, acquired by the Sentinel-1 and Sentinel-2 satellites. Via this approach, we created the first-ever global and quality controlled urban local climate zones classification covering all cities across the globe with a population greater than 300,000 and made it available to the community (https://doi.org/10.14459/2021mp1633461). Statistical analysis of the data quantifies a global inequality problem: approximately 40% of the area defined as compact or light/large low-rise accommodates about 60% of the total population, whereas approximately 30% of the area defined as sparsely built accommodates only about 10% of the total population. Beyond, patterns of urban morphology were discovered from the global classification map, confirming a morphologic relationship to the geographical region and related cultural heritage. We expect the open access of our dataset to encourage research on the global change process of urbanization, as a multidisciplinary crowd of researchers will use this baseline for spatial perspective in their work. In addition, it can serve as a unique dataset for stakeholders such as the United Nations to improve their spatial assessments of urbanization.

RESUMEN

Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps - built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.

Asunto(s)

RESUMEN

Air temperature is a key aspect of urban environmental health, especially considering population and climate change prospects. While the urban heat island (UHI) effect may aggravate thermal exposure, city-level UHI regression studies are generally restricted to temporal-aggregated intensities (e.g., seasonal), as a function of time-fixed factors (e.g., urban density). Hence, such approaches do not disclose daily urban-rural air temperature changes, such as during heatwaves (HW). Here, summer data from Lisbon's air temperature urban network (June to September 2005-2014), is used to develop a linear mixed-effects model (LMM) to predict the daily median and maximum Urban Thermal Signal (UTS) intensities, as a response to the interactions between the time-varying background weather variables (i.e., the regional/non-urban air temperature, 2-hours air temperature change, and wind speed), and time-fixed urban and geographic factors (local climate zones and directional topographic exposure). Results show that, in Lisbon, greatest temperatures and UTS intensities are found in 'Compact' areas of the city are proportional to the background air temperature change. In leeward locations, the UTS can be enhanced by the topographic shelter effect, depending on wind speed - i.e., as wind speed augments, the UTS intensity increases in leeward sites, even where sparsely built. The UTS response to a future urban densification scenario, considering climate change HW conditions (RCP8.5, 2081-2100 period), was also assessed, its results showing an UTS increase of circa 1.0 °C, in critical areas of the city, despite their upwind location. This LMM empirical approach provides a straightforward tool for local authorities to: (i) identify the short-term critical areas of the city, to prioritise public health measures, especially during HW events; and (ii) test the urban thermal performance, in response to climate change and urban planning scenarios. While the model coefficient estimates are case-specific, the approach can be efficiently replicated in other locations with similar biogeographic conditions.

Asunto(s)

RESUMEN

Brazil is the country with the highest social inequality in South America. This socioeconomic disparity reflects not only on the families' income but also on their spatial localization in the city, as well as on the urban design. These urban environments can alter the urban microclimate, and consequently, interfere in dwellers' thermal comfort. This research investigated the relationship between socio-spatial inequalities and thermal comfort in two different Local Climate Zones (LCZ) using a combination of measurement and modeling. Air temperature (Tair) was obtained by on-site measurements in compact high-rise (LCZ1) and compact low-rise buildings (LCZ3) and Mean radiant temperature (Tmrt) was simulated using SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG). The results indicated that in LCZ1 seafront-localized buildings, in which residents have a higher income, the temperature remains in a range classified as comfortable, mainly due to shading and sea breeze. On the other hand, LCZ3, located in the periphery of the city, in which the low-income population is concentrated and is marked by a precariousness urban environment, presented a higher air temperature and Tmrt values, exposing the dwellers to heat stress throughout the year, especially during the summer season. These observations suggested that public and private actions tend to promote better urban designs in areas with a higher concentration of income. Public reforms aimed at improving the urban environment and promoting thermal comfort should be a priority for the warmest LCZ, where the poorest residents live. Public agents should rethink the distribution of environmental resources to promote equitable urban spaces.

Asunto(s)

RESUMEN

Local Climate Zones (LCZ) have become a worldwide standard for identifying land cover classes, according to their climate-relevant morphological parameters. The LCZ's are mostly used to evaluate urban climate performance, particularly the relationship between the urban heat island effect (UHI) and the characteristics of the built-up environment. The World Urban Database and Access Portal Tools (WUDAPT) has provided a supervised LCZ classification method based only on moderate resolution free satellite imagery, mostly Landsat 7 or 8 (30 m pixel size, in the visible spectrum brands); however, its' results are less accurate for European cities. Conversely, alternative geographic information system (GIS)-based methods developed so far require information that is hardly available to all, such as building footprints or heights. Here, the ArcGIS based LCZ from Copernicus Toolbox (LCZC) provides an alternative classification method that uses only freely accessible information from the Copernicus Land Monitoring Service (CLMS), being possible to replicate it in 800 European urban locations. The method combines Urban Atlas (UA) and Corine Land Cover (CLC) with Tree Cover Density, Dominant Leaf Type and Grassland information, to produce a higher-resolution baseline shapefile that is classified according to each feature's dominant characteristics. The LCZC toolbox output is a LCZ raster map. It has been validated in five European cities: Athens, Barcelona, Lisbon, Marseille, and Naples.•The LCZC toolbox provides an alternative LCZ GIS-based classification, based on freely accessible CLMS datasets.•The use of CLMS shapefile higher-resolution inputs, particularly the UA and CLC datasets, ensures an output LCZ map that has greater detail and higher accuracy.•The availability of CLMS information in 800 European urban areas guarantees that the method can be replicated in those locations.

RESUMEN

This paper assesses the influence of land development patterns on intra-urban thermal variation in a densely-developed subtropical city, considering joint effect from greenspace pattern and built-up geometry. Despite growing research on urban climates, research at a scale that can support urban planning with scientificallyinformed strategies is still not as well documented for warm climate cities as for temperate cities. In response, this paper uses land surface temperature and geoinformation to assess the subtropical city of Taipei, Taiwan. Results show cooler environments are not only associated with natural surfaces, but also their interrelation with different spatial arrangement of buildings. An open layout tends to have lower temperature at low- to mid-rise buildings, whereas a compact layout is the coolest form for high-rise buildings. Cooling benefit from open layouts is, however, related to an increase in greenery. Clustering distribution of greenspaces produces more notable cooling. Accordingly, this paper proposes four heat mitigation strategies for Taipei: 1) increasing the amount of water bodies and vegetation, with greater coverage and coherence; 2) taking building height and shadow into account during regeneration/development; 3) increasing spacing and greenery between low- to midrise buildings; and 4) avoiding construction of compact low-rise buildings with corrugated iron steel.

RESUMEN

Taking into account meteorological data in urban planning increases in relevance in the context of changing climate and enhanced urbanisation. The present article focusses on the nocturnal urban heat island intensity (UHII) simulated with a physically based atmospheric model for >200,000 Reference Spatial Units (RSU), which correspond to building patches delimited by roads or water bodies in 42 French urban agglomerations. First are investigated the statistical relationships between the UHII and six predictors: Local Climate Zone, distance to the agglomeration centre, population, distance to the coast, climatic region, and elevation differences. It is found that the maximum UHII of an agglomeration increases proportional to the logarithm of its population, decreases for cities closer than 10 km to the coast, and is shaped by the regional climate. Secondly, a Random Forest model and a regression-based model are developed to predict the UHII based on the predictors. The advantage of the regression-based model is that it is easier to understand than the black box Random Forest model. The Random Forest model is able to predict the UHII with <0.5 K absolute error for 54% of the RSU. The regression-based model performs slightly worse than the Random Forest model and predicts the UHII with <0.5 K absolute error for 52% of the RSU. A future challenge is to conduct a similar investigation at global scale, which is to date limited by the availability of a robust description of urban form and functioning.

RESUMEN

Here, we provide Local Climate Zones (LCZ) map datasets from five Southern European Mediterranean cities: Athens (Greece), Barcelona (Spain), Lisbon (Portugal), Marseille (France) and Naples (Italy). The maps were produced according to a geographic information system (GIS)-based classification method, using freely available Copernicus Land Monitoring Service (CLMS) input data. Several maps are provided: (i) five LCZv1 maps (one per city) depicting urban LCZ's aggregated by density (no building height information); (ii) five LCZv1_leaf maps (one per city), identical to the previously mentioned ones, with tree cover LCZ classes A and B reclassification according to the Dominant Leaf Type (DLT) (deciduous or coniferous); (iii) two LCZv1_BH maps (Athens and Lisbon) distinguishing urban LCZ classes 123 and 456 according to the dominant building height (BH); and (iv) two LCZv1_leaf_BH maps (Athens and Lisbon) identical to the previous ones with added DLT-based land cover classification. The LCZ classification maps are available in both ArcGIS .lyr layer and GeoTIFF raster formats (Appendix 1 and 2), with a spatial resolution of 50×50m pixels, and are suitable to urban climate-related studies, particularly at the metropolitan and city scales of analysis. The data here provided is related to the article entitled «Local Climate Zones in five Southern European cities: an improved GIS-based classification method based on free data from the Copernicus Land Monitoring Service¼ [1], and the corresponding method/ArcGIS based custom Toolbox is freely available in «Local Climate Zones classification from Copernicus Land Monitoring Service datasets: an ArcGIS-based Toolbox¼ [2].

RESUMEN

Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (Pf PR2-10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR2-10. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower Pf PR2-10 (5%-30%) than rural areas (15%-40%). The Pf PR2-10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR2-10. Informal settlements-represented by the LCZ 7 (lightweight lowrise)-have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.

RESUMEN

The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.

RESUMEN

Urban waterbodies can effectively mitigate the increasing UHI effects and thus enhance climate resilience of urban areas. To contribute to our limited understanding in cooling effect of waterbodies on surrounding thermal environments, we examine the quantitative relationship between the spatial distribution of urban waterbodies and the land surface temperature (LST) in Wuhan, China. This paper 1) applies two indicators, the fractional water cover and the gravity water index, for measuring the spatial distribution of urban waterbodies; 2) conducts simple linear regression and spatial regression analyses to explore the LST-water relationship at multiple scales; and 3) compares the individual regression results from different land use types. The results show that the spatial distribution of urban waterbodies affects the LST significantly, and the gravity water index sufficiently explains the LST variation at various scales. Furthermore, the impact of urban waterbody distribution on the LST does vary across different land use types. Conclusions from this study provide insights of the cooling effect of urban waterbodies, which can further assist city planners and decision makers in utilizing cooling effects of waterbodies to improve the thermal environment of urban areas.

RESUMEN

This study uses the MUKLIMO_3 urban climate model (in German, Mikroskaliges Urbanes KLImaMOdell in 3-Dimensionen) and measurements from an urban climate network in order to simulate, validate and analyse the spatiotemporal pattern of human thermal comfort outdoors in the city of Brno (Czech Republic) during a heat-wave period. HUMIDEX, a heat index designed to quantify human heat exposure, was employed to assess thermal comfort, employing air temperature and relative humidity data. The city was divided into local climate zones (LCZs) in order to access differences in intra-urban thermal comfort. Validation of the model results, based on the measurement dates within the urban monitoring network, confirmed that the MUKLIMO_3 micro-scale model had the capacity to simulate the main spatiotemporal patterns of thermal comfort in an urban area and its vicinity. The results suggested that statistically significant differences in outdoor thermal comfort exist in the majority of cases between different LCZs. The most built-up LCZ types (LCZs 2, 3, 5, 8 and 10) were disclosed as the most uncomfortable areas of the city. Hence, conditions of great discomfort (HUMIDEX >40) were recorded in these areas, mainly in the afternoon hours (from 13.00 to 18.00 CEST), while some thermal discomfort continued overnight. In contrast, HUMIDEX values in sparsely built-up LCZ 9 and non-urban LCZs were substantially lower and indicated better thermal conditions for the urban population. Interestingly, the model captured a local increase of HUMIDEX values arising out of air humidity in LCZs with the presence of more vegetation (LCZs A and B) and in the vicinity of larger bodies of water (LCZ G).