Local Climate Zones Research Articles

Climate walking and linear mixed model statistics for the seasonal outdoor thermophysiological comfort assessment in Lisbon

To measure urban outdoor pedestrian thermophysiological comfort mobile roving missions were conducted in all seasons of the year during the day and at night. Six routes were chosen, representing areas of the city with varying urban morphological layouts. The thermal comfort conditions were analysed using the Local Climate Zone spatial scale. This enabled the identification of typologies of areas where pedestrians might feel (un)comfortable. To achieve the proposed objectives, quantitative and qualitative analysis were run, namely a Linear Mixed-Effect Model. This model was useful to understand the thermal behaviour of the roved areas by comparison to a reference area. The city was found to be generally cooler, except at night when it can have a 44% increased UTCI. Most collected values throughout the year had ‘no thermal stress’, except in the summer when ‘moderate’ and ‘strong heat stress’ was predominant. Compact and large low rise urban areas were found to be the most uncomfortable. Significant amplitudes were found in some LCZs, reaching as high as 7 °C in some scenarios. Sparsely wooded areas exhibited higher values in all seasons and at night, except in the summer. This showed that for a local heat stress analysis a higher resolution scale is needed.

Quantifying the effect of surrounding spatial heterogeneity on land surface temperature based on local climate zones using mutual information

Local climate zone (LCZ), a landscape classification scheme that segments urban region into 17 distinct zones, has become the standard for analyzing urban thermal environments since 2012. The characteristic features of each LCZ decide the standard regime for their Land Surface Temperature (LST). In heterogeneous cities, the variation in LST is observed for similar but spatially dispersed LCZs and has not yet been analysed by the researchers. The surrounding spatial heterogeneity could be partially responsible for such variation. Hence, we have presented a framework to analyze the LST variation of similar but spatially dispersed LCZs by considering the surrounding LCZ pattern11LCZ pattern represents the group of LCZs surrounding any considered LCZ.details in the study area of Nagpur (India). The framework uses machine learning techniques like random forest (RF), Xgboost, deep learning to confirm that the LST variation is due to surrounding LCZ pattern. Later it indentifies the factors responsible for LST variation in each LCZ type using our proposed mutual information based Adjacent LCZ preference Estimator (ALPE). Deep learning model captures 90–92 % of the neighbourhood heterogeneity responsible for LST variation as compared to RF (0.83- 0.79) and Xgboost (0.86–0.81). The reduction in bias (by 1.05 °C -1.39 °C) is observed while estimating LST incorporating surrounding LCZ pattern. This confirms that the external heterogeneity affects the LST of the corresponding LCZ. Further, the result analysis of ALPE suggests that characteristics of open LCZ types (when they are present in surrounding) highly influence the LST of compact and open LCZs.

Evaluating the Impact of Heat Mitigation Strategies Using Added Urban Green Spaces during a Heatwave in a Medium-Sized City

Recognizing the growing trend of the urban population and the undeniable fact of global and regional climate change, it becomes increasingly important to explore how we can improve the livability of our cities not only in the distant future but also in the next few years. A critical aspect of this endeavor involves studying how we can effectively mitigate human heat load in urban areas. In our research, in the case of a medium-sized city (Szeged, Hungary), we examined the effect of surface modifications caused by vegetation on human thermal perception during the day and night of two heatwave days. To achieve this, we used the MUKLIMO_3 micro-scale climate model to simulate the thermal climate of Szeged, while the thermal load was assessed with the perceived temperature calculated by the Klima-Michel model. Our analysis also relied on the local climate zone (LCZ) system to describe the original land cover and the additional urban green spaces in the study area. We scrutinized the effects of added vegetation of different types and densities, as well as the presence of protective forests surrounding the city. Our findings revealed that the effect of the added vegetation can only be detected on the modified surfaces and in their immediate vicinity. Notably, dense urban greenery resulted in up to a 2–3 °C reduction in perceived temperature in certain areas during the daytime, highlighting the profound impact of targeted green space development. In addition, it is crucial to consider the airflow-blocking effect of woody vegetation, which can increase thermal load by 1–3 °C in the areas located in a downwind direction. Therefore, the changing regional climatic conditions (e.g., wind direction) and the development of the right type and location of urban green areas deserve special attention during modern urban planning processes.

Urban growth’s implications on land surface temperature in a medium-sized European city based on LCZ classification

The study determined the influence of changes in land use and land cover (LULC) on land surface temperature (LST) over a 33-year period based on a medium-sized European city (Poznań, Poland). The LST was estimated from Landsat 5, 8 and Terra (MOD11A2v6) satellites. The local estimation of climate patterns was based on the Local Climate Zones (LCZ) classification utilised with the methodology proposed by the World Urban Database and Access Portal Tools (WUDAPT). Moreover, the Copernicus’ imperviousness density product (IMD) was used. Between 2006 and 2018 the area with IMD of 41–100% increased by 6.95 km2, 0–20% decreased by 7.03 km2. The contribution of built-up LCZs increased by 7.4% (19.21 km2) between 1988 and 2021 reaching 13% (34 km2) within open mid-rise LCZ. Due to urbanisation and reforestation, low plants LCZ shrunk by 12.7%. For every 10% increase in IMD, LST increases by up to 0.14 °C. Between 1988 and 2021 the LSTm in specific LCZs rose from 1.52 up to 2.97 °C. As per LST models LCZ change from natural to built-up led up to 1.19 °C LST rise. The increase of the LSTm was registered even when the LCZ remained unchanged.

Urbanization enhances channel and surface runoff: A quantitative analysis using both physical and empirical models over the Yangtze River basin

Urbanization has become an irreversible trend and is even more rapid in flood-prone area. However, the responses of both channel runoff and surface runoff to the urbanization and the underlying mechanisms still deserve more in-depth exploration within urban extents with limited data availability. Accordingly, we conducted a comprehensive quantitative analysis of the urbanization effects on channel and surface runoff from both statistical and model-based perspectives over three major urban agglomerations (i.e., Chengyu (CY), middle region of Yangtze River (MRYR), Yangtze River Delta (YRD)) in the Yangtze River Basin. The long-term high-resolution channel and surface runoff under different urbanization scenarios were simulated using both WRF-Hydro and SCS-CN models by integrating multiple remotely sensed, reanalysis, and in-situ datasets. Results demonstrated that the channel runoff increased by 4.4%, 5%, and 12.08% in CY, MRYR, and YRD urban agglomerations, respectively. Meanwhile, the urbanization also contributed to 30%, 21.4%, and 17.7% of the surface runoff changes in three urban agglomerations. Based on the high-resolution local climate zone (LCZ), our results highlighted that the high-rise and middle-rise buildings and compact-arranged regions were more prone to accumulating surface runoff and trigger flooding events due to the weaker infiltration capacity of land surface with the densely distribution of impervious surface. Further machine learning based investigation and case analysis also explored the potential influencing factors and mechanisms of the impact of urbanization on channel and surface runoff. Results in the current research show further evidence that policy makers of urban planning and management need to consider and ensure societal preparedness for extreme runoff and flooding events.

Urban heat island phenomenon in Istanbul: A comprehensive analysis of land use/land cover and local climate zone effect

The relationship between urban heat island (UHI) and land use/land cover (LULC), and local climate zone (LCZ) is apparent and takes rising attention in the current literature. This study aimed to investigate the relationship between meteorological data collected from 30 stations between 2016 and 2022 and Istanbul’s LULCs and LCZs. Several notable findings were uncovered, providing light on the UHI phenomenon and its consequences for the city’s characteristics. The stations in urban areas (typology A) had higher temperatures than stations in rural/suburban (typology B) and forested landscape (typology C). Those yearly values were ∼1°C for monthly mean temperatures and ∼1.5°C for monthly minimum temperatures. Moreover, urban areas possessed +4 and +2 hot days (35°C and above) for typologies B and C, respectively. Another remarkable result was that stations situated close to water surfaces exhibited a lower tendency to exceed temperatures of 35°C. Furthermore, built-type LCZs wind velocity achieved a lower value than land cover type, and humidity in typology A was 5% and 10% less than the typologies B and C, respectively. Consequently, the southern part of Istanbul emerged as the most vulnerable location to the UHI phenomena, suffering greater temperatures.

Present day and future urban cooling enabled by integrated water management

The process of urbanisation has increased public health risks due to urban heat, risks that will be further exacerbated in future decades by climate change. However, the growing adoption of integrated water management (IWM) practices (coordinated stormwater management of water, land, and resources) provides an opportunity to support urban heat amelioration through water supply provision and irrigated and vegetated infrastructure that can provide cooling benefits. This study examines the thermal impacts of future implementations of IWM for nine Australian cities based on a review of Government policy documents in the present and over two future time frames (2030 and 2050) under different greenhouse gas emission scenarios (SSPs 1.2-6, 3.7-0 and 5.8-5). Statistical analysis of the future climate data using historical data shows that future warming is nuanced, with changes variable in both time and place, and with extremes becoming more pronounced in future. We have developed a unique approach to morph the future climate projections onto historical data (derived from the ERA5 Reanalysis product) for the 2010-2020 period. Additionally, we use locally appropriate Local Climate Zones (LCZs) for Australian cities, resulting from a holistic and global approach that is widely adopted by the urban climate modelling community. We developed scenarios for business-as-usual as well as implementation of moderate and high levels of IWM across each of the Australian LCZs and modelled them using TARGET (The Air temperature Response to Green infrastructure Evaluation Tool). Results generated at the LCZ level are aggregated to Australian statistical areas (SA4, the largest sub-city area) and city-wide levels. The thermal impacts associated with the various degrees of IWM were marked and geographically differentiated, depending on the climatic characteristics of the various cities. For the current climate, high IWM intervention provided reductions in annual mean daily maximum temperature ranging from -0.77°C in Darwin, up to -1.86°C in Perth. Generally, the drier southern cities of Sydney, Canberra, Albury, Melbourne, Adelaide, and Perth produced the greatest thermal response to implementation of IWM and the more tropical cities with higher rainfalls the least response. For some southern cities cooling was > -3.0°C at the time of maximum summer temperatures. Interestingly high levels of IWM in winter produced modest warming of minimum overnight temperatures, especially for the cooler southern cities. The cooling benefits of IWM were seen across all future climate scenarios and are a real opportunity to offset-projected temperature increases resulting from climate change.

Heat stress resilience assessment of urban form from physical space dimension: A case study of Guangdong-Hong Kong-Macao Greater Bay Area

Improving urban resilience is crucial to counteract rising high-temperature disasters. This study constructed a framework for a process-based heat stress resilience (HSR_phy) assessment from the urban physical space dimension. Based on the universal thermal climate index (UTCI), the Weather Research and Forecasting (WRF) model, and the Local Climate Zone (LCZ) map generated through WUDAPT, an HSR_phy evaluation method with high spatiotemporal accuracy is proposed. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was studied for heat stress resilience patterns in July 2021. Boosted regression tree (BRT) models analyzed LCZ landscape patterns' impacts on HSR_phy. The results indicated that HSR_phy during the daytime was significantly lower than that at night. The HSR_phy of GBA showed the characteristics of low inland and high periphery. Significant differences in HSR_phy existed among most LCZs. LCZA, LCZC, and LCZ9 showed strong HSR_phy, whereas LCZ2, LCZ8, and LCZ7 were opposite. At the 1500 m scale, the Percentage of Landscape (PLAND), Landscape Shape Index (LSI), and Shannon's Diversity Index (SHDI) of LCZ4, LCZ5, and LCZ6 were the key factors affecting HSR_phy. Our research demonstrates how to quantify heat stress resilience and impact factors of urban physical spaces to provide a basis for developing climate-sensitive urban design strategies.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery.

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Urban microclimate differences in continental zone of China

In modern cities, the block geometry and land cover feature directly affect the heat transfer between ground surface and atmosphere, resulting in significant differences of local microclimate. Clarifying this difference will help to accurately predict regional building energy demand and optimize regional layout design. However, existing microclimate research mainly focuses on tropical, arid, and temperate climate regions, leaving continental areas virtually unexplored. To address this problem and clarify the interactions of the urban blocks and microclimate, the combination of the local climate zone (LCZ) scheme and fixed-point weather station monitoring is employed to investigate the variation characteristics of the thermal and humidity conditions of typical urban blocks in continental zone of China. Firstly, sixty-seven typical urban blocks with self-built, national and regional meteorological stations are selected as the object of study, and classified into twelve LCZ categories, according to their inherent features. Secondly, the spatial and temporal variation patterns of the thermal environment indexes of the twelve LCZ types are analyzed and compared. Additionally, correlation analyses and regression fittings are performed between the thermal environment indexes and the urban morphological parameters. The results indicate significant differences in thermal environment indexes among different LCZ types. The mean temperature of built types is 0.72 °C higher than land cover types. The temperature and humidity exhibit a parabolic relationship with height of roughness elements and building surface fraction respectively, while the humidity has strong negatively correlations with the impervious surface fraction and the height of roughness elements.

Urban heat island analysis based on high resolution measurement data: A case study in Beijing

Urban heat island (UHI) is a widely concerned urban climate which impacts billions of residents worldwide. Nevertheless, inadequate rigorous air temperature (Tair) measurement has become an obstacle to UHI research and mitigation, especially in megacities like Beijing. In this study, we applied a 1-km hourly Tair dataset interpolated from records of 521 stations in Beijing. The dataset enables us to analyze UHI patterns on a block scale and the impacts of landscapes combined with the local climate zones (LCZ) scheme. Our results indicate that: (1) Tair and its temporal variation in each LCZ vary significantly, which are highly relevant to their morphology and thermal-radiative properties. (2) LCZ 1 and LCZ 2 have the highest nocturnal UHI intensities (UHII). Discrepancies among LCZs also cause various temporal variations in their UHII. (3) The spatial pattern of LCZs affects regional temperature. Area ratio and aggregation extent of LCZ 1&2 show significant positive correlations with regional temperature, while LCZ A&D&G have the opposite regularity. (4) Distinct mitigation strategies are put forward for each subarea in Beijing with various endowments of population and the LCZ structure. The study provides insight into Beijing UHI and plausible mitigation advice in megacities like Beijing.

Characterizing the local climate of large-scale archaeological parks in the tropics

PurposeThis study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.Design/methodology/approachThe study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.FindingsThe study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.Practical implicationsBased on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.Originality/valueThe local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

Ambient temperature modelling from surface characteristics and associating urban morphology with thermal discomfort

Urban heat island assessment is of paramount importance when monitoring microclimate changes, increased heat stress, mortality and energy consumption. Simply analysing land surface temperature patterns for human comfort and health assessment is often inadequate. In this study, we attempt to resolve this inadequacy with ambient temperature modelling from multiple surface characteristics for a tropical megacity. A 336 datapoint‐based multilinear regression model was formulated to predict the maximum summer air temperature with a mean absolute error of 0.694°C. Specific locations of critical thermal environments were demarcated with Getis‐Ord statistic. Based on our results, extreme hotspots covered 10.53 per cent of the city on a daily basis at 99 per cent confidence level. The application of an outdoor thermal comfort index highlighted the existence of very strong heat stress zones over 14.67 per cent of the study area. The air temperature hotspots were verified with intra‐urban variation in carbon sequestration. This environmental parameter was used to strengthen the observed results since, carbon sequestration directly links urbanization with degradation of thermal environments. Further, five urban morphological parameters were analysed to conclude that building density and height were the most significant urban design factors leading to increased air temperature. Simultaneous local climate zone mapping depicted that heat islands were dominated by mid and high‐rise built‐up areas, of which 68.44 km2 of that area is suitable for vertical and roof gardening. Another 33.29 km of road stretch was delineated within low‐rise built‐up areas with scope for green landscaping to improve urban sustainability.

Research on Optimal Cooling Landscape Combination and Configuration Based on Local Climate Zones—Fuzhou, China

The deterioration of the urban thermal environment has seriously affected the quality of life of urban residents, and studying the optimal cooling landscape combination and configuration based on local climate zones (LCZs) is crucial for mitigating the thermal environment. In this study, the LCZ system was combined to analyze the spatial and temporal changes to the thermal environment in the central area of Fuzhou, and the 159 blocks in the core area were selected to derive the optimal LCZ combination and configuration. The conclusions are as follows: (1) From 2013 to 2021, the building layout of the study area became more open and the building height gradually increased. The high-temperature areas were mainly clustered in the core area; (2) The LSTs for low-rise buildings (LCZ 3 (41.67 °C), LCZ 7 (40.10 °C), LCZ 8 (42.61 °C), and LCZ 10 (41.85 °C)) were higher than the LSTs for high-rise buildings (LCZ 1 (38.58 °C) and LCZ 4 (38.50 °C)); (3) The thermal contribution index for low building types was higher for dense buildings (LCZ 3 (0.4331), LCZ 8 (0.3149), and LCZ 10 (0.2325)) than for open buildings (LCZ 6 (0.0247) and LCZ 9 (0.0317)); (4) Blocks with an average LST of 36 °C had the most cost-effective cooling, and the combination and configuration of LCZs within such blocks were optimal. Our results can be used to better guide urban planners in managing LCZ combinations and configurations within blocks (the smallest planning unit) at an earlier phase of thermal environment design, and for appropriately adapting existing block layouts, providing a new perspective on urban thermal environment research with important implications for climate-friendly city and neighborhood planning.

A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities

Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).

LCZ framework and landscape metrics: Exploration of urban and peri-urban thermal environment emphasizing 2/3D characteristics

Rapid urbanization has altered the environment and climate, necessitating a thorough grasp of urban thermal dynamics for sustainable development. The lack of detailed urban information poses a considerable challenge. This study employs the Local Climate Zone (LCZ) framework to delve into the intricate relationship between land surface temperature (LST) zones and both 2/3D urban characteristics. The LCZ map for Bojnourd city, Iran, was prepared, consisting of eleven classes categorized into five built types and six land cover types. The mean LST in the summer of 2021 was derived from Landsat8 using the split-window algorithm (SWA) and divided into seven thermal zones. The research reveals distinctive spatial patterns of LCZs across various Land LST zones. Notably, LCZ2 (Compact mid-rise) and LCZ 3 (Compact low-rise), despite differing heights, share the same thermal zone (Zone3), suggesting influences beyond height on thermal zoning. In contrast, LCZ5 (Open midrise), situated in a cooler thermal zone (Zone2), features more vegetation and taller trees, exhibiting a lower LST range compared to LCZ2 and LCZ3. Class-level metrics (AREA_MN and LPI) offer a similar description of LCZ spatial patterns, while landscape-level metrics like SHDI and IJI provide a more insightful depiction of spatial patterns. The study found that the most appropriate spatial pattern for improving urban thermal conditions involves considering a set of factors such as vegetation presence, land cover proximity, height, and density. This ensures sustainable and green urban development that is in tune with climate change and promotes balanced and harmonious thermal patterns in the urban environment.

Exploring the Dynamics of Land Surface Temperature in Jordan’s Local Climate Zones: A Comprehensive Assessment through Landsat Entire Archive and Google Earth Engine

Exploring the Dynamics of Land Surface Temperature in Jordan’s Local Climate Zones: A Comprehensive Assessment through Landsat Entire Archive and Google Earth Engine

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

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.

Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients

ContextUrbanization and local urban climate have multiple impacts on vegetation phenology in urban and suburban areas. Understanding these effects and their interactions with the surface urban heating effect remains limited.ObjectiveWe employed a time series of Earth observation data to analyze land surface phenology (LSP) dynamics and related environmental drivers in the highly urbanized Pearl River Delta (PRD) region.MethodsFirst, local climate zone (LCZ) maps were generated from Earth observation datasets of 2000 and 2019. Second, LSP (i.e., start, end, and length of season) were extracted from vegetation indices for 2000–2019. Thirdly, land surface temperature (LST) was used as an explanatory variable based on the LCZ of cities. Finally, interannual trends of LSP and their association with LST were analyzed, depending on the distance gradient of vegetation to compact high-rise buildings.ResultsUrban surface characteristics showed that LSP in regions dominated by compact and high-rise urban areas presented significant spatiotemporal variation at the start and end of season than those dominated by open, mid-rise, and low-rise areas. The impacts of spring and autumn LST in the daytime on LSP were slightly more substantial than those in the nighttime. The association of decreasing spring LST in the daytime with a delayed start of season is especially pronounced in urban domains.ConclusionsThe results indicate that vegetated areas adjacent to urban domains presented greater spatiotemporal dynamics than suburban and rural regions. Our study emphasizes the dependence of spatiotemporal changes in vegetation phenology on the effects of urban surface warming.

High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning

Abstract. As the frequency and intensity of heatwaves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI). All four submodel predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night.