Daytime Surface Urban Heat Island Research Articles

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

Characteristics of surface urban heat islands in global cities of different scales: Trends and drivers

Given the increasing severity of the surface urban heat island (SUHI) phenomenon, urban residents face heightened heat stress. Consequently, mitigating the effects of SUHI becomes critically important to improve urban livability. Previous studies often overlooked the influence of city size on SUHI, focusing on the correlational between SUHI and drivers and sidelining the synergistic effects of multiple factors on SUHI. To address this issue, we introduced a novel method, “geographical convergent cross-mapping”, coupled with the random forest and Shapley value theory. Our results indicated that: Globally, SUHI exhibited a decreasing trend during day and night, with significant spatiotemporal disparities, and in cities of varying sizes, the cooling capacity of vegetation for SUHI showed significant variation. Locally, in arid cities spanning 150–200 km², vegetation contributed to cooling SUHI by up to 61 %, but this effect lessened in larger cities. Notably, in equatorial cities larger than 200 km², this capability dropped drastically during summer, from 49 % to 7 %. Long-term trends showed an increase in daytime SUHI in equatorial regions and large cities along the southeastern coasts of China and the United States. This study provides key insights for precise urban climate adaptation and sustainable planning in cities of different sizes.

Exploring the role of strategic urban planning and greening in decreasing surface urban heat island intensity

ABSTRACT Surface urban heat islands (SUHIs) are a significant concern in urban areas worldwide owing to their adverse effects on energy consumption, human health, and urban climate. Strategic urban planning incorporating abundant vegetation is an approach to moderate the UHI effect, promoting cooler, greener and more sustainable cities. This study investigates SUHIs in Seoul and Ulsan, South Korea, from 2001 to 2022, using satellite measurements. Summer daytime SUHII holds significant importance due to the frequent presence of heatwaves and its combined effects with UHI phenomenon. Remarkably, Seoul has shown a significant decrease in summer daytime SUHIs (−0.03°C/yr in Terra and −0.05°C/yr in Aqua), attributed to increased vegetation (0.03/decade in Terra and 0.02/decade in Aqua) in the city’s core. In contrast, Ulsan saw rising SUHIs (+0.04°C/yr), linked to reduced summer vegetation. Daytime SUHII is highest in both Seoul and Ulsan during summer months with both Terra and Aqua datasets, and peaks in the month of June with values reaching up to 2.98°C in Seoul and 3.39°C in Ulsan. Increasing urban greenery, including tree planting, and expanded green spaces, proves effective in mitigating SUHIs, offering valuable insights for urban planners and policymakers to enhance urban sustainability, particularly in megacities like Seoul.

Spatial Characteristics and Temporal Trend of Urban Heat Island Effect over Major Cities in India Using Long-Term Space-Based MODIS Land Surface Temperature Observations (2000–2023)

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects.

Urbanization influenced SUHI Of 41 megacities of the world using big geospatial data assisted with google earth engine

The rapid pace of urbanization with climate change pushed megacities into the forefront of human habitation, emphasizing the urgent need to unravel the relationship between urban expansion and environmental dynamics. This study examines the Surface Urban Heat Island (SUHI) within the 41 megacities, to interpret the complex relationship that emerges from the nexus of urbanization and climatic influence. Through a comprehensive analysis, it has been observed that daytime and nighttime temperature is increasing particularly in Asian regions. An interesting SUHI intensity emerges in all megacities of the world. The SUHI does vary greatly across the megacities of the world. Similarly, the average daytime ocean/coastal cities SUHI (1.19 °C) was higher than inland cities (0.77 °C) but at nighttime inland cities had a higher average SUHI (0.95 °C) than ocean/coastal cities (0.93 °C). Notably, the findings illuminate the complex relationship between city size, elevation, and location with inland and ocean/coastal cities. An inverse yet substantively significant relationship emerges between, city size, elevation, and SUHI magnitude. Our study delivers a positive but moderate correlation between daytime and nighttime SUHI and population density (r2 = 0.3/r2 = 0.5, p<0.05). In summary, this study elevates our understanding of SUHI phenomena. These insights are for well-informed urban policies and sustainable development.

Characterizing the indicator-based, day-and-night, and climate-based variations in response of surface urban heat island during heat wave across global 561 cities

Increasing surface urban heat island (SUHI) has been reported to be closely associated with a large number of environmental issues. The response of SUHI during heat wave was important for further mitigation proposal. However, it remained unknown whether the response of SUHI during heat wave varies with different SUHI indicators (intensity, frequency and maximum duration), day-and-night contrast, and climate zones, and how to characterize and explain these response variations. Therefore, this paper quantified and explained for these variations in response of SUHI during heat wave across global 561 cities. There were three major findings. (1) The average percentage of response in intensity, frequency and maximum duration at daytime was -20%, -12% and -47%, while it was 10%, 11% and -10% at nighttime. During heat wave, daytime SUHI decreased in the equatorial and arid climates, while nighttime SUHI increased. (2) The attenuated daytime SUHI during heat wave in the equatorial and arid climates resulted from a decrease in latent heat flux and vegetation cooling effect. (3) Increased solar radiation during heat wave contributed to intensified nighttime SUHI, of which energy sources were determined on surface solar absorption. Additionally, differences in evaporation, vegetation, and albedo during heat wave were discussed.

Surface urban heat island of Iași city (Romania) and its differences from in situ screen-level air temperature measurements

Nowadays, in the field of urban climate there is a scientific need to compare Land Surface Temperature (LST) with screen-level air temperature (2mTair). This is done in our study for Iași city, based on MODIS imagery and in situ observations. We assess firstly the Surface Urban Heat Island (SUHI) under clear-sky conditions, outlining that SUHI is well expressed during the year, while its geometry tends to be more compact and regular during nighttime. In summer daytime SUHI extension reaches its peak, being outlined by the 35°C isotherm, while in winter SUHI is absent during the day. LST increases clearly when imperviousness ratio (IMD) overpasses the 10% threshold, and its maximum is reached for IMD higher than 80%. The comparison with the in situ observations indicates that the LST – 2mTair differences are the highest during daytime in spring and summer (+ 5 to +7°C), while during winter no major differences are observed. For the nighttime the LST is generally 1 to 3°C lower than 2mTair. We also found that intense radiative conditions are prone to increase both the SUHI intensity, and the difference between LST and 2mTair. The results are meant to support the policies aiming to mitigate the heat island effects on human population.

Nonlinear effects of urban and industrial forms on surface urban heat island: Evidence from 162 Chinese prefecture-level cities

Urban and industrial forms were widely concerned as influential factors of surface urban heat island (SUHI) at the macroscale. However, there is still a lack of systematic understanding of urban and industrial forms’ nonlinear and threshold effects on SUHI. This study aimed to disclose their nonlinear effects on daytime and nighttime SUHI based on the random forest (RF) model, using the case of 162 prefecture-level cities in China. The RF results showed that industrial factors collectively contributed 43.38% to daytime SUHI variance, whereas urban factors contributed 20.44% to nighttime SUHI variance. Industrial diversity and urban contiguity were the most critical non-natural factors influencing daytime and nighttime SUHI, respectively. Most factors positively affected daytime and nighttime SUHI, except for the urban elongation. Urban boundary complexity had a distinct effect on daytime and nighttime SUHI. Urban compactness/contiguity and industrial agglomeration/diversity had nonlinear and threshold effects on SUHI. Based on the findings, we have a further understanding of the effects of urban and industrial forms on SUHI in the effective ranges.

Are global cities homogenizing? An assessment of urban form and heat island implications

The rapid growth of cities—along with the increasing connectedness of the world's social, economic, and political systems—has been hypothesized to generate a homogenization of urban form and associated environmental impacts. These hypotheses, however, have rarely been tested. Employing satellite imagery of 150 of the most populous cities in China, India, and the United States, we examine how the area and configuration of built-up land within cities has changed between 1995 and 2015 and assess impacts on the urban heat island effect. We find similar urban form trends across the three countries. The strongest evidence of homogenization is in the connectivity of urban form, while the shape of cities is linked to higher daytime surface urban heat island (SUHI) intensity. In the context of this and other research, we postulate that the identified urban form trends may lead to the homogenization of the biotic and abiotic environment of cities. Homogenization presents an opportunity for cities to learn from each other as they encounter similar ecological outcomes driven, in part, by their increasingly similar urban form.

Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city’s thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001–2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Surface and canopy urban heat islands: Does urban morphology result in the spatiotemporal differences?

Urban heat island (UHI) is a phenomenon wherein urban areas become warmer than surrounding rural areas, which is one of the most important urban issues arising due to human activities. In this study, UHI was investigated at two different layers: canopy urban heat island (CUHI) and surface urban heat island (SUHI). Kitakyushu City, Japan, which is a highly spatially heterogeneous urban area, was utilized as a case study to explore the spatiotemporal differences of the two types of UHIs, both during the daytime and nighttime from January to December 2010. Subsequently, random forest (RF) regression was applied to reveal the correlation between the differences in the two types of UHI intensity (∆UHII) and urban morphology indices. The results showed that (1) the SUHI and CUHI during daytime had similar spatial patterns, while those during nighttime had different spatial patterns; (2) the daytime SUHI intensity (SUHII) could be greatly overestimated compared to the CUHI intensity (CUHII), and this overestimation significantly varied with months; and (3) highly nonlinear correlations existed between ∆UHII and urban morphology indices. Both the internal and external characteristics of urban morphology in a study grid contributed to the generation of ∆UHII, but internal characteristics were more important.

A multicity analysis of daytime Surface Urban Heat Islands in India and the US.

• In the US, urban land surface temperature (LST) was higher compared to rural LST. • Despite urbanization, urban LST in India was lower compared to rural LST. • Non-photosynthetic vegetation (NPV) and barren land types influence the results. • Built-up indices used here do not differentiate sparse vegetation and built-up surfaces. • New methods are needed for quantifying urbanization's impact on LST in India. Daytime surface UHI (SUHI) and the factors influencing it across highly populated cities in India and the United States (US) were evaluated. Urban-rural mean land surface temperature (LST) differences (ΔT S ) were calculated and correlated with vegetation and built-up surface densities, quantified using the Normalized Difference Vegetation Index (NDVI) and Index Based Built-Up Index (IBI) respectively. The results from the US show a conventional SUHI phenomenon, and LST decrease with increasing vegetation and decreasing built-up surfaces. However, in India, despite existing urban densities, urban areas are cooler than the rural surroundings. The average LST was higher and NDVI was lower compared to the US, and NDVI and LST correlations were weak. Furthermore, in India, the average IBI values calculated for built-up areas, cropland, and other sparse vegetation land covers were approximately equal. The prevalence of non-photosynthetic vegetation (NPV) and barren soil in India are impacting spectral indices and the correlations. Differences in ΔT S and spectral indices trends between India and US highlight the shortcomings of the indices used in appropriately identifying Indian LULC types and establish the need for localized SUHI research methods that can specifically analyze the impact of urbanization on SUHI in India.

The Extreme Heat Wave over Western North America in 2021: An Assessment by Means of Land Surface Temperature

In our current global warming climate, the growth of record-breaking heat waves (HWs) is expected to increase in its frequency and intensity. Consequently, the considerably growing and agglomerated world’s urban population becomes more exposed to serious heat-related health risks. In this context, the study of Surface Urban Heat Island (SUHI) intensity during HWs is of substantial importance due to the potential vulnerability urbanized areas might have to HWs in comparison to their surrounding rural areas. This article discusses Land Surface Temperatures (LST) reached during the extreme HW over Western North America during the boreal summer of 2021 using Thermal InfraRed (TIR) imagery acquired from TIR Sensor (TIRS) (30 m spatial resolution) onboard Landsat-8 platform and Moderate Resolution Imaging Spectroradiometer (MODIS) (1 km spatial resolution) onboard Terra/Aqua platforms. We provide an early assessment of maximum LSTs reached over the affected areas, as well as impacts in terms of SUHI over the main cities and towns. MODIS series of LST from 2000 to 2021 over urbanized areas presented the highest recorded LST values in late June 2021, with maximum values around 50 °C for some cities. High spatial resolution LSTs (Landsat-8) were used to map SUHI intensity as well as to assess the impact of SUHI on thermal comfort conditions at intraurban space by means of a thermal environmental quality indicator, the Urban Field Thermal Variance Index (UFTVI). The same high resolution LSTs were used to verify the existence of clusters and employ a Local Indicator of Spatial Association (LISA) to quantify its degree of strength. We identified the spatial distribution of heat patterns within the intraurban space as well as described its behavior across the thermal landscape by fitting a polynomial regression model. We also qualitatively analyze the relationship between both UFTVI and LST clusters with different land cover types. Findings indicate that average daytime SUHI intensity for the studied cities was typically within 1 to 5 °C, with some exceptional values surpassing 7 °C and 9 °C. During night, the SUHI intensity was reduced to variations within 1–3 °C, with a maximum value of +4 °C. The extreme LSTs recorded indicate no significant influence of HW on SUHI intensity. SUHI intensity maps of the intraurban space evidence hotspots of much higher values located at densely built-up areas, while urban green spaces and dense vegetation show lower values. In the same manner, UTFVI has shown “no” SUHI for densely vegetated regions, water bodies, and low-dense built-up areas with intertwined dense vegetation, while the “strongest” SUHI was observed for non-vegetated dense built-up areas with low albedo material such as concrete and pavement. LST was evidenced as a good marker for assessing the influence of HWs on SUHI and recognizing potential thermal environmental consequences of SUHI intensity. This finding highlights that remote-sensing based LST is particularly suitable as an indicator in the analysis of SUHI intensity patterns during HWs at different spatial resolutions. LST used as an indicator for analyzing and detecting extreme temperature events and its consequences seems to be a promising means for rapid and accurate monitoring and mapping.

Global long-term mapping of surface temperature shows intensified intra-city urban heat island extremes

Surface temperatures are generally higher in cities than in rural surroundings. This phenomenon, known as Surface Urban Heat Island (SUHI), increases the risk of heat-related human illnesses and mortality. Past global studies analysed this phenomenon aggregated at city scale or over seasonal and annual time periods, while human impacts strongly depend on shorter term heat stress experienced locally. Here we develop a global long-term high-resolution dataset of daytime SUHI, offering an insight into the space–time variability of the urban–rural temperature differences which is unprecedented at global scale. Our results show that across urban areas worldwide over the period 2003–2020, 3-day SUHI extremes are on average more than twice as high as the warm-season median SUHI, with local exceedances up to 10 K. Over this period, SUHI extremes have increased more rapidly than warm-season medians, and averaged worldwide are now 1.04 K or 31% higher compared to 2003. This can be linked with increasing urbanisation, more frequent heatwaves, and greening of the earth, processes that are all expected to continue in the coming decades. Within many cities there are hotspots where extreme SUHI intensity is 10–15 K higher compared to relatively cooler city parts. Given the limited human adaptability to heat stress, our results advocate for mitigation strategies targeted at reducing SUHI extremes in the most vulnerable and exposed city neighbourhoods.

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in “mega” cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO2 in Amman, Beirut, and Jeddah decreased by −56.6%, −43.4%, and −32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO2 levels in megacities like Tehran (−0.9%) and Cairo (−3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

How urbanisation alters the intensity of the urban heat island in a tropical African city.

Due to the combined effects of urban growth and climate change, rapid urbanisation is particularly challenging in African cities. Areas that will house a large proportion of the urban population in the future coincide with where natural hazards are expected to occur, and where hazard risk management institutions, knowledge, and capacity are often lacking. One of the challenges posed by rapid urbanisation is the Urban Heat Island (UHI) effect, whereby urban areas are warmer than the surrounding rural areas. This study investigates urbanisation patterns and alterations in surface UHI (SUHI) intensity for the Kampala urban cluster, Uganda. Analyses show that between 1995 and 2017, Kampala underwent extensive changes to its urban built-up area. From the centre of the city to adjoining non-built up areas in all directions, the urban land cover increased from 12,133 ha in 1995 to 25,389 ha in 2016. The area of SUHI intensity in Kampala expanded significantly over the 15-year period of study, expanding from 22,910 ha in 2003 to 27,900 ha in 2016, while the annual daytime SUHI of 2.2°C in 2003 had decreased to 1.9°C by 2017. Although SUHI intensity decreased in some parts of the city, elsewhere it increased, suggesting that urbanisation does not always lead to a deterioration of environmental conditions. We postulate that urban development may therefore not necessarily create an undesirable impact on local climate if it is properly managed. Rapidly growing cities in Africa and elsewhere should ensure that the dynamics of their development are directed towards mitigating potentially harmful environmental impacts, such as UHI effect through careful planning that considers both bluespaces and greenspaces.

Urban Forests as Main Regulator of the Evaporative Cooling Effect in Cities

AbstractHigher temperatures in urban areas expose a large fraction of the human population to potentially dangerous heat stress. Green spaces are promoted worldwide as local and city‐scale cooling strategies but the amount, type, and functioning of vegetation in cities lack quantification and their interaction with urban climate in different settings remains a matter of debate. Here we use state‐of‐the‐art remote sensing data from 145 city clusters to disentangle the drivers of surface urban heat islands (SUHI) intensity and quantify urban‐rural differences in vegetation cover, species composition, and evaporative cooling. We show that nighttime SUHIs are affected mostly by abiotic factors, while daytime SUHIs are highly correlated with vegetation characteristics and the wetness of the background climate. Magnitude and seasonality of daytime SUHIs are controlled by urban‐rural differences in plant transpiration and leaf area, which explain the dependence of SUHIs on wetness conditions. Leaf area differences are caused primarily by changes in vegetation type and a loss of in‐city forested areas, highlighting the importance of maintaining “natural reserves” as a sustainable heat mitigation policy.

Reduction in human activity can enhance the urban heat island: insights from the COVID-19 lockdown

The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human–land–atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling through changes in the surface energy budget during the lockdown (1 April to 15 May 2020) in the Indo-Gangetic Basin, one of the world’s most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution compared to a five year baseline, expected to increase incoming solar radiation, was counteracted by a ∼30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting during the lockdown increased surface vegetation cover, causing almost half the regional cooling via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20–0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on rural climate in this region and its indirect impact on the SUHI intensity.

Spatiotemporal Variations in the Urban Heat Islands across the Coastal Cities in the Yangtze River Delta, China

Over 70% of the cities in China are experiencing urbanization, and urban heat island intensity (UHII) evaluation studies have been widely performed. However, under the rapid economic development in China, few studies on surface urban heat island (SUHI) interannual variations have been conducted in coastal cities in the leading economic region of the Yangtze River Delta. In this study, the long-term summer daytime SUHI from 2001 to 2019 is studied based on the remotely sensed land surface temperature (LST) in 11 coastal cities in the Yangtze River Delta. The results show that notable SUHIs occur in the study area with high spatial heterogeneity, particularly in the central area, including Shanghai, Hangzhou, and Ningbo. The SUHI trends are not synchronous across the study area, with suburban areas revealing higher trends than city center areas. In addition, all 11 cities show an increasing trend of the urban heat proportion index (UHPI) over 19 years, which is more profound in Shanghai and Zhoushan but less profound in Lianyungang and Wenzhou. The strong correlation between the UHPI and artificial impervious coverage indicates that artificial impervious coverage plays an important role in determining the spatial and temporal distributions of the summer daytime SUHI in the 11 coastal cities, which are especially notable in Ningbo and Taizhou.