scholarly journals Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

2021 ◽  
Vol 9 ◽  
Author(s):  
Mikhail Varentsov ◽  
Daniel Fenner ◽  
Fred Meier ◽  
Timofey Samsonov ◽  
Matthias Demuzere
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Urban Climate ◽  
Winter Season ◽  
Local Scale ◽  
Driving Factors ◽  
Heat Island ◽  
Local Climate Zones ◽  
Urban Heat ◽  
Climate Studies

Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.

scholarly journals Satellite Based Analysis of Surface Urban Heat Island Intensity

2016 ◽  
Vol 9 (1-2) ◽  
pp. 23-30 ◽  
Author(s):  
Orsolya Gémes ◽  
Zalán Tobak ◽  
Boudewijn van Leeuwen
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Urban Climate ◽  
Seasonal Development ◽  
Heat Island ◽  
Early Afternoon ◽  
Local Climate Zones ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract The most obvious characteristics of urban climate are higher air and surface temperatures compared to rural areas and large spatial variation of meteorological parameters within the city. This research examines the long term and seasonal development of urban surface temperature using satellite data during a period of 30 years and within a year. The medium resolution Landsat data were (pre)processed using open source tools. Besides the analysis of the long term and seasonal changes in land surface temperature within a city, also its relationship with changes in the vegetation cover was investigated. Different urban districts and local climate zones showed varying strength of correlation. The temperature difference between urban surfaces and surroundings is defined as surface urban heat island (SUHI). Its development shows remarkable seasonal and spatial anomalies. The satellite images can be applied to visualize and analyze the SUHI, although they were not collected at midday and early afternoon, when the phenomenon is normally at its maximum. The applied methodology is based on free data and software and requires minimal user interaction. Using the results new urban developments (new built up and green areas) can be planned, that help mitigate the negative effects of urban climate.

scholarly journals Using the dendro-climatological signal of urban trees as a measure of urbanization and urban heat island

2021 ◽  
Author(s):  
Christoph Schneider ◽  
Burkhard Neuwirth ◽  
Sebastian Schneider ◽  
Daniel Balanzategui ◽  
Stefanie Elsholz ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Tree Ring ◽  
Air Temperature ◽  
Tree Species ◽  
Urban Climate ◽  
Ring Width ◽  
Urban Trees ◽  
Pedunculate Oak ◽  
Heat Island ◽  
Urban Heat

AbstractUsing dendroclimatological techniques this study investigates whether inner city tree-ring width (TRW) chronologies from eight tree species (ash, beech, fir, larch, lime, sessile and pedunculate oak, and pine) are suitable to examine the urban heat island of Berlin, Germany. Climate-growth relationships were analyzed for 18 sites along a gradient of increasing urbanization covering Berlin and surrounding rural areas. As a proxy for defining urban heat island intensities at each site, we applied urbanization parameters such as building fraction, impervious surfaces, and green areas. The response of TRW to monthly and seasonal air temperature, precipitation, aridity, and daily air-temperature ranges were used to identify climate-growth relationships. Trees from urban sites were found to be more sensitive to climate compared to trees in the surrounding hinterland. Ring width of the deciduous species, especially ash, beech, and oak, showed a high sensitivity to summer heat and drought at urban locations (summer signal), whereas conifer species were found suitable for the analysis of the urban heat island in late winter and early spring (winter signal).The summer and winter signals were strongest in tree-ring chronologies when the urban heat island intensities were based on an area of about 200 m to 3000 m centered over the tree locations, and thus reflect the urban climate at the scale of city quarters. For the summer signal, the sensitivity of deciduous tree species to climate increased with urbanity.These results indicate that urban trees can be used for climate response analyses and open new pathways to trace the evolution of urban climate change and more specifically the urban heat island, both in time and space.

scholarly journals Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1271
Author(s):  
Yatong Zhang ◽  
Delong Li ◽  
Laibao Liu ◽  
Ze Liang ◽  
Jiashu Shen ◽  
...  
Keyword(s):  
Population Density ◽  
Urban Heat Island ◽  
Driving Factors ◽  
Intensity Change ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Local Climate Zones ◽  
Urban Heat ◽  
Surface Urban Heat Island

The increasing degree of urbanization has continuously aggravated the surface urban heat island (sUHI) effect in China. To investigate the correlation between spatiotemporal changes of sUHI and urbanization in Beijing, land surface temperature in summer from 2000 to 2017 and the distribution of local climate zones (LCZs) in 2003, 2005, 2010, and 2017 was retrieved using remote sensing data and used to analyze the sUHI area and intensity change. The statistical method GeoDetector was utilized to investigate the explanatory ability of LCZs and population as the driving factors. The year of 2006 was identified as the main turning year for sUHI evolution. The variation the sUHI from 2000 showed first an increasing trend, and then a decreasing one. The sUHI pattern changed before and after 2009. Before 2009, the sUHI mainly increased in the suburbs, and then, the enhancement area moved to the central area. The sUHI intensity change under different LCZ conversion conditions showed that the LCZ conversion influences the sUHI intensity significantly. Based on population distribution data, we found that the relationship between population density and sUHI gets weaker with increasing population density. The result of GeoDetector indicated that the LCZ is the main factor influencing the sUHI, but population density is an important auxiliary factor. This research reveals the sUHI variation pattern in Beijing from 2000 and could help city managers plan thermally comfortable urban environments with a better understanding of the effect of urban spatial form and population density on sUHIs.

A study on the spatial patterns of the Moscow megacity urban heat island based on the dense official and crowdsourcing observations

2020 ◽  
Author(s):  
Mikhail Varentsov ◽  
Timofey Samsonov ◽  
Pavel Konstantinov ◽  
Pavel Kargashin ◽  
Daniel Fenner ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Spatial Patterns ◽  
Air Temperature ◽  
Urban Climate ◽  
Basic Research ◽  
Area Fraction ◽  
Heat Island ◽  
Urban Heat ◽  
Non Local ◽  
The City

<p>The presented study is devoted to the investigation of the spatial patterns of the urban-induced air temperature anomaly, known as the urban heat island (UHI) effect, based on the example of Moscow megacity. The numerous previous studies have already shown that Moscow exhibits urban-induced climatic effects (Varentsov e al., 2018) and could serve as a good test-bed for urban climate studies. In the presented study, we have further analyzed the UHI using high-quality observations from the official meteorological networks in Moscow region as well as the uncertified crowdsourcing observations from Netatmo network. The official meteorological networks include more than 70 observational sites in the city and surroundings, while the Netatmo network additionally provides the data from more than 1500 citizen weather stations (CWSs) in Moscow region. Previous studies have shown that CWS observations could be used for urban climate studies after application of the special quality control and filtering routines (Meier et al., 2017).</p><p>The analysis performed for a number of summer and winter seasons has revealed the seasonal variations of the UHI spatial patterns. In order to investigate the driving factors of the observed spatial heterogeneity of the air temperature within the city, we have analyzed its linkages with different qualitative and quantitative parameters of the urban environment, including the Local Climate Zone (LCZ) type, the impervious area fraction, building density, vegetation area fraction, etc. These parameters were obtained using the Landsat and Sentinel satellite images, Copernicus Global Land Cover data and OpenStreetMap data. We have shown that the UHI spatial patterns are shaped both by local and non-local driving factors. The factors such as LCZ type represent the local features of the urban environment, while the non-local drivers represent the influence of remote parts of the megacity, transformed by the atmospheric diffusion and advection. The non-local effects are reflected e.g. in the dependence between the UHI intensity and the distance from the city center; in the differences between similar LCZs, located in the different parts of the city; in the heat advection to the leeward side of the city. The findings of the study clearly illustrate the importance of taking the non-local effects into consideration in urban planning applications, biometeorological assessments and when applying the LCZ approach for big cities.</p><p><strong>Acknowledges:</strong> The processing and analysis of the official and crowdsourcing observations were supported by Russian Foundation for Basic Research (project no. 19-35-70009). The analysis of the impervious surface area fraction data was supported by Russian Foundation for Basic Research (project no. 18-35-20052). The analysis of the impacts of urban vegetation on the urban heat island was supported by Russian Science Foundation (project no. 19-77-30012).</p><p><strong>References: </strong></p><p>Meier F., Fenner D., Grassmann T., Otto M., & Scherer D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Climate, 19, 170–191.</p><p>Varentsov M., Wouters H., Platonov V., & Konstantinov P. (2018). Megacity-Induced Mesoclimatic Effects in the Lower Atmosphere: A Modeling Study for Multiple Summers over Moscow, Russia. Atmosphere, 9(2), 50.</p>

scholarly journals Comments about Urban Bioclimate Aspects for Consideration in Urban Climate and Planning Issues in the Era of Climate Change

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 546
Author(s):  
Andreas Matzarakis
Keyword(s):  
Climate Change ◽  
Urban Heat Island ◽  
Urban Climate ◽  
Heat Island ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Urban Heat

In the era of climate change, before developing and establishing mitigation and adaptation measures that counteract urban heat island (UHI) effects [...]

scholarly journals Including Urban Heat Island in Bioclimatic Early-Design Phases: A Simplified Methodology and Sample Applications

2021 ◽  
Vol 13 (11) ◽  
pp. 5918
Author(s):  
Giacomo Chiesa ◽  
Yingyue Li
Keyword(s):  
Urban Heat Island ◽  
Urban Climate ◽  
Temperate Climate ◽  
Heat Island ◽  
Climate Data ◽  
Degree Days ◽  
Climate Conditions ◽  
Early Design ◽  
Correct Definition ◽  
Urban Heat

Urban heat island and urban-driven climate variations are recognized issues and may considerably affect the local climatic potential of free-running technologies. Nevertheless, green design and bioclimatic early-design analyses are generally based on typical rural climate data, without including urban effects. This paper aims to define a simple approach to considering urban shapes and expected effects on local bioclimatic potential indicators to support early-design choices. Furthermore, the proposed approach is based on simplifying urban shapes to simplify analyses in early-design phases. The proposed approach was applied to a sample location (Turin, temperate climate) and five other climate conditions representative of Eurasian climates. The results show that the inclusion of the urban climate dimension considerably reduced rural HDD (heating degree-days) from 10% to 30% and increased CDD (cooling degree-days) from 70% to 95%. The results reveal the importance of including the urban climate dimension in early-design phases, such as building programming in which specific design actions are not yet defined, to support the correct definition of early-design bioclimatic analyses.

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

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