Inter-/intra-zonal seasonal variability of the surface urban heat island based on local climate zones in three central European cities
Graphical abstract
Introduction
Urbanization is one of the main driving forces of landscape change in Europe [1]. The territory of urban agglomerations, including urban and suburban landscapes, constitutes an ever-increasingly important part of the surface [2]. Urban landscape is characterized by structures, arrangements and compositions of land cover distinctly different from those of rural or natural landscapes. Significant proportions of urban areas are covered with artificial – mostly impervious – surfaces, which substantially impact upon the thermal regime of the landscape and thus modify the climate of urban areas. Increased temperatures in such places may significantly alter many aspects of all kinds of life, including biodiversity [3,4], population health [[5], [6], [7], [8]] and overall sustainability of urban areas [9,10]. Considering that global climate change has led to increases in summer climate indices in many urbanized regions, including central Europe [[11], [12], [13], [14], [15]], the importance of studying any additional heat load in urban areas at a local scale is currently of especially pressing importance.
Higher land surface temperatures (LSTs) in urban landscapes in comparison with surrounding (rural or natural) landscapes occur in particular response to lower albedo, higher degree of sealing and anthropogenic heat emission [16]. Compact areas of increased LST in urban areas give rise to a phenomenon known as the “surface urban heat island” (SUHI), something that has been the subject of many studies in recent decades [[17], [18], [19]]. Several investigations have demonstrated that the intensity and character of SUHI greatly influence the air temperature conditions of a city, contributing to the formation of UHIs – air-temperature-based urban heat islands [[18], [19], [20], [21], [22]]. However, the study of temperature differentiation between urban surfaces has long been limited to description of differences between urban, suburban and rural landscapes/areas [18]. This has reflected the comparatively low spatial resolution of the sensors used, as well as the absence of a uniform and generally-recognized classification of urban cover/use types appropriate to LST analyses. Conventional land use/cover classifications, e.g. CORINE Land Cover [23] 500-m MODIS Land Cover Maps [24], USGS Global Land Cover [25] and GlobeLand30 [26] have not permitted discrimination within the diversity of the urban building environment, whereas urban landscape classification has not reacted to the climatic perspective of the urban landscape [27]. This apparent impasse has recently been resolved, at least in part, by classification based on the concept of local climatic zones (LCZs) [28].
LCZs consist of areas of uniform surface cover structure, material, and human activity. They may cover areas ranging from hundreds of meters to several kilometers on the horizontal scale. There are several advantages associated with the LCZ concept as an approach to analysis: it is a global classification scheme with a limited number of classes (10 built and 7 land-cover types) and these classes are defined by physical properties of the urban environment. Although LCZs were originally conceived for the study of air temperature in urban and suburban landscapes, based on an assumption that each LCZ has a generally unique building height and density, as well as some predominant types of building material (concrete, masonry, metal, glass, wood, etc.), that provide different emissivity, the concept was soon recognized as being particularly suitable for studying LST variation in the urban landscape.
Indeed, recent studies have confirmed assumptions of significant or substantial LST differences among LCZs [[29], [30], [31]].
With the adoption of the LCZ concept for LST/SUHI studies, however, further research perspectives and pending queries have emerged, including the effects of day period, seasonality and/or thermal anisotropy on LST variability in particular LCZs [30]. With respect to the results of studies analyzing air temperature variability in LCZs [13,32,33], it is natural to assume that times of day and seasons generate different effects on LST in given LCZs. Indeed, Geletič et al. [30] presented diurnal/nocturnal LST differences between LCZs in the city of Brno (Czech Republic) and Wang et al. [34] recently reported on diurnal/nocturnal LST differences between LCZs in Phoenix (Arizona) and Las Vegas (Nevada). Seasonal variations of LST among LCZs have been covered recently by Gémes et al. [35], who used a single scene for February, May, July and September to express seasonal LST in LCZs in Szeged (Hungary). Further, Ziaul and Pal [36] presented figures from which seasonal variations in LST in the LCZs of English Bazar (India) may be identified.
While the first efforts to quantify seasonal variability of SUHI within the methodical framework of the LCZ concept are emerging, several studies have recently addressed seasonal variation in SUHI and/or seasonal differences of LST between land-cover/use in diverse world regions [[37], [38], [39], [40], [41], [42], [43], [44]]. The sheer quantity and scope of the above contributions presumably demonstrate the existence and importance of seasonal effects in SUHI analyses.
Based on the review above, we assume that wider recognition and employment of the LCZ concept in studies addressing SUHI analyses help to standardize research and facilitate further progress towards more advanced knowledge of the influence of factors such as building structure and material or vegetation state and type down to micro–mezzo-scale climate patterns.
Therefore, this study takes up the challenge of studying SUHI defined on the basis of LCZs and analyzing seasonal differences of LST among particular LCZs. More precisely, the aims of this study are to: i) investigate SUHI based on LCZs, ii) analyze seasonal variations of LST among LCZs and iii) analyze seasonal differences in the intra-zonal variability of LCZs A–C by imposing new subclasses, using the central European cities of Prague and Brno (Czech Republic) and Novi Sad (Serbia) as examples.
Section snippets
Study areas
In the Czech Republic, the study covers the two largest cities: Prague, the nation's capital and Brno, the regional capital of the eastern province of Moravia. It also takes in Novi Sad, the second-largest city in Serbia (Fig. 1; Table 1).
There are few marked differences in urban morphology and topography between the three cities. Brno and Prague both lie among hills. Their elevations range from under 200 m to over 500 m in Brno and under 200 m–400 m in Prague, while Novi Sad lies in the
Local climate zones and subclasses
The landscape classification used herein consists of 17 standard LCZs, of which 15 are defined by surface structure and cover and two by construction materials and anthropogenic heat emissions. The standard set is divided into “built types” 1–10, and “land-cover types” A–G [28]. Contained within the specialist literature are approach methodologies to LCZ mapping based on: (1) manual [46], (2) GIS-based [47,48], (3) remote sensing [49] and (4) combined methodology [50,51]. Since the satellite
Seasonal differences of LST in LCZs – SUHI intensity
SUHI was not well developed in the winter season in a few cases. In Prague and Novi Sad, LCZ G – in both cities dominated by substantial rivers – was the warmest LCZ in the winter season. Further, in Novi Sad the mean LCZ A DIF of LCZ D was even higher than LCZ A DIF of all built LCZs (“SUHI”). In these cases, we cannot, therefore, strictly speaking, use SUHI in significant discussion. Further, in Novi Sad only small differences in LCZ A DIF among built types of LCZ appeared. A higher LCZ A DIF
Discussion
The results of this study raise several important points and pending issues in SUHI analyses, as well as in the concept of Local Climate Zones, that should be discussed further. However, the advantages and disadvantages of the methods employed herein the must be given careful consideration before any further elaboration.
Conclusions
Urban areas of Prague, Brno (Czech Republic) and Novi Sad (Serbia) and their hinterlands were classified in terms of LCZs and subsequently analyses were performed as to their seasonal SUHI variability. On the assumption that SUHI patterns are strongly influenced by different vegetation situations, additional LCZ subclasses were delineated for three land-cover zones (LCZs A−C). The additional classification system had 11 subclasses, and 10 of them were delineated in the three cities analyzed.
Acknowledgements
This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Program I (NPU I), grant reference LO1415 and with institutional support, ref. RVO: 67985807. A partial contribution was made by the Ministry of Education, Science and Technological Development of the Republic of Serbia through project no. 176020. This work was supported by an internal grant from Palacký University, Olomouc ref. IGA_PrF_2018_018 “Regions and Cities:
References (81)
Landscape change and the urbanization process in Europe
Landsc. Urban Plann.
(2004)- et al.
Urban and rural mortality rates during heat waves in Berlin and Brandenburg, Germany
Environ. Pollut.
(2011) - et al.
Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)
Proc. Environ. Sci.
(2010) - et al.
Thermal remote sensing of urban climates
Remote Sens. Environ.
(2003) - et al.
Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators - an application for the city of Leipzig (Germany)
Ecol. Indicat.
(2012) - et al.
A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30m continuous field tree cover data
Remote Sens. Environ.
(2015) - et al.
Analyzing control of respiratory particulate matter on Land Surface Temperature in local climate zones of English Bazar Municipality and Surroundings
Urban Climate
(2018) - et al.
Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures
Remote Sens. Environ.
(2011) - et al.
Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas
Remote Sens. Environ.
(2013) - et al.
Using Local Climate Zone scheme for UHI assessment: evaluation of the method using mobile measurements
Build. Environ.
(2015)
GIS-based mapping of Local Climate Zone in the high-density city of Hong Kong
Urban Climate
The impact of tree cover loss on land surface temperature: a case study of central Massachusetts using Landsat Thematic Mapper thermal data
Appl. Geogr.
The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring
Remote Sens. Environ.
Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology - a case study of Yangtze River Delta, China
Urban Climate
Comparison of the urban heat island intensity quantified by using air temperature and Landsat land surface temperature in Hangzhou, China
Ecol. Indicat.
Wind observations above an urban river using a new lidar technique, scintillometry and anemometry
Sci. Total Environ.
Heat waves and urban heat islands in Europe: a review of relevant drivers
Sci. Total Environ.
Remote sensing of the urban heat island effect across biomes in the continental USA
Remote Sens. Environ.
Peak power and cooling energy savings of shade trees
Energy Build.
Assessment of the microclimatic and human comfort conditions in a complex urban environment: modelling and measurements
Build. Environ.
Role of street trees in mitigating effects of heat and drought at highly sealed urban sites
Landsc. Urban Plann.
Street greenery and its physical and psychological impact on thermal comfort
Landsc. Urban Plann.
A review of benefits and challenges in growing street trees in paved urban environments
Landsc. Urban Plann.
Urban greening to cool towns and cities: a systematic review of the empirical evidence
Landsc. Urban Plann.
Land use patterns, temperature distribution, and potential heat stress risk - the case study Berlin, Germany
Comput. Environ. Urban Syst.
Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation
ISPRS J. Photogramm.
Land Cover CCI: Product User Guide (Version 2.0)
Effects of urban structure on plant species richness in a large European city
Urban Ecosyst.
Effects of settlement size, urban heat island and habitat type on urban plant biodiversity
Landsc. Urban Plann.
Heat stress and public health: a critical review
Annu. Rev. Public Health
The urban heat island and its impact on heat waves and human health in Shanghai
Int. J. Biometeorol.
Heat wave risk assessment and mapping in urban areas: case study for a midsized Central European city, Novi Sad (Serbia)
Nat. Hazards
Urban heat island research from 1991 to 2015: a bibliometric analysis
Theor. Appl. Climatol.
Intra-urban temperature observations in two Central European cities: a summer study
Idojaras
Projection of drought-inducing climate conditions in the Czech Republic according to Euro-CORDEX models
Clim. Res.
Employing an urban meteorological network to monitor air temperature conditions in the ‘local climate zones’ of Szeged, Hungary
Int. J. Climatol.
Climate change scenarios of heat waves in Central Europe and their uncertainties
Theor. Appl. Climatol.
Spatial modeling of summer climate indices based on local climate zones: expected changes in the future climate of Brno, Czech Republic
Clim. Change
The energetic basis of the urban heat island
Q. J. Roy. Meteorol. Soc.
Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology
Int. J. Remote Sens.
Cited by (114)
Contrasting moist heat across local climate zones in heat and non-heat waves: Insights from 29 Chinese metropolises
2024, Building and EnvironmentSeasonal surface urban heat island analysis based on local climate zones
2024, Ecological Indicators