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Influence of diversified relief on the urban heat island in the city of Krakw, Poland



In cities located in concave landforms, urban heat island (UHI) is an element of a complicated thermal structure and occurs due to the common impact of urban built-up areas and orography-induced processes like katabatic flows or air temperature inversions. Kraków, Poland (760,000 inhabitants) is located in a large valley of the river Vistula. In the years 2009–2013, air temperature was measured with the 5-min sampling resolution at 21 urban and rural points, located in various landforms. Cluster analysis was used to process data for the night-time. Sodar and synoptic data analysis provided results included in the definition of the four types of night-time thermal structure representing the highest and the lowest spatial air temperature variability and two transitional types. In all the types, there are three permanent elements which show the formation of the inversion layer, the cold air reservoir and the UHI peak zone. As the impact of land use and relief on air temperature cannot be separated, a concept of relief-modified UHI (RMUHI) was proposed as an alternative to the traditional UHI approach. It consists of two steps: (1) recognition of the areal thermal structure taking into consideration the city centre as a reference point and (2) calculation of RMUHI intensity separately for each vertical zone.
Influence of diversified relief on the urban heat island in the city
of Kraków, Poland
Anita Bokwa
&Monika J. Hajto
&Jakub P. Walawender
&Mariusz Szymanowski
Received: 14 October 2014 /Accepted: 20 July 2015
#The Author(s) 2015. This article is published with open access at
Abstract In cities located in concave landforms, urban heat
island (UHI) is an element of a complicated thermal structure
and occurs due to the common impact of urban built-up areas
and orography-induced processes like katabatic flows or air
temperature inversions. Kraków, Poland (760,000 inhabitants)
is located in a large valley of the river Vistula. In the years
20092013, air temperature was measured with the 5-min
sampling resolution at 21 urban and rural points, located in
various landforms. Cluster analysis was used to process data
for the night-time. Sodar and synoptic data analysis provided
results included in the definition of the four types of night-
time thermal structure representing the highest and the lowest
spatial air temperature variability and two transitional types. In
all the types, there are three permanent elements which show
the formation of the inversion layer, the cold air reservoir and
the UHI peak zone. As the impact of land use and relief on air
temperature cannot be separated, a concept of relief-modified
UHI (RMUHI) was proposed as an alternative to the tradition-
al UHI approach. It consists of two steps: (1) recognition of
the areal thermal structure taking into consideration the city
centre as a reference point and (2) calculation of RMUHI
intensity separately for each vertical zone.
1 Introduction
The concept of urban heat island (UHI) has been well known
since the nineteenth century; however, numerous methodolog-
ical issues are still the subject of discussion. It is widely ac-
cepted that UHI, i.e. the occurrence of higher air temperature
in urbanized areas than in rural areas, is the effect of combined
influences mainly of changes to the surface geometry in the
built environment due to land use changes and emission of
anthropogenic heat. Lowry (1977) claimed that the value of a
meteorological element measured in a city consists of three
components: general climatic background, impact of local
conditions (e.g. landform and land cover) and impact of urban
areas. One of the local conditions is the impact of the relief on
air temperature in the local scale. Goldreich (1984,2009)
summarized studies on that topic and stated that particular
authors neglected the relief impact or included it only in a
qualitative dimension. Other approaches comprised either
elimination of the landform impact with the use of statistical
procedures or emphasized the role of relief in a study as an
important factor controlling the local climate. Unfortunately,
the last option has not been popular so far. Good examples of
the comprehensive approach are Nkemdirim (1980), Kuttler
et al. (1996) and Suomi and Kayhko (2012), who studied the
impact and spatial range of katabatic flows in cities of various
sizes, located in mountainous valleys (Calgary, Stolberg,
Turku, respectively). Besides, UHI formation and intensity
in cities located in the mountains were studied, e.g. in
Switzerland by Roten et al. (1984; for Fribourg) and Wanner
and Hertig (1984; for Basle, Biel, Berne), in Austria by Lazar
and Podesser (1999; for Graz) and Rupnik (2003;for
Salzburg) or in Slovenia by Hocevar and Petkovsek (1995;
for Ljubljana). Saaroni and Ziv (2010) analysed long-term
changes in UHI intensity in Beersheba, Israel, and took into
account the impact of topographic parameters on permanent
*Anita Bokwa
Institute of Geography and Spatial Management, Jagiellonian
University, 7 Gronostajowa St., Kraków, Poland
Centre for Hydrological and Meteorological Measurement Service,
Satellite Remote Sensing Department, Institute of Meteorology and
Water Management - National Research Institute, 14 Borowego St.,
Kraków, Poland
Institute of Geography and Regional Development, University of
Wrocław, 1 Uniwersytecki Sq., 50-137 Wrocław, Poland
Theor Appl Climatol
DOI 10.1007/s00704-015-1577-9
factors controlling the phenomenon. Ketterer and
Matzarakis (2014) studied the bioclimate of Stuttgart
in relation to UHI and stressed the possible combined
impact of orography and land use on the spatial vari-
ability of the urban microclimate.
As shown by Stewart (2011a,b) for example, the popular
use of ΔT
(i.e. air temperature difference between an urban
and a rural station) to define and quantify the UHI magnitude
is weakening the philosophical and methodological integrity
of the literature. A solution proposed by that author is the
application of a local climate zones (LCZs) scheme. Stewart
also described criteria which have to be considered to ensure
reliable results of UHI studies. One of them is the diversified
relief, a factor forcing, for example, the occurrence of process-
es like katabatic flows or cold air lake formation. Stewart and
Oke (2012) suggested paying special attention to such factors
which exert a combined effect with the LCZs, and to include
them in the quantitative analysis.
The main research problem of this paper is rooted in
the concept developed by Lowry (1977), mentioned
above, who distinguished three components affecting
air temperature in urban areas: general climatic back-
ground, local conditions (including relief) and impact
of urban areas. In cities located in large valleys, like
Kraków, urban areas and surrounding rural areas are
located in various landforms (e.g. valley floor, slopes,
hilltops). Additionally, in terms of population, Kraków
is a much larger city than most of the settlements al-
ready mentioned above where the studies on UHI influ-
enced by relief impact were conducted (e.g. Stolberg 60,
000; Turku 175,000; Fribourg 50,000; Graz 226,000;
Salzburg 143,000; Ljubljana 300,000 inhabitants in the
year of publication). Therefore, the aim of the present
paper is an attempt to first separate the impact of two
factors distinguished by Lowry (relief and land use/land
cover) on air temperature, in a local scale (the third
factor mentioned by Lowry, i.e. general climatic back-
ground, was considered to be uniform for the whole
study area). Then, UHI intensity was determined, unbi-
ased by the impact of relief, for various parts of the city
located in different vertical zones. Land use/land cover
features were defined using various indices. The LCZ
scheme does not include the relief impact; therefore, it
was used only in the initial phase of data analyses.
First, the paper shows the mean air temperature as well
as spatial and temporal variability for selected weather
conditions, in order to highlight the basic methodologi-
cal problems regarding the application of the classic
UHI concept for a city located in a complex relief area.
Then, alternative approaches to the estimation of UHI in
urban areas of diversified relief are presented and the
concept of relief-modified urban heat island (RMUHI) is
2 Study area
Kraków (Cracow) is a city in southern Poland, on the river
Vistula, with an area of 326.8 km
and about 760,000 perma-
nent registered inhabitants (the total number is estimated to
reach one million) (Fig. 1). The city is located in a concave
landform, i.e. in the river valley passing from west to east. The
historical city centre is placed on the bottom of the river
Vistula valley (at about 200 m a.s.l.), on a limestone tectonic
horst (Wawel Hill), emerging from the river valley. To the
north of the river Vistula and the city centre is the Kraków-
Częstochowa Upland, built of limestone and marls, and its
parts located close to Kraków reach up to 300 m a.s.l. The
southern borders of the city run partially in the Carpathian
Foothills, built of Flysch rocks, with an elevation up to
370 m a.s.l. in the area neighbouring Kraków. The river
Vistula valley is narrow in the western part of Kraków (about
1 km) and widens to about 10 km in the eastern part. In the
western part of the valley, there are several limestone tectonic
horsts, reaching about 350 m a.s.l., so the city area is not
surrounded by hills only from the east. The urbanized areas
can be found in the river valley with its terraces and in convex
landforms to the south and north of the city centre. Height
differences between the valley floor and the hilltops next to
the city borders are about 100 m, and the built-up areas do not
reach those hilltops.
Data on land use/land cover (LULC) structure are present-
ed using modified GMES European Urban Atlas classes
(GMES 2010;seeTable1). Within the city borders, built-up
areas (classes 16) cover 43.0 % of the area, while agricultural
and semi-natural areas (class 8) amount to 41.3 %, and the
remaining (green and water) areas cover 15.7 % (Fig. 1c).
In the valley floor, many different land use types can be
distinguished, while in the convex landforms south and north
of the valley, only a few land use types can be found.
Due to the historical development of the city since the early
Middle Ages, buildings with more than four storeys are locat-
ed mainly in the suburbs, and those are districts with blocks of
flats. Areas with compact built-up districts are found mainly in
the old town. In the eastern part of the city, in the river valley
bottom, is the Nowa Huta district with a huge steelworks,
constructed after the Second World War. Originally, the city
occupied only Wawel Hill and its close vicinity; then, it grad-
ually expanded to the river valley floor, but further expansion
to the convex landforms north and south of the city centre took
place as late as the second half of the twentieth century and
that process is still ongoing (Mydel 1996;Zborowski2005).
The local climate of Kraków has been studied by many re-
searchers since the 1960s, mainly in the context of air pollu-
tion dispersion conditions which are very poor, especially in
comparison to other large Polish cities, e.g. Łódź, Warsaw or
Wrocław (Fortuniak and Kłysik 2008;Błażejczyk et al. 2014;
Szymanowski 2004), and have contributed to high pollution
Bokwa A. et al.
Fig. 1 Location of the study area (a), location and division of the measurement points into clusters according to the topographic parameters (b) and land
use/land cover classes in the study area (c). Symbols of measurement points in bas in Table 2;LULCclassesincas in Table 1
Influence of diversified relief on urban heat island in Kraków
concentrations. The location in the valley running from the
west to the east is the reason for significant modification in
the wind direction frequency; winds from the western direc-
tion have the frequency of 21 %, while winds from the east
make up 12 % (Hess 1974). The natural ventilation of the city
is limited due to the location in a semi-basin, and additionally,
air temperature inversions are often formed. Atmospheric
calms are as frequent as 27 % (Hess 1974). The inversions
frequently last for 24 h or more, especially in winter. Among
numerous studies, Lewińska et al. (1982)publishedtheresults
of a 3-year project (19751978) aimed at evaluating the spa-
tial distribution of the main mesoclimate characteristics in
Kraków. UHI magnitude was estimated to reach 2 K, and it
was determined as the minimum air temperature difference
between the city centre and a rural measurement point, both
located in the valley floor, as at that time many contemporary
built-up areas located elsewhere did not exist. Air temperature
inversions were studied in Kraków using various techniques,
but the most complete information was obtained using sodar.
Results of the multi-annual sodar measurements, which have
been conducted in Kraków since 1980 by the Institute of
Meteorology and Water Management, can be found in publi-
cations by Walczewski and Feleksy-Bielak (1988)and
Walczewski (1989,1994), for example, and the outcomes
show that on average during over 60 % of nights of the year,
ground air temperature inversions were observed.
The present study is based on the following reasoning.
In the studies on UHI, it is essential to obtain measure-
ments which show air temperature differences due to
land use/land cover modifications induced by processes
of urbanization. In Kraków, however, relief is an impor-
tant local climate factor, in spite of relatively small
height differences in the study area, as it forces, for
example, the formation of a cold air lake in the valley
floor and air temperature inversions. Therefore, air tem-
perature differences within the city and in rural areas
around the city are formed in different parts of the
valley due to various factors. Consequently, UHI is only
a part of the spatial thermal mosaic, modified by the
local climate factors.
3.1 Location of measurement points
Following the assumptions mentioned above, a network
of 21 permanent measurement points was established in
the area of Kraków and its vicinity (Fig. 1), in the years
20072009. The measurements were designed so as to
obtain information on screen-level temperatures, and
they have been performed according to the recommen-
dations of Oke (2004). The locations of the measure-
ment points were chosen to represent three vertical
floor (separately on the northern and southern slopes)
and hilltops (about 100 m above the river valley bot-
tom, separately on the northern and southern sides of
the valley). One rural point was established in each of
the first two vertical zones; for the hilltops, two rural
points on each side of the valley (N and S) were orga-
nized, and in the case of urban points, their number in
particular zones depended on the number of land use
types to be found there. Additionally, the division of
the city into western and eastern parts, i.e. narrow and
wide parts of the valley, was carried out.
Tabl e 1 Land use/land cover
structure in Kraków (reclassified
on the basis of GMES European
Urban Atlas, GMES 2010)
Class Item Total area (km
) Share (%)
1 Continuous urban fabric 27.0 8.3
2 Discontinuous dense urban fabric 36.3 11.1
3 Discontinuous detached urban fabric 5.8 1.8
4 Industrial, commercial, public, military and private units 44.5 13.6
5 Transportation areas 19.2 5.9
6 Other artificial areas 7.5 2.3
7 Green urban areas 24.1 7.3
8 Agricultural and semi-natural areas 134.9 41.3
9 Forests 21.0 6.4
10 Waters 6.5 2.0
Kraków 326.8 100.0
GMES European Urban Atlas classes included in the classes used in Table 1: class 1: 11100; class 2: 11210; class
3: 11220, 11230 and 11300; class 4: 12100; class 5: 12210, 12220, 12230 and 12400; class 6: 13100, 13300 and
13400; class 7: 14100 and 14200; class 8: 20000; class 9: 30000; class 10: 50000. Data source:
GMES European Urban Atlas PL003L v.1.1 (available on-line at:
Bokwa A. et al.
3.2 Land use/land cover and relief characteristics
The following urban land use types weredistinguished: blocks
of flats, residential built-up areas, compact built-up areas (with
street canyons), urban green areas and water bodies (Bokwa
2010). The network was organized before the first publica-
tions of the LCZ scheme (e.g. Stewart 2009, Stewart and
Oke 2009), but the scheme (Stewart 2011a,StewartandOke
2012) is included in the description of measurement points, so
as to improve further analyses or comparisons (Table 2).
However, in some areas of the study area, spatial variability
of LULC can be observed, due to gradual changes in the area
functions in the last few centuries. Therefore, in case of some
measurement points, two dominating LCZs were suggested.
The source area was defined as the 500-m buffer around each
point. For the buffered areas, land use and relief characteristics
were extracted. The percentage share of different land use
types within the 500-m radius of each point was calculated
on the basis of the European Urban Atlas (GMES 2010). For
each point, the sky view factor (SVF) was estimated using
fish-eye photos, processed with BMSky-view software
(Rzepa and Gromek 2009;Bokwa2010). The measurement
point locations were chosen so as to be representative in terms
of SVF values for the majority of the surrounding area. Other
parameters included in the LCZ classification were estimated
from in situ observations (aspect ratio, building height) or
accepted as given in Stewart 2011a.
The second group of source-area characteristics was de-
rived on the basis of a 30-m-resolution digital elevation model
(DEM) of the study area. It consists of a few common and
specific DEM derivatives:
&Absolute height.
&Relative height, calculated using an BAltitude above chan-
nel network^tool implemented in SAGA GIS software
&Topographic position index (TPI; http://www.jennessent.
com/arcview/tpi.htm), describing topographic position
(concave/convex, slope, flat) of each point in the study
area. For the purpose of this paper two horizontal ranges
were taken into consideration for the calculations: 500 and
2000 m from each point.
&General slope inclination (SLP) calculated for 1-km-
resolution DEM (resampled from the original 30-m
All indices described above were calculated for each mea-
surement point in order to characterize the relief features in
detail. Cluster analysis using the Ward method (Everitt et al.
2001) was used to group the points, each of them character-
ized with the values of the indices mentioned. Cluster analysis
was also used in the further procedure. The division into to-
pographic clusters obtained is shown in Fig. 1b. Six clusters
can be distinguished. Cluster 1 contains points A, 2, 3, 4, 5, 6,
7, 9 and 10, all of them located in the valley floor, with relative
height from 1 to 4 m, negative values of TPI for the 2-km
buffer and SLP below 0.4. In cluster 2, there are points 1, 8
and 14 located in the valley floor and 50 m above it and their
common feature is TPI for the 2-km buffer reaching values of
45. Cluster 3 consists of points B, C, 12 and 13, with TPI
values for the 2-km buffer from 3 to 12 and all located about
50 m above the valley floor. Cluster 4 is composed of two
points, 11 and D, located in the northern part of the study area,
50 and 70 m above the valley floor, respectively; both have
negative TPI values and much higher SLP values (>1) than
other points, as in the regional scale they are located on the
huge slope of the Kraków-Częstochowa Upland. In cluster 5,
there are points E and F, i.e. two hilltops, and cluster 6 in-
cludes only one point, G, at the highest location. For points E,
F and G, the TPI values for the 2-km buffer are the highest,
from 26 to 67. The division shows that some measurement
points which belong to a certain vertical zone (valley floor,
slope, hilltop) have specific relief features, e.g. point G is
located about 100 m higher thanother hilltop points and there-
fore is included in a separate cluster, and point 1 is located
about 10 m higher than most other points in the valley floor,
which is the reason for its exclusion from cluster 1. The land
use/land cover features and landform features were used fur-
ther in the air temperature data interpretation as the LCZ
scheme alone could not be used.
Tab le 2presents the main topographic and land use/land
cover parameters of the measurement points shown in Fig. 1.
3.3 Measurement data
Each measurement point has been equipped with an air tem-
perature data logger. In 16 points, HOBO data loggers have
been operating (HOBO® PRO series Temp Data Logger,
Onset Computer Corporation, Pocasset, MA, USA; operating
range T,30 to 50 °C; resolution, 0.2 °C between 0 and
40 °C), and in five points, those have been MINIKIN data
loggers (EMS Brno, Czech Republic, operating range T,30
to 60 °C; accuracy 0.2 °C between 0 and 40 °C); all of them
were supplied with naturally ventilated solar radiation shields.
The loggers were located 24 m above the ground, depending
on the local conditions and safety demands. Air temperature
values were recorded every 5 min.
The data used come from 308 nights in the period
September 2009August 2013. The night-time was defined
as the time span starting 2 h before sunset and lasting for 5 h
after sunset. Therefore, we have taken into consideration only
the first phases of UHI development, i.e. from its formation to
stabilization. According to other studies (e.g. Oke 1987; Hage
1972; Camilloni and Barrucand 2012; Erell and Williamson
2007,Holmeretal.2007; Linden 2011; Bohnenstengel et al.
2011), maximum UHI intensity is observed around 35hafter
Influence of diversified relief on urban heat island in Kraków
sunset although there are also studies which show that the
maximum intensity can be observed just before sunrise (e.g.
Jauregui et al. 1992; Fortuniak and Kłysik 1998). Preliminary
analysis of the data used in the present study showed that
within the time span of 5 h after sunset, the maximum air
temperature differences in the study area are most often ob-
served. During the selected nights, after sunset, cloudiness did
not exceed 4 oktas and the wind speed was below 4 m s
according to the meteorological data from the Kraków Airport
synoptic station located in Balice about 11 km west of the city
centre (241 m a.s.l.; Fig. 1). For the nights chosen, the com-
plete measurement data were available from at least 19 points
(for 174 nights, the data were available for 21 measurement
points; for another 76 nights, for 20 points; and for another 58
nights, for 19 points).
The results obtained were interpreted using additional me-
teorological data (from the Kraków Airport synoptic station)
and sodar data. The sodar data were used in the study as they
Tabl e 2 Land use/land cover and topographic characteristics of the measurement points used in the study
Measurement points Altitude
[m a.s.l.] SVF Land
use type LULC classes bLCZ c
Valley floor
A Jeziorzany 211 0.956
Theatre 215 0.695
St. 204 0.457
district 203 0.605
Blocks of
4 Szkolne district 205 0.695
Blocks of
5 Bema St. 208 0.822
meadows 203 0.711
areas D/B/2
Garden 206 0.690
areas B/2
St. 222 0.788
areas B/D
9 Wandy bridge 197 0.939
10 Przylasek
Rusiecki 190 0.690
North slopes 50 m above the valley floor
Bokwa A. et al.
allow information to be obtained about air temperature inver-
sions, an important phenomenon controlling the local climate
of Kraków. The present sodar measurement station has
worked continuously since May 2010 and is located in the
eastern part of the city, in the Nowa Huta district, close to
the steelworks (198 m a.s.l.; Fig. 1c).
Sodar equipment which provided the acoustic sound-
ings data was developed in the Institute of Meteorology
and Water Management, Kraków Branch (Walczewski
1989). The latest model of a sounder, called the vertical
Doppler sodar, has been working with a few breaks
from 1994 until now. A sodar loudspeaker emits every
6 s a 0.8-kW sound pulse of 1.6-kHz frequency lasting
60 ms. The loudspeaker is also a microphone which
receives an acoustic backscatter signal (echo).
Subsequently, the amplified echo is recorded and im-
aged as an echogram. The range of the sodar echo is
1 km. Sodar echograms are analysed manually and vi-
sually. Such analysis enables determination of thermal
convection vertical structures and horizontal structures
of air temperature inversion layers (ground-based or el-
evated). Their heights are normally determined as
Tabl e 2 (continued)
B Modlniczka 258 0.975
11 Ojcowska St. 245 0.809
South slopes 50 m above the valley floor
C Rzozów 251 0.968
12 Czajna St. 258 0.838
13 Bojki St. 252 0.691
Blocks of
14 Mała Góra St. 231 0.859
Blocks of
North hilltops 100 m above the valley floor
Murowana 270 0.975
E Kocmyrzów 299 0.968
South hilltops 100 m above the valley floor
F Libertów 314 0.785
G Chorągwica 436 0.940
Symbols like in Fig. 1: numbers 1, 2, 3, etc. indicate urban measurement points; letters A, B, C, etc. indicate rural measurement points
In the areas of 500-m radius around each measurement point, legend of LULC classes like in Fig. 1and Tab. 1
Influence of diversified relief on urban heat island in Kraków
hourly averages. A schematic diagram of the sodar sys-
tem and an example of a 24-h echogram is presented by
Zimnoch et al. (2010).
In the present study, the sodar data from the period
May 2010August 2013 were used. In the whole period, sodar
data were available for 216 nights (i.e. 70 % of the 308 nights
originally selected for analysis), and in particular for 105
nights when air temperature data were available for 21 mea-
surement points, for 74 nights when air temperature data were
available for 20 points and 37 nights when air temperature
data were available for 19 points. As the sodar measurements
were not available for the whole study period, they could only
be used for some additional analyses.
3.4 Methods of data analysis
The data gathered were used first to recognize the basic fea-
tures of the spatial variability of air temperature during the
night-time in the study area. The mean night-time air temper-
ature course was calculated for each point, for all nights. The
courses were grouped using cluster analysis (Ward method).
The results obtained allowed the reference point for further
analysis to be chosen. Then, night-time air temperature spatial
patterns and temporal phases were established. Air tempera-
ture differences between the reference point and all other
points were calculated for each night considered. Next, for
each night, a sequence of the difference courses was com-
posed, using always the same order of stations. All the nights
were grouped using cluster analysis with the k-means method
in order to obtain types of spatial and temporal structure of air
temperature differences in the study area. For each cluster
(structure type), mean courses of air temperature differences
were calculated for each point. Next, hourly averages of the
differences were computed and then the averages from select-
ed 1-h intervals were mapped to present spatially and tempo-
rally the structure types distinguished. The final outcomes
allowed proposals to be formulated concerning UHI and its
intensity definitions for cities located in areas with complex
relief. Application of particular methods allowed to obtain
results which determined next steps of analysis; therefore,
further details concerning methods are provided in Section 4.
4.1 Night-time air temperature spatial patterns
Many studies on UHI showed that urban and rural areas not
only have different air temperatures during the night-time, but
they also differ significantly in the air temperature courses
during the night (e.g. Ketzler et al. 2006). Therefore, in the
first step, for each measurement point, the mean night-time air
temperature course was calculated, using data for 174 nights
(i.e. data from all points were available). In particular, 12
nights occurred in winter (Dec.Feb.), 40 nights in spring
(Mar.May), 78 nights in summer (Jun.Aug.) and 44 nights
in autumn (Sep.Nov.). The mean air temperature courses
were grouped with the Ward method (Fig. 2) in order to see
whether the land use/land cover features, characteristic for
urban and rural points, respectively, turn out to be decisive
in controlling the night-time air temperature changes, as is
the case of flat areas.
The decision on how many clusters should be finally dis-
tinguished is always subjective, even though there are formu-
las which can be used in the initial phase of analysis to esti-
mate the number of clusters which are significantly different
from one another. The application of objective methods used
to determine the number of clusters, developed by e.g. Stanisz
and Grabiński or Mojena (after Panek 2009), allows only two
clusters to be obtained, i.e. the cluster tree should be cut at the
binding distance 40. However, such a division seems to be too
coarse as the first cluster is composed of urban points located
in the valley floor (numbers 1, 2, 3, 4, 5, 7) and urban points
representing areas with blocks of flats at the slopes (13, 14),
but three rural points located at the hilltops (E, F, G) are in-
cluded, too. The second cluster is composed of rural points
located in the valley floor and on the slopes (A, B, C, D),
urban points with residential built-up areas on the slopes (11,
12) and green/water areas in the valley floor (6, 8, 9, 10). That
division shows that the air temperature in the study area is
controlled by various factors which influence particular sites
with different intensities. In the first cluster, points with the
most intensive built-up areas are grouped, regardless of the
landform, but three rural stations at the hilltops are included
as well. In the second cluster, points with no built-up or sparse
built-up areas can be found, located in the valley floor or on
the slopes. In order to study better in the further analyses the
combined impact of the land use and the landform, the divi-
sion into six clusters is proposed. The objects grouped in the
Fig. 2 Cluster analysis result for mean nocturnal air temperature courses
at the measurement points in Kraków and its vicinities, for selected nights
in the period 20092013. Colours of the clustersnumbers correspond to
colours used in Fig. 3
Bokwa A. et al.
analysis were mean nocturnal air temperature courses, calcu-
lated for the data from only chosen nights, with little or no
cloudiness and weak or no wind. It means the data showed
mean conditions during an air temperature inversion situation.
In such situation, the rural points at the hilltops are often above
the inversion layer and therefore, the air temperature there is
higher than in other rural points located lower. In fact, some-
times, it is not much lower than in the urban area in the valley
floor. However, the reason why the temperature is relatively
high in urban areas in the valley floor is the effect of urban
structures, and the reason why the temperature is also relative-
ly high at the hilltops is relief-related factor (i.e. location above
the inversion layer). The urban areas on the slopes are at the
same time urban areas at the suburbs, and in those areas,
sometimes, the land use/land cover factors are dominant,
and sometimes those are relief-dependent factors. Those dif-
ferences can be seen in the division into six clusters.
Figure 3shows the spatial distribution of each cluster
In order to use the data presented above to study UHI, first,
the answer to the following question has to be found: how is
the UHI intensity defined and calculated? In the classic ap-
proach, the reference point is usually a rural location with the
lowest values of air temperature. However, the analysis of the
values in mean night-time air temperature courses, presented
above, showed that there are large differences in thermal
conditions among the rural sites around Kraków, as they are
influenced by various processes linked, for example, to the
inversion layer formation; there is not just one rural station
representative for the whole non-urban area. The first step
towards the definition of UHI in such circumstances was then
the recognition of the thermal structure of the study area, using
air temperature differences, where the reference point was the
one where the air temperature values are always the highest,
i.e. measurement point 1 (Słowackiego Theatre), located in
the valley floor, in the city centre. Table 3shows the main
features of particular clusters.
The cluster analysis presented above shows that some char-
acteristic features of UHI can be seen but they are modified
probably by the processes linked to the relief impact. In the
city centre, located in the valley bottom, which is the area with
the most compact built-up areas, the mean night-time air tem-
perature is the highest, so it has been assigned to the Bpeak^
zone in the UHI scheme (cluster 1). Worth mentioning is the
exclusion of point 6 from cluster 1, as it is a vast, flat, open
green area of 48 ha, and even though it is located close to the
city centre, the air temperature was much lower there than at
nearby urban points. The Bplateau^zone of UHI is not as
uniform as the classic UHI pattern. Cluster 2 represents areas
where mean night-time air temperature is about 1 K lower
than in the peak zone, with blocks of flats located in two
vertical zones and residential built-up areas, but only those
Fig. 3 Spatial distribution of measurement points belonging to particular clusters shown in Fig. 2
Influence of diversified relief on urban heat island in Kraków
located in the valley floor. However, similar mean tempera-
tures are found in rural areas located often above the inversion
layer, on the southern and north-eastern hilltops (points E and
F; cluster 3), with the exception of point G, placed much
higher and influenced by the mesoregional air temperature
pattern. Residential built-up areas located on the slopes (points
11 and 12) were grouped either together with urban green
areas located in the valley floor (cluster 4; point 11) or with
rural points on the slopes (cluster 5; point 12). Such a division
shows that air temperature in areas with residential built-up
districts is more diversified within the city than the tempera-
ture in areas with blocks of flats. While areas with blocks of
flats show similar thermal conditions, regardless of the vertical
zone, areas with residential built-up districts located on the
slopes have significantly lower air temperature than areas with
the same land use but located in the valley floor. Rural points
were included in three clusters, i.e. cluster 6 representing areas
with the lowest night-time air temperature in the whole study
area, located in the valley bottom (so-called cold air lake) and
on the north-western hilltop (points A and D, respectively),
and in clusters 3 and 5, as mentioned above. That pattern is an
indication of frequent air temperature inversions and high het-
erogeneity in the pattern of night-time air temperature spatial
distribution. An interesting feature of the pattern described is
the inclusion of point D, located about 70 m above the valley
floor, in the same cluster as point A, located in the valley floor,
in the extent of the cold air lake. The area located north of the
river Vistula, in the western part of the valley, belongs to a
large landform, i.e. the southern slope of the Jurassic Kraków-
Częstochowa Upland. The slope consists of huge steps and is
cut with deep valleys. Katabatic flows can develop there on a
much larger area and can have larger intensity than inthe areas
located to the south of the river Vistula. Therefore, the cold air
accumulation can occur not only in the valley floor but also on
some slopes and even lower hilltops in the northern part of the
study area.
The results presented above show some spatial features of
the air temperature pattern in Kraków and its vicinity. The next
step towards the modified UHI definition was the recognition
of temporal phases in the air temperature difference courses at
particular points, which are described in the next section.
4.2 Night-time air temperature structure types
In order to recognize the temporal phases in the air tempera-
ture difference courses in the study area, the following proce-
dure was used. The differences between measurement point 1
and the other points were used. Point 1 was used asa reference
point following the results presented in the previous section.
For each night, the air temperature difference courses were
arranged into one numerical sequence, always following the
same order of stations. The k-means method of cluster analysis
was used to group nights, each of them defined with such a set
of air temperature difference courses. That procedure was re-
peated three times, for the datasets containing air temperature
records from 21, 20 and 19 measurement points. For each
dataset, the analysis was conducted for the assumptions of 3,
4 and 5 clusters distinguished. For further analyses, the divi-
sion into four clusters was chosen. The four groups of mean
air temperature difference courses for particular points repre-
sent four types of spatial and temporal structure of air temper-
ature difference during the night-time in Kraków and its vi-
cinity. Data for 21 measurement points were taken for further
processing in order to present the most complete dataset; the
results obtained were very similar to those for 20 or 19 points.
Figure 4presents mean courses of air temperature differences
for particular points calculated for the clusters (structure types)
distinguished. Figure 5shows hourly averages of air
Tabl e 3 Mean air temperature differences at the night-time between
point no. 1 and other points (mean dT), standard deviation for the
averaged night-time air temperature difference course (SD) and the
difference between the highest and the lowest values in the averaged
night-time air temperature difference course (ΔdT), in particular
measurement points belonging to the clusters distinguished
Measurement points Mean dT SD ΔdT
Symbol Name
Cluster 1
2 Krasińskiego St. 0.2 0.4 1.3
7 Botanical Garden 0.4 0.6 1.5
Cluster 2
3 Podwawelskie district 1.1 0.7 2.1
4 Szkolne district 1.3 0.4 1.2
5 Bema St. 1.0 0.7 2.3
13 Bojki St. 0.9 0.3 0.9
14 MałaGóraSt. 1.3 0.4 1.3
Cluster 3
E Kocmyrzów 1.2 0.5 1.6
F Libertów 1.2 0.5 1.6
G Chorągwica 1.9 0.7 2.5
Cluster 4
6Błonia 1.6 1.0 2.7
9 Wandy Bridge 1.6 0.8 2.2
10 Przylasek Rusiecki 1.7 0.7 2.1
11 Ojcowska St. 2.0 1.1 3.4
Cluster 5
8 Malczewskiego St. 2.5 0.8 2.8
12 Czajna St. 2.3 0.8 2.6
B Modlniczka 2.4 1.0 3.1
C Rzozów 2.2 1.0 3.1
Cluster 6
A Jeziorzany 3.3 1.8 5.2
D Garlica Murowana 3.7 1.5 4.5
Symbols as in Fig. 1
Bokwa A. et al.
Fig. 4 Mean courses of air temperature differences (in relation to reference point 1) for the clusters distinguished
Fig. 5 Hourly averages of air temperature differences at particular measurement points from three selected 1-h intervals (H0, H2, H4) for the clusters
distinguished. H0 interval between hours 1 and 0 (with sunset as hour 0), H2 interval between hours 1 and 2, H4 interval between hours 3 and 4
Influence of diversified relief on urban heat island in Kraków
temperature differences from three selected 1-h intervals (H0,
H2 and H4) for the clusters (structure types) distinguished, at
particular measurement points. Data presented in Fig. 5show
the gradual development of the spatial pattern of differences
with respect to the four structures distinguished during the
night-time, and the last time interval (H4) is taken further to
characterize the classic UHI elements.
For the four clusters (structure types), accompanying atmo-
spheric conditions and the air temperature inversion layer
were analysed. Figure 6shows mean hourly values of cloud-
iness, wind speed, air temperature and relative humidity, ob-
served in the Kraków Airport synoptic station, while Fig. 7
shows the frequency of wind directions for particular clusters
and hours. Mean hourly height and frequency of the air tem-
perature inversion layer, derived from the sodar data, for par-
ticular clusters are presented in Fig. 8. The long-term sodar
measurements conducted in Kraków (from the 1980s) con-
firmed that the air temperature inversion layer in Kraków
can occur at wind speeds up to 4 m s
, most probably due
to the location of the city in the valley.
The final four types of spatial and temporal air temperature
difference structures can be described as follows:
1. Structure type (cluster) 1with the highest values (Fig. 4)
and the most intensive changes (Fig. 5) in air temperature
differences between the reference point and the other
points. The mean air temperature decrease in the synoptic
station within the 7-h night-time was the largest (about
11 K), and the mean air temperatures were the largest,
too (1424 °C) (Fig. 6). In this structure type, hourly
averages of air temperature differences (dT) for the mea-
surement points vary spatially the most compared to other
distinguished clusters (Fig. 5). In the fourth hour after
sunset (H4), averaged air temperature at point F is similar
to the value at point 1; for points 2, 5 and 7 as well as E
and G, dT is around 1 K; for points 3, 4, 10, 13 and 14 it is
about 2 K; for points 6, 9, 11 and 12 around 3 K; for points
8, B and C about 4 K; and for points A and D about 5 and
6 K, respectively. This structure type occurred when the
mean cloudiness and wind speed after sunset were the
lowest on average, compared to the nights classified to
other structures (i.e. less than 1 okta and less than
, respectively). The type occurred almost exclu-
sively during summer months; of 46 nights included in
that cluster, 40 belonged to the period JuneAugust.
Additionally, atmospheric calms prevailed (about 45 %
for each hour after sunset; Fig. 7). Those results show first
of all the formation of the inversion layer (data for points
E, F, G where after sunset the differences diminish to
reach values close to 0 at the end of the night-time). The
inversion layer after sunset was the lowest compared to
other clusters (between 140 and 180 m a.g.l.; Fig. 8). The
UHI peak zone seems to be limited to the city centre
(point 1 and about 1 K lower temperature at points 2, 5
and 7). The plateau zone of UHI is dispersed and includes
areas with blocks of flats and residential built-up areas,
located in the valley floor and on the slopes (points 3, 4,
13 and 14). Further use of the classic UHI concept is
problematic, especially in the spatial aspect. At the same
time when the air temperature at rural hilltops is almost
the same as in the city centre in the valley floor, the
highest difference values, up to 67 K, occur in the west-
ern rural part of the valley floor (point A) and on the
slopes on the lower hilltops (point D) in the northern part
of the study area during the whole time after sunset, due to
the formation of the cold air lake and katabatic flow, re-
spectively. An interesting feature is that the temperature
difference at point D, located in northern hilltops, about
Fig. 6 Mean hourly values of
cloudiness (a), wind speed (b),
air temperature (c) and relative
humidity (d) for particular air
temperature structure types,
according to the data from
Kraków Airport synoptic station
Bokwa A. et al.
70 m above the valley floor, is about 6 K, while for point
E, located also in northern hilltops but about 100 m above
the valley floor, it reaches only about 1 K. The relief of the
study area shows asymmetry in both N-S and W-E cross-
sections. Point D is placed in the western, narrow (about
1 km) part of the river valley (in W-E cross-section) while
point E in the eastern, much wider (about 10 km) part of
the valley. Additionally, in the north of point D, there are
areas of increasing altitude, in regional scale, as point D is
in the marginal zone of the Kraków-Częstochowa
Upland. Point E, instead, is placed on an extensive pla-
teau. Therefore, at the area where point D is located, kat-
abatic flows are detected indirectly, while various
observations from the area where point E is placed show
no such phenomena (Bokwa 2010). In areas with residen-
tial built-up districts located 50 m above the valley floor
(points 11 and 12), the air temperature differences are
similar to those in urban green areas and at the water areas
in the valley bottom (points 6, 9 and 10), maximum dif-
ference values reach about 3 K, while in rural areas locat-
ed 50 m above the valley floor (points B and C) as well as
other green urban areas located in the valley bottom (point
8), they increase to 4 K most of the night-time.
2. Structure type (cluster) 2with the lowest values (Fig. 4)
and the least intensive changes (Fig. 5) in air temperature
differences between the reference point and the other
points. The mean air temperature decrease in the synoptic
station is the smallest (about 7 K) and so are the air tem-
peratures: 412 °C, compared to other types (Fig. 6). In
this structure type, hourly averages of air temperature dif-
ferences (dT) for the measurement points are the least
spatially varied compared to other distinguished clusters
(Fig. 5). Four hours after sunset (H4), averaged air tem-
perature at point 2 is similar to the value at point 1; for
points 3, 4, 5, 6, 7 and 13, dT is around 1 K; for points 8, 9
and 14 as well as B, E, F and G, it is about 2 K; for points
10, 11, A, C and D around 3 K; and point 12 is about 4 K
colder than point 1. This structure type occurs when the
mean cloudiness after sunset is similar to that of type 1
(less than 1 okta), but the mean wind speed after sunset is
the largest, compared to the nights classified to other types
(clusters), i.e. about 2.5 m s
. The prevailing wind direc-
tion is NE (Fig. 7).Thetypeoccurredmainlyfromau-
tumn to spring; of 27 nights included in this cluster, 21
belonged to the period SeptemberApril. The results in-
dicate that in structure type 2, the UHI peak zone is more
extended than in structure 1 and includes points 1, 2, 3, 4,
Fig. 8 Mean hourly height (a) and frequency (b) of air temperature
inversion layer for particular air temperature structure types, derived
from the sodar data
Fig. 7 Frequency of wind
directions for particular air
temperature structure types and
hours, according to the data from
Kraków Airport synoptic station
Influence of diversified relief on urban heat island in Kraków
5, 6, 7 and 13, i.e. areas with various urban built-up dis-
tricts located in the valley floor. The plateau zone includes
urban green and water areas, located in the river valley
(points 8, 9, 10). Like in the case of structure type 1, the
development of the inversion layer and cold air res-
ervoir can be traced, taking into consideration data
from Fig. 4, but the dT values indicate lower inten-
sity of the process. The mean height of the inver-
ture type 1 (between 160 and 190 m a.g.l.; Fig. 8).
Urban areas located on the slopes cannot be easily
included in any classic UHI zone.
3. Structure type (cluster) 3together with type 4, it pre-
sents the transitional type of structure, between the pat-
terns with the highest and the lowest air temperature
difference values (Figs. 4and 5). In this structure type,
hourly averages of air temperature differences (dT) for
the measurement points were less spatially varied than
in structure type 1 but more than in structure type 2
(Fig. 5). In the fourth hour after sunset (H4) for points
2, 7 and 13 as well as E, F and G, dT is around 1 K; for
points 3, 4, 5 and 14 it is about 2 K; for points 8, 9, 10,
11 and 12 as well as point B around 3 K; for points 6
and C about 4 K; and for points A and D about 5 K.
The mean air temperature decrease within the 7-h period
in the synoptic station was about 8 K (Fig. 6).
Cloudiness and wind speed are rather low after sunset
(less than 1 okta and about 1 m s
the prevailing wind direction is NE, like in type 2, but
the atmospheric calms occur also quite often after sunset
(about 20 %; Fig. 7). There were 52 nights included in
cluster 3 and they were distributed rather evenly in par-
ticular months. Like in the case of structure 1, the de-
velopment of the inversion layer is well seen in data for
points E, F and G. The height of the inversion layer is
similar to that for type 2 (between 160 and 200 m a.g.l.;
Fig. 8). Points 2 and 7 located in the city centre
(representing the UHI peak zone) show the lowest
values of differences (about 1 K). The next group of
points, representing areas with blocks of flats and resi-
dential built-up districts in the valley floor (points 3,
4, 5) is characterized with the difference values
close to 2 K during most of the night-time and
can be regarded as the UHI plateau zone. The third
group of points (8, 9, 10, 11 and 12) can be distin-
guished to represent the UHI slope zone, with the
differences reaching 3 K. Those are points
representing urban green and water areas in the val-
ley floor and residential built-up areas on the slopes.
Points representing rural areas in the valley floor
and 70 m above it (points A and D) are character-
ized by the same relative rate of cooling, but differ-
ence values reach 5 K, showing the cold air lake
formation. Like in the case of structure 2, blocks
of flats located on the slopes cannot be easily in-
cluded in any classic UHI zones.
4. Structuretype(cluster)4the atmospheric features
which significantly distinguish type 4 from the other
types are the highest relative humidity (Fig. 6), the highest
mean shares of SW and W wind directions in particular
hours (Fig. 7) and the largest height of the inversion layer
(about 220 m a.g.l.; Fig. 8). There were 49 nights included
in cluster 4, and, like in the case of cluster 3, they were
distributed rather evenly throughout the year. In the fourth
hour after sunset (H4), averaged air temperature at points
2, 13 and 14 is similar to the value at point 1; for points 3,
4, 5, 7, 9 and 10 as well as E and F, dT is around 1 K; for
points 6, 11 and 12 as well as points C and G it is about
2 K; for points 8 and B around 3 K; and for points A and
D about 4 K. The identification of the classic UHI zones is
complicated again, as in the case of structure 3. However,
some features observed earlier may be seen. The largest
differences (about 4 K) are observed for points A and D,
as usual indicating the formation of the cold air reservoir.
Almost all urban points located in the valley floor could
be included in the peak zone; however, a difference be-
tween the eastern and western parts of the valley can be
seen; and larger difference values are observed in the
eastern one. Inversion layer formation is visible mainly
in the data for points E and F. The UHI plateau zone could
be defined as green areas in the valley floor and residen-
tial built-up areas on the slopes (points 6, 8, 11, 12), but
very slight difference values in the case of areas with
blocks of flats on the slopes can hardly be interpreted as
the UHI peak zone continuation.
In all four structures distinguished, the following elements
can be seen: inversion layer, cold air reservoir, UHI peak zone
and UHI plateau zone. Table 4shows some further details as a
summary of the above structure descriptions.
The spatial and temporal structure types of air temperature
differences, presented in the previous section, were distin-
guished using data only for the nights with low cloudiness
and wind speed. However, the comparison of structures 1
and 2 shows that even the increase in wind speed to
2.5 m s
changes the spatial pattern of air temperature differ-
ences significantly, while mean cloudiness was almost the
same for all structure types. A similar pattern was described
for Lisbon (Alcoforado and Andrade 2006). During calm
nights, the air temperature at the hilltops surrounding the city
was higher than in the city centre, due to inversion formation
and katabatic flows, while during windy nights, the pattern
Bokwa A. et al.
was the opposite. Another factor which has an impact on the
differences in spatial pattern is connected with the relief im-
pact and it is the height of the inversion layer; the largest
difference values (structure 1) were observed when the inver-
sion layer was the lowest. When the inversion layer was the
highest (structure 4), it was also noticed that the wind direc-
tions W and SW prevailed and the UHI peak zone had the
largest spatial extent. So far, there are no studies on the con-
nection between the inversion layer height and wind direction
in Kraków, and studies on the connection between the atmo-
spheric circulation and air pollution, including the role of the
inversion layer, provide only the information that during the
occurrence of atmospheric circulation types with the advec-
tion from south or west (according to the Litynski classifica-
tion), the height of the inversion layer increases, which indi-
rectly supports the results presented above (Godłowska and
Tom asz ewska 2010).
Particular measurement points belong to various types of
LCZ (Table 2). However, the application of the classic UHI
approach seems to be inefficient, i.e. the connection between
LCZ and UHI intensity is not clear cut in this case with addi-
tional relief effects. Points representing, for example, domi-
nating LCZ 4 (nos. 3, 13, 14) are assigned to different UHI
zones in particular structure types (Table 4). Moreover, at rural
points, changes in air temperature differences are in some
cases similar to those in urban points (Fig. 4). There are, how-
ever, three permanent elements which can be found in all
structure types and also in Fig. 4; these are the points where
the air temperature difference courses always show the forma-
tion of the following:
1. The inversion layer: points E, F and G
2. The cold air reservoir: points A and D
3. The UHI peak zone: point 2 and 7
The results presented above show that some measurement
points located in the same LCZ (Fig. 4) show different types
of air temperature difference changes during the night-time,
depending on their orography location (Fig. 2). It can be con-
cluded that in the inner part of the urban area, the orography-
driven phenomena have little impact on air temperature
courses, but the suburbs can be affected significantly. In the
case of inner parts of urban areas, the increased roughness is a
strong barrier for the katabatic flows, so their impact is rather
limited. As shown e.g. by Kuttler et al. (1996) or Nkemdirim
(1980), in case of cities located in narrow, mountain valley,
during clear and calm nights, the cold air that reaches a city
with katabatic flows is first blocked and high air temperature
differences between urban and rural areas are observed.
However, later in the night, the cold air enters a city interior
and the differences decrease. That is not the case in Kraków
where large differences in the valley floor are observed until
sunrise. Air temperature difference changes in the areas with
urban built-up land use located in the valley floor always show
the features characteristic for urban areas, while areas with
urban built-up land use located on the slopes sometimes ex-
perience conditions similar to those occurring in rural areas
(Figs. 4and 5). It means that in the suburban zone, at the
slopes, the katabatic flows enter the built-up areas occasional-
ly. The comparison of data presented in Table 2,Fig.2and
Section 4.2 shows that the impact of topographic and landuse/
land cover factors on night-time air temperature changes is
modified by the influence of local climate features to a large
extent. For example, rural points at the hilltops like E, F or G
can experience air temperatures almost as high as those in
urban areas with dense built-up land use, while other rural
areas with similar land use/land cover features (A and D) are
about 6 K colder than the city centre. As already mentioned in
the previous section, in the description of structure type 1, the
relief of the study area is diversified in both N-S and W-E
cross-sections which is the reason for the larger intensity of
katabatic flow effects in the western part of the city than in the
eastern part. The large diversity of thermal conditions in the
rural areas around Kraków was also observed for the day time
land surface temperature obtained from Landsat satellite data
(Walawender et al. 2014).
In the case of cities like Kraków, the application of the
traditional UHI intensity concept is problematic. The core
assumptionof the UHI intensity is the air temperature increase
in urban areas due only to land use/land cover changes and
impact. Urban areas belonging to various LCZs are compared
with one rural reference point. The first issue to be discussed is
the determination of stations which should be compared to
obtain urbanrural air temperature difference. The well-
Tabl e 4 Selected characteristic features of the air temperature difference structures distinguished
Characteristics Structure (cluster)
Peak UHI zone 1, 2, 5, 7 1, 2, 3, 4, 5, 6, 7, 13 1, 2, 7 1, 2, 3, 4, 5, 7, 9, 10
Plateau UHI zone 3, 4, 13, 14 8, 9, 10 3, 4, 5 6, 8, 11, 12
Special feature Highest difference values Lowest difference values Transitional structure Transitional structure; the eastern
part of the city warmer than the western one
Influence of diversified relief on urban heat island in Kraków
known UHI concept has already been questioned, e.g. in rela-
tion to the cold front passages (Szymanowski 2005)orpartic-
ular land use conditions (Stewart 2011b). In the present study,
the impact of relief parallel to the land use/land cover impact is
the factor to be considered. It is relatively easy to identify in
Kraków the urban core area, located in the valley floor, with
the highest air temperature. However, the rural surroundings
are highly diversified in terms of thermal conditions, from the
cold air reservoir to areas located above the inversion layer;
even the rural areas in the valley floor are quite diversified,
with clear differences between the very cold western part and
the much warmer eastern part. Therefore, it is proposed to
define UHI and its intensity in the context of the whole ther-
mal structure presented above. That task can be realized in two
First, the analyses presented above suggest that it is not
possible to separate the influence of relief from the land use/
land cover impact on air temperature in particular parts of the
city and the valley. The results obtained show that measure-
ment points located in similar relief conditions or in similar
land use/land cover conditions can belong to one cluster or be
included in different clusters. Additionally, the situation
changes with relatively small change in meteorological con-
ditions, which can be interpreted as an interruption of the
impact of land use/land cover factors that are usually rather
stable during calm, cloudless nights. It is a complicated
process, generated by the interaction between relief and
land use/land cover. Thus, we have to reject the hypoth-
esis that these two thermal signals can be separated.
And therefore, we introduce a concept of a relief-
modified urban heat island (RMUHI). It consists of
two steps: (1) recognition of the areal thermal structure
taking into consideration the city centre as a reference
point and (2) calculation of RMUHI intensity separately
for each vertical zone.
Following the already-mentioned concept of Lowry
(1977), air temperature in various parts of a city located in a
valley is a result of large-scale weather conditions modified by
a combined impact of the land use and relief. Rural stations are
located in different thermal conditions, so none of them can be
chosen as a reference point. That is why the reference point
should be the city centre, representing the RMUHI peak zone.
That approach allows the whole thermal structure of the study
area to be seen. However, a methodological problem of
RMUHI intensity arises. As mentioned above, in the classic
UHI approach, the UHI intensity is defined for each urban
LCZ separately, by calculating the air temperature differences
between the rural reference station and a certain urban station.
In the case of RMUHI, data from all urban measure-
ment points are compared with the city centre, i.e. also
an urban point. RMUHI intensity for that part of the
valley where the city centre is located (in the case of
Kraków, it is the valley floor) can be defined as the
difference between the city centre and a rural point lo-
cated in the same landform. However, in the case of the
parts of the city located in other landforms, the second
step of the procedure can be proposed.
The analyses shown in previous sections show that rural
stations present various patterns of air temperature differences.
Additionally, the maximum value of the differences varies a
great deal among the rural stations. That is the effect of the
processes forced by the presence of the diversified relief, e.g.
katabatic flows or inversion layer formation. Those processes
can only partially be traced in the air temperature patterns for
urban points, most probably because of the increase in rough-
ness mentioned above and also because most urban areas are
not affected by the lifted inversion layer, often forming above
the urban canopy layer. The urban structure shows a large
inertia, and the local mesoclimatic phenomena can modify
the air temperature significantly only in the suburbs.
However, the extent of the modification depends on weather
conditions. According to the goal of the paper described in
Section 1, the results presented show that it is not possible to
clearly separate the impact of the relief from the impact of the
land use/land cover. Still, it seems reasonable to propose the
definition of RMUHI intensity to be established separately for
the vertical zones, i.e. urban stations from particular zones
should be compared with rural stations from the same zones.
Such approach is in accordance with the solutions proposed
by Goldreich (1984,2009) who suggested to establish UHI
intensity in cities with diversified relief using urban and rural
stations located at similar altitude and height above the valley
floor. However, a few important assumptions have to be
highlighted. In each vertical zone, there are different local
climatic processes affecting the air temperature in rural and
urban areas. Air temperature in urban and rural areas in a
particular vertical zone is not affected in the same way by
those processes. Therefore, the RMUHI intensity should be
established separately for each particular vertical zone, but it is
not recommended to compare such magnitudes between the
zones, for the same land use/land cover types. For example, in
Kraków, there are urban areas with blocks of flats located in
the valley floor and on a slope. In order to calculate the inten-
sity of RMUHI, the area with blocks of flats in the valley floor
will be compared with the area of a cold air lake while the
other one with an area experiencing katabatic flow.
Additionally, the first urban area mentioned above is not af-
fected by the local mesoclimatic processes while the other one
might be affected by those processes depending on weather
6 Conclusion
In the case of Kraków, a city located in a large valley, outside
mountain areas, the application of the LCZ and UHI concept
Bokwa A. et al.
is problematic, due to the significant impact of relief-induced
mesoclimatic processes on the thermal structure of the study
area. Therefore, the paper introduces a modification of the
classic approach to UHI definition. The concept of RMUHI
(i.e. relief-modified UHI) is proposed as an alternative solu-
tion. The RMUHI concept focuses mainly on UHI calculation
assumptions, following Lowrys model as mentioned above
showing the common impact of relief and land use/land cover.
Concerning the RMUHI concept, UHI is first presented as an
element of a complex thermal structure influenced by land
use/land cover, topographic features and local climate pro-
cesses forced by the presence of a diversified relief. To show
that complex thermal structure first, the city centre is taken for
the reference point, as it is the only point which always be-
longs to the same element of the thermal structure within the
whole study area, i.e. to the area where the air temperature is
the highest during the night-time. Rural areas surrounding
Kraków are highly diversified in terms of thermal conditions,
due to the influence of the cold air lake and inversion layer
formation, and katabatic flows. Hence, it is not possible to
choose one rural measurement point as a reference point.
Additionally, the thermal structure is sensitive to the relatively
small changes in weather conditions, e.g. the increase in the
wind speed up to 2.5 m s
. Following the above reasoning, in
the second step, the magnitude of RMUHI in Kraków can be
defined as a set of maximum values in night-time mean air
temperature difference courses, calculated separately for the
two vertical zones in which built-up areas are located and for
the four types of the RMUHI structure types distinguished. In
each case, the difference concerns a rural station located in a
certain vertical zone and an urban station representative for a
certain LULC:
1. Valley floor
(a) Dense urban built-up areas in the city centre (air tem-
perature difference between points 1 and A): from
6.6 K (structure type 1) to 3.6 K (structure type 2)
(b) Blocks of flats (points 3 and A): from 3.7 K (struc-
ture type 1) to 2.5 K (structure type 4)
(c) Residential built-up areas (points 5 and A): from
4.1 K (structure type 1) to 2.4 K (structure type 2)
(d) Urban green areas (points 6 and A): from 2.7 K
(structure type 3) to 2.3 K (structure type 4)
2. North slopes 50 m above the valley floor
(a) Residential built-up areas (points 11 and B): from 1.4 K
(structure type 1) to 0.8 K (structure type 3)
3. South slopes 50 m above the valley floor
(a) Blocks of flats (points 13 and C): from 2.6 K (struc-
ture type 1) to 1.7 K (structure type 4)
(b) Residential built-up areas (points 12 and C): from
1.5 K (structure type 1) to 0.6 K (structure type 4)
The values presented above should be interpreted taking
into consideration the particularities of the study area, i.e.
values mentioned in point 1(a) show the air temperature dif-
ference between the city centre and the cold air reservoir.
The RMUHI concept can be used in other cities where the
relief is not only diversified but also generates mesoclimatic
processes which have a significant impact on air temperature
not only in the urbanized area itself but also in surrounding
rural areas. The procedure described in this paper can be
regarded as an initial phase in elaborating a spatially continu-
ous thermal structure in the urban area, but the mobile mea-
surements of air temperature profile across the city of Kraków
conducted so far (Bokwa 2010) indicated that no simple
modelling approach could be used.
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... The UHI intensity increases proportionally to the size and population of the urban area (Oke 1973) and under calm (wind speed <2 m/s), clear-sky, and dry-air conditions (Oke 1982;Morris et al. 2001;Mestayer et al. 2005;Hoffmann and Schlünzen 2013;Arnds et al. 2017). In addition, strong UHIs are often associated with thermal inversions even in relatively flat cities (Oke and Maxwell 1975;Nkemdirim 1980;Goldreich 1984;Kuttler et al. 1996;Szymanowski 2005;Hidalgo et al. 2010;Bokwa et al. 2015). Elevation and landforms have been shown to modulate UHIs, e.g. with air temperature cooling with elevation during the day and colder conditions on northern than southern slopes (in the Northern Hemisphere; Zhao et al. 2016;Peng et al. 2020). ...
... Topography is associated with almost systematic temperature decrease with elevation during the day and frequent (up to 30% of the time) nighttime thermal inversions. Such a diurnal reversal in the altitudinal gradients corroborates previous studies on urban environments with contrasted topography (Nkemdirim 1980;Goldreich 1984;Bokwa et al. 2015). Yet, in our case, there are only a few sensors located beyond 300 m above sea level. ...
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... Most of the studies conducted for the country's territory covered such manifestations of climate change as heat and cold waves , UHI (Błażejczyk et al., 2016;Majkowska, Kolendowicz, Półrolniczak, Hauke, & Czernecki, 2017), change in precipitation pattern Ziernicka-Wojtaszek & Kopcińska, 2020), floods and urban floods (Kundzewicz & Kowalczak, 2014), extreme weather events and natural hazards (Piasecki & Ż mudzka, 2022). In addition, some of them showed a relation between climate change and human mortality Rabczenko et al., 2016), bioclimatic comfort (Błażejczyk et al., 2016) or suggested introduction of new indices for better description of climate change (Bokwa, Hajto, Walawender, & Szymanowski, 2015). Since adaptation to climate change is not among the most important policy priorities in small and medium-sized cities of Poland (Karaczun, Bojanowski, Zawieska, & Swoczyna, 2022), we decided to focus our study on major Polish cities in terms of population and economic potential. ...
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... Urban climates are influenced not only by the main local climatic background/meteorological conditions (Cheval et al., 2009;, geographic topography (Bokwa et al., 2015;Liu et al., 2020a), and vegetation phenology (Walawender et al., 2014) but also by vegetation cover (VC), impervious cover (Imhoff et al., 2010;Connors et al., 2013), urban spatial patterns (Scarano and Sobrino, 2015;Yin C. H. et al., 2018;Liu et al., 2020bLiu et al., , 2021, and socioeconomic factors such as anthropogenic heat (Wang et al., 2017), GDP, and population (Liu et al., 2018). Due to these factors and the different development characteristics of different cities (Oke et al., 2020), the spatial distribution of climate characteristics in each city may be unique, likely resulting in the inconsistency of factors influencing the local climate and their influence thresholds. ...
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... SUHIs generally have clear spatial and temporal characteristics (van Hove et al., 2015). Their size and morphology are affected not only by the SUHI estimation methods used (Schwarz et al., 2011;Martin et al., 2015) but also by numerous other factors, such as climate types and weather conditions (Cheval et al., 2009;Du et al., 2016), phenology (Walawender et al., 2014), topography (Bokwa et al., 2015), types of land use (He et al., 2007), vegetation coverage and impermeable surface coverage (Imhoff et al., 2010;Connors et al., 2013), albedo (Peng et al., 2012), urban spatial morphology (Scarano and Sobrino, 2015;Yin et al., 2018), and anthropogenic heat (Wang et al., 2017). Given these factors and their different development characteristics, cities may each have unique SUHI distribution patterns (Oke et al., 2021). ...
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How does the urban spatial landscape (USL) pattern affect the land surface urban heat islands (SUHIs) and canopy urban heat islands (CUHIs)? Based on satellite and meteorological observations, this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area (CUA) of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to 2018. Eleven USL metrics—building height (BH), building density (BD), standard deviation of building height (BSD), floor area ratio (FAR), frontal area index (FAI), roughness length (RL), sky view factor (SVF), urban fractal dimension (FD), vegetation coverage (VC), impervious coverage (IC), and albedo (AB)—with a 500-m spatial resolution in the CUA are extracted for comparative analysis. The results show that SUHI is higher than CUHI at night, and SUHI is only consistent with CUHI at spatial—temporal scales at night, particularly in winter. Spatially, all 11 metrics are strongly correlated with both the SUHI and CUHI at night, with stronger correlation between most metrics and SUHI. VC, AB, and SVF have the greatest impact on both the SUHI and CUHI. High SUHI and CUHI values tend to appear in areas with BD ⩾ 0.26, VC ⩽ 0.09, AB ⩽ 0.09, and SVF ⩽ 0.67. In summer, most metrics have a greater impact on the SUHI than CUHI; the opposite is observed in winter. SUHI variation is affected primarily by VC in summer and by VC and AB in winter, which is different for the CUHI variation. The collective contribution of all 11 metrics to SUHI spatial variation in summer (61.8%) is higher than that to CUHI; however, the opposite holds in winter and for the entire year, where the cumulative contribution of the factors accounts for 66.6% and 49.6%, respectively, of the SUHI variation.
... The LCZ scheme is currently considered a standard for linking landscape to air pollutants at an urban scale. Generally, this classification scheme can be applied in three areas: (1) study of urban heat islands (Emmanuel and Krüger 2012;Alexander and Mills 2014;Leconte et al. 2015;Lehnert et al. 2015), (2) Modeling (such as: Surface Urban Energy and Water Balance model (SUEWS), digital elevation model (DEM)) (Alexander et al. 2015;Bokwa et al. 2015;Geletič et al. 2016), and (3) Land cover mapping (Bechtel and Daneke 2012;Lelovics et al. 2014;Danylo et al. 2016). One of the advantages of LCZ classification is the complete description of land-use types in an urban environment because it meets the standards for measuring physical properties and urban morphology (Stewart and Oke 2010). ...
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ContextUrban expansion has led to land use changes in metropolises, which in turn cause landscape pattern changes and ecological issues in urban areas.Objective The main objective of this research is to investigate the relationship between different land use patterns and air pollutants (NO2, SO2, CO, O3) in the metropolis of Tehran.Methods The Local Climate Zone scheme and Landsat 8 satellite images were used to extract urban land uses in Tehran. Additionally, Sentinel-5P satellite images were used to calculate and evaluate air pollutants in summer (2020) and winter (2021).Then, the relationship between the spatial composition and configuration of urban land uses and air pollutants was computed.ResultsThe results show that the correlation of the distribution or concentration of air pollutants is different from the spatial pattern of land use. The spatial composition and configuration of anthropogenic land uses, including the classes of compact mid-rise, compact low-rise, large low-rise, and heavy industry, had a positive correlation with NO2 and SO2 (P < 0/05). In contrast, the pollutant CO had a significant negative correlation with the green spaces of types A (dense trees) (P < 0/01) and B (scattered trees) (P < 0/05). Conversely, the spatial composition and configuration of anthropogenic land uses had a negative correlation with O3 (P < 0/05) while had a positive correlation with green spaces (P < 0/05).Conclusion Generally, the spatial pattern of the anthropogenic land uses had a direct and positive correlation in both spatial configurations with NO2, SO2, and CO and a negative correlation with O3.
... This is true for many cities in Poland. The influence of topography on the diversity of the city's thermal field in Krakow led to the development of the concept "relief-modified UHI effect" (Bokwa 2010;Bokwa et al., 2015). Bokwa et al. (2018) studied the impact of fog on UHI intensity in Kraków, Poland, and found that fog can decrease the UHI effect by about 1 K under specific weather conditions. ...
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This study investigates the characteristics of the temperature regimes at an urban station (Litewski square) in Lublin city in Poland and a nearby rural station (Radawiec), and the Urban Heat Island (UHI) effect in Lublin city. In winter, spring, summer, and autumn at both urban and rural stations frequency distributions of daily minimum (Tmin), and maximum (Tmax) air temperature in 1998–2020 have shifted towards a warmer climate compared to the frequency distributions in 1974–1997. At both stations in 1974–2020, in all seasons, the annual Tmin and Tmax display increasing trends. At Litewski square and Radawiec, Tmax shows increasing trends of 0.083 and 0.088 ºC/year in summer, respectively. This is the largest increase in all four seasons. Furthermore, it is revealed that the heatwaves at both the urban and rural stations have increased in number over time. However, cold waves at both stations show a declining trend. The UHI effect in Lublin city has not increased significantly during 1974–2020. Population in Lublin city has declined over the period 1995–2020, but the population in the surrounding rural counties has increased. It is speculated that this is one of the causes of no clear increase in the UHI intensity. Apart from that, the city’s large green coverage (about 40%) is probably acting as a heating inhibitor. The annual Tmin and Tmax projected by 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs indicate that the temperature regimes at both urban and rural stations show significant increasing trends during 2015–2100 under the selected SSPs, with the highest increase under high emission scenario (SSP5-8.5) and the lowest increase under the low emission scenario (SSP1-2.6). During 2015–2100, the UHI effect in Lublin city does not show any significant increasing or decreasing trends for the majority of t