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ORIGINAL ARTICLE

Assessment of bioclimatic conditions on Crete Island, Greece

Anastasia Bleta • Panagiotis T. Nastos •

Andreas Matzarakis

Received: 3 May 2012 / Accepted: 30 August 2013 / Published online: 13 September 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract The objective of this study was to assess and

analyze the human bioclimatic conditions of Crete Island,

by applying two human thermal indices: physiological

equivalent temperature (PET), derived from the Munich

Energy-balance Model for Individuals human energy bal-

ance model, and Universal Thermal Climate Index (UTCI),

based on the Fiala multi-node model of human thermo-

regulation. Human bioclimatic studies provide a frame-

work for considering the effects of climatic conditions on

human beings and highlighting the social/economic factors

that mitigate or amplify the consequences of environmental

changes. In order to estimate the thermal effect of the

environment on the human body, it has been considered

that the total effects of all thermal components, not only of

individual parameters, should be taken into account. The

climatic data (air temperature, relative humidity, cloudi-

ness, wind speed) used in this study were acquired from the

archives of the Hellenic National Meteorological Service,

regarding ten meteorological stations in Crete Island. These

data, covering the 30-year period 1975–2004, were used for

the calculation of PET and UTCI in order to assess thermo-

physiological stress levels. The findings of this analysis,

such as bioclimatic diagrams, temporal and spatial distri-

butions of PET and UTCI as well as trends and variability,

will help stake holders to understand and interpret the

island’s current bioclimate, in order to make any necessary

adaptations and become more resilient to the foreseen

climate change.

Keywords Bioclimatic conditions � PET � UTCI �Crete Island

Introduction

The quality of life in an urban or rural environment is

influenced significantly by bioclimatic conditions, both

short and long terms. Bioclimatic conditions are modified

due to environmental factors and the complex structure and

development of urban agglomerations as well. There has

been extended human biometeorological research indicat-

ing the impact of urban bioclimate on human morbidity

(Schwartz et al. 2004; Nastos and Matzarakis 2006; Mi-

chelozzi et al. 2007), mortality (Curriero et al. 2002; An-

alitis et al. 2008; Baccini et al. 2008; Hajat and Kosatky

2010; Almeida et al. 2010; Nastos and Matzarakis 2012),

tourism potential and decision making (de Freitas 2003;

Didaskalou and Nastos 2003; Hamilton and Lau 2005; Lin

et al. 2006; Matzarakis and Nastos 2011), and urban

planning (Carmona et al. 2003; Nikolopoulou and Steemers

2003; Nikolopoulou and Lykoudis 2006; Thorsson et al.

2004; 2007). Over the last years, many attempts have been

made to formulate a reliable and user-friendly index for the

assessment of the physiological thermal response of the

human body to climatic conditions (d’Ambrosio Alfano

et al. 2011), but only physiological equivalent temperature

(PET) and Universal Thermal Climate Index (UTCI) seem

to meet these requirements. PET is recommended for the

evaluation of the thermal component of different climates

by VDI Guideline 3787 (VDI 1998). PET is based on the

A. Bleta � P. T. Nastos (&)

Laboratory of Climatology and Atmospheric Environment,

Department of Geography and Climatology, Faculty of Geology

and Geoenviroment, National and Kapodistrian University of

Athens, Panepistimiopolis, 15784 Athens, Greece

e-mail: [email protected]

A. Matzarakis

Meteorological Institute, Albert Ludwigs University of Freiburg,

79085 Freiburg, Germany

123

Reg Environ Change (2014) 14:1967–1981

DOI 10.1007/s10113-013-0530-7

Munich Energy-balance Model for Individuals (MEMI),

which describes the thermal conditions of the human body

in a physiological relevant way (Fanger 1972; Hoppe

1993). UTCI was developed following the concept of an

equivalent temperature and has been applied in recent

human biometeorological studies, such as daily forecasts

and extreme weather warnings, bioclimatic mapping, urban

and regional planning, environmental epidemiology, and

climate impacts research (Jendritzky et al. 2012). The

UTCI represents an equivalent temperature that equals to

the ambient air temperature resulting in the same level of 7

crucial physiological parameters, which an average person

would experience in a reference environment (Jendritzky

et al. 2002, 2008; Fiala et al. 2012).

The bioclimatic analysis in this study concerns Crete

Island, a climate-sensitive area, affected by frequent Sah-

aran dust episodes especially during spring and summer

periods, when the development of suitable synoptic mete-

orological conditions often occurs (Kaskaoutis et al. 2008).

Furthermore, the topography of the island and the conse-

quent Fohn winds develop worse bioclimatic conditions

(Nastos et al. 2011), which are exacerbated due to atmo-

spheric pollution in urban agglomerations and strongly

impact public health.

Climate change is considered to be the main driving

force and its effects are expected to be more intense in the

forthcoming years. The stake, therefore, lies in mitigating

the environmental, social, and economic impacts on the

lucrative sectors of the region and tourism, agriculture, and

services related to civil protection. An overarching priority

in future research is the adaptation to climate and envi-

ronmental changes and related issues such as sustainable

cities and coastal zones as well as resilient societies, as

identified in the 7th Framework Program. Coastal areas are

highly threatened since they are hotspots for pollution,

increased water demands, and high urbanization pressures.

Located on the land and sea boundary, they are particularly

vulnerable to climate and global changes. Thus, the

purpose of the present study is to quantify the bioclimatic

conditions on Crete Island, during the 1975–2004 period,

based on PET and UTCI human thermal indices.

Data and analysis

The climatic data used in this study concern mean daily

values of air temperature, relative humidity, cloudiness,

and wind speed, acquired from the archives of the Hellenic

National Meteorological Service (HNMS) concerning ten

meteorological stations in Crete, during the period

1975–2004. The geographic characteristics of the HNMS

stations are presented in Table 1. The stations are divided

into three groups: stations located on the northern coasts,

mountainous stations, and stations on the southern coasts of

Crete Island.

The data sets have been tested for homogeneity in pre-

vious studies (Matzarakis and Nastos 2011; Nastos et al.

2002) and were used for the calculation of PET and UTCI

in order to assess the level of the thermo-physiological

stress.

For bioclimatic purposes, the wind velocity for all the

examined stations was adjusted according to the following

formula (Kuttler 2000):

WS1:1 ¼WSh � ð1:1=hÞa a ¼ 0:12 � z0 þ 0:18

where WSh is the wind speed (m s-1) at the station’s height

(h, usually 10 m), a is an empirical exponent, depending on

the surface roughness, and z0 is the roughness length. Wind

velocity was estimated at 1.1 m, which is the center of

gravity of the human body, and builds the reference level

for human biometeorological studies. In this study, three

different values of roughness length were applied

depending on the landscape around the examined station.

Thus, z0 = 0.2 for agricultural land with many houses,

shrubs, and plants, or 8-m-tall sheltering hedgerows within

a distance of about 250 m (stations: Palaiochora and

Table 1 Characteristics of the

meteorological stations in Crete

Island

Code Station Latitude

(degrees)

Longitude

(degrees)

Altitude

(m)

Period

1 746 SOUDA 35.3 24.0 10 1975–2004

2 758 RETHYMNO 35.4 24.5 7 1975–2004

3 754 HERAKLION 35.3 25.2 39 1975–2004

4 757 SITIA 35.2 26.1 22 1975–2004

5 755 FOURNI 35.3 25.7 315 1975–2004

6 752 ANOGEIA 35.3 24.9 822 1975–2004

7 760 KASTELLI 35.2 25.3 333 1975–2004

8 751 PALAIOCHORA 35.2 23.7 4 1975–2004

9 759 TYBAKI 35.0 24.5 7 1975–2004

10 756 IERAPETRA 35.0 25.7 13 1975–2004

1968 A. Bleta et al.

123

Kastelli); z0 = 0.4 for villages, small towns, agricultural

land with many or tall sheltering hedgerows, forests, and

very rough and uneven terrain (stations: Souda, Anogeia,

Fourni, Tymbaki); z0 = 0.8 for larger cities with tall

buildings (stations: Heraklion, Ierapetra, Sitia, Rethymno).

The aforementioned values of roughness length for a spe-

cific terrain were derived from the European Wind Atlas

(Troen and Petersen, 1989).

PET is equivalent to the air temperature at which—in a

typical indoor setting (without wind and solar radiation)—

the heat balance of the human body (work metabolic rate

80 W of light activity that should be added to the basic

metabolic rate 86.5 W, Stolwijk and Hardy 1977; heat

resistance of clothing 0.9 clo, which is the reference

clothing insulation value used for the formulation of PET)

is maintained with core and skin temperatures equal to

those of the under assessment conditions (Mayer and

Hoppe 1987; Hoppe 1999). The following assumptions are

made for indoor reference climate: Mean radiant temper-

ature equals air temperature (Tmrt = Ta). Air velocity is

set to 0.1 m/s. Water vapor pressure is set to 12 hPa

(approximately equivalent to relative humidity of 50 % at

Ta = 20.0 �C). The PET assessment scale (Table 2) is

derived by calculating Fanger’s (1972) PMV for varying

air temperatures in the reference environment using the

settings for the PET reference person (height: 1.75 m,

weight: 75 kg, age: 35 years, and sex: male; work meta-

bolic rate 80 W of light activity that should be added to the

basic metabolic rate and heat resistance of clothing 0.9

clo,) (Matzarakis et al. 1999). According to Hoppe (1999),

the assumption of constant values for clothing and activity

in the calculation of PET were made in order to define an

index independent of individual behavior.

UTCI is defined as the equivalent ambient temperature

(�C) of a reference environment providing the same

physiological response of a reference person as the actual

environment (Weihs et al. 2012). The calculation of the

physiological response to the meteorological input is based

on a multi-node model of human thermoregulation (Fiala

et al. 2001), associated with a clothing model. The UTCI

assessment scale is presented in Table 3. The assessment

scale is based on different combinations of rectal and skin

temperatures, sweat rate, shivering, etc., that might indicate

‘‘identical’’ strain causing non-unique values for single

variables like rectal or mean skin temperature in different

climatic conditions with the same value of UTCI (Błaz-

ejczyk et al. 2012). Static clothing insulation is adjusted to

the ambient temperature considering seasonal clothing

adaptation habits of Europeans, which notably affects

human perception of the outdoor climate (Havenith et al.

2012). In this point, it is worth mentioning that while in

principle the PET scale represents thermal sensations of

thermal environment experienced by specific population,

the UTCI scale represents heat/cold stress intensities

regardless the population type. The UTCI scale can be

applied to a very wide range of temperatures, from -70 to

?50 �C.

The input variables in modeling mean daily PET and

UTCI are mean daily values of air temperature, relative

humidity, wind speed, and radiant temperature. The input

variable of mean daily radiant temperature (the uniform

temperature of a surrounding surface that results in the

same radiation energy gain on a human body as the pre-

vailing radiation fluxes) is modeled based on mean daily

cloud coverage. In the study, both PET and UTCI were

calculated using ‘‘RayMan’’ model, appropriate to calcu-

late radiative heat transfer and human biometeorological

indices (Matzarakis et al. 1999, 2010). The ‘‘RayMan’’

model, developed according to Guideline 3787 of the

German Engineering Society (VDI 1998), calculates the

radiative heat transfer in both simple and complex envi-

ronments. The main feature and advantage of ‘‘RayMan’’

compared to other similar models is the calculation of

short- and long-wave radiation fluxes, the possibility to

Table 2 Physiological equivalent temperature (PET) for different

grades of thermal sensation and physiological stress on human beings

(Matzarakis et al. 1999)

PET (�C) Thermal sensation Physiological stress level

\4 Very cold Extreme cold stress

4–8 Cold Strong cold stress

8–13 Cool Moderate cold stress

13–18 Slightly cool Slight cold stress

18–23 Comfortable No thermal stress

23–29 Slightly warm Slight heat stress

29–35 Warm Moderate heat stress

35–41 Hot Strong heat stress

[41 Very hot Extreme heat stress

Table 3 UTCI Assessment Scale: UTCI categorized in terms of

thermal stress (Brode et al. 2012b)

UTCI (�C) Stress category

[ ?46 Extreme heat stress

?38 to ?46 Very strong heat stress

?32 to ?38 Strong heat stress

?26 to ?32 Moderate heat stress

?9 to ?26 No thermal stress

?9 to 0 Slight cold stress

0 to -13 Moderate cold stress

-13 to -27 Strong cold stress

-27 to -40 Very strong cold stress

\-40 Extreme cold stress

Bioclimatic conditions on Crete Island 1969

123

evaluate the thermal environment throughout the year for

both cold and hot seasons, and the use of a common unit

(�C).

The Mann–Kendall rank statistic method was applied

concerning the trends of the time series of annual number

of days with mean daily PET/UTCI falling in the extreme

classes of PET/UTCI (Mitchell et al. 1966).

Results and discussion

The northern, mountainous and southern meteorological

stations of HNMS were kept in different groups (Table 1).

The northern coasts of Crete are under the influence of the

northerly winds, especially during summer time and early

autumn, when the ‘‘Etesians’’ prevail. These are periodical

winds of the north section established over the Aegean Sea,

when a high-pressure center in the central and south Eur-

ope is combined with the Indian low-pressure system over

Asia Minor and the Eastern Mediterranean Sea (Metaxas

and Bartzokas 1994). A characteristic effect of the Etesians

regime is summer droughts and uniform weather conditions

in Greece (Nastos et al. 2002). This prevailing weather type

(cool and dry winds over the Aegean Sea) in summer

moderates the intensity of heat stress at the northern coasts

of Crete. The beneficial impacts of ‘‘Etesians’’ winds do

not appear at the southern coasts, due to the blocking

induced by the mountain ranges, leading to increased heat

stress within the summer period. In addition, the southern

coasts are protected against the winter northerly winds and,

thus, mild bioclimatic conditions prevail during the cold

period of the year. It is evident that the topography of the

island is responsible for extreme weather conditions. This

is the case of Heraklion city located in the basin northward

the Mount Psiloritis (2,456 m), which is perpendicular to

the southern air mass flow, causing the so-called Fohn

winds. Coming down from the lee of the mountain, these

winds are getting warm at a dry adiabatic rate and arrive at

lower elevations both warmer and drier than they were at

corresponding levels on the windward side. Descending to

the lowlands on the leeward side of the range, the air

arrives as a strong, gusty, desiccating wind. The wind often

lasts for 3 days or more, gradually weakening after the first

or second day. Sometimes, it stops very abruptly (Nastos

et al. 2011).

Figures 1, 2, and 3 depict the bioclimatic diagrams for

PET with respect to the stations under examination taking

into consideration the aforementioned division in three

groups (northern, southern, and mountainous stations). The

human thermal bioclimatic conditions are expressed in

percentages of the occurrence of PET classes (Table 2) for

each month. The respective bioclimatic diagrams for UTCI

are not shown for brevity, but are also discussed along with

PET diagrams further below.

As far as the stations on the northern coasts of the island

(Souda, Rethymno, Heraklion, and Sitia) are concerned, the

analysis of the bioclimatic diagrams showed that the thermal

comfort class (18 �C \ PET \ 23 �C) occurs throughout

the year in all stations. The frequency of the thermal comfort

class ranges from 0.3 % (August, in Souda) to 38.4 %

(October, in Heraklion). The thermal comfort class presents

frequencies from 1.3 % (August, in Tybaki) to 38.4 %

(March, in Palaiochora) throughout the year, concerning the

southern coastal cities, while this class disappears in July

and August in Palaiochora. Our findings indicate that the

thermal comfort class counts for higher frequencies for the

northern stations against the southern ones.

Taking into account the thermal index UTCI, the class

of thermal comfort (9 �C \ UTCI \ 26 �C) for northern

stations is missing in December and January in Souda,

while it reaches 90.9 % in November in Heraklion. Con-

cerning the southern stations, UTCI appears to be between

0.1 % (August, in Palaiochora) and 95.7 % (December, in

Palaiochora).

The frequency of the strong heat stress class for PET on

the northern coasts of the island does not appear in cold

months (December to February, in Souda and Rethymno;

November to March, in Sitia; December to March, in

Heraklion), while the maximum frequency (31.8 %) comes

up in August, in Rethymno. Concerning the southern

coastal cities, the strong heat stress class does not appear in

January in Tybaki, from December to January in Ierapetra,

and from December to March in Palaiochora, while the

highest frequency is 52.1 % in September, in Palaiochora.

On the other hand, the strong cold stress class is absent

from May to June and October in Sitia, and from May to

October in Souda, Rethymno, and Heraklion, regarding the

northern coastal stations. Similar patterns occur in the

southern coastal cities, where this class does not appear

from May to October at any of the stations, whereas it

reaches maximum frequency (15.3 %) in February, in

Ierapetra.

The strong heat stress for UTCI index on the north

coasts of the Crete Island does not appear from September

to May in Souda, from November to April in Heraklion and

Rethymno, and from November to March in Sitia, reaching

its maximum frequency (55.8 %) in August, in Rethymno.

Regarding the southern coastal stations, this class does not

come up from December to April in Palaiochora, from

November to April in Tybaki, and from November to

March in Ierapetra, while the maximum frequency

(79.0 %) appears in August in Palaiochora. The strong cold

stress class is really scarce for northern stations. More

specifically, this class does not occur at all in Souda and


123

Fig. 1 Frequencies of various

classes of PET index for

northern coastal areas of Crete

Island (period 1975–2004)


123

does not appear from April to December in Heraklion, from

March to September and November in Rethymno, and from

March to November and January in Sitia. The maximum

frequency is 0.6 % in February, in Heraklion. For the

southern coastal stations, the strong cold stress does not

exist throughout the year for Palaiochora and Tybaki,

neither does it appear from April to November in Ierapetra.

However, it reaches the maximum frequency 0.83 % in

February, in Ierapetra.

The extreme heat class for PET at the northern stations

is absent from November to March in Souda, Rethymno,

and Sitia, and from November to April in Heraklion, pre-

senting the maximum frequency (13.7 %) in July in Ret-

hymno. Similar temporal patterns appear at the southern

stations; there is no extreme heat stress from November to

April in Palaiochora, from November to February in Ty-

baki, and from November to March in Ierapetra. The

maximum frequency (36.5 %) occurs in July in



southern coastal areas of Crete

Island (period 1975–2004)


123

Palaiochora. Taking into account the frequencies of the

extreme cold class for northern stations, the highest fre-

quency (4.9 %) appears in February in Souda, while this

class does not exist from April to October in Souda, from

April to November in Heraklion, from April to September

and November in Rethymno, and in April, May, July,

September, and October in Sitia. Regarding the southern

stations, the class of extreme cold stress does not appear

from March to November in Palaiochora, from April to

November in Tybaki, and from April to June, and August

to October in Ierapetra, presenting the maximum frequency

(4.2 %) in February, in Ierapetra.

The very strong heat class for UTCI at the northern

stations ranges from 0.2 % (August, in Sitia) to 4.3 %

(July, in Rethymno) against 0.1 % (November in Palai-

ochora and September in Ierapetra) to 14.9 % (July, in

Palaiochora). The very strong cold class is not apparent at

the northern and southern stations throughout the year. The



mountain areas of Crete Island

(period 1975–2004)


123


123

extreme heat class for UTCI index (UTCI [ 46 �C) does

not exist at the northern stations, with the exception of

Sitia, where the class slightly appears from June to August

presenting the highest frequency (0.5 %) in July. Besides,

this class is absent at the southern stations with the

exception of Palaiochora, where it presents a value of

0.4 % in July. The extreme cold class of the assessment

scale (UTCI \ -40 �C) does not appear at any of the

northern and southern stations.

Regarding the mountain stations, the main characteristic

is that the extreme cold stress class for PET appears within

7 months (October to April) in Anogeia and Kastelli, and

6 months (November to April) in Fourni, ranging from

0.1 % (April, in Fourni and October in Kastelli) to 18.8 %

(January, in Fourni). The strong cold stress is apparent

within 8 months (October to May) in Kastelli and Anogeia,

and 7 months (October to April) in Fourni, ranging from

0.1 % (October, in Kastelli) to 27.4 % (January, in Kas-

telli). On the other hand, the class of extreme heat stress

has been decreased to 5–6 months (May to September in

Fourni and Anogeia; April to September in Kastelli),

ranging from 0.1 % (September, in Anogeia and May in

Fourni) to 6.9 % (July, in Anogeia). The strong heat stress

is estimated to appear from March to November (in Kas-

telli and Anogeia) and from April to October in Fourni,

having frequencies from 0.1 % (March, in Anogeia) to

37.5 % (July, in Anogeia). Finally, the thermal comfort

conditions range from 0.3 % (January, in Fourni) to 38.9 %

(May, in Kastelli).

The extreme cold stress class for UTCI does not appear

at any of the mountain stations, while the class of the very

strong cold stress comes up only in January (0.1 %) in

Fourni. The strong cold stress is estimated to appear within

2 months (January to February in Kastelli and Anogeia)

and 4 months (December to March in Fourni), ranging

from 0.1 % (February, in Anogeia) to 1.9 % (February, in

Fourni). On the other hand, the extreme heat stress class is

missing throughout the year with an exception in July

(0.1 %) in Fourni, against very strong heat stress, which

appears within 4 months (June to September in Kastelli

and May to August in Anogeia) and 2 months (July and

August in Fourni), ranging from 0.1 % (May, in Anogeia)

to 2.5 % (July, in Fourni). The strong heat stress is esti-

mated to be within 7 months (April to October, in Fourni

and Anogeia) and 5 months (May to September in Kas-

telli), taking frequencies from 0.1 % (April in Anogeia) to

41.7 % (July in Anogeia). Finally, the thermal comfort

class ranges from 0.3 % (August, in Anogeia) to 93.2 %

(November, in Anogeia). It is worth noting that the small

frequencies of extreme classes of UTCI can be explained

by the nature of this index as its values and scale represent

a very wide range of air temperatures. The climate of Crete

does not present extremely low or high temperatures,

which accounts for the absence of extreme UTCI classes.

The findings extracted by the analysis of bioclimatic

diagrams using ground-based observations are in good

agreement with the results presented by Matzarakis and

Nastos (2011), who used climate data from the 10-min

climatology (New et al. 1999, 2000) in order to create a

PET map of high spatial resolution (1 km 9 1 km) for

Crete Island, within the period 1961–1990.

The intra-annual variations of the daily composite

(averages) of mean daily PET, as well as the highest and

lowest mean daily PETs for each Julian day through the

period 1975–2004 for the examined stations in Crete are

depicted in Fig. 4 (right graphs). Looking at the extreme

values of the mean daily PET, the highest mean daily PET

at the northern coastal stations ranges between 14.1 �C

(February 20, in Heraklion) and 55.8 �C (July 27, in Sou-

da), against 17.1 �C (January 16, in Ierapetra) and 56.8 �C

(July 20, in Tybaki), for the southern coastal stations. The

estimated highest mean daily PET remains above 41.0 �C

(extreme heat stress) for a long period in southern stations

compared to northern ones. As mentioned above, this is

very likely due to the blocking of the northerly cold winds

by the mountain ranges along the island. Taking into

account the lowest mean daily PET, it appears within

-6.1 �C (December 10, in Sitia) and 28.7 �C (August 9, in

Souda) concerning the northern stations, against -3.0 �C

(February 9, in Ierapetra) to 33.0 �C (August 14, in Pal-

aiochora), for the southern stations. Regarding the moun-

tain stations, the highest mean daily PET varies between

13.0 �C (January 20, in Fourni) and 52.0 �C (July 10, in

Kastelli), while the lowest mean daily PET presents values

within the range -10.0 �C (February 18, in Anogeia) and

29.0 �C (July 19 in Anogeia).

The intra-annual variations of the daily composite

(averages) of mean daily UTCI, as well as the highest and

lowest mean daily UTCIs for each Julian day through the

period 1975–2004 for the examined stations of HNMS in

Crete are depicted in Fig. 5 (right graphs). The highest

mean daily UTCI at the northern coastal stations ranges

between 18.0 �C (January 12, in Herkalion) and 42.0 �C

(July 27, in Souda). At the southern coastal stations, the

highest mean daily UTCI lies between 21.0 �C (January

10, in Ierapetra) and 47.0 �C (July 7, in Palaiochora). On

the other hand, the lowest mean daily UTCI for northern

coastal stations varies from -21.0 �C (February 18, in

Heraklion) to 29.4 �C (August 9, in Souda), against

-23.0 �C (February 9, in Ierapetra) and 32.3 �C (August

Fig. 4 Time series of annual number of days within extreme classes

of mean daily PET, along with linear trends, (left graphs) and intra-

annual variation of the daily composite (averages) of mean daily

PET, as the highest and lowest mean daily PETs for each Julian day

through the period 1975–2004 for the examined stations of HNMS in

Crete (right graphs)

b


123


123

14, in Palaiochora), with respect to the southern coastal

stations. Finally, the highest mean daily UTCI for the

mountain station ranges between 16.7 �C (January 20, in

Fourni) and 49.2 �C (July 5, in Fourni), while the lowest

mean daily UTCI presents values within the range

-30.0 �C (January 7, in Fourni) and 29.3 �C (August 10, in

Anogeia).

An analysis of the trends of the time series of annual

number of days with mean daily PET within classes of

strong heat stress, extreme heat stress, strong cold stress,

and extreme cold stress is presented, during the period

1975–2004 (Fig. 4, left graphs). We only focus and com-

ment on statistically significant trends of p \ 0.05

(Table 4). Regarding the northern coastal cities, only in the

case of Sitia, the days with strong cold stress show an

increasing trend (0.460 days/year), while days with

extreme and strong heat stress have a decreasing trend.

Concerning the mountainous stations, Fourni station indi-

cates an increasing trend in extreme heat stress, against

Kastelli, where an increasing trend in extreme cold stress

occurs. On the southern coasts, Tybaki presents an

increasing trend of extreme cold stress and decreasing

trends of strong/extreme heat stress during the study per-

iod. Furthermore, an increasing trend of strong heat stress

appears in Ierapetra.

Additionally, the trends of the time series of annual

number of days with mean daily UTCI within extreme

classes during the period 1975–2004 are depicted in Fig. 5

(left graphs). It is worth mentioning that the time series of

very strong/strong cold stress and very strong heat stress is

missing for the majority of the examined stations with the

exceptions of Palaiochora and Tybaki (southern coastal

stations) presenting increasing and decreasing trends of

strong heat stress, respectively (Table 5). The dominant

class is that of strong heat stress indicating significantly

increasing trends in Souda and Ierapetra, and decreasing

trends in Sitia and Tybaki. This is in agreement with the

trends appearing in the time series of PET strong heat stress.

Further, a comparison between the examined thermal

indices, UTCI, and PET is made. High correlations were

found for these two thermal indices (Table 6), namely the

R-squared varies from 89.7 % in Fourni to 96.4 % in

Anogeia, with slopes from 0.715 in Palaiochora to 0.954 in

Ierapetra. This is in agreement with the findings of Błaz-

ejczyk et al. (2012), who concluded that the values for

indices deriving from human heat balance models, i.e.,

PET, PT (perceived temperature), and SET* (standard

effective temperature), were most similar to those of UTCI,

because these indices indicate equivalent temperature.

In our case, the lowest mean daily PET values range

from ?0.2 �C (at Palaiochora) to -10.1 �C (at Anogeia),

against the lowest mean daily UTCI values ranging from

-7.6 �C (at Palaiochora) to -30.0 �C (at Fourni). The

scattering pattern of UTCI versus PET increases with

decreasing UTCI, and this can be explained due to the PET

fixed clothing insulation under cold conditions against the

wind-dependent clothing insulation of UTCI. As Blaz-

ejczyk et al. (2012) commented UTCI is very sensitive to

changes in air temperature, solar radiation, humidity, and

especially wind speed, whereas PET is more closely related

to air temperature.

Thorsson et al. (2007) showed good agreement of PET

with the perception of interviewees by comparing the

results of structured interviews and of PET evaluation in an

Fig. 5 Time series of annual number of days within extreme classes

of mean daily UTCI, along with linear trends, (left graphs) and intra-

annual variation of the daily composite (averages) of mean daily

UTCI, as well as the highest and lowest mean daily UTCIs for each

Julian day through the period 1975–2004 for the examined stations of

HNMS in Crete (right graphs)

Table 4 Trends of the time series of the annual number of days with mean daily PET within specific classes, representing the extreme/strong

cold and heat stress

Station PET \ 4 �C 4 �C \ PET \ 8 �C 35 �C \ PET \ 41 �C PET [ 41 �C

b ± SE b ± SE b ± SE b ± SE

SOUDA n.s. n.s. n.s. n.s.

RETHYMNO n.s. n.s. n.s. n.s.

HERAKLION n.s. n.s. n.s. n.s.

SITIA n.s. 0.460 ± 0.155* -0.634 ± 0.138* -0.328 ± 0.090*

FOURNI n.s. n.s. n.s. 0.144 ± 0.062*

ANOGEIA n.s. n.s. n.s. n.s.

KASTELLI 0.371 ± 0.145* n.s. n.s. n.s.

PALAIOCHORA n.s. n.s. n.s. n.s.

TYBAKI 0.110 ± 0.044* n.s. -1.001 ± 0.273* -1.352 ± 0.325*

IERAPETRA n.s. n.s. 0.950 ± 0.172* n.s.

* Statistically significant values at p \ 0.05, while n.s. for not significant relationships

b


123

urban park and a square in Matsudo, a satellite city near

Tokyo, Japan. Besides, PET has been used in urban built-

up area with complex shading patterns and generated

accurate predictions of thermal environments (Thorsson

et al. 2007; Gulyas et al. 2006). Svensson et al. (2003)

presented the PET distribution map of Goteborg using the

geographic information system. Gulyas et al. (2006) eval-

uated thermal comfort in Szeged, Hungary. Bouyer et al.

(2007) evaluated the thermal comfort in two stadia (Stade

de France, Paris, and the Ataturk Olympic stadium, Istan-

bul), using wind tunnel experiments and PET. They con-

cluded that PET maps should not be regarded as

‘‘absolute’’ results, and detailed analysis of the main heat

transfers, due to wind and radiative contributions, is nec-

essary. A high (or even unacceptable) level of wind

velocity combined with a high amount of solar loads leads

to PET values that could have been obtained with mild

conditions.

Further to the above discussion, we must acknowledge a

constraint related to the PET assessment scale. We used the

PET scale for Western/Middle European countries taking

into consideration that Crete Island has a temperate cli-

mate, not significantly different from the climate of Wes-

tern/Middle European countries. Thus, the use of the PET

assessment scale for Western/Middle European countries is

almost acceptable. A recent study by Cohen et al. (2012)

confirms our assumption, revealing that the acceptable

comfort range of PET for the Mediterranean climate of Tel

Aviv is 19–26 �C, against 18–23 �C for the acceptable

comfort range of PET for Western/Middle European

countries. Besides, the assessment scale is frequently used

in international literature (Matzarakis et al. 1999) to esti-

mate bioclimatic conditions in different places, such as

Istanbul and Paris (Bouyer et al. 2007), Goteborg (Svens-

son et al. 2003), Szeged (Gulyas et al. 2006), Matsudo (a

satellite city near Tokyo), Japan (Thorsson et al. 2007),

Far-Eastern Federal District of the Russian Federation

(temperate monsoon climate zone, which is characterized

by an extreme continental regime of annual temperatures)

(Grigorieva and Matzarakis 2011). Another important issue

of using this assessment scale concerns international tour-

ists, who visit the Island of Crete, meaning that the thermal

comfort of international tourists coming from areas of high

latitude (or moderate climate zones) differentiates from

that of local tourists on Crete Island. International tourists

want to know the bioclimatic conditions of a region

according to their thermal sensation and not to the

respective one of the local people. This conclusion is in

agreement with Lin (2009), who compared a modified PET

scale for Taiwan with the PET scale for Western/Middle

European countries. He concluded that compared to the

thermal acceptable range on Western/middle European

scale (18–23 �C PET), the thermal acceptable range in

Table 5 Trends of the time series of the annual number of days with mean daily UTCI within specific classes, representing the very strong/

strong cold and heat stress

Station -40 �C \ UTCI \ -27 �C -27 �C \ UTCI \ -13 �C 32 �C \ UTCI \ 38 �C 38 �C \ UTCI \ 46�CC

b ± SE b ± SE b ± SE b ± SE

SOUDA – – 0.820 ± 0.388* –

RETHYMNO – – n.s. –

HERAKLION – – n.s. –

SITIA – – –0.963 ± 0.208* –

FOURNI – – n.s. –

ANOGEIA – – n.s. –

KASTELLI – – n.s. –

PALAIOCHORA – – n.s. 0.377 ± 0.145*

TYBAKI – – -1.080 ± 0.321* -0.174 ± 0.754*

IERAPETRA – – 1.010 ± 0.195* –

* Statistically significant values at p \ 0.05, while n.s. for not significant relationships

Table 6 Statistical characteristics of the relationship between Uni-

versal Thermal Climate Index (UTCI) and physiological equivalent

temperature (PET), for the examined sites in Crete Island, during the

period 1975–2004

Stations Slope R-squared (%)

SOUDA 0.813 94.2

RETHYMNO 0.894 91.4

HERAKLION 0.940 91.5

SITIA 0.901 91.7

FOURNI 0.922 89.7

ANOGEIA 0.728 96.4

KASTELLI 0.887 92.8

PALAIOCHORA 0.715 95.2

TYBAKI 0.829 92.6

IERAPETRA 0.954 90.3


123

Taiwan, which has a hot and humid climate, is much higher

(21.3–28.5 �C PET), confirming the different thermal

requirements of people living in different regions.

Concerning UTCI, one limitation might be the applica-

tion in studies concerning health risk assessment under

extreme conditions (in extreme hot or extreme cold envi-

ronments), where a one-scale deviation might already lead

to a critical decision argument (Weihs et al. 2012). UTCI

has been applied in order to differentiate cities in terms of

thermal stress. Idzikowska (2010) examining the biocli-

matic conditions on the basis of the UTCI in four cities

such as Rome, Budapest, Paris, and Warsaw showed that

‘‘no thermal stress’’ occurs throughout the year in all four

cities. Extreme values of heat appeared in Rome and

Budapest, while cold stress was observed in Warsaw, Paris,

and Budapest. Furthermore, the application of UTCI in

bioclimatic research was performed in Warsaw by Lindner

(2011) and in Czech Republic by Novak (2011). Bakowska

(2010) performed bioclimatic analysis concerning the daily

course of UTCI in Koszalin, Warsaw, and Wroclaw for

different air masses such as arctic, polar maritime, polar

continental, and tropical. The results showed that advec-

tions of polar continental air masses in winter caused most

severe bioclimatic conditions in Poland, while these masses

were responsible for strong heat stress in summer. Kunert

(2010), simulating UTCI in the landscape of the Warsaw

Lowland in different weather conditions, found that relief

features have the greatest influence on the modification of

thermal conditions. Differences of UTCI between the

warmest and the coldest areas, in similar weather condi-

tions, rise above 30 �C. Besides, the impacts of bioclimatic

conditions with respect to UTCI in organic-cause mortality

in Athens and Bangladesh have been analyzed by Nastos

and Matzarakis (2012) and Burkart and Endlicher (2010),

respectively. Besides, UTCI can serve as a suitable plan-

ning tool for urban thermal comfort in subtropical regions

(Brode et al. 2012a, b).

Conclusions

The assessment of the bioclimatic conditions on Crete

Island was carried out based on the analysis of the human

thermal indices PET and UTCI. PET is a universal index

and is irrespective of clothing and metabolic activity, while

UTCI is very sensitive to changes in air temperature, solar

radiation, humidity, and especially wind speed. Besides,

the accuracy of PET and UTCI calculations depends on the

accuracy and the uncertainties of the used meteorological

parameters.

The estimated UTCI thermal comfort class presents

higher frequencies and duration throughout the year

compared to the respective PET class, for all the examined

stations; especially, during the cold months, this UTCI

class dominates and in many cases exceeds 90 % against

PET thermal comfort class, reaching approximately 38 %.

Another finding is that the UTCI values of extreme/very

strong/strong cold stress and very strong heat stress are

missing for the majority of the examined stations with the

exceptions of Palaiochora and Tybaki (southern coastal

stations) showing increasing and decreasing trends of

strong heat stress, respectively. Taking into consideration

the scatter plots of UTCI versus PET, we found that scat-

tering increases with decreasing UTCI, and this can be

explained due to the PET fixed clothing insulation under

cold conditions against the wind-dependent clothing insu-

lation of UTCI.

The analysis showed that during the summer period, the

intensity of heat stress decreases in urban areas on the

northern coasts of Crete, due to the cooling impacts of

‘‘Etesians’’ winds, which do not appear on the southern

coasts. The ‘‘Etesians’’ winds are blocked by the mountain

ranges along the island and thus the southern coastal

regions experience longer periods with estimated PET

extreme/strong heat stress. Nevertheless, the blow of

southern winds (associated with Saharan dust episodes in

spring/summer time) exacerbates the bioclimatic condi-

tions at northern coastal urban areas (such as Heraklion),

because of the development of the hot and dry Fohn winds

coming from the lees of the mountain ranges. On the other

hand, the bioclimatic conditions over the mountainous

regions indicate that the estimated PET extreme heat stress

class shows low frequencies from June to August against

moderate frequencies of extreme cold stress appearing

from October to April.

In the region of Crete Island, the time series of the

annual number of days with mean daily extreme values of

PET/UTCI do not present clear either increasing or

decreasing trends during the 30-year period 1975–2004. It

is worthy to remark that the examined human thermal

indices are based on the energy balance of the human body

and not on individual climatic parameters. Thus, the

observed global warming, mainly referring to air temper-

ature trends, has had no important impacts on human

physiology in the region of Crete Island until now. This is

very likely due to some kind of compensation among the

climatic parameters included in PET/UTCI modeling, such

as air temperature, relative humidity, wind speed, and

cloudiness, and to the synergistic effects of all these

parameters on thermal perception.

Acknowledgments This research is supported by HERAKLEITOS

project, which is co-funded by EU and National Resources. The

authors would like to acknowledge the Hellenic National Meteoro-

logical Service for providing the long meteorological time series.


123

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