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World Applied Sciences Journal 12 (10): 1662-1675, 2011
ISSN 1818-4952
© IDOSI Publications, 2011
Analysis of Spatially Distributed Annual, Seasonal and
Monthly Temperatures in Istanbul from 1975 to 2006
Ahmet Karaburun, Ali Demirci and Fatih Kara
Faculty of Arts and Science, Geography Department, Fatih University, Turkey
Abstract: The present study is about the analysis of mean, mean maximum and mean minimum temperatures
carried out on annual, seasonal and monthly time-scales examining the data from 8 meteorological stations in
Istanbul for the period 1975-2006. Various spatial and statistical tools were used to display and analyze trends
in temperature data. ArcGIS was used to produce the spatially distributed temperature data by using Thiessen
method. The non-parametric Mann-Kendall test was used to determine whether there is a positive or negative
trend in data with their statistical significance. Sen’s method was also used to determine the magnitude of the
trends. The results reveal positive trends in annual mean and mean maximum temperatures with 95% and 99%
significance. Annual mean temperatures increased 0.83°C while annual mean maximum temperatures increased
1.6°C over the 32-years period in Istanbul. On a seasonal basis, a statistically significant positive trend was
observed in summer. On a monthly basis, July and August has experienced significant warming trend in mean,
maximum and minimum temperatures. The analysis of the whole record reveals a tendency towards warmer
years, with significantly warmer summer, spring and autumn periods and slightly colder winters. These warming
patterns may have important impacts on energy consumption, water supply, human health and natural
environment in Istanbul.
Key words: Thiessen polygon
Temperature
Mann-Kendall
INTRODUCTION
The world has been witnessing the effects of global
warming in many different areas during the last century.
Rising sea levels, glacier and snow cover retreat and
changing patterns of agriculture are among the many
consequences which are attributed to global warming
[1-4]. The increase in global surface temperature and its
direct and indirect effects have already caused many
widespread social, economic and environmental problems
throughout the world. Changing patterns and timing of
extreme weather events such as tropical cyclones,
expansion of contagious diseases and increasing duration
of heat waves have been cited among negative effects
of global warming on human life in many studies [5-8].
Studies on monitoring global and regional
temperature change have intensified over the last few
decades because global and regional effects of global
warming became apparent. One of the most important
associations providing data on global temperature
changes is the Intergovernmental Panel on Climate
Change (IPCC) which is a scientific intergovernmental
body established in 1988. According to IPCC’s recent
report, the global average surface air temperature has
increased by 0.13 °C (± 0.03°C) per decade over the last 50
years [9]. As estimated in many studies, global
temperature increased 0.8°C in the past century and the
warming was not continuous but occurred principally
during 1920-45 and after 1975 [10,11,12]. Cooling was
significant during the intervening period (1945-75)
especially for North America, the Arctic and Africa [10].
Analysis of worldwide air temperature changes have
also shown that temperature has increased in both
hemispheres over the last century; however, warming
was more dominant in the Northern Hemisphere since
the 1950s [12]. Many regional studies have also found a
positive trend in temperature although the changes
slightly vary from one region to another [13-21]. As
Zhang et al. [22] indicated, the annual mean temperature
has increased between 0.5 and 1.58°C in the southern
regions of Canada in the 20th century. A strong warming
trend of +0.08°C ± 0.03°C per decade was detected over
Europe within the 20th century with the highest increase
as 0.43°C over the last 30 years [14].
Corresponding Author: Ahmet Karaburun, Faculty of Arts and Science, Geography Department, Fatih University, Turkey.
Tel: +90-212-8663300(2038), Fax: +90-212-8663402, E-mail: [email protected].
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Analysis of temperature data throughout the world
has shown that change was not only observed in mean
annual temperature but also in seasonal, monthly and
minimum-maximum temperatures. As Pielke et al. [23]
stated, minimum temperatures have risen about 50% faster
than maximum temperatures globally since 1950. Winter
minimum and maximum temperatures significantly
increased in USA, Europe and many parts of Asia while
small increase was observed on minimum and maximum
temperatures during the summer [24]. The highest
increases in temperature were found in winter in the
interior region of Alaska as 2.2°C within the last 50 years
[22].
Mediterranean region is among the most affected
areas in the world from global warming. Studies on global
warming in the Mediterranean basin indicate that the rise
in temperature which intensified over the last few decades
increased the potential evapotranspiration, water deficit
and forest fire risk [25]. Brunet et al. [17] studied
temperature variability in Spain during 1850-2005 and
found a significant warming of 0.10°C per decade for the
annual mean temperature. According to the same study,
average autumn and winter temperatures have increased
slightly more than spring and summer temperatures.
Founda et al. [15] studied mean, maximum and minimum
temperature in Athens from 1897 to 2001 and concluded
that an increase of 1.23 and 0.34°C has been observed in
the mean summer and mean winter temperature
respectively. A significant increase has also been
observed for both warm and cold seasons in the same
study between 1992 and 2001.
A number of studies used different methods and
predicted the changes in average, maximum and minimum
temperature in Turkey with changing patterns from region
to region [26-32]. Turke et al. [26] had found cooling in
many stations of Turkey during the second half of the
20th century. However, as Tayanc et al. [31] indicated,
almost all parts of Turkey experienced a warming trend
over the last half century except for the northern parts. A
more widespread significant warming trend was reported
in the minimum temperatures in the southern, western and
continental parts of Turkey in the same study. Black Sea
region, however, has experienced a cooling period from
1975 to 1993, but a warmer period from 1994 to 2007 [30].
As seen in some recent studies, predicting the change in
annual, seasonal mean, maximum and minimum
temperatures for the whole Turkey is problematic
since different trends are observed in different
climatic regions of the country [26,30,31,33].
For this reason, mean, maximum and minimum temperature
changes have to be analyzed regionally in Turkey by
using different methods in order to understand the trends
in temperature and make correct predictions for future
temperature changes.
Understanding of climate change requires a careful
examination and interpretation of climatic data to see if
there is a change in the average of the data in a given time
period. Several parametric and non-parametric statistical
data processing methods are used to analyze trends in
climate change studies. Sequential Mann–Kendall trend
test, Sen’s slope estimator and Spearman’s rank–order
correlation tests are used to analyze the direction and
magnitude of possible trends in temporal observed data
[55, 56, 57, 58]. Many researchers used Mann-Kendall
tests to identify trends in hydrologic variables such as
river flow, sediments and meteorological variables such as
temperature and precipitation in many studies [59, 60, 61,
62, 29, 63]. The purpose of this study is to analyze trends
in annual, seasonal and monthly mean, minimum and
maximum temperatures in Istanbul for 32 years time period
(1975-2006) by using Mann-Kendall non-parametric test
and Sen’s method applied on spatially distributed
monthly temperature data produced by Thiessen polygon
method.
Study Area: Study area is the financial capital of Turkey,
Istanbul, with its population over 12 million [64]. The city
is located in northwest Turkey along both sides of the
Bosphorus, where the western side of the city is in Europe
and its eastern side is in Asia (Fig. 1). It is located
between 40° 48'– 41° 36' N latitudes and 27° 58'–29° 56' E
longitudes. Istanbul covers a surface area of 5,390 km2
with 32 sub-provinces administered by the Istanbul
Metropolitan Municipality. Istanbul is surrounded by
Black Sea in the north and Sea of Marmara in the south.
These two water bodies along with the Mediterranean Sea
determine the climatic characteristics of the region. The
climate of the city shows a transition between the
Mediterranean and temperate climates characterized by
cool and wet winters and warm and humid summers [65].
The average annual temperature is 13.7°C with daily mean
temperature 6.3°C in winter and 22.4°C in summer [66].
According to data taken Florya Meteorological station
which is located in the south, the hottest months are July
(23.2°C) and August (23.7°C) while the coldest months are
January (5.3°C) and February (5.5°C) [66]. Total annual
precipitation is 734 mm in the city [67]. Like temperature,
the amount of precipitation varies from south to north.
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World Appl. Sci. J., 12 (10): 1662-1675, 2011
Fig. 1: Study area map
The total annual precipitation is over 1.000 mm in the
north while it decreases towards Sea of Marmara in the
south below 600mm [67]. The topography in European
and Asian sides of the city is characterized by low
plateaus. Average elevation of the area is between 150
and 200 meters, though some mountains are higher than
500 meters. The elevation decreases from north to south
in general over Istanbul. Rapid increase in population and
urbanization has resulted significant changes in land
cover of the city especially over the last fifty years. Being
located mostly in the northern parts, forest areas cover
37.3% of the total surface are of the city while urban areas
account for 14.3% [68].
Methodology: Estimating the spatial distribution of
climatic data has become an important part of studies
helping to understand climate change and its effects
throughout the world [34]. While many of the studies
analyze climatic data in local meteorological stations,
spatial distribution of climatic data is produced by
estimating data at non monitored locations based on
registered values at neighboring sites [34]. Different
methods were proposed in several studies to estimate
spatial distribution of climatic data mainly precipitation
and temperatures [36-43, 34]. The methods which are
commonly used to present spatial distribution of climatic
data can be classified into three main groups; graphical,
topographical and numerical [42]. Graphical methods
include precipitation-elevation analysis, isohyets
mapping, [44, 37] and Thiessen’s [36]. Topographical
methods include the correlation of point climate data
obtained from topographic and synoptic parameters such
as slope, exposure, elevation, the location of barriers,
wind speed and direction [45, 46, 38, 47]. Numerical
methods are the most attempted methods in order to
estimate spatial distribution of climate data over the last
years. Optimal interpolation [48], kriging and its variants
[41] and smoothing splines [39] are examples in numerical
methods.
In Thiessen method, each meteorological station is
weighted in direct proportion based on its area in the total
area of region without consideration of topography or
other local physical characteristics. In this method, it is
accepted that the area defined by each meteorological
station is closer to that station than to any other station
over the study area. The Polygons that represent the
influence area for each station are created by connecting
the intersections of perpendicular bisectors of lines that
are drawn between stations. The spatial distribution of
climatic data is calculated by multiplying the climate data
of each station with their areal ratio over total area. Many
researchers used Thiessen method to estimate the spatial
distribution of precipitation and temperature in several
studies [49-53]. This method can be used for the areas
which have insignificant topographic differences and
inadequate meteorological station density.
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Table 1: Characteristics of meteorological stations
Fig. 2: Thiessen Polygon over study area
Several spatial and statistical methods were used in
the study to produce spatially distributed temperature
data and to detect the trend on annual, seasonal and
monthly mean, maximum and minimum temperatures in
Istanbul for a 32-years period from 1975 to 2006. Monthly
temperature data were obtained from 8 meteorological
stations one of which is located in the west, outside of the
provincial border of the city (Fig 2). Seven meteorological
stations located in the city were those from which
consistent temperature data were available for a 32-years
period [54]. The only station which is located outside of
the city was used to create Thiessen polygons in the
western part of the city where no other stations were
available for the study. Table 1 displays the location and
elevation of these meteorological stations. As seen from
the table, elevation is below 200 meters in every station.
This was helpful to use Thiessen method to produce
spatially distributed temperature data over the study area.
After Thiessen method, spatially distributed data were
analyzed by Mann-Kendall test and Sen’s Method for
trend analysis.
Producing Spatially Distributed Temperature Data: In
Thiessen method, the area is divided into a set of
polygons with the meteorological station in the middle of
each polygon without taking into account topography
and physical characteristics of study area. All points that
fall inside of a Thiessen polygon are closer to center of
that polygon than to center of neighbor polygons in
study area [69]. The centers of polygons are represented
by meteorological stations. Temperature value of each
Thiessen polygon is assumed to be constant and equal to
value of its representative meteorological station in
Station Name
Latitude
Longitude
Elevation
Bahcekoy
41 10 35
28 59 35
130
Corlu
41 09 25
27 49 05
183
Florya
40 58 55
28 47 20
37
Goztepe
40 58 42
29 03 21
32
Kartal
40 53 23
29 10 57
27
Kirecburnu
41 08 51
29 03 02
58
Kumkoy
41 15 04
29 02 19
38
Sile
41 10 11
29 36 03
83
the center of Thiessen polygon. Thus this method allows
estimating temperature over a large area using limited
numbers of meteorological stations. This method is
probably one of the most used method to model the
spatial distribution of climatic data [49, 50, 51, 52, 53].
Discrete change in data because of discontinuous surface
that occur at the boundaries of polygons is the negative
effect of this method [70]. The spatially distributed
temperature is estimated by determining the weights of
each Thiessen polygon over the study area. The area
percentage of each Thiessen polygon is used to
determine the weights. Firstly, triangles are created by
connecting stations with lines. Secondly, perpendicular
lines are created for each bisector of lines of triangles.
Those perpendicular lines will intersect and form a vertex
of Thiessen. Thiessen polygon is created using those
vertexes. The weights for Thiessen polygons of study
area computed using the following equation;
Wi =
Ap
A
(1)
Wi : The weighted area for Thiessen polygon
AP : The area defined by Thiessen polygon
A : The total area
Spatial weighted temperature of each meteorological
station is calculated by multiplying its spatial weight by
its observed temperature value. The average temperature
of study area is calculated by adding all aerial weighted
temperature of meteorological stations over study area
using following equation;
P=
n
∑ i =1Wi Pi
(2)
P : The average temperature of study area
Pi : The meteorological station temperature for each
Thiessen polygon
n : The number of Thiessen polygon over study area
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World Appl. Sci. J., 12 (10): 1662-1675, 2011
Table 2: Station Weights
Station
Weight
CORLU
0.196
FLORYA
0.326
BAHCEKOY
0.086
KUMKOY
0.011
KIRECBURNU
0.073
SILE
0.164
KARTAL
0.107
GOZTEPE
0.037
All geographical information system software offer
tools to create Thiessen polygons. The locations of
meteorological stations are created using geographical
coordinates provided by Turkish State Meteorological
Service. The Thiessen polygons (Fig.2) that represent the
influence area of each meteorological station were
produced using Arcgis 9.2 software. The weights of
stations are given in Table 2.
Since Sen’s slope estimator is not affected from gross
data error or outliers, it is used to estimate the slope for
Mann-Kendall test even with missing data. Possible
slopes between all possible data pairs can be calculated
by Sen’s slope estimator. The initial value of the MannKendall test statistic S is set to 0 and it indicates there is
no trend. It increases the S value by 1 when a laterobserved value is higher than earlier-observed value and
decreases by 1 when a later-observed value is lower than
an earlier-observed value. The final value of S is
computed by the net results of those increases and
decreases.
Mann-Kendall method firstly calculates S
statistic which indicates the sum of the difference
between the data points indicated in following
equation.
=
S
n −1
n
∑ ∑ sgn(x j − xk )
k= 1 j= k +1
(3)
Where; xj is observed value at time j, xk is observed value
at time k, j is time after time k and n is the length of the
data set.
The sign of the value is defined as follows:
Determining the Trend Analysis of Spatially Distributed
Temperature Data: Spatially distributed temperature data
obtained from Thiessen method was grouped into annual,
seasonal and monthly mean data series in the period of
 1 if x j - xk > 0
1975-2006 to be used in Mann-Kendall test and Sens’

method. Before applying those test to temperature data
=
sgn (x j -xk )
=
 0 if x j - xk 0
(4)
series time period was divided into two periods. The

-1
if
x
x
0
<
j
k


Mann-Kendall test was employed to detect presence of
positive or negative trend in time series data while Sen’s
The statistical significant of S is checked using a test
method was used to compute the magnitude of the
statistic
(or z score):
change in temperature.
 S −1
One of the most used non-parametric tests to
 Var(S ) ; S > 0
identify presence of trends in time series data is The


(5)
Mann-Kendall test. Missing values in time series data =
Z =
0
; S
0

 S +1
are allowed in Mann-Kendall trend test and the data do

; S < 0
not require conforming to any particular distribution
 Var(S )
[71]. The sign of difference is identified after all
subsequent observed values are compared and the
The variance of S can be computed as:
difference between the later-observed values and earlierq
observed values are compared in the Mann-Kendall test.
n( n − 1)(2n + 5) t p (t p − 1)(2t p + 5)
Each-later observed value is analyzed with all earlieri =1
(6)
Var(S ) =
observed values. If later-observed values tend to be larger
18
than earlier-observed values, an increasing trend can be
Where n indicates the number of data points, q indicates
identified while a decreasing trend can be recognized if
the number of tied groups (a tied group is a set of sample
later-observed values tend to be smaller than earlierdata having the same value) and tp indicates the number
observed values.
of data points in the pth group.
Linear regression method is used to estimate the
The Mann-Kendall method test the null hypothesis,
slope of possible linear trend in time series but if there are
Ho, is that data indicate no distinct trend against the
gross data errors or outliers in time series, the estimated
alternative hypothesis Hi is that data indicate a trend.
slope can be different from the true slope of linear trend.
∑
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World Appl. Sci. J., 12 (10): 1662-1675, 2011
RESULTS
Two tailed test is used to decide whether or not the null
hypothesis should be rejected in favor of hypothesis test.
The null hypothesis, Ho is tested by the Z test statistic
value. A decreasing trend is identified if Z is negative and
an increasing trend is identified if the Z is positive. Ho is
rejected at 0.1, 0.05, 0.01 and 0.001 significance levels if
the absolute value of Z is greater than Z1- /2, where Z1- /2 is
taken from the standard normal cumulative distribution
tables. The magnitude of the trend over time is estimated
according to Sen [72]. The median slope of all pair wise
comparisons indicates the trend slope and the slopes of
all data pairs are calculated by;
Qi =
xi - x k
j-k
Spatially distributed temperature data which were
produced by using Thiessen method and their analysis
with Mann-Kendall test and Sen’s method provided an indepth analysis of annual, seasonal and monthly mean,
maximum and minimum temperature changes in Istanbul
during the last 32 years (1975-2006).
(7)
Where
Qi : Slope between data points
xj
: Data measurement at time j,
xk
: Data measurement at time k
j > k.
If there are n values of xj in the time series Sen’s
slope estimator is the median of n(n-1)/2 pairwise slopes.
The Sen's estimator is:
Q = Q  N +1 


 2 
if N is odd,
(8)
Annual Temperatures: Table 2 presents the spatially
distributed annual mean, maximum and minimum
temperatures. The analysis of the whole dataset within the
table indicates that annual mean temperature is 13.7°C in
Istanbul. As table 3 indicates, mean annual temperatures
varied between 12.6°C and 14.9°C in Istanbul from 1975 to
2006. The mean annual temperature was the lowest in 1976
while it was the highest in 2001 with 2.3°C difference
between the two. If the data in table 3 are examined in two
different time periods (1975-1990 and 1991-2006), annual
mean temperatures seem to have differed slightly. Annual
mean temperature is 13.5°C in the first period while it is
13.9°C in the second period. This result reveals that
annual mean temperatures increased especially after 1990
(Figure 3).
Before comparing the mean, minimum and maximum
temperature data of two time periods, the Student’s t test
was applied to assess temperature differences between
two time periods whether they are statistically different
from each other. A statistically significant difference has



1
(9)
 Q N  + Q N + 2   if N is even.
2
2
 2 


  
Table 3: Spatially distributed annual mean, maximum and minimum temperatures in Istanbul from 1975 to 2006
Q
=
Temperatures (°C)
Temperatures (°C)
-----------------------------------------------------------------
-------------------------------------------------------------------
Year
Mean
Max.
Min.
Year
Mean
Max.
Min.
1975
13,8
24,4
5,1
1991
13,2
23,8
5,0
1976
12,6
23,4
3,6
1992
13,1
25,6
4,1
1977
13,7
25,7
4,7
1993
13,1
26,0
4,4
1978
13,5
24,5
4,9
1994
14,7
26,8
5,5
1979
14,1
25,5
4,1
1995
14,0
25,0
4,9
1980
13,2
24,6
4,2
1996
13,3
24,3
5,2
1981
13,6
24,2
4,5
1997
13,1
25,1
3,9
1982
13,2
24,8
4,4
1998
14,1
25,6
5,1
1983
13,4
24,8
4,2
1999
14,8
25,3
5,6
1984
13,6
25,1
4,8
2000
14,2
26,3
4,6
1985
13,1
25,1
3,4
2001
14,9
26,2
5,3
1986
13,5
23,9
4,4
2002
14,5
26,3
5,6
1987
12,9
25,1
2,7
2003
13,6
25,8
4,5
1988
13,4
24,6
4,2
2004
14,0
26,0
3,8
1989
13,7
25,6
4,9
2005
13,9
24,6
4,1
1990
14,1
25,6
4,6
2006
13,9
25,0
4,6
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16,00
Tmean
15,50
15,00
14,50
14,00
13,50
13,00
12,50
12,00
1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Tmax
27,00
26,50
26,00
25,50
25,00
24,50
24,00
23,50
23,00
1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Fig. 3: Annual Tmean, Tmax, Tmin and their linear trends for Istanbul between 1975 and 2006.
Table 4: Annual and seasonal temperature trends of Istanbul over the 32-years period by using Mann-Kendall test and Sen’s method (spring: March-May;
summer: June-August; autumn: September-November; winter: December-February)
Tmean (°C)
Tmean, max (°C)
Tmean, min (°C)
Spring
0.67
1.15
-0.32
1.92**
Summer
2.24***
1.73*
Autumn
0.83+
1.38
0.51
Winter
-0.16
0.16
-0.48
Year
0.83*
1.6**
0.54
+
Significance level <= 90%. *Significance level <= 95%. **Significance level <= 99%. ***Significance level <= 99.9%
been found at 0.05 level of significance between data
series displayed in two time periods as a result of
the Student’s t test. According to the same data
presented in Table 3, mean annual maximum and
minimum temperatures are 25.1°C and 4.5°C respectively
in Istanbul. Mean annual maximum and minimum
temperatures display more variability between years
in comparison with annual mean temperatures.
The lowest mean annual maximum temperature was
observed in 1976 as 23.4°C while the highest value was
seen in 1994 as 26.8°C with 3.4°C difference between the
two. Comparison of the mean annual maximum
temperatures within two time periods (1975-1990 and
1991-2006) reveals that there is 0.7°C difference between
the two. The mean annual maximum temperature was
found to be 24.8°C in the first period from 1975 to
1990 while it increased to 25.5°C in the second time
period from 1991 to 2006. The mean annual minimum
temperatures also display similar pattern between years
although the increase in values slightly lower than the
mean annual maximum temperatures. The lowest mean
annual minimum temperature was observed in 1987 as
2.7°C while the highest value was seen in 1999 and 2002
as 5.6°C. Analysis of the mean annual minimum
temperature within the two time periods reveals that the
second period (1991-2006) experienced a more warming
than the first period (1975-1990). The mean annual
minimum temperature was 4.3°C in the first period (19751990) while the same figure was 4.8°C in the second period
(1991-2006).
The application of the Mann-Kendall test and Sen’s
method demonstrated a positive trend in annual mean,
maximum and minimum temperatures over the 32-years
period in Istanbul. The trends in annual mean and
maximum temperatures are significant at the 95% and 99%
respectively while annual mean minimum temperature is
not statistically significant (Table 4). As seen from the
table 3, the annual mean warming trend in Istanbul was
0.83°C during a 32-years period (1975-2006). This result
indicates a warmer trend in the study area than the
average annual increase in global temperatures which was
0.13 °C (± 0.03°C) per decade over the last 50 years [9].
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Table 4: Spatially distributed seasonal mean, maximum and minimum temperatures in Istanbul over the 32-years period (spring: March-May; summer: JuneAugust; autumn: September-November; winter: December-February)
Spring
Summer
Autumn
Winter
Tmean (°C)
Tmean, max (°C)
Tmean, min (°C)
11,5
22,3
15,1
5,9
25,0
32,7
26,4
16,5
2,0
13,4
6,0
-3,3
Table 5: Spatially distributed monthly mean, maximum and minimum temperatures in Istanbul over the 32-years perio
Months
Tmean (°C)
Tmean, max (°C)
January
5,3
15,6
February
5,1
17,1
March
7,1
21,0
April
11,5
25,5
May
16,0
28,5
June
20,7
32,3
July
23,1
33,0
August
23,0
32,8
September
19,5
30,6
October
15,2
27,0
November
10,5
21,5
December
7,2
16,9
Annual
13,7
25,1
Although there is a positive trend in annual mean
maximum and minimum temperatures, trend is more
important for maximum temperatures. Annual mean
maximum temperature increased 1.6°C, while annual mean
minimum temperature experienced 0.54°C increase over the
period between 1975 and 2006 (Table 4). As this result
reveals, warming trend in annual mean maximum
temperature has played an important role in the increase
in annual mean temperature.
Fig. 3 shows annual Tmean, Tmax, Tmin trends for Istanbul
on over the period 1975-2006 using Sen’s model. As
clearly seen from the figure, the warming trend is more
obvious in annual Tmax altough annual Tmean and Tmin have
also experienced positive trends.
Seasonal Temperatures: Analysis of seasonal
temperature data provided a better understanding of
warming trend in Istanbul. Table 5 represents spatially
distributed seasonal mean, maximum and minimum
temperatures in Istanbul over the 32-years period. As the
table reveals, mean temperatures were 11.5°C in spring,
22.3°C in summer, 15.1°C in autumn and 5.9°C in winter.
Mean maximum temperatures varied from 16.5°C in winter
to 32.7°C in summer. Mean maximum temperatures in
spring and autumn were 25°C and 26.4°C respectively.
Mean minimum temperatures dropped below zero as 3.3°C in winter while it was 13.4°C in summer. Spring and
autumn have experienced slightly low mean minimum
temperatures as 2°C and 6°C respectively in the same
period.
Tmean, min (°C)
-3,7
-4,1
-2,0
2,0
6,1
11,1
14,5
14,6
10,7
6,2
1,0
-2,1
4,5
The analysis of the evaluation of T mean by seasons
with Mann-Kendall test and Sen’s method indicates a
positive trend in all seasons except winter in Istanbul over
the period between 1975 and 2006. The trends in T mean for
spring and winter are not statistically significant.
However, the trends in summer and autumn are significant
at the 99.9% and 90% respectively (Table 4). As table 4
reveals, summer is the season that experienced the
highest warming trend in T mean. Over the 32-years period
from 1975 to 2006, T mean has increased 2.24°C in summer in
Istanbul. Spring and autumn have also experienced a
warming over the same time period as 0.67°C, 0.83°C
respectively. Although the trend is not statistically
significant, winter has experienced 0.16°C decrease in
Tmean over the same period in Istanbul. Fig. 4 shows
seasonal Tmean and their linear trends for Istanbul from
1975 to 2006 using Sen’s model. Highest warming trend in
summer and slight cooling trend in winter is obvious in
Tmean from the figure.
Mann-Kendall test has revealed a positive warming
trend in Tmax in all seasons in Istanbul for the 32-years
period (Table 4). Warming trends in T max were found
statistically insignificant in all seasons except summer.
The trend in Tmax in summer is significant at 95%. As the
results in table 4 reveals, summer was the season
experienced the highest warming in Tmax as 1.73°C over the
32-years period in Istanbul. Spring and autumn have also
experienced warming trends in T max over the same priod as
1.15°C and 1.38°C respectively. Winter did not experience
a significant increase in Tmax so it only increased 0.16°C in
1669
World Appl. Sci. J., 12 (10): 1662-1675, 2011
15,00
Spring
25,00
14,00
24,00
13,00
23,00
12,00
22,00
11,00
21,00
10,00
20,00
9,00
19,00
8,00
1970 1975 1980
1985 1990
1995 2000
18,00
1970 1975 1980
2005 2010
Summer
1985 1990
Years
1995 2000 2005 2010
Years
Fig. 4: Seasonal Tmean and their linear trends for Istanbul between 1975 and 2006.
32 years. A different trend was observed in Tmin for
seasons. As table 4 reveals, spring and winter have
experienced negative trends in Tmin over the 32-years
period although the trends were not statistically
significant. Tmin has decreased 0.32°C and 0.48°C over the
same period in spring and winter respectively in the city.
The analysis of the evolution of Tmin by seasons indicates
1.92°C increase in summer over the periods of 32-years
with 99% significance level. Although it is not statistically
significant, autumn has also experienced 0.51°C increase
in Tmin over the same period in Istanbul.
Monthly Temperatures: Table 5 displays the spatially
distributed monthly mean, maximum and minimum
temperatures for Istanbul over the period from 1975 to
2006. As seen from the table, the July and August are the
warmest months in Istanbul with temperatures 23.1°C and
23°C respectively. According to the mean temperatures,
January and February are the coldest months in Istanbul
with temperatures 5.3°C and 5.1°C respectively. The
highest monthly mean maximum temperatures were
observed in July with 33°C while the same figure was
slightly lower in August as 32.8°C. The lowest monthly
mean maximum temperatures were experienced in January
with 15.6°C. Mean minimum temperatures show a similar
pattern. July and August experienced the highest mean
minimum temperatures as 14.5°C and 14.6°C respectively
while the same figure was the lowest in February as 4.1°C.
Table 6 presents the results of applying the MannKendall test and Sen’s method on monthly mean,
maximum and minimum temperatures of Istanbul over the
32-years period. It was observed that mean temperatures
have experienced a positive trend in all months except
November and December. The most important T mean
increases were detected in July and August with 99.9%
significance. August experienced the highest increase in
Tmean as 2.94°C while the increase was 2.37°C in July from
1975 to 2006. A significant warming trend in Tmean was also
observed in May, June and September (Table 6). T mean has
increased 1.25°C in May, 1.18°C in June and 1.12°C in
September from 1975 to 2006. Although summer months
experienced a significant warming trend, November and
December had a cooling trend in Tmean over the 32-years
period in Istanbul. Although the trend is not statistically
significant, Tmean decreased 0.26°C in November and 0.38°C
in December in Istanbul from 1975 to 2006. A quite small
increase was detected in Tmean in January and March
although the trend was statistically insignificant. T mean has
only increased 0.03°C in January and 0.10°C in March
over the 32-years period in Istanbul.
Results of Mann-Kendall test and Sen’s method
revealed that the trend in monthly T max and T min is different
from that of Tmean. As seen from the table 6, all months
except March demonstrated a positive trend in Tmax. The
most important warming trend in T max was observed in
August with 99.9% significance. Over the 32-years period
from 1975 to 2006, Tmax increased 4.13°C in August in
1670
World Appl. Sci. J., 12 (10): 1662-1675, 2011
Table 6: Monthly temperature trends of Istanbul over the 32-years period (1975-2006) by using Mann-Kendall test and Sen’s method
Months
January
February
March
April
May 1.25+
June 1.18*
July 2.37***
August
September
October
November
December
+
Tmean (°C)
0.03
0.38
0,10
0.45
2.18
0.64
0.67
2.94***
1.12+
1.38
-0.26
-0,38
Tmean, max (°C)
0.90
1.38
-0.29
1.50
-0,10
1.76+
1.76*
4.13***
2.08*
0.86
1.18
0.58
Tmean, min (°C)
0.35
0.13
0.26
-1.50
2.05**
1.41
0.90
-0.99
-1.76
Significance level <= 90%. *Significance level <= 95%. **Significance level <= 99%. ***Significance level <= 99.9%
26,00
Tmean
39,00
25,00
37,00
24,00
23,00
35,00
22,00
33,00
21,00
31,00
20,00
29,00
19,00
1970
Tmax
1980
1990
Years
2000
27,00
1970 1975 1980 1985 1990 1995 2000 2005 2010
2010
Years
18,00
Tmin
17,00
16,00
15,00
14,00
13,00
12,00
11,00
1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Fig. 5: Tmean, Tmax, Tmin and their linear trends in August for Istanbul between 1975 and 2006
Istanbul. Another striking result revealed from Sen’s
method is about the increase in Tmax in September. Tmax
increased 2.08°C in September with 95% significance while
June and July have experienced a lower warming trend as
0.64 and 0.67 respectively over the 32-years period.
Although they had a negative trend in Tmean, November
and December experienced a warming trend in Tmax as
1.18°C and 0.58°C respectively over the same period.
Although statistically insignificant, increase in Tmax in
January and February is much pronounced than Tmean.
These two months have experienced 0.90°C and 1.38°C
increase in their Tmax respectively over the same period
(Table 6). March was the only month that experienced a
cooling trend in Tmax. Tmax decreased 0.29°C over the same
period in March.
The analysis of the evalution of Tmin by months with
Mann-Kendall test and Sen’s method indicates a negative
trend in April, May, November and December while other
months have experienced a positive trend over the period
between 1975 and 2006. The trends in T min are not
statistically significant in the months except June, July
and August. The trends in these months are significant at
90%, 95% and 99% respectively. August experienced the
highest increase in Tmin as 2.05°C over the 32-years period.
As seen from the fig. 5, T mean, T max and T min have increased
in August with over 99% significance. June and July have
also demonstrated a 1.76°C increase in their T min over 32years period. In contrast to its T mean and T max, T min has
displayed a cooling trend in April and May. Although T min
decreased 1.50°C in April, the cooling was only 0.10°C in
1671
World Appl. Sci. J., 12 (10): 1662-1675, 2011
May over the same period. As the results reveal,
November and December had experienced a significant
decrease in Tmin over 32-years period as 0.99°C and 1.76°C
respectively.
CONCLUSION
The present study analyses the evolution of
annual, seasonal and monthly mean, minimum and
maximum temperatures in Istanbul for a 32-years period
between 1975 and 2006 by using Mann-Kendall test
and Sen’s method applied on spatially distributed
monthly temperature data produced by Thiessen
polygon method. The results presented in this paper
reveal positive trends in annual Tmean, Tmax and
Tmin. Annual Tmean has increased 0.83°C while Tmax
and Tmin had experienced an increase of 1.6°C and 0.54°C
respectively in Istanbul over the 32-years period. As
these results indicate, Istanbul has experienced a higher
warming trend than many parts of the world especially the
European continent in annual Tmean over the last three
decades. The increase in Tmean were reported as 0.8 °C in
the world in the last century [10] and as 0.43°C in Europe
over the last 30 years [14]. The annual Tmean has
increased nearly double in Istanbul in comparison with
European continent over nearly the same time period.
The analysis of temperature evolution carried out on
a seasonal basis reveals a stronger statistically significant
positive trend in summer in Tmean, Tmax and Tmin.
Tmean has increased 2.24°C in summer while Tmax and
Tmin have experienced the increase as 1.73°C and 1.92°C
respectively in the same 32-years period. Spring and
autumn have also experienced a significant warming trend
in Tmean, Tmax and Tmin except spring having a negative
trend in its Tmin. In contrast to warming trend found in
other seasons, winter has demonstrated a negative trend
over the same period. Although it is not statistically
significant, Tmean had decreased 0.16°C in winter. The
change on seasonal temperatures over the last few
decades has been observed differently in many parts of
the world. Although a warming trend has been reported
on summer temperatures of different regions in other
studies [24], 2.24°C increase on summer annual mean
temperatures is striking in Istanbul. The negative trends
observed in this study on winter temperatures of Istanbul
are also quite different from what was measured in other
areas around the world [15, 22]. This indicates that global
and regional studies on global warming should not be
used alone and should be evaluated together with local
studies to predict how climate will change in local areas.
The analysis of monthly temperature data in
Istanbul has provided a better understanding of
the change in Tmean, Tmax and Tmin over the
32-years period. The most important warming trend
was observed in August in Tmean, Tmax and Tmin
with statistical significance over 99%. Tmean has
increased 2.94°C in August while Tmax and Tmin had
experienced the increase as 4.13°C and 2.05°C
respectively. July has also experienced a significant
warming trend in Tmean with 99.9% significance over
the same period. Although it is not statistically
significant, November and December have experienced a
decrease in Tmean as 0.26°C and 0.38°C over the period
from 1975 to 2006.
The
study
clearly
shows
very strong
temperature increase in summer and a little decrease in
winter in Istanbul during the last 32 years (1975-2006).
The study revealed that the warming trend especially
observed in the northern hemisphere over the last
half century was also experienced in Istanbul.
The results of the study comply with other
researches which indicated a warming trend all over
Turkey especially in summer months [29, 30, 31]. If the
tendencies continue in the near future, warmer
summers and a bit cooler winters are to be expected
in the city. These differentiated warming patterns may
have important impacts on energy consumption, water
supply, human health and natural environment in
Istanbul. Therefore, future research should concentrate
on the effects of climate change on different physical and
human characteristics of the city. Apart from regular
monitoring of climatic data, strategies and plans have to
be put forward in order to cope with the negative effects
of climate change.
REFERENCES
1.
2.
3.
1672
Cazenave, A., C. Cabanes, K. Dominh, M.C. Gennero
and C. Le Provost, 2003 Present-day sea level
change: Observations and causes, Space Science
Reviews, 108: 131-144.
Bosello, F., R. Roson and R.S.J. Tol, 2007. Economwide estimates of the implications of climate change:
Sea level rise, Environmental and Resource
Economics, 37: 549-571.
Bolch, T., 2007. Climate change and glacier retreat in
northern Tien Shan (Kazakhstan/Kyrgyzstan) using
remote sensing data, Global and Planetary Change,
56: 1-12.
World Appl. Sci. J., 12 (10): 1662-1675, 2011
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Masiokas, M., R. Villalba, B.H. Luckman,
M.E. Lascano, S. Delgado and P. Stepanek, 2008. 20th
century glacier recession and regional hydroclimatic
changes in northwestern Patagonia, Global and
Planetary Change, 60: 85-100.
Epstein, P.R., 2005. Climate change and human health,
The new England J. Med., 353: 1433-1436.
Khasnis, A. and M. Nettleman, 2005. Global warming
and infectious disease, Archives of Medical
Research, 36: 689-696.
Emanuel, K., 2005. Increasing destructiveness of
tropical cyclones over the past 30 years, Nature, 436:
686-688.
Ramon, M.M. and J. Schwartz, 2007. Temperature,
temperature rextremes and mortality: a study of
acclimatisation and effect modification in 50 US cities,
Occupational and Environ. Med., 64: 827-833.
IPCC., 2007. Summary for Policymakers. In: Climate
Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel
on Climate Change [Solomon, S., D. Qin, M.
Manning, Z. Chen, M. Marquis, K.B. Averyt, M.
Tignor and H.L. Miller (eds.)]. Cambridge University
Press, Cambridge, United Kingdom and New York,
NY, USA.
Jones, P.D. and A. Moberg, 2003. Hemispheric and
large-scale surface air temperature variations: An
extensive revision and an update to 2001, Journal of
Climate, 16: 206-223.
Hanseni, J., M. Sato, R. Ruedy, K. Lo, D.W. Lea and
M. Medina-Elizade, 2006. Global temperature change,
Proceedings of the National Academy of Sciences of
the United States of America, 103: 14288-14293.
Rebetez, M. and M. Reinhard 2008. Monthly air
temperature trends in Switzerland 1901-2000 and
1975-2004, Theoretical and Appl. Climatol., 91: 27-34.
Stafford, J.M., G. Wendler and J. Curtis, 2000.
Temperature and precipitation of Alaska: 50 year
trend analysis, Theoretical and Appl. Climatol.,
67: 33-44.
Luterbacher, J., D. Dietrich, E. Xoplaki, M. Grosjean
and H. Wanner, 2004. European seasonal and annual
temperature variability, trends and extremes since
1500, Science, 303: 1499-1503.
Founda, D., K.H. Papadopoulos, M. Petrakis,
C. Giannakopoulos and P. Good, 2004. Analysis of
mean, maximum and minimum temperature in Athens
from 1897 to 2001 with emphasis on the last decade:
trends, warm events and cold events, Global and
Planetary Change, 44: 27-38.
16. Boo, K.O., W.T. Kwon and H.J. Baek, 2006. Change
of extreme events of temperature and precipitation
over Korea using regional projection of future climate
change. Geophys Res 33, L01701, doi:10.1029/
2005GL023378.
17. Brunet, M., P.D. Jones, J. Sigro, O. Saladie, E. Aguilar,
A. Moberg, P.M. Della-Marta, D. Lister, A. Walther
and D. Lo´pez, 2007. Temporal and spatial
temperature variability and change over Spain during
1850-2005, J. Geophys. Res., 112, D12117, doi:10.1029/
2006JD008249.
18. Beniston, M., D.B. Stephenson, O.B. Christensen,
C.A.T. Ferro, C. Frei, S. Goyette, K. Halsnaes,
T. Holt, K. Jylha, B. Koffi, J. Palutikof, R. Scholl,
T. Semmler and K. Woth, 2007. Future extreme
events in European climate: An exploration of
regional climate model projections, Climatic Change,
81: 71-95.
19. Przybylak, R., 2007. Recent air-temperature changes
in the Arctic, Ann. Glaciol., 46: 316-324.
20. Alexander, L.V., P. Hope, D. Collins and
B. Trewin, 2007. Trends in Australia’s climate means
and extremes: A global context, Aust. Met. Mag.,
56: 1-18.
21. Toreti, A. and F. Desiato, 2008. Temperature trend
over Italy from 1961 to 2004, Theoretical and Appl.
Climatol., 91: 51-58.
22. Zhang, X., L.A. Vincent, W.D. Hogg and A. Niitsoo,
2000. Temperature and precipitation trends in
Canada during the 20th century, Atmosphere-Ocean,
38: 395-429.
23. Pielke, R.A., C.A. Davey, D. Niyogi, S. Fall,
J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai,
Y.K. Lim, H. Li, J. Nielsen-Cammon, K. Gallo, R.
Hale, R. Mahmood, S. Foster, R.T. McNider
and P. Blanken, 2007. Unresolved issues with the
assessment of multidecadal global land surface
temperature trends, J. Geophysical Res., 112,
D24S08,doi:10. 1029/2006JD008229.
24. Caesar, J., L. Alexander and R. Vose, 2006. Largescale changes in observed daily maximum and
minimum temperatures: Creation and analysis of a
new gridded data set, J. Geophysical Res., 111,
D05101, doi:10.1029/2005JD006280.
25. Miro, J.J., M.J. Estrela and M. Milla´n, 2006.
Summer temperature trends in a Mediterranean
area (Valencia Region), Intl. J. Climatol., 26: 1051-1073.
26. Turkes, M., U.M. Sumer and G. Kilic, 1995. Variations
and trends in annual mean air temperatures in Turkey
with respect to climatic variability. Intl. J. Climatol.,
15: 557-569.
1673
World Appl. Sci. J., 12 (10): 1662-1675, 2011
27. Alexandrow, V., S. Dakova, H. Aksoy and
A. Dahamsheh, 2004. Analysis of climate change in
southeastern Bulgaria and northwestern Turkey,
Proceedings of the Conference on Water
Observation and Information System for Decision
Support, pp: 1-11, BALWOIS, Ohrid, FY Republic of
Macedonia, 25-29 May 2004, http://balwois.mpl.ird.fr/
balwois/administration/full_paper/ffp-1o-148.pdf
Accessed 16 June 2009.
28. Sönmez, F.K., A.Ü. Kömü çü, A. Erkan and E. Turgut,
2005. An analysis of spatial and temporal dimension
of drought vulnerability in Turkey using the
standardized precipitation index, Natural Hazards,
35: 243-264.
29. Partal, T. and E. Kahya, 2006. Trend analysis in
Turkish precipitation data, Hydorological Processes,
20: 2011-2026.
30. Tecer, L.H., 2009. Temperature trends and changes
in Rize, Turkey for the period 1975 to 2007, Clean,
37(2): 150-159.
31. Tayanç, M., U. Im, M. Do ruel and M. Karaca,
2009. Climate change in Turkey for the last half
century, Climatic Change, 94: 483-502
32. Albek, M., 2008. The effects of climate change on
rivers in the mediterreanean region of Turkey,
Fresenius Environmental Bulletin, 17(9b): 1492-1500
33. Tatl , H., H.N. Dalfes and S.S. Mente , 2005. Surface
air temperature variability over Turkey and its
connection to large-scale upper air circulation via
multivariate techniques. Intl. J. Climatol., 25: 331-350.
34. Price, D.T., D.W. McKenney, I.A. Nalder,
M.F. Hutchinson and J.L. Kesteven, 2000. A
comparison of two statistical methods for spatial
interpolation of Canadian monthly climate data.
Agr. For. Meteorol., 101: 81-94.
35. Rodriquex-Lado, L., G. Sparovek, P. Vidal-Torrado,
D. Dourado-Neto and F. Macias-Vazquez, 2007.
Modelling air temperature for the state of Sao Paulo,
Brazil, Scientia Agricola, 64, doi: 10.1590/S010390162007000500002.
36. Thiessen, A.H., 1911. Precipitation averages for large
areas. Mon. Wea. Rev., 39: 1082-1084.
37. Peck, E.L. and M.J. Brown, 1962. An approach to the
development of isohyetal maps for moun tainous
areas. J. Geophys. Res., 67: 681-694.
38. Schermerhorn, V.P., 1967. Relations between
topography
and
annual
precipitation in
western Oregon and Washington. Water Res.
Res., 3: 707-711.
39. Hutchinson, M.F. and R.J. Bischof, 1983. A new
method of estimating mean seasonal and annual
rainfall for the Hunter Valley, New South Wales.
Aust. Meteorol. Mag., 31: 179-184.
40. Dingman, S.L.,
D.M. Seely-Reynolds and
R.C. Reynolds, 1988. Application of kriging to
estimating mean annual precipitation in a
region of orographic influence. Water Resources B.,
24: 329-339.
41. Phillips, D.L., J. Dolph and D. Marks, 1992. A
comparison of geostatistical procedures for spatial
analysis of precipitation in mountainous terrain.
Agric. For. Meteorol., 58: 119-141
42. Daly, C., R.P. Nielson and D.L. Phillips, 1994. A
statistical-topographic model for mapping climatological precipitation over mountainous terrain.
J. Appl. Meteorol., 33: 140-158.
43. Nalder, I.A. and R.W. Wein, 1998. Spatial
interpolation of climatic normals: test of a new
method in the Canadian boreal forest. Agr. For.
Meteorol., 9: 211-225
44. Reed, W.G. and J.B. Kince, 1917. The preparation of
precipitation charts. Mon. Wea. Rev., 45: 233-235.
45. Spreen, W.C., 1947. A determination of the effect
of topography upon precipitation. Trans. Am.
Geophys. Union, 28: 285-290.
46. Burns, J.I., 1953. Small-scale topographic effects on
precipitation distribution in San Dimas Experimental
Forest. Trans. Am. Geophys. Union, 34: 761-768.
47. Houghton, J.G., 1979. A model for orographic
precipitation in the North-Central Great Basin. Mon.
Wea. Rev., 107: 1462-1475.
48. Gandin, L.S., 1963. Objective Analysis of
Meteorological Fields. Gidrometeoizdat, Leningrad,
p. 287. (English translation: Israel Program for
Scientific Translations, Jerusalem, 1965.
49. Bayraktar, H.F., F.S. Turalioglu and Z. Sen, 2005.
The estimation of average areal rainfall by percentage
weighting polygon method in Southeastern
Anatolia Region, Turkey. Atmospheric Res.,
73(1-2): 149-160.
50. Cheung, W., G.B. Senay and A. Singh, 2008. Trends
and spatial distribution of annual and seasonal
rainfall in Ethiopia. Intl. J. Climatol., 28(13): 1723-1734.
51. Chaoka, R.T., B.F. Alemaw and D.M. Tsiege, 2007.
Modeling and Understanding the Relationship
between Vegetation and Rainfall of Tropical
Watershed using Remote Sensing Data and GIS. J.
Spatial Hydrol., 7(2): 47-61.
1674
World Appl. Sci. J., 12 (10): 1662-1675, 2011
52. Fiedler, F.R., 2003. Simple, Practical Method for
Determining Station Weights Using Thiessen
Polygons and Isohyetal Maps. J. Hydrol.
Engineering, 8(4): 214-218.
53. Li, M.H., M.J. Yang, R. Soong and H.L. Huang,
2005. Simulating Typhoon Floods with Gauge Data
and Mesoscale-Modeled Rainfall in a Mountainous
Watershed J. Hydrometeorol., 6: 306-323.
54. Tumas, 2009 Meteorolojik Veri Ar iv Sistemi
http://tumas.dmi.gov.tr/wps/portal. Accessed 20
June 2009.
55. Tonkaz, T., M. Çetin and K. Tülücü, 2007. The impact
of water resources development projects on water
vapor pressure trends in a semi-arid region, Turkey
Climatic Change, 82:195-209.
56. Türke , M., 2002. Spatial and Temporal Variations
in Precipitation and Aridity Index Series of Turkey.
in: Mediterranean Climate Variability and Trends.
Regional Climate Studies. Springer Verlag,
Heidelberg, pp: 181-213.
57. Sneyers, R., 1990. On the statistical analysis of series
of observations. WMO, Technical Note No. 143,
Geneva, Switzerland, pp: 192.
58. Kukul, Y.S., S. Anaç, E. Yeþil rmak and J.M. Moreas,
2007. Trends of precipitation and stream-flow in
Gediz River Basin, Western Turkey, Fresenius
Environmental Bulletin, 16(5): 477-488.
59. Gan, Y.T., 1998. Hydroclimatic Trends and Possible
Climatic Warming in the Canadian Praires. Water
Resources, 34(11): 3009-3015.
60. Yue, S. and M. Hashino, 2003. Long Term
Trends of Annual and Monthly Precipitation in
Japan. J. American Water Resources Association,
39(3): 587-596.
61. Fu, G., S. Chen, C. Liu and D. Shepard, 2004. HydroClimatic Trends of the Yellow River Basin for the Last
50 Years. Climatic Change, 65(1-2): 149-178.
62. Gemmer, M., S. Becker and T. Jiang, 2004. Observed
Monthly Precipitation Trends in China 1951-2002.
Theoretical and Appl. Climatol., 77(1-2): 39-45.
63. Wang, W., X. Chen, P. Shi and P.H.A.J.M. van
Gelder, 2008. Detecting changes in extreme
precipitation and extreme streamflow in the
Dongjiang River Basin in southern China. Hydrol.
Earth Syst. Sci., 12: 207-221.
64. TÜIK., 2008. Adrese dayal nüfus kay t sistemi
(ADNKS), 2007 Nüfus Say m
Sonuçlar .
http://tuikapp.tuik.gov.tr/adnksdagitimapp/adnks.zu.
Accessed 29 July 2008.
65. Tayanç, M., 2000. An assessment of spatial and
temporal variation of sulfur dioxide levels over
Istanbul, Turkey, Environ. Pollution, 101: 61-69.
66. Anteplio lu, U., S. Topçu and S. Incecik, 2002.
Air Pollution Modeling and Its Application XV,
Edited by Borrego and Schayes “An application of a
photochemical model for urban airshed in Istanbul,
Kluwer Academic/Plenum Publishers, New York,
pp: 167-175.
67. Demirci, A., 2001. Types and distribution of
landslides in the eastern parts of Buyukcekmece
Lake, using GIS, Unpublished Master Thesis, Fatih
University, Social Science Institute, Istanbul.
68. Karaburun, A., A. Demirci and Suen I-Shian, 2009.
Impacts of urban growth on forest cover in Istanbul
(1987-2007), Environmental Monitoring and
Assessment, DOI 10.1007/s10661-009-1000-z
69. Gold, C.M., 1991. Problems with handling spatial
data--the Voronoi approach. C.I.S.M. J., 45: 65-80.
70. Ball, J.E. and K.C. Luk, 1998. Modeling Spatial
Variability of Rainfall Over A Catchment, J. Hydrol.
Engineering, 3: 122-130.
71. Yu, Y.S., S. Zou and D. Whittemore, 1993.
Nonparametric trend analysis of water quality data of
rivers in Kansas. J. Hydrol., 150: 61-80.
72. Sen, P.K., 1968. Estimates of regression coefficients
based on Kendall’s tau. Journal of the American
Statistical Association, 63(1968): 1379-1389.
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