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Trend analysis of streamflow in Turkey
Ercan Kahyaa,*, Serdar Kalaycıb
aIstanbul Technical University, Civil Engineering Department, Hydraulics Division, Maslak, 34469 Istanbul, TurkeybSelcuk University, Civil Engineering Department, Konya, Turkey
Received 10 December 2002; revised 27 October 2003; accepted 13 November 2003
Abstract
This paper presents trends computed for the 31-year period of monthly streamflows obtained from 26 basins over Turkey.
Four non-parametric trend tests (the Sen’s T, the Spearman’s Rho, the Mann-Kendall, and the Seasonal Kendall which are
known as appropriate tools in detecting linear trends of a hydrological time series) are adapted in this study. Moreover, the Van
Belle and Hughes’ basin wide trend test is included in the analysis for the same purpose. Homogeneity of trends in monthly
streamflows is also tested using a procedure developed by Van Belle and Hughes. Thus, this study includes a complete
application of both the Van Belle and Hughes’ tests for homogeneity of trends and basin wide trend (originally developed for
trend detection in water quality data) on a hydroclimatic variable. As a result, basins located in western Turkey, in general,
exhibit downward trend, significant at the 0.05 or lower level, whereas basins located in eastern Turkey show no trend. In most
cases, the first four tests provide the same conclusion about trend existence. Use of the Seasonal Kendall, which involves a
single overall statistic rather than one statistic for each season, is justified by the homogeneity of trend test. Moreover, some
basins located in southern Turkey exhibit a global trend, implying the homogeneity of trends in seasons and stations together,
based on the Van Belle and Hughes’ basin wide trend test.
q 2003 Elsevier B.V. All rights reserved.
Keywords: Climate change; Mann-Kendall test; Non-parametric tests; Streamflow variability; Trend analysis; Turkey
1. Introduction
In general, observational and historical hydrocli-
matologic data are used in planning and designing
water resources projects. There is an implicit
assumption, so called stationarity implying time-
invariant statistical characteristics of the time series
under consideration, in all water resources engineer-
ing works. Such an assumption can no longer be valid
if the presumed changes in global climate as a result
of the increase of greenhouse gases in the atmosphere.
This, of course, results in major problems (e.g.
dislocation and inefficiencies) in regional water
resources management. From a more specific percep-
tion, for example, floods are considered as an outcome
of stationary, independent and identically distributed
random process by hydrologists for a long time.
Nevertheless some investigators (i.e. Cayan and
Peterson, 1989; Lins and Slack, 1999; Jain and Lall,
2000) have reported evidence of trends (possibly due
to anthropogenic influences) and long-term variability
of climate.
Journal of Hydrology 289 (2004) 128–144
www.elsevier.com/locate/jhydrol
0022-1694/$ - see front matter q 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2003.11.006
* Corresponding author. Tel.: 212-285-6802; fax: 212-285-6587.
E-mail addresses: ercan.kahya@itu.edu.tr (E. Kahya);
skalayci@selcuk.edu.tr (S. Kalaycı).
Among regional streamflow-trend studies in the
world, Zhang et al. (2001) stated that monthly mean
streamflow in Canada for most months decreased,
with the strongest decrease in summer and autumn
months, and there was almost no basin exhibiting
upward trend. In contrast, Lettenmaier et al. (1994)
presented the upward streamflow trend pattern, at its
peak in midwinter, covering most of the United States
with the exception of a small number of downtrends
concentrated in the Northwest, Florida, and coastal
Georgia. Lins and Slack (1999) came to similar
conclusion studying streamflow trends calculated for
selected quantiles of discharge. Lettenmaier et al. also
stressed that the trend in streamflow are not fully
parallel to the changes in precipitation and tempera-
ture due to a combination of climate and water
management effects. However, Burn and Elnur (2002)
indicated the similarities in trends and patterns in the
hydrological variables and in meteorological
variables at chosen locations in Canada, implying
the relations between the two groups of variables.
All previous studies regarding trends in surface
climatic variables in Turkey concentrated on
temperature and precipitation patterns. For example,
Turkes et al. (1995) used various non-parametric tests
to identify abrupt changes and trends in the long-term
mean temperature of both individual stations and
geographical regions in Turkey during the period
1930–1992. They found that climate tended to be
warmer in the eastern Anatolia and to be cooler
particularly in the Marmara and Mediterranean
regions using regional mean temperature series.
Turkes (1996) worked with the area-averaged annual
rainfall series during the period 1930–1993 and
pointed out that slightly insignificant decreases were
generally observed over Turkey, particularly in the
Black Sea and Mediterranean regions. Kadıoglu
(1997) examined trends in the mean annual tempera-
ture records during the period 1939–1989 in the
eighteen stations across Turkey and found insignif-
icant increasing trends in the mean annual tempera-
tures. He also indicated that a regional increase in
mean minimum temperatures, which could be attrib-
uted to the urban heat island effect, appeared around
1955. His results are inconclusive for the existence of
long-term trends. In contrast, Tayanc et al. (1997)
found statistically significant cooling in mean
temperatures mostly in northern Turkey and warming
mostly in large urban locations. In the same context,
Karaca et al. (1995) showed the urban heat island
intensity in Istanbul although it is surrounded by the
Black Sea and the Marmara Sea.
To stress the importance of trend analysis of
hydrologic variables (streamflow as the most attract-
ing variable), in a watershed which is assumed not to
be exposed to anthropogenic influences; the following
explanations based on the work of Zhang et al. (2001)
are presented. Under certain geomorphic conditions,
the nature of river reflects the integrated watershed
response to climatic forcing. This critical point was
previously noted by Cayan and Peterson (1989);
Kahya and Dracup (1993) in searching teleconnec-
tions between surface hydroclimatic variables and the
large-scale atmospheric circulation. Since the
geomorphologic evolution of watershed is quite
slow in comparison with climate change, the
detectable changes in the hydrologic regimes of
stable, unregulated watersheds may be considered as
the reflection of changes in climate. Consequently
hydrologic variables might be used as indicators to
detect and monitor climate change.
Because of the review of major trend studies
covering Turkey and the fact of streamflow being a
privileged variable as stated earlier, a study regarding
streamflow trend analysis in the geography of Turkey
seemed to be an important necessity. The objective
of this investigation is to document trend character-
istics of Turkish streamflow data for evidence of
climate change.
2. Data
Monthly mean streamflow records compiled by
EIE (General Directorate of Electrical Power
Resources Survey and Development Administration)
are used in this study. In most hydroclimatologic
studies, a completely homogeneous data set has been
rarely used. Thus, the common practice in most cases
is to put forward reasonable criteria for the condition
of homogeneity. For example, Lins (1985) included
streamflow stations on watercourses where diversion
amounts have been less than 10% of the mean flow
and storage capacity amounted less than 10% of the
mean annual runoff. In order to comply with the
homogeneity condition, a total of 83 streamflow
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 129
gauging stations distributed over 26 river basins
(Fig. 1) have been selected among more than 300
stations and from where there was no reported
regulation or diversion in upstream. This data set is
the same as used by Kahya and Karabork (2001) who
confirmed the homogeneity by checking out the data
for man-made changes, such as jumps due to
relocation of station, regulation or diversion due to
the presence of dams or weirs. Table 1 presents the
number of selected stations for the analysis among
available stations in each basin as well as the basic
features of river basins in Turkey. It should be
recognized that there is a difficulty associated with
differentiating between natural variability and trends.
By taking the preceding arguments into consideration,
we have not applied a test for homogeneity of
streamflow records in this study. The majority of
streamflow records include observations of 31 years
spanning from 1964 to 1994. However, we were
obligated to include some shorter records [Station 212
(1965–1993); Station 1003 (1969–1993); Station
1102 (1965–1993); Station 2505 (1972–1993) and
Station 2507 (1969–1993)] in the analysis for the sake
of at least having one data coverage in each basin.
Hydroclimatologists are concerned with analysing
time series by concentrating on differences in 30-year
normals along the whole period of records. This is why
the period of 30-year is assumed to be long enough for
a valid mean statistic. It also amounts to describing
hydroclimatic time series as non-stationary with local
periods of stationary (Kite, 1991). The length of data
set in our study, mostly 31 years, suffices the minimum
required length in searching evidence of climatic
change in hydroclimatic time series. Burn and Elnur
(2002) stated that the selection of stations in a climate
change research is substantial at the initial step and
that a minimum record length of 25 years ensures
validity of the trend results statistically.
For the climatology of Turkey (readers are
referred to Turkes (1996) for details); precipitation,
the main component of runoff process, displays a
considerable temporal and spatial variability in
Turkey. Annual rainfall totals, in general, decrease
from the coastal belts to the interior. Annual rainfall
amount exceeds 1000 mm and reaches to national
maximum of 2304 mm on far eastern of the coastline
of the Black Sea where rainfall shows almost uniform
distribution over time. Along the Mediterranean
coast, the precipitation mostly occurs in the winter
and the mean annual total of this region is above
800 mm. In the central Anatolia, as a result of being
protected from the moisture bearing air masses, the
range of mean annual precipitation totals are from
350 to 500 mm. Over the continental southeastern
and eastern Anatolia, annual precipitation totals
increase from south (400 mm) to north (800 mm).
In the Marmara and Aegean Sea regions, annual
precipitation totals vary from 600 to 800 mm. The
Atlantic Ocean and the Mediterranean Sea are major
sources of moist air masses, which cause precipi-
tation during late autumn, winter and early spring
over Turkey. These mid-latitude storms from the
Atlantic are predominantly governed by the North
Atlantic Oscillation (NAO) shown in Cullen and
deMenocal (2000). Significant relations between the
NAO and Turkish surface climatic parameters
(precipitation, streamflow, and temperature) have
been recently shown by Karabork et al. (2003).
3. Methodology
The first work in this section is to rationalize the
use of a group of methodological approaches,
successfully applied in other disciplines, in a hydro-
logic variability study. Tests for trend have been of
keen interest in environmental sciences during the
final quarter of the last century. Unfortunately many
existing water quality data in a region is not amenable
to analysis by standard methods. The assumptions of
classical parametric methods (i.e. normality, linearity,
and independence) are mostly not satisfied by water
quality data whose elements sometimes might be
missing to some extent or censored (Van Belle and
Hughes, 1984). These analysis difficulties motivated
some investigators to compare existing trend methods
and develop new methods to overcome the mentioned
problems. But streamflow data set, compared to water
quality data, have less similar problems including the
length of records. Therefore it is worthwhile to
consider the trend analysis techniques used in water
quality studies when examining streamflow data for
the same purpose.
In the climatic and hydrologic literature, only one
non-parametric method (almost always the Mann-
Kendall) has been used in similar trend studies.
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144130
Fig. 1. Locations of streamflow gauging stations used in the study with their basins. Integer in circle indicates the basin identification number.
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In order to have more confidence on the existence of
trend in a streamflow time series, we have decided to
apply five different tests in this study. We assume that
the existence of trend in a streamflow station should
be approved by at least two methods. Similarly,
Yu et al. (1993) utilized three different non-para-
metric trend tests (the Mann-Kendall, the Seasonal
Kendall, and the Sen’s T) and the Van Belle and
Hughes tests to identify linear trends in water quality
data. Alike techniques were applied to search trends in
water quality data across a river basin (e.g. Kahya
et al., 1998; Kalaycı and Kahya, 1998) and in lake
water level data (e.g. Kalaycı et al., 2002) in Turkey.
Yu and his collaborators compared the performances
of these methods by using the Monte Carlo simulation
for each selected sample size, n ¼ 3; 5, 9, and 15
years. For n ¼ 9 and 15, there are no significant
differences in relative power between these methods.
They came up with the same conclusion of Van Belle
and Hughes (1984) that both the Sen’s T and the
Mann-Kendall (aligned rank methods) are asympto-
tically more powerful than intrablock methods such as
the Seasonal Kendall.
Among various widely used techniques, five
non-parametric tests are selected to analyze linear
trends in streamflow data over Turkey. All tests
initially require rank transformation of the original
data and then following usual parametric pro-
cedures. Brief descriptions of the techniques are
briefly presented here. Readers are particularly
referred to Van Belle and Hughes (1984); Yu et al.
(1993) for the details.
Table 1
Summary of major characteristics of basins in Turkey
Basin
number
Basin name Available
stations
Selected
stations
Area of river
basin (£1000 km2)
Basin average
height (m)
Average precipitation
(mm/year)
Total streamflow
(mm/year)
1 Meric 8 1 14.560 56.63 604.0 91.35
2 Marmara 8 1 24.100 42.25 728.7 345.64
3 Susurluk 18 7 22.399 201.56 711.6 242.42
4 Aegean 8 2 10.003 63.75 624.2 208.94
5 Gediz 18 4 18.000 220.06 603.0 108.33
6 Little Menderes 1 1 6.907 4.00 727.4 172.29
7 Big Menderes 23 3 24.976 413.83 664.3 121.32
8 West Mediterranean 15 3 20.953 383.47 875.8 426.19
9 Middle Mediterranean 13 2 19.577 248.85 1,000.4 564.95
10 Burdur Lake 1 1 6.374 910.00 446.3 78.44
11 Afyon 8 1 7.605 1,016.67 451.8 64.43
12 Sakarya 39 11 58.160 508.62 524.7 110.04
13 West Black Sea 29 4 29.598 325.67 811.0 335.50
14 Yesilırmak 24 5 36.114 695.63 496.5 160.60
15 Kızılırmak 27 6 78.180 748.48 446.1 82.89
16 Middle Anatolia 19 2 53.850 1,139.37 416.8 83.94
17 East Mediterranean 19 3 22.048 269.05 745.0 502.09
18 Seyhan 22 2 20.450 749.68 624.0 391.69
19 Hatay 6 2 7.796 159.17 815.6 150.08
20 Ceyhan 21 2 21.982 684.81 731.6 326.63
21 Euphrates 54 7 127.304 1,009.87 540.1 248.30
22 East Black Sea 34 4 24.077 443.24 198.2 618.85
23 Coruh 18 2 19.872 757.39 629.4 317.03
24 Aras 20 2 27.548 1,652.65 432.4 168.07
25 Van Lake 7 2 19.405 1,829.29 474.3 123.16
26 Tigris 24 3 57.614 844.79 807.2 370.22
Total Total Total Average Average Average
484 83 779.452 591.49 620.4 246.67
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144132
3.1. Techniques for trend detection
Sen’s T test: This technique is an aligned rank
method having procedures (Sen, 1968a,b) that first
removes block (season) effect from each datum,
then sum the data over blocks, and finally produce a
statistic from these sums. The aligned rank test is
more powerful than its counterpart (intrablock
procedures) and is distribution free and not affected
by seasonal fluctuations (Van Belle and Hughes,
1984). The original monthly data at a station are
deseasonalized and then converted to the ranks to
calculate the test statistic ‘T’ whose distribution
follows Nð0; 1Þ under the null hypothesis of no
trend. If lTl . za; a trend exists in that station at
the a level. Mathematical developments of the test
are well given in Sen (1968a); Van Belle and
Hughes (1984).
Spearman’s Rho test: A quick and simple test to
determine whether correlation exists between two
classifications of the same series of observations is
the Spearman’s rank correlations test. In this test,
there is a significant trend only if the correlation
between time steps and streamflow observations are
found to be significant. Account of the test statistic
z based on rs was not presented here, since it can
easily be found in statistical books. For n (sample
size) .30, the distribution of rs will be normal,
so that the normal distribution tables can be used.
In this case, the test statistic ðrsÞ is computed by
z ¼ rs
ffiffiffiffiffiffiffin 2 1
p: If lzl . za at a significance level of a;
then the null hypothesis of no trend (on the other
word, values of observations are identically
distributed) is rejected.
Mann-Kendall test:This technique, commonly
known as the Kendall’s tau statistic, has been widely
used to test for randomness against trend in climato-
logical time series (Zhang et al., 2001). In this test, the
null hypothesis Ho states that the deseasonalized data
ðx1;…; xnÞ are a sample of n independent and
identically distributed random variables (Yu et al.,
1993). The alternative hypothesis H1 of a two-sided
test is that the distribution of xk and xj are not identical
for all k; j # n with k – j: The test statistic S is
calculated with Eqs. (1) and (2) which
S ¼Xn21
k¼1
Xn
j¼kþ1
sgnðxj 2 xkÞ ð1Þ
sgnðxj 2 xkÞ ¼
þ1 if ðxj 2 xkÞ . 0
0 if ðxj 2 xkÞ ¼ 0
21 if ðxj 2 xkÞ , 0
8>><>>:
9>>=>>;
ð2Þ
has mean zero and variance of S; computed by
VarðSÞ ¼ ½nðn 2 1Þð2n þ 5Þ2P
t tðt 2 1Þð2t þ 5Þ�=18;
and is asymptotically normal (Hirsch and Slack,
1984), where t is the extent of any given tie andP
t
denotes the summation over all ties. For the cases that
n is larger than 10, the standard normal variate z is
computed by using the following equation (Douglas
et al., 2000).
z ¼
S 2 1ffiffiffiffiffiffiffiffiVarðSÞ
p if S . 0
0 if S ¼ 0
S þ 1ffiffiffiffiffiffiffiffiVarðSÞ
p if S , 0
8>>>>><>>>>>:
9>>>>>=>>>>>;
ð3Þ
Thus, in a two-sided test for trend, the Ho should
be accepted if lzl # za=2 at the a level of
significance. A positive value of S indicates an
‘upward trend’ and a negative value indicates a
‘downward trend’.
Seasonal Kendall test: This test can be used for
time series with seasonal variation and does not
require normality of the time series (Hirsch et al.,
1982; Yu et al., 1993). This test is intended to assess
the randomness of a data set X ¼ ðX1;…;X12Þ and
Xi ¼ ðxi1;…; xi nÞ; where X is a matrix of the entire
monthly data over n years at a station. The test statistic
is a sum of the Mann-Kendall statistic ðS; similar to
that in Eq. (1)) computed for each month. The
interpretation of the rest of the test is similar to that
of the Mann-Kendall test.
Sen’s estimator of slope: If a linear trend is present,
the true slope (change per unit time) can be estimated
by using a simple non-parametric procedure devel-
oped by Sen (1968b). In computational procedures,
the slope estimates of N pairs of data are first
computed by Qi ¼ ðxj 2 xkÞ=ðj 2 kÞ for i ¼ 1;…;N;
where xj and xk are data values at times j and k ðj .
kÞ; respectively. The median of these N values of Qi is
Sen’s estimator of slope. If there is only one datum in
each time period, then N ¼ nðn 2 1Þ=2 where n is the
number of time periods. If N is odd, then Sen’s
estimator is computed by Qmedian ¼ QðNþ1Þ=2 and if N
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 133
is even, then Sen’s estimator is computed by
Qmedian ¼ ½QðNÞ=2 þ QðNþ2Þ=2�=2: The detected value
of Qmedian is tested by a two-sided test at the
100(1 2 a) % confidence interval and true slope
may be obtained by the non-parametric test.
3.2. Van Belle and Hughes’ homogeneity of trend test
The Sen’s T, the Mann-Kendall, the Seasonal
Kendall, and the Spearman’s Rho tests include an
implicit assumption of trend homogeneity between
seasons. Using an imaginary data set, Van Belle and
Hughes (1984) demonstrated that the overall statistic
indicates no trend although a trend is apparent for
each season. As a result, an overall trend test at a
station leads to an ambiguous conclusion when the
trend, in fact, is heterogeneous between seasons. For
this purpose, they suggest a preliminary test for
homogeneity of trend based on the study of cross-
classified data. The overall statistic ðx2totalÞ is parti-
tioned into two parts as x2total ¼ x2
homogeneousþx2trend:
The computation of these three chi-square terms
mainly involves with a standard normal deviate ðZÞ
which is based on the Mann-Kendall statistic ðSÞ for
each season.
For homogeneity in seasonal trends at a station, the
following statistic is calculated.
x2homogeneous ¼x2
total2x2trend ¼
Xm
i¼1
ðZiÞ22mð �ZÞ2 ð5Þ
The values of ðZiÞ and ðZÞ are calculated by
Zi ¼Siffiffiffiffiffiffiffiffiffi
VarðSiÞp and �Z ¼
1
m
Xm
i¼1
Zi
ðm ¼ 12 for monthly dataÞ
ð6Þ
where Si is the Mann-Kendall statistic for month i:
Two possible cases are concerned: (a) if x2homogeneous
exceeds the a level critical value for the chi-square
distribution with ðm 2 1Þ degrees of freedom (df), the
null hypothesis of homogeneous seasonal trends over
time (referring to trends in the same direction) must be
rejected; (b) if x2homogeneous does not exceed, then the
calculated value for x2trend is referred to the chi-square
distribution with df ¼ 1 to test for a common trend in
all seasons. The chi-square statistics are computed
from equations shown in Table 2 (not presented here)
of Van Belle and Hughes (1984). The acceptance or
rejection of the hypothesis can then be determined by
comparing the computed values of x2station; x
2season and
x2station2season with the a level critical values in
the standard chi-square table with ðk 2 1Þ; ðm 2 1Þ
and ðk 2 1Þ: ðm 2 1Þ degree of freedom, respectively.
3.3. Van Belle and Hughes’ trend test for the general
case
In this section, an alternative approach to those
given in the preceding sections is suggested to
recourse if one wants to make a basin-wide
statement about all possible trend features using a
single method. Following to Van Belle and Hughes
(1984), the data from several streamflow stations in
a basin are combined into a single global trend test.
The analysis procedures are similar to analysis of
variance except the use of x2 tests instead of F tests.
For making a basin wide statement about trend in a
variable, Van Belle and Hughes suggest to combine
m seasons of analysis data from k stations for n
years in a basin into a single global trend test. For
this purpose, the following four questions are of
interest: (i) Is the degree of trend homogeneous
between seasons?, (ii) Is the degree of trend
homogeneous between stations?, (iii) Is there
evidence of station-season interaction?, and (iv)
What can be supposed about an overall trend
within-season trend, within-station trend, or within
station-season trend?
Partitioning of the overall statistic ðx2totalÞ is given
in Table 2 of Van Belle and Hughes (1984).
The analysis procedure involves computing the
value of various chi-squares (i.e. x2total; x
2homogeneous;
x2station; x
2season; x
2trend and x2
station2season) for testing the
trend heterogeneity. It will be convenient to present
the rest of the analysis procedures when presenting the
outcomes of this test in Section 4.
4. Results and discussions
4.1. Trend results
The spatial distribution of trends in monthly mean
streamflow for the study period is shown in Fig. 2.
Basins located in western Turkey (marked as dark
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144134
grey in Fig. 2) completely display negative trends,
suggesting decrease in monthly mean streamflow.
Similar findings appear in the west part of south-
eastern Anatolia region. A small region situated
Table 2
Results of homogeneity of trends between months based on the Van
Belle and Hughes’ Homogeneity of Trend test
Basin no Station no x2homogeneous x2
trend
1 101 14.92 39.21*
2 212 17.01 47.54*
3 302 9.19 105.21*
311 2.84 109.38*
314 23.79
316 18.31 17.97*
317 13.38 65.47*
321 30.47þ
324 53.82þ
4 406 44.27þ
407 15.74 126.02*
5 509 7.02 59.81*
510 4.58 139.02*
514 15.22 72.64*
518 0.99 146.79*
6 601 3.04 130.35*
7 701 12.85 82.05*
706 4.95 160.87*
713 23.51þ
8 808 18.09 43.05*
809 6.80 78.24*
812 7.05 80.84*
9 902 9.88 22.33*
912 5.18 11.07*
10 1003 2.73 101.42*
11 1102 10.54 39.44*
12 1203 4.32 193.02*
1216 18.28 103.27*
1221 2.03 103.05*
1222 4.72 47.50*
1223 18.56 118.36*
1224 6.68 143.74*
1226 71.22þ
1233 8.17 27.33*
1237 9.23 32.04*
1242 7.90 97.48*
1243 4.61 54.10*
13 1302 9.91 4.81*
1307 9.05 0.00
1314 7.10 12.67*
1335 7.47 6.07*
14 1401 20.69þ
1402 24.28
1413 7.14 5.46*
1414 27.86þ
1418 10.66 3.55
Table 2 (continued)
Basin no Station no x2homogeneous x2
trend
15 1501 9.70 0.00
1517 2.39 6.54*
1524 11.39 2.53
1528 11.74 1.82
1532 9.93 0.87
1535 7.02 0.00
16 1611 10.32 16.06*
1612 3.94 11.52*
17 1708 4.17 15.68*
1712 8.33 29.60*
1714 11.11 34.55*
18 1801 5.04 1.72
1805 4.67 0.02
19 1905 31.95þ
1906 17.04 50.81*
20 2006 7.93 18.21*
2015 1.72 16.69*
21 2122 16.51 0.10
2124 3.09 15.17*
2131 1.38 33.57*
2132 3.91 61.56*
2145 3.59 19.51*
2147 7.34 0.66
2151 7.13 4.88*
22 2213 7.15 2.33
2218 10.60 1.52
2232 6.01 1.10
2233 5.79 0.06
23 2304 13.31 0.28
2323 14.08 1.18
24 2402 2.66 1.38
2409 2.33
25 2505 14.87 0.61
2507 51.30þ
26 2603 3.60 2.89
2610 6.41 0.52
2612 5.63 0.26
*, refers to that monthly trends are homogeneous; þ , refers to
that monthly trends are heterogeneous; The critical values of
x2homogeneous and x2
trend at a ¼ 0.05 level equal to 19.68 and 3.84,
respectively. See the text for notational explanations.
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 135
Fig. 2. Results of trend analysis. Regions with light (dark) grey reveal a significant downward (upward) trend. The remaining regions (shown in white) demonstrate no significant
trend.
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mostly on northern Yesilırmak basin (marked as light
grey in Fig. 2) displays positive trends, suggesting
increase in monthly mean streamflow. In contrast,
basins in the middle and eastern Turkey (marked as
white in Fig. 2), in general, show no trends. It is noted
that all trends detected are significant at the 0.05 level.
Among 83 stations, a total number of stations
containing positive or negative trend is 56 (56, 47, 61)
based on the Mann-Kendall (the Seasonal Kendall, the
Spearman’s Rho, the Sen’s T) test. In general, 47 (5,
3) stations in 19 (4, 3) basins result in the same
conclusion based on the four (three, two) tests.
Therefore it is readily said that the majority of
detected trends in Fig. 2 were confirmed by at least
three different tests. However, stations 1335, 1401,
1413 and 1414 in Fig. 1 have a trend confirmed only
by one test (mostly by the Seasonal Kendall). Three
stations are located in Kızılırmak basin and their trend
indications are not as much reliable as the other
stations.
4.2. Van Belle and Hughes’ homogeneity of trend test
When analyzing monthly data at a station, the first
condition to check out, in fact, should be the
homogeneity of monthly trends, which is an implicit
assumption in the trend tests. To see the validity of
this assumption in the trend results presented in
Section 4.1, the procedures of Van Belle and Hughes’
homogeneity of trend test are applied for individual
stations within 26 basins and its outcomes are
summarized in Table 2. For example, all computed
x2homogeneous values of three stations in the East
Mediterranean basin (No: 17 in Fig. 1) are less than
the critical x2 (equal to 19.68) with df ¼ 12 2 1 at
the a ¼ 0:05 significance level (Table 2). Since the
x2homogeneous values are not significant, the x2
trend values
for the three stations can be compared to the critical x2
value with df ¼ 1 at the same significance level. As a
result, all three stations have x2trend larger than x2
critical
(equal to 3.84), thus monthly streamflow trends are
homogeneous. In other words, trends in all months
have the same direction (downward). Moreover, the
four (three) tests confirmed the implied trend in
stations 1712 and 1714 (station 1905) in the East
Mediterranean basin.
In general, if x2homogeneous exceeds x2
critical (with
df ¼ 11Þ; the null hypothesis of homogeneous
seasonal trends over time (implying that trends in all
months have the same direction and magnitude)
should be rejected. In this case, the use of the
Seasonal Kendall test becomes questionable, but the
Mann-Kendall test is suggested to apply for each
individual season by considering the effect of positive
serial correlation (Zhang et al., 2001; Burn and Elnur,
2002). Table 2 summarizes the results of Van Belle
and Hughes’ homogeneity of trend test for each
station. Only 14% of stations (12 out of 83) in the
study domain result in heterogeneity in monthly
trends. When inspecting the results given in the third
column of Table 2, the reliability of the trend
indications in Fig. 2 is shown by another approach
since seasonal trends in most stations come out to be
homogeneous. Fig. 3 shows the outcomes of testing
the homogeneity of seasonal trends in graphical
fashion and its resemblance to Fig. 2 is fairly obvious.
Therefore, the analysis results obtained in Section 4.1
do not need to be revised. This evaluation has been
expected, since the indications of both the Mann-
Kendall and the Seasonal Kendall tests for monthly
streamflow were similar for all stations. In contrast,
existing monthly trends are in different directions
according to the Van Belle and Hughes’ Homogeneity
of Trend test in few stations (i.e. stations 314, 321,
406, 713 and 1905) although the four non-parametric
tests indicated a downward trend for these stations.
Therefore, trend testing at these stations is suggested
to be carried out separately for each individual month
as in Zhang et al. (2001). In the other case, if the value
of x2homogeneous is less than x2
critical (with df ¼ 11), then
x2trend is referred to the table of chi-square distribution
with ðdf ¼ 1Þ to demonstrate the possibility of a valid
test for a common trend (for all seasons) at a station.
The results of this possibility are presented in the
fourth column of Table 2, which is completely
consistent with the indications of the third column
(except for station 1418). Thus, a common trend is
present for all seasons in streamflow data, having a
significant trend.
4.3. Van Belle and Hughes’ trend test for the general
case
In this section, our purpose is to test the
homogeneity of trend directions in streamflow at
different stations. This test would be easier if no
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 137
Fig. 3. Homogeneity of seasonal trends based on the Van Belle and Hughes’ homogeneity of trend test. Basins with dark grey have homogeneous monthly trends as the opposite for
the basins with light grey. Basins with white indicate no trend.
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8
seasonal cycles are the component of streamflow time
series. When seasonality is present in the data set, the
four chi-square statistics (i.e. x2total; x
2trend; x
2station, and
x2season) are first computed by using equations given by
Van Belle and Hughes (1984). Then the values of
x2station; x
2season; and x2
station2season are compared with the
relevant critical chi-square values if they are
significant. Before taking these three statistics into
consideration for the global trend analysis, it will be
useful to comment the spatial distributions of stations
whose the chi-square statistics are significant (Fig. 4).
Fig. 4a reveals a picture of the degree of trends
homogeneous between stations. It is meaningless to
obtain a conclusion in some basins having only one
station (i.e. basins with number of 1, 2, 6, and 11; all
shown in white in Fig. 4a) for the statistic x2station:
This is also true for the next cases in Fig. 4b and c.
Basins with the number of 8, 9, 13, 16, 17, and 20 are
also marked as those having a significant trend in Fig.
2. Fig. 4b displays the degree of trends between
seasons for the combined two situations of all
stations in a basin. When comparing this figure
with Fig. 3 (corresponding to the single situation of
all stations in a basin), the basins with the number of
5, 7, 9, 15, 16, 17, 19, 20, and 21 reflect the
homogeneity of seasons for the two distinct cases.
Finally, the station-season interaction is evident in
most basins as shown in Fig. 4c.
In the analysis procedures, the following four cases
are examined:
(a) When all three statistics (x2station; x2
season; and
x2station2season) are not significant, then the x2
trend
statistic can be compared to the value of x2critical
(with df ¼ 1Þ to test for overall or global trend in
a basin. In our analysis, this case was only
possible for eight basins to be tested, but only
four basins (namely, the Mid-Mediterranean, the
Mid-Anatolia, the East-Mediterranean, and the
Ceyhan) located in southern Turkey reveal a
significant global trend. Hence no evidence of
trend heterogeneity is found either between
seasons or between stations. Therefore the
streamflow data could be combined and are
said to have decreasing trend over the period
1964–1994.
(b) When x2season is significant (implying hetero-
geneous seasonal trends), but x2station is not
significant (implying homogeneity of stations);
then different trend direction in each season
should be tested (see Van Belle and Hughes
(1984) for details). Three basins (No: 8, 13, and
23) shown with light grey in Fig. 5 confirm this
condition. The m seasonal statistics are needed to
be calculated in these basins. Then each refers to
the value of x2critical (with df ¼ 1) at the a ¼ 0:05
significance level. In the West Mediterranean
basin (No: 8), the statistic ðkpZ2j :Þ ðj is the index
for season) becomes larger than x2critical (with
df ¼ 1) ¼ 3.84 for each individual month. This
basin has been also previously shown as one of
those having significant downward trend and
seasonal homogeneity (Figs. 2 and 3). In the
West Black Sea basin (No: 13), the statistic is
found to be significant for February, May, June,
July, August, and December months. However,
the western part of this basin was also shown as
an area having significant and homogeneous
monthly streamflow trends (Figs. 2 and 3).
This discrepancy may be due to the effects of
dominant statistics belonging to the months
when computing the overall test statistic. Finally
the Coruh basin (No: 23) is designated as an area
having insignificant trend in which no significant
trend previously was detected in Section 4.1.
This conclusion is also verified by the present
analysis, resulting in insignificant trends for
eleven months.
(c) When the opposite case to that in (b) occurs,
then test for trend at each station is required.
The results of this case are depicted in Fig. 5,
indicating that trend analysis should be carried
out for individual stations in six basins with
dark grey (No: 3, 4, 12, 14, 19, and 25). The k
station statistics are needed to be computed in
these basins. Then each refers to the value of
x2critical (with df ¼ 1) at the a ¼ 0:05 signifi-
cance level. In the Gediz and the Big
Menderes basins (No: 5 and 7), the statistic
ðmpZ2·1Þ (l is the index for station) is found to
be larger than x2critical (with df ¼ 1) ¼ 3.84 for
each individual station, thus the existence of
trend is confirmed. In the Fırat basin (No: 21),
the relevant statistics appear to be less than
the critical value only for stations 2122 and
2147, thus the remaining five stations reveal
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 139
Fig. 4. (a) Spatial distribution of the x2station statistic in the Van Belle and Hughes’ global trend test. In basins with dark grey, the x2
station statistic is
insignificant, referring to the homogeneity of stations. (b) Same as in (a) except for the x2season statistic. (c) Same as in (a) except for the
x2station2season statistic.
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144140
Fig. 5. Results of the Van Belle and Hughes’ global trend analysis for streamflow in Turkey for the cases other than in Fig. 6. Basins with light grey have homogeneous stations and
heterogeneous seasonal trend whereas basins with dark grey have the reverse conditions.
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1
Fig. 6. Results of the Van Belle and Hughes’ global trend analysis for streamflow in Turkey. Basins with light grey have heterogeneous stations and seasonal trends or significant
station-season interaction.
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2
significant trend. However, only station 1517
indicates significant trend in the Kızılırmak
basin (No: 15). In the Hatay and Van Lake
basins (No: 19 and 25), one out of two
stations (1906 and 2507), results in a signifi-
cant trend. The whole findings in this analysis
phase are completely consistent with those
summarised in Fig. 3.
(d) When both x2station and x2
season are significant
(implying that both stations and seasons are
heterogeneous) or x2station2season is significant
(implying that there is a substantial station-
season interaction), then the only meaningful
trend tests can be done for individual station-
season combinations. These tests are carried out
by comparing each Zjl statistic with the critical
value of standard normal distribution. In this
case, Zjl statistic is supposed to be recomputed
with inclusion of the correction of continuity
(Yu et al., 1993). Fig. 6 displays the final case, in
which few basins have heterogeneity in both
seasonal and station trends or in station-season
interaction. Since the x2trend test cannot be done
for these basins, meaningful trend tests can be
applied for stations in the Aegean Water, the
Susurluk, and the Van Lake basins.
Consequently the results presented so far are quite
convincing on the existence of linear trend in
monthly mean streamflow data over Turkey.
Although the analyses in Sections 4.1 and 4.2 are
mainly based on testing at individual station, their
outcomes seem to be consistent with those of the Van
Belle and Hughes’ trend test for the general case in
Section 4.3 which reflects a regional testing.
However, the following facts should be remembered
in evaluating trends in a hydrologic series: (i) there is
a difference between natural low frequency varia-
bility, such as a phase shift in the NAO and the
human-induced climate change and (ii) the multi-
decadal variability could appear as a trend in a
relatively short sample (for example, 30-year).
5. Conclusions
The application of trend detection techniques to
26 Turkish basins has resulted in the identification of
significant trends appearing in the western and south
eastern parts of the country. The direction of trends
is, in general, downward. The both aligned (i.e. the
Sen’s T test) and intrablock (i.e. the Seasonal
Kendall) methods produce more or less similar
conclusions.
The homogeneity of trend directions in multiple
streamflow stations and in months is tested by the Van
Belle and Hughes method. In fact, the homogeneity of
seasonal trends should be tested by this method before
the non-parametric tests in Section 3.1, which
implicitly assume homogeneous seasonal trends in
the time series under consideration, are conducted.
This substantial point somehow has been ruled out in
germane investigations in the literature. It is shown
that this issue did not appear as a problem in the
present study. Moreover the Van Belle and Hughes’
trend test for the general case is first applied to
monthly streamflow data as a comprehensive
approach for the trend detection purpose. In general,
its results seem fairly consistent with those of the
individual non-parametric tests.
As a common conclusion often made in the
relevant previous studies, it would be inappropriate
to express that the observed trends in Turkish
streamflow pattern have occurred as a consequence
of climate change. Moreover, the trend attribution
and the relation between the observed streamflow
trends and climate change should be addressed in
future studies with the inclusion of the influences of
precipitation and temperature variables. Physical
interpretations for the appearance of trend in a
surface hydroclimatologic variable may logically be
related to the greenhouse effects, urban heat islands
aerosol or a contentious subject of global warming
(Balling, 1992). It is wise not to rule out the
possibility that this type of inconclusive (due to
several inherent reasons) changes in a hydroclimato-
logic time series is mostly due to natural variability.
The presence of trends in Turkish streamflow
patterns may be attributed to the observed decreases
in rainfall and, to some extent, to increases in
temperature. Since there is an increasing attention
given to coupling streamflow processes with the
atmospheric circulation models, it is essential to
investigate the nature of streamflow trends over large
domains and how they are related to trends in
precipitation and temperature, that have been better
E. Kahya, S. Kalaycı / Journal of Hydrology 289 (2004) 128–144 143
understood (Lettenmaier et al., 1994). Therefore it
will be plausible to examine whether relations exist
between trends in these three climatologic variables
in Turkey.
Acknowledgements
We thank Dr H. Kerem Cıgızoglu and Dr Mehmet
Karaca for thoughtful reviews. We also appreciate the
fruitful comments of two anonymous reviewers.
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