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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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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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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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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