ORIGINAL PAPER

Identification of trends in hydrological and climatic variablesin Urmia Lake basin, Iran

Farshad Fathian & Saeed Morid & Ercan Kahya

Received: 2 August 2013 /Accepted: 1 February 2014# Springer-Verlag Wien 2014

Abstract The drawdown trend of the water level in UrmiaLake poses a serious problem for northwestern Iran which hashad negative impacts on agriculture and industry. This re-search investigated likely causes of the predicament by esti-mating trends in the time series of hydroclimatic variables ofthe basin. Three non-parametric statistical tests, the Mann–Kendall, Spearman rho, and Sen’s T, were applied to estimatethe trends in the annual and seasonal time series of tempera-ture, precipitation, and streamflow at 95 stations throughoutthe basin. The Theil–Senmethodwas also used to estimate theslopes of trend lines of annual time series. The results showeda significant increasing trend of temperature throughout thebasin and an area-specific precipitation trend. The tests alsoconfirmed a general decreasing trend in the basin streamflowthat was more pronounced in the downstream stations. Theannual trend line slope was found to be from 0.02 to 0.14 °C/year, −7.5 to 3.8 mm/year, and −0.01 to −0.4 m3/s/year fortemperature, precipitation, and streamflow, respectively. Thehomogeneity of the monthly trends was also evaluated usingthe Van Belle and Hughes tests as confirmation. Temporalanalyses of the trends for the temperature and streamflow ofthe basin detected significant increasing trends beginning inthe mid-1980s and 1990s. The correlations betweenstreamflow and climate variables (temperature and precipita-tion) were detected by Pearson’s test. The results showed that

the streamflow in Urmia Lake basin is more sensitive tochanges in temperature than precipitation. In general, thedecline in the lake water level can be related to both theincrease of temperature in the basin and an improvement inover-exploitation of the water resources.

1 Introduction

Urmia Lake has been shrinking for the last 15 years, and itsarea has decreased from 6,100 to 4,750 km2 (Jalili 2010)resulting in about 6 m drawdown in water level. The declineof the water lake has jeopardized the region’s industrial andagricultural sectors. Furthermore, the decrease in water levelof the once vibrant and vivid lake has led to buried-underwatersalt exposure. Persistence of this situation allows the exposedsalt to blow away, causing a serious threat to the health of theinhabitants of the region. Various reasons have been stated asthe major causes of this predicament, including changes inhydroclimatic variables, human activities (development ofagricultural lands due to increasing water diversion for irrigat-ed agriculture), and mismanagement (Eimanifar and Mohebbi2007; Golabian 2011; Zarghami 2011; Hassanzadeh et al.2012). Thus, a trend analysis of rivers’ inflows to the lake isa first step to investigate the plausible causes. However, theexistence of an increasing or decreasing trend in a hydrologictime series can also be explained by changes in precipitationand temperature as two of the most effective meteorologicaldrivers in rainfall–runoff processes.

A number of statistical techniques have been used to iden-tify significant trends in climate variables using either para-metric or non-parametric tests (e.g., Kahya and Kalaycı 2004;Partal and Kahya 2006; Bandyopadhyay et al. 2009; Pal andAl-Tabbaa 2011; Tabari et al. 2012; Kumar et al. 2009; Zhaoet al. 2010; Shahid 2011). The former trend tests are morepowerful than the latter ones; however, they require data to be

F. Fathian (*)Department of Water Resources Engineering, Tarbiat ModaresUniversity, Box: 14115-139, Tehran, Irane-mail: farshad.fathian@yahoo.com

S. MoridDepartment of Water Resources Engineering, Tarbiat ModaresUniversity, Box: 14115-139, Tehran, Iran

E. KahyaHydraulics Division, Civil Engineering Department, IstanbulTechnical University, Maslak, 34469 Istanbul, Turkey

Theor Appl ClimatolDOI 10.1007/s00704-014-1120-4

independent and normally distributed. In contrast, the non-parametric trend tests only require the data to be indepen-dent and can tolerate outliers of the data (Huth and Pokorná2004; Zhang et al. 2006; Chen et al. 2007). The Mann–Kendall (MK), Spearman’s rho (SR), and Sen’s T (ST) testsare typical examples of some non-parametric techniques.Moreover, the Van Bell and Hughes (VH) test used todetect the homogeneity of seasonal trends has been usedin few studies (Kahya and Kalaycı 2004; Dinpashoh et al.2011; Jhajharia et al. 2012).

In Urmia Lake basin (ULB), previous studies have notmuch concentrated on climate variables, except thosewith focus on temperature and precipitation. Streamflow,as the most important data are used in planning anddesigning water resources projects, has received lessattention. Thus, there is an obvious need for more re-search in this area to provide an integrated prospectiveabout status of streamflow, and no study has yet beenexclusively conducted for streamflow trend in Iran, es-pecially in ULB.

Large body of research throughout the world has involvedtrend analysis of hydrological and climatic variables withimportant indications of climate change. In this context, areview of the literature showed that temperature is increasingthroughout the world (Stafford et al. 2000; Brunetti et al.2000; Yue and Hashino 2003; Feidas et al. 2004; Wu et al.2007; Zhao et al. 2010; Fan and Wang 2011), that is, a factemphasized by the Intergovernmental Panel for ClimateChange (IPCC 2001). However, it is not the case for precip-itation as their results have not appeared to be consistent tothose of temperature (Zhang et al. 2000; Stafford et al. 2000;Partal and Kahya 2006; Kumar et al. 2009; Pal and Al-Tabbaa2011; Fan and Wang 2011). The analysis results showed thatthere are different results of precipitation trends depending onthe region of the study. For example, decreasing trends ofannual and seasonal rainfall were noted in Italy (Brunetti et al.2000), India (Duan et al. 2006; Pal and Al-Tabbaa 2011), SirLanka (Zubair et al. 2008), southeastern Australia (Murphyand Timbal 2008), and Turkey (Partal and Kucuk 2006). Onthe other hand, increasing rainfall trends were found inSpain (Mosmann et al. 2004), the USA (Groisman et al.2001), and Canada (Zhang et al. 2000). Trend analysis forstreamflow is more complicated, since it is affected byclimate variables as well as land processes. Streamflowtrends have been extensively analyzed in different partsof the world to document long-term hydrologic trends andpossible effect of climate change on hydrology. Studiesinclude both analyzing trends at catchment (Zhang et al.2006; Masih et al. 2010; Zhao et al. 2010) and nationalscale (Lettenmaier et al. 1994; Kahya and Kalaycı 2004;Kumar et al. 2009).

Specifically, Jahanbakhsh-Asl and Ghavidel Rahimi(2003) used the linear and polynomial regressions to analyze

the temporal trend of annual precipitation trends in ULB.Their results showed extreme fluctuations in annual precipi-tation during 39 years so that this trend is decreasing in moststations over basin. Katiraei et al. (2006) and RezaeiBanafsheh et al. (2010) focused on daily precipitation. Theyapplied the MK test for the period 1960 to 2001 and foundsimilar results about precipitation trend in the basin. Theirresults showed that most stations located in the ULB havedecreasing precipitation trend. In another study, Jalili (2010)also used the MK test and reported no trend in the precipita-tion time series of synoptic stations in the basin for the period1990 to 2005 as increasing temperature trends at moststations.

The objective of present study is to explore temporalmonotonic trends in the time series of temperature, precip-itation, and streamflow in the ULB using three non-parametric statistical techniques, namely the Mann–Ken-dall, Spearman rho, and Sen’s T tests. Trends in one or bothof these variables could be seen as potential evidence ofclimate change and its impact on the hydrologic cycle,which could eventually lead to shifts in the availability ofwater across the ULB. In order to locate the beginningyear(s) of a trend and estimate the slope of trend, weadopted two respective tests: the sequential Mann–Kendall(SMK) and Theil–Sen (TS). Moreover, the Van Belle andHughes homogeneity test is used to check the homogeneityof trends.

2 Data and methodology

2.1 Study area and data

The ULB is located in northwest Iran and covers an area of51,800 km2 (Fig. 1). It is the largest lake in the country and isalso one of the world’s saltiest bodies of water. The lake basinincludes 14 main subbasins that surround the lake with theareas that vary from 431 to 11,759 km2. The most importantrivers are ZarrinehRoud, SiminehRoud, and Aji Chai. Numer-ous hydrometeorological stations exist in the basin; becausesome had short record lengths, not all were applicable. Theselected stations are shown in Fig. 1. They comprise 35 raingauge stations, 35 stream gauge stations, and 25 temperaturegauge stations (Table 1). The gauging stations selected foranalysis were based on a large record of data (>30 years) forvalidity of the time series and trend analysis results andcontinuity of their records as evenly distributed throughoutthe basin as possible (Githui 2009). All records started from1950s, 1960s, and 1970s and ended on with the year 2007 forall analysis. Stations were selected having records with min-imum continuous 30 years of observations. In addition, datarecorded annually from 1966 to 2008 of the lake level atGolmankhaneh station was also prepared and applied for

F. Fathian et al.

further analysis. We applied three non-parametric tests, name-ly Spearman, Mann–Whitney, and run test to evaluate theindependence, homogeneity, and randomness status of data,respectively. The results confirmed the quality of data underconsideration.

2.2 Methodology

We selected three non-parametric methods in this studyto detect and confirm an existing trend with more confi-dence, namely the Mann–Kendall (Mann 1945; Kendall1975), Spearman rho (Sneyers 1991), and Sen’s T (Yueet al. 1993) test. In addition, we applied the sequentialMann–Kendall (Sneyers 1991) and Theil–Sen (Theil1950; Sen 1968) test in order to locate the beginningyear(s) of a trend and to estimate the slope magnitude if

a linear trend is present in a time series, respectively.Since the time scale of our analysis is monthly, the VanBelle and Hughes homogeneity test is used to check thehomogeneity of the monthly results (Van Belle andHughes 1984).

MK test This test, commonly known as the Kendall’s τ,has been widely used to test stationary statistics againsttrend statistics in hydrology and climatology (Burn andElnur 2002). It is a rank-based procedure and good for usewith skewed variables. The MK trend test starts first withcomputing the test statistic S as:

S ¼Xn−1k¼1

Xj¼kþ1

n

Sgn x j−xk� � ð1Þ

Fig. 1 Map showing the studyarea

Identification of trends in hydrological and climatic variables

Table1

Listin

gof

temperature,precipitatio

n,andhydrogauge

stations

used

inthisstudy

Basin

no.Stationnumber

Statio

nname

Location

Height(m)

Span

oftemperature

Length(years)

Span

ofprecipitatio

nLength(years)

Span

ofstream

flow

Length(years)

11

Sahzab

Aghim

onChaiR

iver

1,900

––

1972–2007

361975–2007

33

2Saransar

AjiChaiR

iver

1,660

––

1972–2007

361975–2007

33

3Vanyar

AjiChaiR

iver

1,450

––

1972–2007

361950–2007

58

4Sahlan

SanikhChaiR

iver

1,330

1973–2007

35–

––

–

5Mirkooh

TajyarsarabRiver

1,400

1973–2007

35–

––

–

6Akholeh

AjiChaiR

iver

1,310

––

1972–2007

361975–2007

33

7Sarab

AjiChaiR

iver

1,682

1978–2007

30–

––

–

8Tabriz

AjiChaiR

iver

1,361

1951–2007

57–

––

–

29

Yangjeh

GhalehChaiR

iver

1,650

––

1972–2007

361975–2007

33

10Sh

ishvan

GhalehChaiR

iver

1,270

––

1972–2007

361975–2007

33

311

Alavian

SofiC

haiR

iver

1,600

––

1972–2007

361974–2007

34

12Chekan

ChekanChaiR

iver

1,440

––

1972–2007

361975–2007

33

13Maraghe

SofiChaiR

iver

1,478

1978–2007

30–

––

–

414

Moghanj

Moghanj

ChaiR

iver

1,500

1978–2007

30–

––

–

15Gheshlagh

Amir

Mardogh

ChaiR

iver

1,450

––

1972–2007

361975–2007

33

16Sh

irinkand

LeylanChaiR

iver

1,380

––

1972–2007

361974–2007

34

517

Ghabghablou

Saghez

ChaiR

iver

1,500

––

1970–2007

381975–2007

33

18Nezam

Abad

ZarrinehR

oudRiver

1,283

––

1972–2007

361975–2007

33

19Po

lAnian

JighatoChaiR

iver

1,460

1978–2007

301972–2007

361975–2007

33

20Safakhaneh

SaroghChaiR

iver

1,475

––

1972–2007

361975–2007

33

21Senteh

KherkherehChaiR

iver

1,434

––

1972–2007

361975–2007

33

22SariGhamish

ZarrinehR

oudRiver

1,380

––

1967–2007

411956–2007

52

23SadSh

ahidKazem

iZarrinehR

oudRiver

1,473

1978–2007

30–

––

–

24Saghez

ZarrinehR

oudRiver

1,523

1961–2007

47–

––

–

25Takab

ZarrinehR

oudRiver

1,682

1978–2007

30–

––

–

626

Bokan

Sim

inehRoudRiver

1,350

––

1967–2007

411951–2007

57

27Tazekand

SiminehRoudRiver

1,290

1974–2007

341972–2007

361975–2007

33

28SadNoroozlou

Sim

inehRoudRiver

1,330

1978–2007

30–

––

–

729

Kotar

Mahabad

ChaiR

iver

1,380

––

1971–2007

371975–2007

33

30Po

lSorkh

Mahabad

ChaiR

iver

1,350

1975–2007

33–

31Po

lBahramlou

Gadar

ChaiR

iver

1,285

––

1967–2007

411958–2007

50

32Mahabad

Mahabad

ChaiR

iver

1,352

1978–2007

30–

––

–

833

PeyGhaleh

Gadar

ChaiR

iver

1,500

1978–2007

301967–2007

411966–2007

42

34Naghadeh

Gadar

ChaiR

iver

1,340

––

1967–2007

411966–2007

42

935

Ghasemlou

BalanjC

haiR

iver

1,380

1978–2007

301969–2007

391974–2007

34

36Babaroud

BarandozChaiR

iver

1,285

––

1967–2007

411950–2007

58

1037

MirAbad

Shahr

ChaiR

iver

1,525

1978–2007

301967–2006

401974–2007

34

F. Fathian et al.

where n is the number of observations, xj is the jth observa-tion, and Sgn(.) is the sign function, which can be computedas:

Sgn x j−xk� � ¼

þ10−1

24 if

ifif

x j−xk� �

> 0x j−xk� � ¼ 0x j−xk� �

< 0ð2Þ

The mean of S is zero and its variance can be computed(Kendall 1975) as:

Var Sð Þ ¼n n−1ð Þ 2n þ 5ð Þ−

Xi¼1

m

t t−1ð Þ 2t þ 5ð Þ

18ð3Þ

wherem is the number of groups of tied ranks, each with ti tiedobservations. Mann–Kendall is designated by Z and is com-puted as (Douglas et al. 2000):

Z ¼

S−1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þp0

S−1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þp

if S > 0if S ¼ 0if S < 0

8>>>><>>>>:

ð4Þ

Thus, in a two-sided test for trends, the null hypothesis shouldbe accepted if at the α level of significance. A positive valueof Z indicates an upward trend. The critical value at a 0.10significance level of the trend test is ±1.64.

SR test A quick and simple test to determine whether corre-lation exists between two classifications of the same series ofobservations is the Spearman rank correlations test. Given asample data set {Xi, i=1,2,…,n}, the null hypothesisH0 of theSR test over the trend tests is that Xi is independent andidentically distributed. The alternative hypothesis is that Xiincreases or decreases with i, that is, a trend exists. The teststatistic is:

rs ¼ 1− 6X

R X ið Þ− i½ �2� �.

N3−N� � ð5Þ

ZSR ¼ rS

ffiffiffiffiffiffiffiffiffiffiffin−21−r2S

sð6Þ

where R(Xi) is the rank of the ith observation Xi in a sample ofsize n. Positive values of ZSR indicate upward trends, whilenegative ZSR indicate downward trends in the time series. TheTa

ble1

(contin

ued)

Basin

no.Stationnumber

Statio

nname

Location

Height(m)

Spanof

temperature

Length(years)

Span

ofprecipitatio

nLength(years)

Span

ofstream

flow

Length(years)

38BandUrm

iaShahr

ChaiR

iver

1,390

––

1967–2007

411950–2007

58

39Kam

pUrm

iaShahr

ChaiR

iver

1,381

1978–2007

30–

––

–

1140

GoyjaliA

slan

NazlouChaiR

iver

1,285

––

1968–2007

401975–2007

33

41Kalhor

Rozeh

ChaiR

iver

1,500

––

1977–2007

371975–2007

33

1242

Tapik

NazlouChaiR

iver

1,450

––

1967–2007

411951–2007

57

43Abajalousofla

NazlouChaiR

iver

1,290

1978–2007

301969–2007

391965–2007

43

44MarzSarv

Bardook

River

1,640

1971–2007

37–

––

–

45Urm

iaNazlouChaiR

iver

1,328

1951–2007

57–

––

–

1346

ChehrighO

liaZolaChaiR

iver

1,600

1978–2007

301968–2007

401975–2007

33

47Nazar

Abad

Darik

ChaiR

iver

1,620

––

1971–2007

371975–2007

33

48Tamr

KherkherehChaiR

iver

1,410

––

1971–2007

371970–2007

38

49YalghozAghaj

ZolaChaiR

iver

1,300

1978–2007

301972–2007

361975–2007

33

1450

Daryan

DaryanChaiR

iver

1,600

––

1972–2007

361975–2007

33

51Sh

arafkhaneh

DaryanChaiR

iver

1,270

1968–2007

40–

––

–

52Sh

anjan

Shanjan

River

1,650

1971–2007

37–

––

–

Identification of trends in hydrological and climatic variables

ZSR statistic is approximately normally distributed for the SRstatistic (Yue et al. 2002).

ST test This technique is an aligned rank method havingprocedures expressed in a matrix such as, where n denotesthe number of years andm denotes the number of seasons. Thetest is based on the calculation of the test statistic T, under thenull hypothesis of no trend. In the present study, in order todetect trends for each season, ST test was applied to eachindividual season. If |T| > za, a trend exists at that station at theα level. Mathematical developments of the test are well de-scribed by Partal and Kahya (2006).

SMK test This method analyzes the temporal trends ofhydroclimatic time series (Zhang et al. 2005). The time seriesis assumed for n variables as x1, x2, … xn; pi denotes thecumulative samples where xi>xj (1≤j≤i); dk is calculated as(Zhao et al. 2010):

dk ¼Xi¼1

k

Pi 2 ≤ k ≤ nð Þ ð7Þ

When the original time series is random and independent, themean and the variance of dk are defined as:

E dk½ � ¼ k k−1ð Þ4

ð8Þ

Var dk½ � ¼ k k−1ð Þ 2k þ 5ð Þ72

2 ≤ k ≤ nð Þ ð9Þ

Under the above assumption, the statistic index UFk (MK testbased on the data) is calculated as:

UFk ¼ dk −E dk½ �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar dk½ �p k ¼ 1; 2; 3;…; n ð10Þ

where UFk satisfies the normal distribution and the null hy-pothesis can be rejected at the significance level of α, if |UF|>UF1−α/2. Also, UF1−α/2 is the critical value of the standardnormal distribution with a probability exceeding α/2. Com-puting UB (MK test based on adverse sequence of the data) isrepeated based on the adverse sequence of the above, meaningthat the sequence is from xn to x1.When the UF and UB curvesintersect, the intersection point denotes the jumping (or turn-ing) point (Zhang et al. 2005). In other words, the sequentialversion of the Mann–Kendall is considered as an effectual

way of locating the beginning year(s) of a trend (Partal andKahya 2006).

TS method The magnitude of the slope of the trend is estimat-ed using the approach developed by Theil (1950) and Sen(1968). The slope is estimated using Eq. 11 where Xt and Xs

are data values at time t and s (t>s), respectively (Kumar et al.2009).

β ¼ X t −X s

t−sð11Þ

The median of N=n(n−1)/2 for βi is Sen’s estimator of slopewhere n is the number of time periods. The value of βmedian istested using a two-sided test at the 100(1−α)% confidenceinterval, and the true slope is obtained using the non-parametric test.

2.2.1 Test of homogeneity of trends

The three non-parametric trend tests used in our study implic-itly assume trend homogeneity between seasons. Using animaginary data set, Van Belle and Hughes (1984) demonstrat-ed that the overall statistic indicates no trend, although a trendis apparent for each season. As a result, an overall trend test ata station leads to an ambiguous conclusion when the trend, infact, is heterogeneous between seasons. They suggested com-puting the following three chi-square terms with a standardnormal deviate (Z) based on the MK statistic for each season(Kahya and Kalaycı 2004). Homogeneity of seasonal trends ata station can be calculated as:

χ2homogenous ¼ χ2

total−χ2trend ¼

Xi¼1

m

Z2i −m Z

� �2ð12Þ

Zi and Z are:

Zi ¼ SiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sið Þp and Z ¼ 1

m

Xi¼1

m

Zi m ¼ 12 for monthly datað Þ

ð13Þ

where Si is the MK statistic for month i. This produces twopossible results: (a) If xhomogeneous

2 exceeds the critical valuefor the chi-square distribution of (m−1) degrees of freedom(df), the null hypothesis of homogeneous seasonal trends overtime (trends in the same direction) must be rejected; (b) ifxhomogeneous2 does not (m−1) df, then the value of the x2 trend isa chi-square distribution where df=1 to test for a commontrend in all seasons.

F. Fathian et al.

3 Results and discussion

3.1 Annual and monthly trend analysis in ULB

In this section, we present trend analysis results for eachvariable at the both annual and monthly scales. The threenon-parametric methods nearly resulted in the same number

of temperature stations exhibiting upward or downward trendsfor monthly and annual time scales. The results of trendanalysis using the selected methods for hydrologic and cli-matic variables at 10 % or lower significant level are shown inFig. 2. The number of stations having significant upward(positive) or downward (negative) trends in temperature isshown in Fig. 2a. It has been observed that 20, 21, and 22

-5

0

5

10

15

20

25

Jan

Feb

Mar

Apr

May Jun

July

Aug

Sep Oct

Nov

Dec

Ann

ual

tnacifi

ngis

gniva

hn

oitatsf

ore

bm

uN

po

siti

ve a

nd

neg

ativ

e tr

end

s

Time

Temperature

MK SR ST

a

-10

-5

0

5

10

15

20

25

30

35

Jan

Feb

Mar

Apr

May Jun

July

Aug

Sep Oct

Nov

Dec

Ann

ual

tnacifi

ngis

gniva

hn

oitatsf

ore

bm

uN

po

siti

ve a

nd

neg

ativ

e tr

end

s

Time

Precipitation

MK SR ST

b

-30

-25

-20

-15

-10

-5

0

5

10

Jan

Feb

Mar

Apr

May Jun

July

Aug

Sep Oct

Nov

Dec

Ann

ual

tnacifi

ngis

gniva

hn

oitatsf

ore

bm

uN

po

siti

ve a

nd

neg

ativ

e tr

end

s

Time

Streamflow

MK SR ST

c

Fig. 2 Number of stationsshowing significant upward(positive) and downward(negative) trends for time series ofa temperature, b precipitation,and c streamflow variables,obtained through the MK, SR,and ST methods at 10 % or lowersignificant levels in ULB

Identification of trends in hydrological and climatic variables

out of 25 stations show a significant increasing trend intemperature on the annual time scale according to the MK,SR, and ST tests, respectively.

Spatial distribution of annual temperature trends depictedin Fig. 3a implies that the entire basin almost demonstratedunique upward trend behavior. Temperature data showed asignificant decreasing trend in only two small portion of thewestern (Urmia station) and southern (Saghez station) basin. Itis important to note that at least two tests confirmed theindicated trends in our analysis.

On the monthly time scale, a large number of positivesignificant trends are observed in June, August, and Octoberas a low number of those in January, February, and December(Fig. 4). It can be also noticed that most of the stationsdemonstrated statistically significant increasing trends in thesummer and autumn seasons. Negative significant trends weredetected in few stations (that is to say, two to four of 25stations) at both annual and monthly time scale, except forFebruary andMarch months. Considering the extended periodof May to October, it can be said that prevailing trend-typetemperature behavior is in upward mode across the ULB. It isalso evident that there is no prevailing trend-type temperaturebehavior across the ULB in the period of November to April,indicating a persistent pattern in relation to climate change.Consequently, it can be expected that evaporation and evapo-transpiration trends would be increasing as noted by thestudies of Dinpashoh et al. (2011) and Sabziparvar et al.(2010) for northwestern Iran.

In the evaluation of precipitation trend results, Fig. 2bshows that the number of precipitation stations having signif-icant upward and downward trends is not only less than thoseof temperature and streamflow, but somewhat unparalleledindications of the three methods outcomes are also evident.Unlike temperature, 11 to 37 % of all stations revealed signif-icant trends on the annual time scale, depending on testingmethod. Out of the 35 stations, the total numbers of stations

having significant either positive or negative precipitationtrends are 9 for MK, 10 for SR, and 20 for ST at the annualtime scale. Spatial distribution of annual precipitation trends isdepicted in Fig. 3b implying that the entire basin almostdemonstrated unique no trend behavior (e.g., 25 out of 35stations). Exceptions are downward trends in four precipita-tion stations in the upper western and one station in the easternbasin.

On the monthly time scale, a large number of no significanttrends are almost observed in all months, with more appear-ance in April and October (Fig. 5). This finding is fairlyconsistent with that of annual scale. A number of increasing(decreasing) trends are mainly detected in the period of July toNovember (March to June) in the ULB.

Jalili (2010) also reported that there was no trend in theprecipitation time series of synoptic stations in the basin from1990 to 2005 using the MK test whereas an increasing tem-perature trend was observed at most stations. In contrast,Ghahraman and Taghvaeian (2008) as well as Dinpashohet al. (2013) showed a significant decreasing trend in annualprecipitation over the northwest Iran (covering ULB) usingthe linear regression and MK methods, respectively.

In the evaluation of streamflow trend results, Fig. 2cshows the number of streamflow stations having both sig-nificant upward and downward trends (in most cases).Drastic negative significant trends (reduction of inflows)were detected in a number of stations varying from 20 to71 % of 35 stations. The MK, SR, and ST methods cameout to be the same conclusion being quite consistent innumber as the case of temperature.

The spatial distribution of streamflow trends can be seen inFig. 3c. Negative trends were located in the northwest, south-ern, and eastern of ULB. The downstream subbasins showmore significant non-stationary and negative trends (17 out of35 stations). Most of these areas were agricultural regions, andthis reflects the effect of human interference and the growing

Fig. 3 Spatial variation of annual trends for a temperature, b precipita-tion, c streamflow with increasing trend (up-pointing triangles), decreas-ing trend (down-pointing triangles), and no trend (circles) at the 10 % or

lower significance level in main subbasins of ULB (the numbers neareach station represent the station number according to Table 1)

F. Fathian et al.

exploitation of the upper subbasins. According to the study ofHassanzadeh et al. (2012), various factors have influenced the

water level decline in Urmia Lake so that 65 % of the declinewas from changes in inflow caused by climate change and

Fig. 4 Spatial variation of monthly trends for temperature with increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), andno trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

Identification of trends in hydrological and climatic variables

diversion of surface water for upstream use. It can be alsonoticed that there are no increasing significant trends for

streamflow and the stations with no trend also have negativetrend, but not significantly.

Fig. 5 Spatial variation ofmonthly trends for precipitationwith increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), andno trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

F. Fathian et al.

The highest number of negative (positive) monthly trends(Fig. 6) was reported for October (August). A view of

decreasing trends in annual streamflow in ULB seems to becombined effects of decreasing trend tendency in all months

Fig. 6 Spatial variation of monthly trends for streamflow with increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), andno trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

Identification of trends in hydrological and climatic variables

particularly in January, February, September, and October.Furthermore, comparison of the results shows that the total

number of stations having negative significant streamflowtrends is 17 for MK, 14 for SR, and 25 for ST for the annual

-0.03

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Slo

pe

of

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amfl

ow

(m

3 /s/

year

)

Station Number

Streamflow Slope

b

c

Fig. 7 Estimated trend slopes ofannual mean: a temperature, bprecipitation, and c streamflowcalculated by the Theil–Senmethod in ULB

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1968 1973 1978 1983 1988 1993 1998 2003 2008

Ave

rag

es o

f n

orm

aliz

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ime

seri

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Time

Temperature Streamflow Urmia Lake Level

Surrounds Temperature Precipitation

Fig. 8 Averages of normalizedtime series for annualtemperature, precipitation,discharge at selected stations, andlake level in ULB

F. Fathian et al.

time scale. For the monthly time scale, ST test, unlike theother methods, detected more stations with a significant trend.It should be noted that the results belonging to theMK and TStests for trend detection of monthly and annual temperature,precipitation, and streamflow were similar, and it was ignoredto show in Fig. 2.

3.2 Trend slope of hydrological and climatic variablesin ULB

The respective variations in significant upward and downwardtrends of hydrological and climatic variables using slopevalues calculated by the TS method for each station are shownin Fig. 7. Temperature slopes for 23 out of 25 (92 %) stationsare located above zero line (Fig. 7a). In case of precipitation,

this is 20 out of 35 (57 %) and 15 stations are located belowzero line (Fig. 7b); also for streamflow, 33 out of 35 (95 %)stations show negative slope (Fig. 7c). It can be said that theannual mean increase in basin temperatures varies from 0.02to 0.14 °C/year and the average value is about 0.05 °C/year forthe stations under consideration. Similarly, the annual meanvalues vary from −7.5 to 3.8 (mm/year) for precipitation andfrom −0.01 to −0.4 (m3/s/year) for discharge. Generally, it canbe concluded that the slope values of the annual mean tem-perature and streamflow are positive and negative, respective-ly, as being alternating positive and negative for precipitation.

Moreover, the general behavior of these time series hasbeen evaluated for all selected stations in this study. Figure 8shows the averages of normalized time series for the recordedannual temperature, precipitation, and discharge at selected

0

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4 5 7 8 13 14 19 23 24 25 27 28 30 32 33 35 37 39 43 44 45 46 49 51 52

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ho

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a Critical value of X2

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X2

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s st

ream

flo

w

Station Number

Critical value of X2

b

c

Fig. 9 Results of Van Belle andHughes homogeneity test of atemperature, b precipitation, and cstreamflow monthly trends inULB (crossing the criticalthreshold confirms heterogeneityof monthly trends within astation)

Identification of trends in hydrological and climatic variables

stations and the lake level. The figure shows continuouspositive values for temperature and negative values for dis-charge and lake level. There was a doubt that this positivetrend is a local phenomenon, and it relates to reduction in thesize of the lake and increased available energy to heat airinstead of evaporating the lake’s water body. To accommodatethis, several stations around the basin were selected, andaverages of their standardized values were calculated andadded to Fig. 8. As seen in this figure, the general pattern of

temperature at the basin stations and the surrounding ones issimilar. A general increase in temperature and decrease instreamflow and lake level in the basin are observable afterthe 1990s.

3.3 Homogeneity of detected trends

Figure 2 showed that the monthly trends do not necessarilysimilar nor do all months have significant trends.

0

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4 5 7 8 13 14 19 23 24 25 27 28 30 32 33 35 37 39 43 44 45 46 49 51 52

X2

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a Critical value of X2

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1 2 3 6 9 10 11 12 15 16 17 18 19 20 21 22 26 27 29 31 33 34 35 36 37 38 40 41 42 43 46 47 48 49 50

X2

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atio

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Critical value of X2

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1 2 3 6 9 10 11 12 15 16 17 18 19 20 21 22 26 27 29 31 33 34 35 36 37 38 40 41 42 43 46 47 48 49 50

X2

tren

d s

trea

mfl

ow

Station Number

Critical value of X2

b

c

Fig. 10 Results of Van Belle andHughes trend test of atemperature, b precipitation, and cstreamflow monthly trends inULB (crossing the criticalthreshold confirms homogeneityand existence of commonmonthly trends)

F. Fathian et al.

Nevertheless, when analyzing the hydroclimatic monthly timeseries at a station, homogeneity of monthly trends can beverified (Kahya and Kalaycı 2004). To test the validity of thisassumption, the VH homogeneity test was applied for indi-vidual stations within the 14 subbasins.

As stated, this was done in two steps. First, the homogene-ity of the results was evaluated (Fig. 9), and xhomogeneous

2 of thestations was compared to its critical value at α=0.01 andfound to be about 24.72 with a df of 11. Since xhomogeneous

2

for all stations was less than critical, the monthly trends werehomogenous; otherwise, they were heterogeneous. Thus, thenull hypothesis of homogeneity of the stations can be accepted(Zhang et al. 2001; Burn and Elnur 2002). For temperature,the VH homogeneity of trend test showed that six out of 25stations result in heterogeneity in monthly trends (Fig. 9a).But, in case of precipitation, all station showed homogeneityof monthly trends (Fig. 9b). In addition, nine out of 35streamflow stations show heterogeneity of monthly trends(Fig. 9c).

Once the homogeneity of the monthly trends in a stationhas been confirmed, the second step is to compare x2 trendwith its critical value (6.63 where df=1 at α=0.01),confirming that trends for all months have the same direction.Figure 10 summarizes the results of the VH trend test oftemperature, precipitation, and streamflow at the stations un-der consideration. We found that 88, 28, and 74 % of stations(numerically 22 out of 25, 10 out of 35, and 26 out of 35,respectively) have Xtrend

2 larger than Xcritical2 (equal to 6.68).

Monthly trends in temperature, precipitation, and streamfloware thus homogeneous in the study area. In other words, trendsin all months for each station have the same direction (upwardor downward). Kahya and Kalaycı (2004) also showed thehomogeneity of streamflow trends in Turkey, based on the VHbasin wide trend test.

The spatial status of the homogeneity of trends in thebasin is shown in Fig. 11. The heterogeneity monthlytrends of stations mean that we cannot assume a monotonictrend between months. In other words, nonexistence of

homogeneity in hydroclimatic trends between months im-plies that some months exhibited upward trends, whereasothers show downward trends. The result of estimatedmonthly trends is shown in Fig. 11a, and 19 out of 25temperature stations are homogenous (only six out of 25stations located in the southeastern and western of the basinare heterogeneous), and their resemblance to Fig. 3a isevident. This evaluation was expected because the resultsof three non-parametric tests for monthly temperature weresimilar for all stations. This conclusion was also confirmedin term of precipitation, and there are no heterogeneousmonthly trends for all stations (Fig. 11b). In case ofstreamflow, most of the subbasins showed homogenoustrends, and nine out of 35 stations located in northeasternand southern of basin are heterogeneous (Fig. 11c). Overall,the homogeneity test in the present study revealed thatstations trends are homogenous. This means that similardirection of trends for average value of Z statistics formonths is shown almost for all stations. Moreover, homog-enous nature of monthly trends suggests that trends for allmonths were in the same direction for average values ofZ’s for the station.

3.4 The results of SMK test

The trends of the annual mean values of the hydroclimaticstations were analyzed using the SMK test for the stations thatshowed that significant trends based on at least two tests wereperformed to confirm a significant trend. The time seriesshowed a downward trend when UF<0 (Eq. 10) and anincreasing trend if UF>0.Where UF is greater than the criticalvalue in the figure (the two dashed lines above and belowzero), the upward or downward trend is at 10 % significancelevel (UF and UB=±1.64 lines). Finally, the UF curve shows achanging trend for the hydroclimatic variables at specifictimes (Zhang et al. 2005).

Figure 12 shows the results of the turning point of the MKanalysis of the annual temperature for stations having

Fig. 11 Spatial variation using VH monthly trend test for a temperature, b precipitation, and c streamflow with homogenous monthly trends (up-pointing triangles), heterogeneitymonthly trends (down-pointing triangles), and no trend (circles) at the 1% significance level in main subbasins of ULB

Identification of trends in hydrological and climatic variables

significant trends (23 out of 25). Two general patterns can berecognized for the basin: (i) a continuously increasing trend

began in the 1980s (at Tabriz, Sahlan, Mir Kooh, Tazekand,Takab, Marz Sarv, and Shanjan stations), and (ii) an other

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MahabadUF

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TabrizUF

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SaghezUF

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UrmiaUF

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TakabUF

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SahlanUF

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Mirkooh

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TazekandUF

UB

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Marz Sarv

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AbajalousoflaUF

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Yalghoz AghajUF

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GhasemlouUF

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Pol AnianUF

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Kamp UrmiaUF

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PolsorkhUF

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Chehrigh OliaUF

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Mir AbadUF

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ShanjanUF

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Sad Shahid KazemiUF

UB

Fig. 12 Annual temperature trends turning point detected by the SMK method in ULB

F. Fathian et al.

increasing trend was observable beginning in the 1990s (atMaragheh, Sarab, Mahabad, Abajalousofla, Chehrigh Olia,Pol Anian, and so on stations). As shown in the figure, allUF curves intersected the upper critical value lines (dashedline), confirming the significant trends. In addition, when theUF and UB curves intersect at a point in time, theintersection point denotes the jumping time between1990 and 2000 in the second pattern. For Saghez andUrmia stations, the figures also show decreasing signifi-cant trend beginning in the 1990s and 1970s (where theturning point is present), respectively.

Similarly, Fig. 13 shows the results of the SMK analysisfor stations with significant trends for precipitation (10 outof 35) in the basin. There is no dominant pattern forprecipitation as the both positive and negative trends can

be seen. For instance, Akholeh, Ghabghablou, and Kalhorstations showed increasing trends starting from 1980s thatcrossed the upper bond of the critical value in the middle2000s. In contrast, Saransar, Tapik, and Nazar Abad sta-tions showed decreases in precipitations in the 1970s thatwere also significant in the 1990s. All of 10 stations showjumping (or turning) points where the UB and UF curvescross. At Chekan station, an obvious jumping point alsooccurred at the beginning of the 1980s; the UF curveincreased continuously and crossed the upper critical linein 1992.

Figure 14 shows the MK analysis of the streamflow sta-tions with significant trends (14 out of 35). The results revealthat the trends for the majority of stations are negative, but notnecessarily significant. The UF curves passed the lower

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ChekanUF

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GhabghablouUF

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Mir AbadUF

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Goyjali AslanUF

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KalhorUF

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F &

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TapikUF

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Fig. 13 Annual precipitation trends turning point detected by the SMK method in ULB

Identification of trends in hydrological and climatic variables

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AlavianUF

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NaghadehUF

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Chehrigh OliaUF

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Nazar AbadUF

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TamrUF

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Yalghoz AghajUF

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SafakhanehUF

UB

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YangjehUF

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ShirinkandUF

UB

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UF

& U

B V

alu

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AbajalousoflaUF

UB

Fig. 14 Annual streamflow trends turning point detected by the SMK method in ULB

F. Fathian et al.

critical values at all stations, and all of stations except NezamAbad had more than one intersection of the UF and UB curveswith many of the most recent intersections occurring in 1990s.Two general patterns can be also recognized for temporaltrends of streamflow in the basin: (i) a decreasing trend beganin the middle 1990s and the UF and UB curves intersectbetween 1995 and 2000 (Vanyar, Akholeh, Ghasemlou, andNazar Abad stations), and (ii) a continuously decreasing sig-nificant trend began in the 1990s (Shishvan, Nezam Abad,and Tazekand stations).

The SMK test was also applied in studies of Zhang et al.(2005) and Zhao et al. (2010) for hydroclimatic variables inChina. The results of their studies confirmed the similar resultsof our study for climate variables. Yet in term of streamflow,the trend has increased after 1980s.

The trends and changing points of the streamflowwere alsocompared with the dates of exploitation from the basin dams(Fig. 15). According to the ministry of energy reports of Iran(2010), there are six important dams that are exploiting inULB, and three of them have gauging stations in downstreamof their rivers. The figure shows the status of the river timeseries for the first gauging stations after the dams (Mahabad,ShahidKazemi, and Shahr Chai dams). It is surprising that nosignificant changes occurred in streamflow after the exploita-tion dates.

3.5 Relationship between Urmia Lake level, streamflow,and climate variables trends

Figure 16 shows time series of the level of Lake Urmia from1966 to 2008. A drastic decrease of more than 5 m in the levelis seen in the last decade. The figure also shows that UF andUB values detect approximately the year of the trend changein the lake level time series as 1998. Any correlation betweenthe lake levels, the dates of exploitation of the main basindams, the detected years of trend change in temperature, anddischarge time series has been added to the figure. As shown,the significant change in the lake level is close to the detectedyear of trend changes in temperature and discharge (around1998).

Since streamflow is correlated with climate variables, weattempted to investigate the linear relationship between annualstreamflow at each station and climate variables in the asso-ciated climate stations using correlation analysis. In the samecontext, it is expected that the lake levels are correlated withstreamflow; thus, we performed correlation analysis betweenthe two variables. For this purpose, Pearson coefficient, r, wasestimated (McCuen 2003). This analysis was performed for17 out of 35 streamflow stations that showing significantdecreasing trends in ULB (Fig. 3c) in order to better explainthe probable causes of decline in the lake level. The climate

-3

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1947 1953 1959 1965 1971 1977 1983 1989 1995 2001 2007

UF

& U

B V

alu

es

Band UrmiaUF

UB

Fig. 15 Status of annual streamflow trend turning points and date of ULB dam exploitation for aMahabad, b ShahidKazemi, and c Shahr Chai dams

Explo

itation M

ahabad d

am

Exp

loita

tio

n o

f S

ha

hid

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ze

mi d

am

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nd c

hanges o

f te

mpera

ture

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nd c

hanges o

f dis

charg

e

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ha

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ai d

am

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B v

alu

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ter

La

ke

Le

ve

l (m

)

Time

Water Lake Level UFUB Critical value lines

Fig. 16 Time series of the lakelevel, UF, and UB of ULB from1966 to 2008. Also, the dates ofexploitation of the main basindams and the detected years oftrend change in temperature anddischarge time series have beenshown

Identification of trends in hydrological and climatic variables

data were also selected in the stations near the streamflowstation. It can be noticed that defining the significance of rvalues varies with the number of observations and selectingthe confidence bound, i.e., in case of 30 observations, thevalues outside the range of ±0.361 are defined as significantat 95 % confidence level.

Correlation coefficients (CC) between annual streamflowand precipitation ranged from 0.11 to 0.68 (Table 2), and 12out of 17 stations showed significant correlation, while all of17 temperature stations exhibited significant correlation (CCranged from −0.31 to −0.64), indicating that annualstreamflow and temperature are well correlated. Comparably,precipitation is not completely associated with streamflow(Table 2), although the CC of most stations shows positivevalues. Furthermore, according to spatial distribution plot(Fig. 3a, b), unlike streamflow decreasing trends, precipitationtrends do not show sensible change over ULB. Furthermore,area showing decreasing trends in streamflow do not exhibitdecreasing trend in precipitation. The presence of high corre-lations at annual scale is in agreements with similar studies indifferent basins (Masih et al. 2010; Zhao et al. 2010). In thecase of lake levels, CC with streamflow showed significantcorrelation ranging from 0.32 to 0.62 (Table 2). This may beconcluded from the geographical distribution of streamflow

stations, which seem to be concentratedmostly in downstreamof subbasins and play a key role as inflow coming into the lake(Fig. 7c). Consequently, as the lake level declines, the exposedlakebed is left with a covering of salts and making a vast saltydesert on more than 400 km2 of lost surface area (Golabian2011).

4 Conclusions

This study documentedmonotonic trend behaviors inmonthlyand annual time series of temperature, precipitation, andstreamflow in ULB using a number of non-parametric statis-tical methods. Our results can be summarized as follows:

(i) Temperature has significantly increased throughout thebasin. Unlike temperature, trends in precipitation werenot basin-wide. There was no trend for about 75 % of thestations. Both decreasing and increasing trends were ob-served in the remaining stations. The streamflow resultswere closer to the temperature trends, and about 80 % ofthem show a decreasing trend.

(ii) Spatial distribution of significant trends in streamflowrevealed that the downstream subbasins face decreasingtrends (a decrease in water flowing into the system) andconsequently have declined water level in Urmia Lake.Therefore, it can be primarily attributed to climate changeand over-exploitation of the upper catchments.

(iii) A comparison of estimated monthly and annual trends bythe non-parametric methods showed that they do notobey the same pattern in a given station. To check thegeneral homogeneity of trends, the Van Belle andHughes method was applied. This test confirmed thehomogeneity of the trends in 76, 100, and 75 % fortemperature, precipitation, and streamflow gaugingstations, respectively.

(iv) Timing of trends in the study variables showed tworegional patterns for temperature. One trend increasedcontinually beginning in the 1980s and another began in1995. For streamflow, two general patterns can be alsodiscerned in the basin. One decreasing trend beginningin the middle 1990s between 1995 and 2000 and theother began in 1990s, but not significant. However,changing points in these time series are mainly observ-able in the mid-1990s.

(v) There are three large dams in the basin. No correlationwas observed from the SMK analysis between the ex-ploitation dates of dams and the detected trends or thechanging points of the annual time series of streamflow.

(vi) In general, this research showed that there are two mainreasons for the decline of the lake water level. One isrelated to the increase in temperature and the associatedincreases in evaporation and evapotranspiration, and the

Table 2 Correlation (r) of streamflow with precipitation, temperature,and lake levels for the selected stations in ULB

Station numberof streamflow

Station nameof streamflow

Precipitation Temperature Lakelevels

3 Vanyar 0.26 −0.53c 0.40b

6 Akholeh 0.11 −0.64c 0.49c

9 Yangjeh 0.60c −0.46c 0.32a

10 Shishvan 0.23 −0.37b 0.53c

11 Alavian 0.46c −0.48c 0.37b

15 Gheshlagh Amir 0.56c −0.48c 0.43b

16 Shirinkand 0.53c −0.41b 0.38b

18 Nezam Abad 0.13 −0.31a 0.54c

20 Safakhaneh 0.36b −0.36b 0.31a

27 Tazekand 0.33a −0.49c 0.39b

34 Naghadeh 0.67c −0.60c 0.38b

35 Ghasemlou 0.51c −0.60c 0.59c

43 Abajalousofla 0.41b −0.38b 0.41b

46 ChehrighOlia 0.34a −0.58c 0.45b

47 Nazar Abad 0.13 −0.51c 0.62c

48 Tamr 0.40a −0.58c 0.34a

49 YalghozAghaj 0.68c −0.56c 0.53c

a Significant correlation at 90 % confidence levels indicated by boldnumbersb Significant correlation at 95 % confidence levels indicated by boldnumbersc Significant correlation at 99 % confidence levels indicated by boldnumbers

F. Fathian et al.

other is caused by over-exploitation in upstream. Aquantitative analysis of these is under investigation bythe authors.

(vii) A comparison of precipitation trends in ULB with stud-ies in Turkey (Partal and Kahya 2006), which is alsoaffected by the Mediterranean atmospheric system, re-vealed similar results with the exception of streamflow.For instance, Kahya and Kalaycı (2004) reported notrend for the eastern rivers of Turkey, which are adjacentto the western boundaries of ULB. This fact confirmsthe susceptibility of basin hydrology to significant hu-man impact.

(viii) Generally, the results of trend analysis show a relation-ship between observed streamflow trends and changes inclimatic variables (temperature and precipitation). Thesemay not completely explain the variability in streamflowcaused by the changes in other catchment properties.More importantly, this study can provide a base foradditional research that estimates the effects of humanactivities on the hydrological processes. For instance,land use change can also significantly influence theannual and seasonal streamflow, although the effect ofclimate is the dominant factor in annual streamflow.Overall, it is concluded that this study provides a usefuloverview of hydroclimatic time series trends in ULB forfurther research.

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