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Thus, a trend analysis of rivers' inflows to the lake is a first step to investigate the plausible causes. However, the existence of an increasing or decreasing trend ...
Theor Appl Climatol DOI 10.1007/s00704-014-1120-4

ORIGINAL PAPER

Identification of trends in hydrological and climatic variables in 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 Urmia Lake poses a serious problem for northwestern Iran which has had negative impacts on agriculture and industry. This research investigated likely causes of the predicament by estimating trends in the time series of hydroclimatic variables of the basin. Three non-parametric statistical tests, the Mann– Kendall, Spearman rho, and Sen’s T, were applied to estimate the trends in the annual and seasonal time series of temperature, precipitation, and streamflow at 95 stations throughout the basin. The Theil–Sen method was also used to estimate the slopes of trend lines of annual time series. The results showed a significant increasing trend of temperature throughout the basin and an area-specific precipitation trend. The tests also confirmed a general decreasing trend in the basin streamflow that was more pronounced in the downstream stations. The annual 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 for temperature, precipitation, and streamflow, respectively. The homogeneity of the monthly trends was also evaluated using the Van Belle and Hughes tests as confirmation. Temporal analyses of the trends for the temperature and streamflow of the basin detected significant increasing trends beginning in the mid-1980s and 1990s. The correlations between streamflow and climate variables (temperature and precipitation) were detected by Pearson’s test. The results showed that F. Fathian (*) Department of Water Resources Engineering, Tarbiat Modares University, Box: 14115-139, Tehran, Iran e-mail: [email protected] S. Morid Department of Water Resources Engineering, Tarbiat Modares University, Box: 14115-139, Tehran, Iran E. Kahya Hydraulics Division, Civil Engineering Department, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey

the streamflow in Urmia Lake basin is more sensitive to changes in temperature than precipitation. In general, the decline in the lake water level can be related to both the increase of temperature in the basin and an improvement in over-exploitation of the water resources.

1 Introduction Urmia Lake has been shrinking for the last 15 years, and its area has decreased from 6,100 to 4,750 km2 (Jalili 2010) resulting in about 6 m drawdown in water level. The decline of the water lake has jeopardized the region’s industrial and agricultural sectors. Furthermore, the decrease in water level of the once vibrant and vivid lake has led to buried-underwater salt exposure. Persistence of this situation allows the exposed salt to blow away, causing a serious threat to the health of the inhabitants of the region. Various reasons have been stated as the major causes of this predicament, including changes in hydroclimatic variables, human activities (development of agricultural lands due to increasing water diversion for irrigated agriculture), and mismanagement (Eimanifar and Mohebbi 2007; Golabian 2011; Zarghami 2011; Hassanzadeh et al. 2012). Thus, a trend analysis of rivers’ inflows to the lake is a first step to investigate the plausible causes. However, the existence of an increasing or decreasing trend in a hydrologic time series can also be explained by changes in precipitation and temperature as two of the most effective meteorological drivers in rainfall–runoff processes. A number of statistical techniques have been used to identify significant trends in climate variables using either parametric or non-parametric tests (e.g., Kahya and Kalaycı 2004; Partal and Kahya 2006; Bandyopadhyay et al. 2009; Pal and Al-Tabbaa 2011; Tabari et al. 2012; Kumar et al. 2009; Zhao et al. 2010; Shahid 2011). The former trend tests are more powerful than the latter ones; however, they require data to be

F. Fathian et al.

independent and normally distributed. In contrast, the nonparametric trend tests only require the data to be independent and can tolerate outliers of the data (Huth and Pokorná 2004; Zhang et al. 2006; Chen et al. 2007). The Mann– Kendall (MK), Spearman’s rho (SR), and Sen’s T (ST) tests are typical examples of some non-parametric techniques. Moreover, the Van Bell and Hughes (VH) test used to detect the homogeneity of seasonal trends has been used in few studies (Kahya and Kalaycı 2004; Dinpashoh et al. 2011; Jhajharia et al. 2012). In Urmia Lake basin (ULB), previous studies have not much concentrated on climate variables, except those with focus on temperature and precipitation. Streamflow, as the most important data are used in planning and designing water resources projects, has received less attention. Thus, there is an obvious need for more research in this area to provide an integrated prospective about status of streamflow, and no study has yet been exclusively conducted for streamflow trend in Iran, especially in ULB. Large body of research throughout the world has involved trend analysis of hydrological and climatic variables with important indications of climate change. In this context, a review of the literature showed that temperature is increasing throughout 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 fact emphasized by the Intergovernmental Panel for Climate Change (IPCC 2001). However, it is not the case for precipitation as their results have not appeared to be consistent to those of temperature (Zhang et al. 2000; Stafford et al. 2000; Partal and Kahya 2006; Kumar et al. 2009; Pal and Al-Tabbaa 2011; Fan and Wang 2011). The analysis results showed that there are different results of precipitation trends depending on the region of the study. For example, decreasing trends of annual and seasonal rainfall were noted in Italy (Brunetti et al. 2000), India (Duan et al. 2006; Pal and Al-Tabbaa 2011), Sir Lanka (Zubair et al. 2008), southeastern Australia (Murphy and Timbal 2008), and Turkey (Partal and Kucuk 2006). On the other hand, increasing rainfall trends were found in Spain (Mosmann et al. 2004), the USA (Groisman et al. 2001), and Canada (Zhang et al. 2000). Trend analysis for streamflow is more complicated, since it is affected by climate variables as well as land processes. Streamflow trends have been extensively analyzed in different parts of the world to document long-term hydrologic trends and possible effect of climate change on hydrology. Studies include both analyzing trends at catchment (Zhang et al. 2006; Masih et al. 2010; Zhao et al. 2010) and national scale (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 precipitation during 39 years so that this trend is decreasing in most stations over basin. Katiraei et al. (2006) and Rezaei Banafsheh et al. (2010) focused on daily precipitation. They applied the MK test for the period 1960 to 2001 and found similar results about precipitation trend in the basin. Their results showed that most stations located in the ULB have decreasing precipitation trend. In another study, Jalili (2010) also used the MK test and reported no trend in the precipitation time series of synoptic stations in the basin for the period 1990 to 2005 as increasing temperature trends at most stations. The objective of present study is to explore temporal monotonic trends in the time series of temperature, precipitation, and streamflow in the ULB using three nonparametric statistical techniques, namely the Mann–Kendall, Spearman rho, and Sen’s T tests. Trends in one or both of these variables could be seen as potential evidence of climate change and its impact on the hydrologic cycle, which could eventually lead to shifts in the availability of water across the ULB. In order to locate the beginning year(s) of a trend and estimate the slope of trend, we adopted two respective tests: the sequential Mann–Kendall (SMK) and Theil–Sen (TS). Moreover, the Van Belle and Hughes homogeneity test is used to check the homogeneity of trends.

2 Data and methodology 2.1 Study area and data The ULB is located in northwest Iran and covers an area of 51,800 km2 (Fig. 1). It is the largest lake in the country and is also one of the world’s saltiest bodies of water. The lake basin includes 14 main subbasins that surround the lake with the areas that vary from 431 to 11,759 km2. The most important rivers are ZarrinehRoud, SiminehRoud, and Aji Chai. Numerous hydrometeorological stations exist in the basin; because some had short record lengths, not all were applicable. The selected stations are shown in Fig. 1. They comprise 35 rain gauge stations, 35 stream gauge stations, and 25 temperature gauge stations (Table 1). The gauging stations selected for analysis were based on a large record of data (>30 years) for validity of the time series and trend analysis results and continuity of their records as evenly distributed throughout the basin as possible (Githui 2009). All records started from 1950s, 1960s, and 1970s and ended on with the year 2007 for all analysis. Stations were selected having records with minimum continuous 30 years of observations. In addition, data recorded annually from 1966 to 2008 of the lake level at Golmankhaneh station was also prepared and applied for

Identification of trends in hydrological and climatic variables Fig. 1 Map showing the study area

further analysis. We applied three non-parametric tests, namely Spearman, Mann–Whitney, and run test to evaluate the independence, homogeneity, and randomness status of data, respectively. The results confirmed the quality of data under consideration.

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

2.2 Methodology

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

We selected three non-parametric methods in this study to detect and confirm an existing trend with more confidence, namely the Mann–Kendall (Mann 1945; Kendall 1975), Spearman rho (Sneyers 1991), and Sen’s T (Yue et al. 1993) test. In addition, we applied the sequential Mann–Kendall (Sneyers 1991) and Theil–Sen (Theil 1950; Sen 1968) test in order to locate the beginning year(s) of a trend and to estimate the slope magnitude if



n n −1 X X

  Sgn x j −xk

k¼1 j¼kþ1

ð1Þ

10

9

8

7

6

5

4

3

2

Aghimon Chai River Aji Chai River Aji Chai River Sanikh Chai River Tajyarsarab River Aji Chai River Aji Chai River Aji Chai River Ghaleh Chai River Ghaleh Chai River Sofi Chai River Chekan Chai River Sofi Chai River Moghanj Chai River Mardogh Chai River Leylan Chai River Saghez Chai River ZarrinehRoud River Jighato Chai River Sarogh Chai River Kherkhereh Chai River ZarrinehRoud River ZarrinehRoud River ZarrinehRoud River ZarrinehRoud River SiminehRoud River SiminehRoud River SiminehRoud River Mahabad Chai River Mahabad Chai River Gadar Chai River Mahabad Chai River Gadar Chai River Gadar Chai River Balanj Chai River Barandoz Chai River Shahr Chai River

Sahzab Saransar Vanyar Sahlan Mirkooh Akholeh Sarab Tabriz Yangjeh Shishvan Alavian Chekan Maraghe Moghanj Gheshlagh Amir Shirinkand Ghabghablou Nezam Abad Pol Anian Safakhaneh Senteh Sari Ghamish Sad ShahidKazemi Saghez Takab Bokan Tazekand Sad Noroozlou Kotar Pol Sorkh Pol Bahramlou Mahabad PeyGhaleh Naghadeh Ghasemlou Babaroud Mir Abad

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Location

Basin no. Station number Station name 1,900 1,660 1,450 1,330 1,400 1,310 1,682 1,361 1,650 1,270 1,600 1,440 1,478 1,500 1,450 1,380 1,500 1,283 1,460 1,475 1,434 1,380 1,473 1,523 1,682 1,350 1,290 1,330 1,380 1,350 1,285 1,352 1,500 1,340 1,380 1,285 1,525

– – – 1973–2007 1973–2007 – 1978–2007 1951–2007 – – – – 1978–2007 1978–2007 – – – – 1978–2007 – – – 1978–2007 1961–2007 1978–2007 – 1974–2007 1978–2007 – 1975–2007 – 1978–2007 1978–2007 – 1978–2007 – 1978–2007

– – – 35 35 – 30 57 – – – – 30 30 – – – – 30 – – – 30 47 30 – 34 30 – 33 – 30 30 – 30 – 30 1972–2007 1972–2007 1972–2007 – – 1972–2007 – – 1972–2007 1972–2007 1972–2007 1972–2007 – – 1972–2007 1972–2007 1970–2007 1972–2007 1972–2007 1972–2007 1972–2007 1967–2007 – – – 1967–2007 1972–2007 – 1971–2007 – 1967–2007 – 1967–2007 1967–2007 1969–2007 1967–2007 1967–2006

1975–2007 1975–2007 1950–2007 – – 1975–2007 – – 1975–2007 1975–2007 1974–2007 1975–2007 – – 1975–2007 1974–2007 1975–2007 1975–2007 1975–2007 1975–2007 1975–2007 1956–2007 – – – 1951–2007 1975–2007 – 1975–2007 1958–2007 – 1966–2007 1966–2007 1974–2007 1950–2007 1974–2007

36 36 36 – – 36 – – 36 36 36 36 – – 36 36 38 36 36 36 36 41 – – – 41 36 – 37 41 – 41 41 39 41 40

50 – 42 42 34 58 34

33 33 58 – – 33 – – 33 33 34 33 – – 33 34 33 33 33 33 33 52 – – – 57 33 – 33

Height (m) Span of temperature Length (years) Span of precipitation Length (years) Span of streamflow Length (years)

Table 1 Listing of temperature, precipitation, and hydrogauge stations used in this study

F. Fathian et al.

58 – 33 33 57 43 – – 33 33 38 33 33 – – 1950–2007 – 1975–2007 1975–2007 1951–2007 1965–2007 – – 1975–2007 1975–2007 1970–2007 1975–2007 1975–2007 – –

where n is the number of observations, xj is the jth observation, and Sgn(.) is the sign function, which can be computed as:   2 þ1 if    x j − xk  > 0 Sgn x j − xk ¼ 4 0 if ð2Þ  x j − xk  ¼ 0 −1 x j −xk < 0 if

41 – 40 37 41 39 – – 40 37 37 36 36 – –

The mean of S is zero and its variance can be computed (Kendall 1975) as: nðn− 1Þð2n þ 5Þ−

Band Urmia Kamp Urmia GoyjaliAslan Kalhor Tapik Abajalousofla MarzSarv Urmia ChehrighOlia Nazar Abad Tamr YalghozAghaj Daryan Sharafkhaneh Shanjan

Shahr Chai River Shahr Chai River Nazlou Chai River Rozeh Chai River Nazlou Chai River Nazlou Chai River Bardook River Nazlou Chai River Zola Chai River Darik Chai River Kherkhereh Chai River Zola Chai River Daryan Chai River Daryan Chai River Shanjan River

1,390 1,381 1,285 1,500 1,450 1,290 1,640 1,328 1,600 1,620 1,410 1,300 1,600 1,270 1,650

– 1978–2007 – – – 1978–2007 1971–2007 1951–2007 1978–2007 – – 1978–2007 – 1968–2007 1971–2007

– 30 – – – 30 37 57 30 – – 30 – 40 37

1967–2007 – 1968–2007 1977–2007 1967–2007 1969–2007 – – 1968–2007 1971–2007 1971–2007 1972–2007 1972–2007 – –

VarðS Þ ¼

14

13

12

11

m X i¼1

18

t ðt − 1Þð2t þ 5Þ ð3Þ

where m is the number of groups of tied ranks, each with ti tied observations. Mann–Kendall is designated by Z and is computed as (Douglas et al. 2000): 8 S −1 > > pffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > < VarðS Þ if S > 0 ð4Þ Z ¼ if S ¼ 0 0 > −1 > if S < 0 > pSffiffiffiffiffiffiffiffiffiffiffiffiffi ffi > : VarðS Þ

Thus, in a two-sided test for trends, the null hypothesis should be accepted if at the α level of significance. A positive value of Z indicates an upward trend. The critical value at a 0.10 significance level of the trend test is ±1.64. SR test A quick and simple test to determine whether correlation exists between two classifications of the same series of observations is the Spearman rank correlations test. Given a sample data set {Xi, i=1,2,…,n}, the null hypothesis H0 of the SR test over the trend tests is that Xi is independent and identically distributed. The alternative hypothesis is that Xi increases or decreases with i, that is, a trend exists. The test statistic is:  X .  rs ¼ 1 − 6 ½RðX i Þ− iŠ2 ð5Þ N3 −N

Z SR 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

Basin no. Station number Station name

Table 1 (continued)

Location

Height (m) Span of temperature Length (years) Span of precipitation Length (years) Span of streamflow Length (years)

Identification of trends in hydrological and climatic variables

sffiffiffiffiffiffiffiffiffiffiffi n− 2 ¼ rS 1− r2S

ð6Þ

where R(Xi) is the rank of the ith observation Xi in a sample of size n. Positive values of ZSR indicate upward trends, while negative ZSR indicate downward trends in the time series. The

F. Fathian et al.

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

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

ST test This technique is an aligned rank method having procedures expressed in a matrix such as, where n denotes the number of years and m denotes the number of seasons. The test is based on the calculation of the test statistic T, under the null hypothesis of no trend. In the present study, in order to detect trends for each season, ST test was applied to each individual season. If |T| > za, a trend exists at that station at the α level. Mathematical developments of the test are well described by Partal and Kahya (2006).

TS method The magnitude of the slope of the trend is estimated 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).

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

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

dk ¼

k X

P i ð 2 ≤ k ≤ nÞ

ð7Þ

i¼1

When the original time series is random and independent, the mean and the variance of dk are defined as: E ½d k Š ¼

k ðk −1Þ 4

Var½d k Š ¼

k ðk −1Þð2k þ 5Þ ð2 ≤ k ≤ nÞ 72

ð8Þ

ð9Þ

Under the above assumption, the statistic index UFk (MK test based on the data) is calculated as: d k − E ½d k Š UFk ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k ¼ 1; 2; 3; …; n Var½d k Š

ð10Þ

β ¼

X t −X s t −s

ð11Þ

2.2.1 Test of homogeneity of trends The three non-parametric trend tests used in our study implicitly assume 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. They suggested computing the following three chi-square terms with a standard normal deviate (Z) based on the MK statistic for each season (Kahya and Kalaycı 2004). Homogeneity of seasonal trends at a station can be calculated as: χ2homogenous ¼ χ2total − χ2trend ¼

m X i¼1

 2 Z 2i − m Z

ð12Þ

Zi and Z are: Si 1X Z i ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi and Z ¼ Z i ðm ¼ 12 for monthly dataÞ m VarðS i Þ i¼1 m

ð13Þ where UFk satisfies the normal distribution and the null hypothesis can be rejected at the significance level of α, if |UF|> UF1−α/2. Also, UF1−α/2 is the critical value of the standard normal distribution with a probability exceeding α/2. Computing UB (MK test based on adverse sequence of the data) is repeated based on the adverse sequence of the above, meaning that the sequence is from xn to x1. When the UF and UB curves intersect, the intersection point denotes the jumping (or turning) point (Zhang et al. 2005). In other words, the sequential version of the Mann–Kendall is considered as an effectual

where Si is the MK statistic for month i. This produces two possible results: (a) If x2homogeneous exceeds the critical value for the chi-square distribution of (m−1) degrees of freedom (df), the null hypothesis of homogeneous seasonal trends over time (trends in the same direction) must be rejected; (b) if x2homogeneous does not (m−1) df, then the value of the x2 trend is a chi-square distribution where df=1 to test for a common trend in all seasons.

Identification of trends in hydrological and climatic variables

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

3 Results and discussion 3.1 Annual and monthly trend analysis in ULB In this section, we present trend analysis results for each variable at the both annual and monthly scales. The three non-parametric methods nearly resulted in the same number

a

Temperature

MK

SR

ST

Mar

Apr

May

20 15 10 5 0

Time

b

Annual

Dec

Nov

Oct

Sep

Aug

July

Jun

Feb

-5 Jan

N u m b e r o f s ta ti o n h a v i n g s i g n i fi c a n t positive and negative trends

25

Precipitation

MK

SR

ST

Mar

Apr

May

30 25 20 15 10 5

0 -5

Aug

Sep

Oct

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Dec

Annual

Aug

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Dec

Annual

July

Jun

Feb

-10 Jan

N u m b e r o f s ta ti o n h a v i n g s i g n i fi c a n t positive and negative trends

35

Time

c

Streamflow

MK

SR

ST

Mar

Apr

May

10 5 0 -5 -10 -15

-20 -25

July

Jun

Feb

-30 Jan

N u m b e r o f s ta ti o n h a v i n g s i g n i fi c a n t positive and negative trends

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

Time

F. Fathian et al.

out of 25 stations show a significant increasing trend in temperature on the annual time scale according to the MK, SR, and ST tests, respectively. Spatial distribution of annual temperature trends depicted in Fig. 3a implies that the entire basin almost demonstrated unique upward trend behavior. Temperature data showed a significant decreasing trend in only two small portion of the western (Urmia station) and southern (Saghez station) basin. It is important to note that at least two tests confirmed the indicated trends in our analysis. On the monthly time scale, a large number of positive significant trends are observed in June, August, and October as a low number of those in January, February, and December (Fig. 4). It can be also noticed that most of the stations demonstrated statistically significant increasing trends in the summer and autumn seasons. Negative significant trends were detected in few stations (that is to say, two to four of 25 stations) at both annual and monthly time scale, except for February and March months. Considering the extended period of May to October, it can be said that prevailing trend-type temperature behavior is in upward mode across the ULB. It is also evident that there is no prevailing trend-type temperature behavior 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 evapotranspiration trends would be increasing as noted by the studies of Dinpashoh et al. (2011) and Sabziparvar et al. (2010) for northwestern Iran. In the evaluation of precipitation trend results, Fig. 2b shows that the number of precipitation stations having significant upward and downward trends is not only less than those of temperature and streamflow, but somewhat unparalleled indications of the three methods outcomes are also evident. Unlike temperature, 11 to 37 % of all stations revealed significant trends on the annual time scale, depending on testing method. Out of the 35 stations, the total numbers of stations

having significant either positive or negative precipitation trends are 9 for MK, 10 for SR, and 20 for ST at the annual time scale. Spatial distribution of annual precipitation trends is depicted in Fig. 3b implying that the entire basin almost demonstrated unique no trend behavior (e.g., 25 out of 35 stations). Exceptions are downward trends in four precipitation stations in the upper western and one station in the eastern basin. On the monthly time scale, a large number of no significant trends are almost observed in all months, with more appearance in April and October (Fig. 5). This finding is fairly consistent with that of annual scale. A number of increasing (decreasing) trends are mainly detected in the period of July to November (March to June) in the ULB. Jalili (2010) also reported that there was no trend in the precipitation time series of synoptic stations in the basin from 1990 to 2005 using the MK test whereas an increasing temperature trend was observed at most stations. In contrast, Ghahraman and Taghvaeian (2008) as well as Dinpashoh et al. (2013) showed a significant decreasing trend in annual precipitation over the northwest Iran (covering ULB) using the linear regression and MK methods, respectively. In the evaluation of streamflow trend results, Fig. 2c shows the number of streamflow stations having both significant upward and downward trends (in most cases). Drastic negative significant trends (reduction of inflows) were detected in a number of stations varying from 20 to 71 % of 35 stations. The MK, SR, and ST methods came out to be the same conclusion being quite consistent in number as the case of temperature. The spatial distribution of streamflow trends can be seen in Fig. 3c. Negative trends were located in the northwest, southern, and eastern of ULB. The downstream subbasins show more significant non-stationary and negative trends (17 out of 35 stations). Most of these areas were agricultural regions, and this reflects the effect of human interference and the growing

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

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

Identification of trends in hydrological and climatic variables

Fig. 4 Spatial variation of monthly trends for temperature with increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), and no trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

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

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

F. Fathian et al.

Fig. 5 Spatial variation of monthly trends for precipitation with increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), and no trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

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

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

Identification of trends in hydrological and climatic variables

Fig. 6 Spatial variation of monthly trends for streamflow with increasing trend (up-pointing triangles), decreasing trend (down-pointing triangles), and no trend (circles) at the 10 % or lower significance level in main subbasins of ULB from January to December

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 be combined effects of decreasing trend tendency in all months

F. Fathian et al. 0.15

a

0.13

Temperature Slope

0.11 0.09 0.07 0.05 0.03 0.01

52

51

46

49

44

45

43

39

35

37

33

32

30

27

28

25

24

23

19

14

13

7

4

-0.03

8

-0.01 5

Slope of Temperature (oC/year)

Fig. 7 Estimated trend slopes of annual mean: a temperature, b precipitation, and c streamflow calculated by the Theil–Sen method in ULB

4 2 0 -2 -4 -6 -8

b

Precipitation Slope

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

Slope of Precipitation (mm/year)

Station Number

Station Number

0.05 -0.05 -0.15 -0.25 -0.35

c -0.45

Streamflow Slope

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

Slope of streamflow (m3/s/year)

0.15

Station Number

Fig. 8 Averages of normalized time series for annual temperature, precipitation, discharge at selected stations, and lake level in ULB

Averages of normalized time series

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

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

2 1 0 -1 -2 -3 1968

1973

Temperature

Streamflow

Surrounds Temperature

Precipitation

1978

1983

1988 Time

Urmia Lake Level

1993

1998

2003

2008

Identification of trends in hydrological and climatic variables 70 X2 homogenous temperature

Fig. 9 Results of Van Belle and Hughes homogeneity test of a temperature, b precipitation, and c streamflow monthly trends in ULB (crossing the critical threshold confirms heterogeneity of monthly trends within a station)

Critical value of X2

a

60 50 40 30 20 10 0 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

Station Number

b

Critical value of X2

25 20 15

10 5 0

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 homogenous precipitation

30

Station Number

c

70

Critical value of X2

60 50 40 30 20 10 0

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time scale. For the monthly time scale, ST test, unlike the other methods, detected more stations with a significant trend. It should be noted that the results belonging to the MK and TS tests for trend detection of monthly and annual temperature, precipitation, and streamflow were similar, and it was ignored to show in Fig. 2. 3.2 Trend slope of hydrological and climatic variables in ULB The respective variations in significant upward and downward trends of hydrological and climatic variables using slope values calculated by the TS method for each station are shown in Fig. 7. Temperature slopes for 23 out of 25 (92 %) stations are located above zero line (Fig. 7a). In case of precipitation,

this is 20 out of 35 (57 %) and 15 stations are located below zero line (Fig. 7b); also for streamflow, 33 out of 35 (95 %) stations show negative slope (Fig. 7c). It can be said that the annual mean increase in basin temperatures varies from 0.02 to 0.14 °C/year and the average value is about 0.05 °C/year for the stations under consideration. Similarly, the annual mean values vary from −7.5 to 3.8 (mm/year) for precipitation and from −0.01 to −0.4 (m3/s/year) for discharge. Generally, it can be concluded that the slope values of the annual mean temperature and streamflow are positive and negative, respectively, as being alternating positive and negative for precipitation. Moreover, the general behavior of these time series has been evaluated for all selected stations in this study. Figure 8 shows the averages of normalized time series for the recorded annual temperature, precipitation, and discharge at selected

F. Fathian et al.

160 140 X2 trend temperature

Fig. 10 Results of Van Belle and Hughes trend test of a temperature, b precipitation, and c streamflow monthly trends in ULB (crossing the critical threshold confirms homogeneity and existence of common monthly trends)

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stations and the lake level. The figure shows continuous positive values for temperature and negative values for discharge and lake level. There was a doubt that this positive trend is a local phenomenon, and it relates to reduction in the size of the lake and increased available energy to heat air instead of evaporating the lake’s water body. To accommodate this, several stations around the basin were selected, and averages of their standardized values were calculated and added to Fig. 8. As seen in this figure, the general pattern of

temperature at the basin stations and the surrounding ones is similar. A general increase in temperature and decrease in streamflow and lake level in the basin are observable after the 1990s. 3.3 Homogeneity of detected trends Figure 2 showed that the monthly trends do not necessarily similar nor do all months have significant trends.

Identification of trends in hydrological and climatic variables

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

Nevertheless, when analyzing the hydroclimatic monthly time series at a station, homogeneity of monthly trends can be verified (Kahya and Kalaycı 2004). To test the validity of this assumption, the VH homogeneity test was applied for individual stations within the 14 subbasins. As stated, this was done in two steps. First, the homogeneity of the results was evaluated (Fig. 9), and x2homogeneous of the stations was compared to its critical value at α=0.01 and found to be about 24.72 with a df of 11. Since x2homogeneous for all stations was less than critical, the monthly trends were homogenous; otherwise, they were heterogeneous. Thus, the null 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 25 stations result in heterogeneity in monthly trends (Fig. 9a). But, in case of precipitation, all station showed homogeneity of monthly trends (Fig. 9b). In addition, nine out of 35 streamflow stations show heterogeneity of monthly trends (Fig. 9c). Once the homogeneity of the monthly trends in a station has been confirmed, the second step is to compare x2 trend with 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 of temperature, precipitation, and streamflow at the stations under 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 X2trend larger than X2critical (equal to 6.68). Monthly trends in temperature, precipitation, and streamflow are thus homogeneous in the study area. In other words, trends in all months for each station have the same direction (upward or downward). Kahya and Kalaycı (2004) also showed the homogeneity of streamflow trends in Turkey, based on the VH basin wide trend test. The spatial status of the homogeneity of trends in the basin is shown in Fig. 11. The heterogeneity monthly trends of stations mean that we cannot assume a monotonic trend between months. In other words, nonexistence of

homogeneity in hydroclimatic trends between months implies that some months exhibited upward trends, whereas others show downward trends. The result of estimated monthly trends is shown in Fig. 11a, and 19 out of 25 temperature stations are homogenous (only six out of 25 stations located in the southeastern and western of the basin are heterogeneous), and their resemblance to Fig. 3a is evident. This evaluation was expected because the results of three non-parametric tests for monthly temperature were similar for all stations. This conclusion was also confirmed in term of precipitation, and there are no heterogeneous monthly trends for all stations (Fig. 11b). In case of streamflow, most of the subbasins showed homogenous trends, and nine out of 35 stations located in northeastern and southern of basin are heterogeneous (Fig. 11c). Overall, the homogeneity test in the present study revealed that stations trends are homogenous. This means that similar direction of trends for average value of Z statistics for months is shown almost for all stations. Moreover, homogenous nature of monthly trends suggests that trends for all months were in the same direction for average values of Z’s for the station. 3.4 The results of SMK test The trends of the annual mean values of the hydroclimatic stations were analyzed using the SMK test for the stations that showed that significant trends based on at least two tests were performed to confirm a significant trend. The time series showed a downward trend when UF0. Where UF is greater than the critical value in the figure (the two dashed lines above and below zero), the upward or downward trend is at 10 % significance level (UF and UB=±1.64 lines). Finally, the UF curve shows a changing trend for the hydroclimatic variables at specific times (Zhang et al. 2005). Figure 12 shows the results of the turning point of the MK analysis of the annual temperature for stations having

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

significant trends (23 out of 25). Two general patterns can be recognized 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

Identification of trends in hydrological and climatic variables 3 2

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

increasing trend was observable beginning in the 1990s (at Maragheh, Sarab, Mahabad, Abajalousofla, Chehrigh Olia, Pol Anian, and so on stations). As shown in the figure, all UF curves intersected the upper critical value lines (dashed line), confirming the significant trends. In addition, when the UF and UB curves intersect at a point in time, the intersection point denotes the jumping time between 1990 and 2000 in the second pattern. For Saghez and Urmia stations, the figures also show decreasing significant trend beginning in the 1990s and 1970s (where the turning point is present), respectively. Similarly, Fig. 13 shows the results of the SMK analysis for stations with significant trends for precipitation (10 out of 35) in the basin. There is no dominant pattern for precipitation as the both positive and negative trends can

be seen. For instance, Akholeh, Ghabghablou, and Kalhor stations showed increasing trends starting from 1980s that crossed the upper bond of the critical value in the middle 2000s. In contrast, Saransar, Tapik, and Nazar Abad stations showed decreases in precipitations in the 1970s that were also significant in the 1990s. All of 10 stations show jumping (or turning) points where the UB and UF curves cross. At Chekan station, an obvious jumping point also occurred at the beginning of the 1980s; the UF curve increased continuously and crossed the upper critical line in 1992. Figure 14 shows the MK analysis of the streamflow stations with significant trends (14 out of 35). The results reveal that the trends for the majority of stations are negative, but not necessarily significant. The UF curves passed the lower

F. Fathian et al. 5

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Identification of trends in hydrological and climatic variables

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

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

critical values at all stations, and all of stations except Nezam Abad had more than one intersection of the UF and UB curves with many of the most recent intersections occurring in 1990s. Two general patterns can be also recognized for temporal trends of streamflow in the basin: (i) a decreasing trend began in the middle 1990s and the UF and UB curves intersect between 1995 and 2000 (Vanyar, Akholeh, Ghasemlou, and Nazar Abad stations), and (ii) a continuously decreasing significant 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 in China. The results of their studies confirmed the similar results of our study for climate variables. Yet in term of streamflow, the trend has increased after 1980s. The trends and changing points of the streamflow were also compared 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 in ULB, and three of them have gauging stations in downstream of their rivers. The figure shows the status of the river time series for the first gauging stations after the dams (Mahabad, ShahidKazemi, and Shahr Chai dams). It is surprising that no significant changes occurred in streamflow after the exploitation dates. 1278

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Fig. 16 Time series of the lake level, UF, and UB of ULB from 1966 to 2008. Also, the dates of exploitation of the main basin dams and the detected years of trend change in temperature and discharge time series have been shown

Figure 16 shows time series of the level of Lake Urmia from 1966 to 2008. A drastic decrease of more than 5 m in the level is seen in the last decade. The figure also shows that UF and UB values detect approximately the year of the trend change in the lake level time series as 1998. Any correlation between the lake levels, the dates of exploitation of the main basin dams, the detected years of trend change in temperature, and discharge time series has been added to the figure. As shown, the significant change in the lake level is close to the detected year of trend changes in temperature and discharge (around 1998). Since streamflow is correlated with climate variables, we attempted to investigate the linear relationship between annual streamflow at each station and climate variables in the associated climate stations using correlation analysis. In the same context, it is expected that the lake levels are correlated with streamflow; thus, we performed correlation analysis between the two variables. For this purpose, Pearson coefficient, r, was estimated (McCuen 2003). This analysis was performed for 17 out of 35 streamflow stations that showing significant decreasing trends in ULB (Fig. 3c) in order to better explain the probable causes of decline in the lake level. The climate

F. Fathian et al. Table 2 Correlation (r) of streamflow with precipitation, temperature, and lake levels for the selected stations in ULB Station number Station name of streamflow of streamflow

Precipitation Temperature Lake levels

3 6 9 10 11 15 16 18 20

Vanyar Akholeh Yangjeh Shishvan Alavian Gheshlagh Amir Shirinkand Nezam Abad Safakhaneh

0.26 0.11 0.60c 0.23 0.46c 0.56c 0.53c 0.13 0.36b

−0.53c −0.64c −0.46c −0.37b −0.48c −0.48c −0.41b −0.31a −0.36b

0.40b 0.49c 0.32a 0.53c 0.37b 0.43b 0.38b 0.54c 0.31a

27 34 35 43 46 47 48 49

Tazekand Naghadeh Ghasemlou Abajalousofla ChehrighOlia Nazar Abad Tamr YalghozAghaj

0.33a 0.67c 0.51c 0.41b 0.34a 0.13 0.40a 0.68c

−0.49c −0.60c −0.60c −0.38b −0.58c −0.51c −0.58c −0.56c

0.39b 0.38b 0.59c 0.41b 0.45b 0.62c 0.34a 0.53c

a

Significant correlation at 90 % confidence levels indicated by bold numbers b Significant correlation at 95 % confidence levels indicated by bold numbers c

Significant correlation at 99 % confidence levels indicated by bold numbers

data were also selected in the stations near the streamflow station. It can be noticed that defining the significance of r values varies with the number of observations and selecting the confidence bound, i.e., in case of 30 observations, the values outside the range of ±0.361 are defined as significant at 95 % confidence level. Correlation coefficients (CC) between annual streamflow and precipitation ranged from 0.11 to 0.68 (Table 2), and 12 out of 17 stations showed significant correlation, while all of 17 temperature stations exhibited significant correlation (CC ranged from −0.31 to −0.64), indicating that annual streamflow and temperature are well correlated. Comparably, precipitation is not completely associated with streamflow (Table 2), although the CC of most stations shows positive values. Furthermore, according to spatial distribution plot (Fig. 3a, b), unlike streamflow decreasing trends, precipitation trends do not show sensible change over ULB. Furthermore, area showing decreasing trends in streamflow do not exhibit decreasing trend in precipitation. The presence of high correlations at annual scale is in agreements with similar studies in different basins (Masih et al. 2010; Zhao et al. 2010). In the case of lake levels, CC with streamflow showed significant correlation ranging from 0.32 to 0.62 (Table 2). This may be concluded from the geographical distribution of streamflow

stations, which seem to be concentrated mostly in downstream of subbasins and play a key role as inflow coming into the lake (Fig. 7c). Consequently, as the lake level declines, the exposed lakebed is left with a covering of salts and making a vast salty desert on more than 400 km2 of lost surface area (Golabian 2011).

4 Conclusions This study documented monotonic trend behaviors in monthly and annual time series of temperature, precipitation, and streamflow in ULB using a number of non-parametric statistical methods. Our results can be summarized as follows: (i) Temperature has significantly increased throughout the basin. Unlike temperature, trends in precipitation were not basin-wide. There was no trend for about 75 % of the stations. Both decreasing and increasing trends were observed in the remaining stations. The streamflow results were closer to the temperature trends, and about 80 % of them show a decreasing trend. (ii) Spatial distribution of significant trends in streamflow revealed that the downstream subbasins face decreasing trends (a decrease in water flowing into the system) and consequently have declined water level in Urmia Lake. Therefore, it can be primarily attributed to climate change and over-exploitation of the upper catchments. (iii) A comparison of estimated monthly and annual trends by the non-parametric methods showed that they do not obey the same pattern in a given station. To check the general homogeneity of trends, the Van Belle and Hughes method was applied. This test confirmed the homogeneity of the trends in 76, 100, and 75 % for temperature, precipitation, and streamflow gauging stations, respectively. (iv) Timing of trends in the study variables showed two regional patterns for temperature. One trend increased continually beginning in the 1980s and another began in 1995. For streamflow, two general patterns can be also discerned in the basin. One decreasing trend beginning in the middle 1990s between 1995 and 2000 and the other began in 1990s, but not significant. However, changing points in these time series are mainly observable in the mid-1990s. (v) There are three large dams in the basin. No correlation was observed from the SMK analysis between the exploitation dates of dams and the detected trends or the changing points of the annual time series of streamflow. (vi) In general, this research showed that there are two main reasons for the decline of the lake water level. One is related to the increase in temperature and the associated increases in evaporation and evapotranspiration, and the

Identification of trends in hydrological and climatic variables

other is caused by over-exploitation in upstream. A quantitative analysis of these is under investigation by the authors. (vii) A comparison of precipitation trends in ULB with studies in Turkey (Partal and Kahya 2006), which is also affected by the Mediterranean atmospheric system, revealed similar results with the exception of streamflow. For instance, Kahya and Kalaycı (2004) reported no trend for the eastern rivers of Turkey, which are adjacent to the western boundaries of ULB. This fact confirms the susceptibility of basin hydrology to significant human impact. (viii) Generally, the results of trend analysis show a relationship between observed streamflow trends and changes in climatic variables (temperature and precipitation). These may not completely explain the variability in streamflow caused by the changes in other catchment properties. More importantly, this study can provide a base for additional research that estimates the effects of human activities on the hydrological processes. For instance, land use change can also significantly influence the annual and seasonal streamflow, although the effect of climate is the dominant factor in annual streamflow. Overall, it is concluded that this study provides a useful overview of hydroclimatic time series trends in ULB for further research.

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