Evaluation of the effects of climate change on meteorological and hydrological parameters using climatic models and Mann – Kendall test (case study: Urmia Lake)

J. Bio. & Env. Sci. 2014 

Journal <strong>of</strong> Biodiversity and Environmental Sciences (JBES) 

ISSN: 2220-6663 (Print) 2222-3045 (Online) 

Vol. 4, No. 4, p. 112-124, 2014 

http://www.innspub.net 

RESEARCH PAPER 

OPEN ACCESS 

<strong>Evaluation</strong> <strong>of</strong> <strong>the</strong> <strong>effects</strong> <strong>of</strong> <strong>climate</strong> <strong>change</strong> on meteorological 

and hydrological parameters using climatic models and Mann – 

Kendall test (case study: Urmia Lake) 

Somayeh Mahmoodi Khaneshan 1 , Zabihollah Khani Temeliyeh *2 , Hossein Rezaie 3 

1,2 

Graduated Master <strong>of</strong> Water Resources Engineering, Urmia University, Urmia, Iran 

2 

Graduated Master <strong>of</strong> Water Resources Engineering, Urmia University, Urmia, Iran 

3 

students (Candidate) in Water Resources Engineering, Urmia University, Urmia, Iran 

Article published on April 05, 2014 

Key words: Climate <strong>change</strong>, Urmia, Mann-Kendall, ANN, SDSM. 

Abstract 

Climate <strong>change</strong> and increase <strong>of</strong> global temperature are important environmental issues on which various studies 

have been conducted in recent years. This issue has a high importance due to environmental, economic and social 

impacts because, human activities are based on <strong>climate</strong> stability. In this article, <strong>effects</strong> <strong>of</strong> <strong>climate</strong> <strong>change</strong> on 

meteorological and hydrological parameters <strong>of</strong> Urmia Lake watershed have been investigated and forecasted for 

period 2010-2100. In order to forecast meteorological parameters, Atmosphere General Circulation Model was 

used. Temperature, precipitation and evaporation data were downscaled and calibrated using LARS s<strong>of</strong>tware by 

SDSM model and observed data. In continue, Artificial Neural Network (ANN) was used to simulate model <strong>of</strong> 

precipitation to run<strong>of</strong>f. The models outputs mostly showed increase <strong>of</strong> temperature and evaporation and decrease 

<strong>of</strong> precipitation in future periods. Also, <strong>the</strong> results <strong>of</strong> Mann-Kendall indicated that, <strong>climate</strong> <strong>change</strong> and global 

warming is not significant on long-term trend <strong>of</strong> hydrological factors affecting Urmia Lake. Therefore, <strong>the</strong> factors 

<strong>of</strong> rapid reduction <strong>of</strong> <strong>the</strong> lake water level in recent years should be explored among climatic fluctuations such as 

wet and drought, and human factors such as dam constructing, uncontrolled extraction <strong>of</strong> groundwater and 

unsuitable irrigation methods. 

* Corresponding Author: Zabihollah Khani Temeliyeh z.k30040@gmail.com 

112 | Khaneshan et al


Introduction 

Increasing greenhouse gases after industrial 

revolution disturbs <strong>the</strong> earth energy balance and has 

causes <strong>the</strong> earth to be warm. According to <strong>the</strong> reports 

<strong>of</strong> Intergovernmental Panel on Climate Change 

(IPCC), if emission <strong>of</strong> greenhouse gases is not 

reduced, mean temperature <strong>of</strong> <strong>the</strong> earth increases by 

1.1 to 4.4 ⁰C. Global warming phenomenon and its 

resulted <strong>climate</strong> <strong>change</strong> have significant impacts on 

water cycle and water resources system. Climate 

<strong>change</strong> results in <strong>change</strong> in run<strong>of</strong>f volume, duration 

and time, and consequence <strong>of</strong> this happening will 

make much <strong>change</strong>s in water resources management 

(IPCC, 2007). 

In studies <strong>of</strong> <strong>climate</strong> <strong>change</strong> investigation <strong>of</strong> its 

impacts on different sections, various uncertainties 

affect <strong>the</strong> final result. Greenhouse gases emission 

scenario and Atmosphere Ocean General Circulation 

Model (AOGCM) are uncertainties in climatic 

simulations. If <strong>the</strong>se uncertainties are not considered 

in analysis, validation <strong>of</strong> <strong>the</strong> results will be reduced. 

Therefore, use <strong>of</strong> only AOGCM model under an 

emission scenario for <strong>climate</strong> <strong>change</strong> analysis cannot 

cover whole <strong>the</strong> range <strong>of</strong> related uncertainties and will 

provide abnormal results. So, <strong>the</strong> researchers have 

attempted to consider <strong>the</strong>se uncertainty sources by 

more than one climatic model under emission 

scenarios in <strong>climate</strong> <strong>change</strong> studies (Massah Bavani, 

2006). For example, Kamga (2005) used three 

climatic models under emission scenarios A2 and B2, 

Massah Bavani (2006) used seven climatic models 

under emission scenarios A2 and B2. Maurer et al. 

(2009) used 16 climatic models under emission 

scenarios A2 and B2. Khani Tamalieh et al. (2012) 

used a climatic model under scenario A2 in evaluation 

<strong>of</strong> <strong>the</strong> <strong>effects</strong> <strong>of</strong> <strong>climate</strong> <strong>change</strong> on drought traits. 

They found that, in future periods <strong>of</strong> <strong>climate</strong> <strong>change</strong>s 

<strong>the</strong>re will be more variations for parameters <strong>of</strong> 

precipitation and temperature in <strong>the</strong> east <strong>of</strong> Urmia 

Lake than its west. Grieser and Tromel (2002) 

investigated temperature <strong>of</strong> 100 years in Europe and 

demonstrated that, annual temperature fluctuations 

are significantly increased in <strong>the</strong> Eastern Europe and 

almost has had increasing trend throughout <strong>the</strong> 

region. In a same research in which Wilby and 

Dettinger (2006) investigated <strong>the</strong> effect <strong>of</strong> <strong>climate</strong> 

<strong>change</strong> on <strong>the</strong> amount <strong>of</strong> low flows <strong>of</strong> Teams River in 

England, uncertainty sources associated with AOGCM 

Model, downscaling methods, greenhouse gases 

emission scenario, various models <strong>of</strong> simulating 

precipitation-run<strong>of</strong>f and uncertainty related to <strong>the</strong>ir 

parameters considering different weights and Monte 

Carlo method were simulated. 

The results showed that, uncertainties associated with 

AOGCM models have <strong>the</strong> highest proportion and 

greenhouse gases have <strong>the</strong> minimum proportion in 

estimating <strong>the</strong> probability function <strong>of</strong> run<strong>of</strong>f. Minville 

et al. (2008) investigated <strong>the</strong> uncertainty <strong>of</strong> <strong>the</strong> effect 

<strong>of</strong> <strong>climate</strong> <strong>change</strong> on <strong>the</strong> run<strong>of</strong>f <strong>of</strong> Canada watershed 

using HSAMI. They used five GCM models and two 

emission scenarios. Results reveals an increase in 

temperature by 1 to 14 ⁰C and seasonal precipitation 

by -9 to 55%. Also, <strong>the</strong> amount <strong>of</strong> run<strong>of</strong>f <strong>of</strong> <strong>the</strong> 

watershed affected by <strong>climate</strong> <strong>change</strong> will <strong>change</strong>. In 

a research conducted by Samadi and Massah Bavani 

(2008), downscaling <strong>of</strong> precipitation and temperature 

was done by statistical method (SDSM) and Artificial 

Neural Network (ANN). At <strong>the</strong> end <strong>of</strong> <strong>the</strong> mentioned 

research it was concluded that, successfulness <strong>of</strong> both 

methods has been same for temperature downscaling 

while, ANN was more successful than SDSM for 

downscaling <strong>of</strong> precipitation (Samadi et al., 2008). In 

ano<strong>the</strong>r research carried out by Ash<strong>of</strong>teh et al. 

(2008), <strong>the</strong> effect <strong>of</strong> <strong>climate</strong> <strong>change</strong> on <strong>the</strong> peak 

discharge <strong>of</strong> Aidoghmoush in period 2040-2069 for 

scenario A2 was investigated. Seven AOGCM models 

had been used in this study. The results show that, 

temperature <strong>of</strong> <strong>the</strong> watershed increases in period 

2050 between 1 and 6 ⁰C compared with <strong>the</strong> base 

period. Also, <strong>the</strong> range <strong>of</strong> precipitation variations will 

be -80 to 100%. Comparison <strong>of</strong> <strong>the</strong> samples flood 

intensity in various return periods in two future 

decades showed that, peak discharge <strong>of</strong> <strong>the</strong> watershed 

affected by <strong>climate</strong> <strong>change</strong> will <strong>change</strong> (Ash<strong>of</strong>tehsadat 

et al., 2008). Ghorbani & Soltani (2002) studied <strong>the</strong> 

<strong>climate</strong> <strong>change</strong> <strong>of</strong> Gorgan meteorology station using 



40 years statistics <strong>of</strong> precipitation and temperature. 

The results <strong>of</strong> this research showed that, <strong>climate</strong> 

<strong>change</strong> in this region has not had tangible effect on 

temperature but, it has caused to decrease 

precipitation. Taghavi (2005) studied <strong>the</strong> trend <strong>of</strong> 

temperature and precipitation indices in 16 synoptic 

stations in order to detect <strong>climate</strong> <strong>change</strong> in various 

regions. Then it was concluded that, most <strong>of</strong> warm 

indices have increasing trend as well as decreasing 

trend for cold indices. Azizi et al., (2008) used 

multivariate statistical analysis and Box-Jenkins 

model in order to investigate <strong>the</strong> existence <strong>of</strong> 

significant trend in temperature and humidity 

variables <strong>of</strong> <strong>the</strong> western half <strong>of</strong> Iran. Seyf et al. (2009) 

in <strong>the</strong> study <strong>of</strong> <strong>climate</strong> <strong>change</strong> <strong>of</strong> Sabzevar city in a 50 

year statistical period concluded that, mean annual 

precipitation does not show a significant trend but, 

<strong>the</strong> data <strong>of</strong> mean annual temperature has a rising 

trend. Khosravi et al. (2010) investigated <strong>the</strong> effect <strong>of</strong> 

<strong>climate</strong> <strong>change</strong> on water resources <strong>of</strong> <strong>the</strong> Middle East 

using World Bank and IPCC. The results <strong>of</strong> this 

research explain that, according to IPCC <strong>climate</strong> 

<strong>change</strong> scenario, <strong>the</strong>re will increase by 1 to 2 ⁰C and 

precipitation will decrease by 20%. 

Change in water level in <strong>the</strong> lakes is mostly due to 

<strong>change</strong> in river flows and <strong>change</strong> in precipitation on 

<strong>the</strong> lakes and evaporation from <strong>the</strong>ir surface. Water 

level <strong>of</strong> a number <strong>of</strong> lakes has been dropped 

throughout <strong>the</strong> world during <strong>the</strong> past decades because 

<strong>of</strong> human activities (Khosravi et al., 2010). 

Atmosphere General Circulation Models are threedimension 

models which have been developed based 

on various climatic scenarios in order to simulate <strong>the</strong> 

effect <strong>of</strong> greenhouse impact on current <strong>climate</strong> <strong>of</strong> <strong>the</strong> 

earth, those are able to forecast future <strong>climate</strong> <strong>of</strong> <strong>the</strong> 

earth (IPCC, 2007). One <strong>of</strong> <strong>the</strong> main limitations in <strong>the</strong> 

use <strong>of</strong> climatic outputs <strong>of</strong> general circulation models 

is that, <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong>se models is about 200 km 

which is inappropriate for investigation <strong>of</strong> 

mountainous regions and climatic parameters such as 

precipitation and temperature. SDSM is one <strong>of</strong> <strong>the</strong> 

most famous generating models <strong>of</strong> random data on 

wea<strong>the</strong>r, and is used to produce daily precipitation, 

radiation, minimum and maximum daily temperature 

in a station under current and future climatic 

conditions (Dehghanipoor et al., 2012). Downscaling 

algorithm <strong>of</strong> SDSM model has been used with much 

appropriate results in many meteorological, 

hydrological and environmental branches in 

geographical ranges from Europe, Nor<strong>the</strong>rn America 

and Sou<strong>the</strong>astern <strong>of</strong> Asia (Willy et al., 2002). The 

purpose <strong>of</strong> this study is to investigate <strong>the</strong> effect <strong>of</strong> 

<strong>climate</strong> <strong>change</strong> on variation trend <strong>of</strong> meteorological 

and hydrological parameters <strong>of</strong> some stations located 

in <strong>the</strong> watershed <strong>of</strong> Urmia Lake using climatic models 

and Mann Kendall test. 

Materials and methods 

Study area 

Urmia Lake is located in <strong>the</strong> northwest <strong>of</strong> Iran and 

between Western and Eastern Azerbaijan provinces. 

This lake is <strong>the</strong> second most saline lake <strong>of</strong> <strong>the</strong> world 

after Bahrolmeyet in Palestine. Length <strong>of</strong> <strong>the</strong> lake is 

13 to 140 km and its width is 15 to 85 km, its mean 

depth is 6 m and <strong>the</strong> height is 1274 m from sea level. 

Area <strong>of</strong> Urmia Lake has been reported by 5000 to 

5500 km 2 . Urmia Lake watershed has been placed 

between nor<strong>the</strong>rn latitude 35⁰ 29ʹ to 38⁰ 40ʹ and 

eastern longitude 44⁰ 13ʹ and 47⁰ 53ʹ. The highest 

point <strong>of</strong> <strong>the</strong> watershed is close to Sabalan 

mountaintop by 3850 m. Therefore, existing elevation 

difference in <strong>the</strong> watershed is estimated by 2576 m. 

Considering that <strong>the</strong> province has been located on <strong>the</strong> 

path <strong>of</strong> air masses entrance from <strong>the</strong> west and 

northwest, and existence <strong>of</strong> Zagros mountains in <strong>the</strong> 

south and southwest, heights in <strong>the</strong> west and Ararat 

in <strong>the</strong> north, <strong>the</strong> maximum amount <strong>of</strong> precipitation 

has been occurred in this region and it has generated 

high flow rivers. 

The purpose <strong>of</strong> this research is to investigate <strong>the</strong> 

effect <strong>of</strong> <strong>climate</strong> <strong>change</strong> on meteorological and 

hydrological parameters <strong>of</strong> Urmia Lake watershed. 

With regard to <strong>the</strong> importance <strong>of</strong> temperature, 

precipitation and evaporation parameters in 

investigating <strong>the</strong> <strong>effects</strong> <strong>of</strong> <strong>climate</strong> <strong>change</strong>, <strong>the</strong>se 

meteorological parameters were used in this research. 



Observed data <strong>of</strong> indexed synoptic stations in this 

study belong to <strong>the</strong> period 1961-2001. In order to 

investigate <strong>the</strong> effect <strong>of</strong> <strong>climate</strong> <strong>change</strong> on <strong>the</strong> rivers 

run<strong>of</strong>fs <strong>of</strong> Urmia Lake watershed, <strong>the</strong> data <strong>of</strong> stations 

in mountainous areas and on <strong>the</strong> main rivers were 

used. Position <strong>of</strong> 8 hydrometric stations used in this 

study has been shown in Table (2) and Figure (1). At 

first, data homogeneity was investigated <strong>the</strong>n, Mann- 

Kendall test was applied on <strong>the</strong> data and after that, 

data <strong>of</strong> temperature and precipitation were 

downscaled and predicted and ultimately, predicted 

temperature and precipitation and neural network 

MLP were used to model precipitation to run<strong>of</strong>f. 

Considering long-term statistics and data continuity 

<strong>of</strong> temperature, precipitation and evaporation as daily 

in Urmia and Tabriz stations, statistics <strong>of</strong> <strong>the</strong>se 

stations were used in this study. Position <strong>of</strong> <strong>the</strong>se 

index synoptic stations has been shown in table (1). 

Figure (1) shows <strong>the</strong> position <strong>of</strong> index synoptic and 

hydrometric stations in <strong>the</strong> concerned area. 

Fig. 1. Position <strong>of</strong> hydrometric and synoptic stations 

in Urmia Lake watershed. 

General Circulation Models (GCM) 

These models are three-dimensional and are able to 

simulate climatic system considering most <strong>of</strong> 

processes in global or continental scale. The models 

require calculation, saving and replication <strong>of</strong> 

computations in each point <strong>of</strong> <strong>the</strong> network to 

calculate each climatic variable (Shahabfar & Ghiami, 

2001). These models are not applicable in practical 

studies with smaller dimensions, for instance, most <strong>of</strong> 

hydrological studies are faced with small-scale 

processes and sub-basins which <strong>the</strong>ir scale is much 

smaller than <strong>the</strong> scale given by general circulation 

models. Global models should be downscaled to be 

used in hydrological studies. (Wilby et al., 1997). 

There are two approaches for downscaling and 

obtaining <strong>the</strong> data in local or regional scale, from 

global <strong>climate</strong> scenarios generated by general 

circulation models, including dynamical and 

statistical approaches. In <strong>the</strong> first approach, high 

speed computers are needed but, in <strong>the</strong> second 

approach, domestic computers can be used and is a 

fast and low-cost approach. In statistical downscaling, 

<strong>the</strong> maximum limitation is for recorded observations 

(Benestad et al., 2004). 

Multilayer Perceptron network (MLP) 

This network consists <strong>of</strong> an input layer, one or a 

number one or more hidden layers and an output 

layer. For training this network, Back Propagation 

(BP) algorithm is usually used. During <strong>the</strong> training <strong>of</strong> 

MLP network using Back Propagation (BP) learning 

algorithm, <strong>the</strong> computations start from <strong>the</strong> network 

input toward <strong>the</strong> network output and <strong>the</strong>n, calculated 

error values are propagated into <strong>the</strong> previous layers. 

Output computations are conducted as layer by layer 

and output <strong>of</strong> each layer is <strong>the</strong> input <strong>of</strong> <strong>the</strong> next layer. 

In Back Propagation state, <strong>the</strong> output layer is 

adjusted since, <strong>the</strong>re is a desired value for each output 

layer neurons, and <strong>the</strong> weights can be adjusted with 

<strong>the</strong>ir help and update rules. Despite Back Propagation 

(BP) algorithm has presented much proper results in 

resolving <strong>the</strong> problems, it acts weakly in some 

problems which can be due to <strong>the</strong> learning time being 

long or unclear, unsuitable selection <strong>of</strong> learning 

coefficient or random distribution <strong>of</strong> initial weights. 

In some case also, learning process is disturbed due to 

existence <strong>of</strong> local minimum. Training stages using 

this algorithm including (Dayh<strong>of</strong>f, 1990): A) assigning 

random weight matrix to each connections. B) 

selection <strong>of</strong> input vector and its consistent output. C) 

calculation <strong>of</strong> neuron output in each layer and 

consequently calculation <strong>of</strong> neuron output in output 

layer. D) updating <strong>the</strong> weights by network error 

propagation into <strong>the</strong> previous layers, that <strong>the</strong> error is 



resulted from <strong>the</strong> difference between real output and 

calculated output. E) <strong>Evaluation</strong> <strong>of</strong> trained network 

function using some defined indexes such as Mean 

Square Error (MSE) and ultimately returning to <strong>the</strong> 

end <strong>of</strong> training. In this research, artificial neural 

network was used to estimate precipitation-run<strong>of</strong>f 

(Figure 2). 

Mann-Kendall test 

Mann-Kendall test is considered one <strong>of</strong> <strong>the</strong> most 

common nonparametric methods for analysis <strong>of</strong> 

hydrological and meteorological series. Various 

conducted studies using this method state its 

importance and wide application in analysis <strong>of</strong> time 

series. This test was presented by Mann in 1945 for 

<strong>the</strong> first time and <strong>the</strong>n, it was developed by Kendall in 

1948. Use <strong>of</strong> this method has been recommended by 

Global Meteorology Organization. Among strengths 

<strong>of</strong> Mann-Kendall method, suitability <strong>of</strong> this method 

for time series which do not follow a specific 

distribution can be mentioned (Hajam et al., 2008). 

Negligible effect <strong>of</strong> limit values on this method is 

ano<strong>the</strong>r advantage <strong>of</strong> this method (Ghahraman et al., 

2009). Assumption <strong>of</strong> being zero for this test proves 

being randomly and lack <strong>of</strong> trend in data series and 

assumption <strong>of</strong> being 1, means <strong>the</strong> existence <strong>of</strong> trend in 

data series. Stages <strong>of</strong> this test calculation are as 

below: 

A) Calculation <strong>of</strong> difference between <strong>the</strong> observations 

to each o<strong>the</strong>r and applying sign function and 

extraction <strong>of</strong> parameter S as below: 

Where m represents <strong>the</strong> number <strong>of</strong> series in which 

<strong>the</strong>re are at least one duplicate data and t is <strong>the</strong> 

frequency <strong>of</strong> data with <strong>the</strong> same value. 

Finally, Z is determined via one <strong>of</strong> <strong>the</strong> following 

equations: 

In a bilateral test for routing <strong>the</strong> data series, <strong>the</strong> null 

hypo<strong>the</strong>sis is accepted if 

Z Z / 2 

while, is a 

significant level which has been considered for <strong>the</strong> 

test and 

Z 

is <strong>the</strong> factor <strong>of</strong> standard normal 

distribution in <strong>the</strong> significance level <strong>of</strong> that / 2 

is used considering that <strong>the</strong> test is two-tailed. If Z is 

positive, <strong>the</strong> data series trend is considered ascending 

and if it is negative, descending trend is considered. 

Regression analysis 

This approach is a parametric test for which, 

hypo<strong>the</strong>sis <strong>of</strong> normal data exists. A simple linear 

regression relationship to obtain long-term trend <strong>of</strong> 

<strong>the</strong> data is as below: 

Where n is <strong>the</strong> number <strong>of</strong> observations <strong>of</strong> series, xi 

and xj are jth and kth data <strong>of</strong> <strong>the</strong> series. Sign function 

also is calculated as following: 

Where y is atmospheric variable, x is time and a, b are 

regression coefficients which are calculated using <strong>of</strong> 

minimum squares method. By obtaining t value with 

freedom degree <strong>of</strong> n-2, significant regression gradient 

is tested by: 

Where, MSE is mean squares error and SSx is 

calculated as below: 



Where xi is <strong>the</strong> concerned variable and x is <strong>the</strong> mean 

<strong>of</strong> concerned variable. 

If t t , n 2 , regression gradient is considered 

2 

insignificant (hypo<strong>the</strong>sis <strong>of</strong> H0 equal with gradient b 

is zero and if gradient b is significantly different with 

zero, it is represents <strong>the</strong> existence <strong>of</strong> trend). 

Results and Discussion 

In this research, <strong>the</strong> amounts <strong>of</strong> precipitation and 

temperature was predicted in order to investigate <strong>the</strong> 

<strong>effects</strong> <strong>of</strong> <strong>climate</strong> <strong>change</strong> on hydrological parameters 

<strong>of</strong> Urmia Lake watershed and also, trend <strong>of</strong> water 

level variations from precipitation, maximum and 

minimum temperature, relative humidity, sunshine 

hours data using downscaling <strong>of</strong> GCM and HADCM3 

models. Also, monthly discharge data in hydrometric 

index stations and mean monthly temperature, 

precipitation and evaporation were used to train, 

validating and testing <strong>the</strong> model. Variations trend <strong>of</strong> 

<strong>the</strong> lake water level was investigated using Mann- 

Kendall which its results have been explained in 

continue. 

Table 1. Synoptic stations position. 

X 

14 /80 

13 /80 

Mann-Kendall 

Y 

80 /80 

83 /38 

stations 

Tabriz 

Urmia 

In this study, <strong>the</strong> data from base studies <strong>of</strong> 

meteorological and hydrological reports <strong>of</strong> integrated 

management <strong>of</strong> water resources <strong>of</strong> Urmia Lake 

watershed have been investigated. The studied data 

should be homogenous statistically. Investigation <strong>of</strong> 

data homogeneity was done using double mass curve. 

After data preparation <strong>the</strong> trend <strong>of</strong> meteorological 

and hydrological factors affecting Urmia Lake 

watershed was investigated. In this regard, <strong>the</strong> 

variations trend <strong>of</strong> temperature, precipitation, 

evaporation in <strong>the</strong> lake surface, surface run<strong>of</strong>f 

entering <strong>the</strong> lake, variations <strong>of</strong> <strong>the</strong> Lake water level 

and volume parameters were investigated using 

Mann-Kendall test and linear regression. The results 

<strong>of</strong> Mann-Kendall and regression method have been 

reported in Tables (3) and (4). 

Mean precipitation volume on <strong>the</strong> lake and 

evaporation from <strong>the</strong> lake surface have had a 

significant reduction. Also, results <strong>of</strong> Mann-Kendall 

test show an ascending trend for temperature 

parameter. According to <strong>the</strong> results <strong>of</strong> linear 

regression test, descending trend <strong>of</strong> mean inflow to 

Urmia watershed and evaporation has an important 

trend. Also, <strong>the</strong> results <strong>of</strong> this test, reveals <strong>the</strong> 

importance <strong>of</strong> temperature ascending trend. 

Evaporation from <strong>the</strong> lake surface has experienced a 

temperature descending trend during <strong>the</strong> recent years 

with regard to ascending trend <strong>of</strong> temperature which 

can be due to reduction <strong>of</strong> Urmia Lake area and 

increase <strong>of</strong> <strong>the</strong> lake water salinity. Generally, <strong>the</strong> 

results <strong>of</strong> this research suggest that, mean 

precipitation in Urmia is between 300 and 350 mm 

and mean temperature was 11 to 15 ⁰C so; <strong>the</strong> amount 

<strong>of</strong> temperature was not as much to be attributed 

entirely for <strong>the</strong> lake water level variations. These 

results are also predictable for o<strong>the</strong>r watersheds in 

Iran and should be considered. 

Table 2. Hydrometric stations position. 

H 

4488 

4188 

4838 

4188 

4388 

4383 

4888 

4808 

Temperature 

X 

14 /83 

14 /1 

14 /43 

13 /3 

13 /88 

11 /03 

13 /83 

13 /48 

Y 

83 /13 

84 /84 

84 /43 

84 /40 

83 

83 /18 

83 /80 

84 /3 

Stations 

Tazekand 

Polanian 

Dashband 

Beytas 

Peyghale 

Mirabad 

Dizag 

Kuter 

Table (5) shows <strong>the</strong> results <strong>of</strong> Mann-Kendall test for 

monthly minimum and maximum temperature in 

index synoptic stations. 

The results show that, temperature has an ascending 

trend or no trend in Tabriz station while, Urmia has a 

descending trend or no trend. Mean amount <strong>of</strong> 



temperature in <strong>the</strong> observation period (1961-2001) 

and its mean value in <strong>the</strong> prediction period (2010- 

2100) have been shown in Table (3), (4) and (6). The 

difference between <strong>the</strong>se values represents 

temperature variations in <strong>the</strong> future. 

Precipitation 

Table (7) presents <strong>the</strong> results <strong>of</strong> Mann-Kendall test 

for monthly and annual mean precipitation in two 

index synoptic stations. The results show that, <strong>the</strong> 

stations have descending trend or no trend. Mean 

values <strong>of</strong> precipitation in observations and 

predictions period have been presented in Fig.5 and 

Table (8). 

Table 3. Results <strong>of</strong> Mann-Kendall (for confidence level <strong>of</strong> 95%). 

Time series 

Autocorrelation 

Zs 

Possibility <strong>of</strong> significant trend 

coefficient 

existence level 

Mean water level <strong>of</strong> <strong>the</strong> lake 

0.82 

-0.17 

Yes 

Mean precipitation height on <strong>the</strong> lake 

0.3 

-0.22 

No 

Mean surface inflow 

0.17 

-2.2 

Yes 

Mean precipitation volume on <strong>the</strong> lake 

0.3 

-1.9 

Yes 

Mean evaporation height from <strong>the</strong> pan 

0.48 

-3.4 

Yes 

Mean evaporation height from <strong>the</strong> lake 

0.68 

-2.7 

Yes 

Lake volume variations 

0.74 

-0.6 

No 

Temperature variations 

0.43 

2.6 

Yes 

Table 4. Results <strong>of</strong> linear regression test. 

Time series 

Regression equation 

Gradient in confidence P value 

R2 

range <strong>of</strong> 90% 


Y= -8/ 

8X 

+ 4830 

- 8/8 ،8/88 

8 /838 

0 /1% 


low 


Y= -8/ 

43X 

+ 838 

- 8/43 ،4/88 

8 /08 

8 /84% 


low 


Y= -03/ 

8X 

+ 48880 

- 03/8 ،28/8 

8 /880 

48 /1% 


high 


Y= -1/ 

38X 

+ 4110 

- 1/38 ،0/2 

8 /80 

8 /4% 

low 


Y= -3/ 

8X 

+ 4238 

- 3/8 ،8/2 

- 3 E 8 /81 

88 /4% 


high 


Y= -88/ 

0X 

+ 3313 

- 88/0 ،43/8 

8 /884 

81 /8% 


high 


Y= -48/ 

84X 

+ 48481 

- 48/84 ،48481 

8 /30 

8 /8% 


low 


Y= -8/ 

81X 

+ 0/82 

- 8/81 ،8/88 

8 /881 

48 /8% 

high 

Evaporation 

Table (9) show <strong>the</strong> results <strong>of</strong> Mann-Kendall test for 

monthly evaporation in index synoptic stations. The 

results show that, <strong>the</strong> stations have an ascending 

trend in <strong>the</strong> amount <strong>of</strong> evaporation. Mean values <strong>of</strong> 

evaporation in observation and prediction period 

have been shown in Fig.6 and Table (10). 



Table 5. Results <strong>of</strong> Mann-Kendall test as monthly and annual temperature. 

temperature stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann. 

Minimum Tabriz N N N U U U U U U U N N U 

Urmia N N N N L N L L L L L N N 

Maximum Tabriz N N U U N U N U U N N N U 

Urmia N N N N N N L N L N N N N 

U: Ascending trend N: Without trend L: Descending trend. 

Rivers run<strong>of</strong>f 

Table (11) presents <strong>the</strong> results <strong>of</strong> Mann-Kendall test 

for monthly and annual mean discharge in eight 

hydrometric index stations. Annual results show that 

generally, <strong>the</strong>re is no trend in eight hydrometric index 

stations. Results <strong>of</strong> precipitation-run<strong>of</strong>f model in all 

<strong>the</strong> stations have been shown in Table 12. 

Table 6. Predicted temperature by SDSM. 

Maximum Minimum Temperature 

Tabriz Urmia Tabriz Urmia Stations 

17.9 17.4 7 5.1 Observed 

20.6 16.1 9.8 3.6 Predicted 

-2.7 -1.3 2.8 -1.5 Difference 

The results <strong>of</strong> prediction shows increase <strong>of</strong> in Tabriz station and temperature reduction in Urmia station. 

Table 7. Results <strong>of</strong> Mann-Kendall test for monthly and annual mean precipitation. 

station Jan Feb Mar Apr May Jun Jul Aug Sep Oct No Dec Ann. 

v 

Tabriz N L L N N L N N N N N N L 

Urmia L N N N N N N N N N N N L 


Results <strong>of</strong> training and calibration <strong>of</strong> precipitationrun<strong>of</strong>f 

model show that, <strong>the</strong> results from MLP model 

is valid. Monthly mean discharge in <strong>the</strong> observation 

period (1961-2001) and also, <strong>the</strong> values predicted by 

MLP model have been presented in Table (13) and 

Fig. 7. 

Table 8. 

Tabriz Urmia Station 

24.3 28.1 Observed 

21.8 24.6 Predicted 

10.3 12.5 Reduction 

percentage 

The results <strong>of</strong> predictions show reduction <strong>of</strong> 

precipitation in both synoptic stations. 

Fig 7 shows that, <strong>the</strong> rivers run<strong>of</strong>f has increased in <strong>the</strong> 

early spring and decreased in <strong>the</strong> summer which is 

due to increase <strong>of</strong> temperature that has caused early 

snow reserves melting. 

These results suggest that, predicted run<strong>of</strong>f in 

stations Tazekand, Polanian, Dashband and Kuter 

decreases while, it increases in Peyghale, Mirabadand 

Dizag. Never<strong>the</strong>less, <strong>the</strong> amount <strong>of</strong> discharge 

variations is not considerable except Tazekand 

station. At <strong>the</strong> end <strong>of</strong> present study it was found that, 

<strong>climate</strong> <strong>change</strong> does not have a significant effect on 

<strong>the</strong> amount <strong>of</strong> Urmia Lake watershed rivers run<strong>of</strong>f 

until 2100 but, it may <strong>change</strong> monthly distribution <strong>of</strong> 

discharge in this period. Results show that: 



Predicted values <strong>of</strong> temperature show that, 

temperature has increased in Khoy and Tabriz 

stations and slightly decreased in Urmia station. 

Table 9. Results <strong>of</strong> Mann-Kendall test for monthly and annual evaporation. 

station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann. 

Tabriz N N N N U U U U U U N N U 

Urmia N N N N U N U U U U N N U 

Predicted values <strong>of</strong> precipitation show that, <strong>the</strong> 

amount <strong>of</strong> precipitation reduces in all <strong>the</strong> index 

synoptic stations. 

Predicted values <strong>of</strong> evaporation show that, <strong>the</strong> 

amount <strong>of</strong> evaporation increases in all <strong>the</strong> index 


Table 10. Predicted evaporation by SDSM. 

Tabriz Urmia Station 

31.4 30.4 Observed 

37.4 35.1 Predicted 

19.1 15.5 Reduction 

percentage 

The rivers run<strong>of</strong>f has decreased in <strong>the</strong> early spring 

and summer due to increase <strong>of</strong> temperature and early 

melting <strong>of</strong> snow reserves. 

Table 11. Results <strong>of</strong> Mann-Kendall test for monthly and annual mean discharge. 

Station Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Ann. 

Tazekand L L L L N N N N N N N N N 

Polanian N N N N N N N N N N N N N 

Dashband L L L N N N L L L L L L L 

Kuter L L L L N N N N N N N N N 

Beytas N N N N N N N N N N N N N 

Peyghale L N L N N N N N N N N N N 

Dizag L N L L L N N N N L L L N 

Mirabad L L L N N N N N N N N N N 


Table 12. Training and calibration <strong>of</strong> precipitation-run<strong>of</strong>f model. 

Station Training Validation Test 

Tazekand 8 / 88 

8 / 03 

8 / 04 

Polanian 8 / 83 

8 / 03 

8 / 04 

Dashband 8 / 88 

8 / 08 

8 / 33 

Beytas 8 / 83 

8 / 08 

8 / 08 

Peyghale 8 / 88 

8 / 03 

8 / 08 

Mirabad 8 / 88 

8 / 01 

8 / 38 

Dizag 8 / 88 

8 / 014 

8 / 38 

Kuter 8 / 82 

8 / 38 

8 / 38 



Table 13. 

پل Station Beytas Kuter Dashband Polanian 

Kind observation predicted observation predicted observation predicted observation predicted 

run<strong>of</strong>f (m3/s) 4 / 38 4 / 30 3 / 81 3 / 88 43 / 82 43 / 88 43 / 28 43 / 48 

Discharge (%) - 8/ 84 

- 8/ 82 

- 8/ 83 

- 8/ 8 

Station Tazekand Peyghale Dizag Mirabad 

Kind observation predicted observation predicted observation predicted observation predicted 

run<strong>of</strong>f (m3/s) 8 / 38 8 / 31 0 / 3 0 / 30 3 / 88 0 / 808 1 / 83 3 / 44 

Discharge (%) - 88/ 01 

8 / 8 

8 / 80 

8 / 28 

Although, <strong>climate</strong> <strong>change</strong> has no effect on <strong>the</strong> rivers 

run<strong>of</strong>f until 2100 in Urmia Lake watershed, but it 

will <strong>change</strong> monthly distribution <strong>of</strong> discharge in this 

period. 

Fig. 5. Predicted precipitation by SDSM. 

Fig. 2. Multilayer Perceptron network (Hosaini, 

2009). 

Fig 6. Estimation <strong>of</strong> evaporation values in period 

2010-2100 in synoptic stations. 

Fig. 3. Estimation <strong>of</strong> temperature values in 2010- 

2100 in synoptic stations <strong>of</strong> Urmia. 

Fig. 4. Estimation <strong>of</strong> temperature values in 2010- 

2100 in synoptic stations <strong>of</strong> Tabriz. 

Fig. 7. Comparison <strong>of</strong> monthly mean run<strong>of</strong>f in 

observation and prediction periods. 



Conclusion 

In this research, <strong>the</strong> effect <strong>of</strong> <strong>climate</strong> <strong>change</strong> on 

meteorological and hydrological parameters was 

predicted in for period 2010-2100 Urmia Lake 

watershed. In this regard, statistical period <strong>of</strong> 1961- 

2001 was considered as mutual statistical period 

considering statistical downscaled models and index 

meteorology stations to use HADCM3 model, and 

temperature estimation for <strong>the</strong> period 2010-2100 was 

considered under scenario A2. Then, data production 

and downscaling was done in station scale in <strong>the</strong> 

prediction period 2010-2100 using statistical 

downscaled model SDSM. After that, artificial neural 

network was used for precipitation-run<strong>of</strong>f model. The 

results show that: 

1) Although, <strong>climate</strong> <strong>change</strong> has no effect on <strong>the</strong> rivers 

run<strong>of</strong>f until 2100 in Urmia Lake watershed, it can 

<strong>change</strong> monthly distribution <strong>of</strong> discharge in this 

period as an aggravating factor along with <strong>the</strong> factors 

<strong>of</strong> <strong>change</strong> in ecosystem (dam construction), 

uncontrolled extraction <strong>of</strong> groundwater, 

inappropriate irrigation methods, etc. 

2) Predicted temperature values show that, 

temperature has increased in Tabriz station and it 

slightly decreases in Urmia station. 

3) Predicted precipitation values show that, <strong>the</strong> 

amount <strong>of</strong> precipitation decreases in all <strong>the</strong> index 


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Evaluation of the effects of climate change on meteorological and hydrological parameters using climatic models and Mann – Kendall test (case study: Urmia Lake)

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