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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)

Abstract Climate change and increase of 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 climate stability. In this article, effects of climate change on meteorological and hydrological parameters of 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 software by SDSM model and observed data. In continue, Artificial Neural Network (ANN) was used to simulate model of precipitation to runoff. The models outputs mostly showed increase of temperature and evaporation and decrease of precipitation in future periods. Also, the results of Mann-Kendall indicated that, climate change and global warming is not significant on long-term trend of hydrological factors affecting Urmia Lake. Therefore, the factors of rapid reduction of the 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 of groundwater and unsuitable irrigation methods.

Abstract
Climate change and increase of 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 climate stability. In this article, effects of climate change on meteorological and hydrological parameters of 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 software by SDSM model and observed data. In continue, Artificial Neural Network (ANN) was used to simulate model of precipitation to runoff. The models outputs mostly showed increase of temperature and evaporation and decrease of precipitation in future periods. Also, the results of Mann-Kendall indicated that, climate change and global warming is not significant on long-term trend of hydrological factors affecting Urmia Lake. Therefore, the factors of rapid reduction of the 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 of groundwater and unsuitable irrigation methods.

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J. Bio. & Env. Sci. 2014<br />

C<strong>on</strong>clusi<strong>on</strong><br />

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

<strong>meteorological</strong> <strong>and</strong> <strong>hydrological</strong> <strong>parameters</strong> was<br />

predicted in for period 2010-2100 <strong>Urmia</strong> <strong>Lake</strong><br />

watershed. In this regard, statistical period <str<strong>on</strong>g>of</str<strong>on</strong>g> 1961-<br />

2001 was c<strong>on</strong>sidered as mutual statistical period<br />

c<strong>on</strong>sidering statistical downscaled <strong>models</strong> <strong>and</strong> index<br />

meteorology stati<strong>on</strong>s to use HADCM3 model, <strong>and</strong><br />

temperature estimati<strong>on</strong> for <str<strong>on</strong>g>the</str<strong>on</strong>g> period 2010-2100 was<br />

c<strong>on</strong>sidered under scenario A2. Then, data producti<strong>on</strong><br />

<strong>and</strong> downscaling was d<strong>on</strong>e in stati<strong>on</strong> scale in <str<strong>on</strong>g>the</str<strong>on</strong>g><br />

predicti<strong>on</strong> period 2010-2100 <strong>using</strong> statistical<br />

downscaled model SDSM. After that, artificial neural<br />

network was used for precipitati<strong>on</strong>-run<str<strong>on</strong>g>of</str<strong>on</strong>g>f model. The<br />

results show that:<br />

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

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

<str<strong>on</strong>g>change</str<strong>on</strong>g> m<strong>on</strong>thly distributi<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> discharge in this<br />

period as an aggravating factor al<strong>on</strong>g with <str<strong>on</strong>g>the</str<strong>on</strong>g> factors<br />

<str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>change</str<strong>on</strong>g> in ecosystem (dam c<strong>on</strong>structi<strong>on</strong>),<br />

unc<strong>on</strong>trolled extracti<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> groundwater,<br />

inappropriate irrigati<strong>on</strong> methods, etc.<br />

2) Predicted temperature values show that,<br />

temperature has increased in Tabriz stati<strong>on</strong> <strong>and</strong> it<br />

slightly decreases in <strong>Urmia</strong> stati<strong>on</strong>.<br />

3) Predicted precipitati<strong>on</strong> values show that, <str<strong>on</strong>g>the</str<strong>on</strong>g><br />

amount <str<strong>on</strong>g>of</str<strong>on</strong>g> precipitati<strong>on</strong> decreases in all <str<strong>on</strong>g>the</str<strong>on</strong>g> index<br />

synoptic stati<strong>on</strong>s.<br />

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122 | Khaneshan et al

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