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 />
Predicted values <str<strong>on</strong>g>of</str<strong>on</strong>g> temperature show that,<br />
temperature has increased in Khoy <strong>and</strong> Tabriz<br />
stati<strong>on</strong>s <strong>and</strong> slightly decreased in <strong>Urmia</strong> stati<strong>on</strong>.<br />
Table 9. Results <str<strong>on</strong>g>of</str<strong>on</strong>g> <strong>Mann</strong>-<strong>Kendall</strong> <strong>test</strong> for m<strong>on</strong>thly <strong>and</strong> annual evaporati<strong>on</strong>.<br />
stati<strong>on</strong> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann.<br />
Tabriz N N N N U U U U U U N N U<br />
<strong>Urmia</strong> N N N N U N U U U U N N U<br />
Predicted values <str<strong>on</strong>g>of</str<strong>on</strong>g> precipitati<strong>on</strong> 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> reduces in all <str<strong>on</strong>g>the</str<strong>on</strong>g> index<br />
synoptic stati<strong>on</strong>s.<br />
Predicted values <str<strong>on</strong>g>of</str<strong>on</strong>g> evaporati<strong>on</strong> 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> evaporati<strong>on</strong> increases in all <str<strong>on</strong>g>the</str<strong>on</strong>g> index<br />
synoptic stati<strong>on</strong>s.<br />
Table 10. Predicted evaporati<strong>on</strong> by SDSM.<br />
Tabriz <strong>Urmia</strong> Stati<strong>on</strong><br />
31.4 30.4 Observed<br />
37.4 35.1 Predicted<br />
19.1 15.5 Reducti<strong>on</strong><br />
percentage<br />
The rivers run<str<strong>on</strong>g>of</str<strong>on</strong>g>f has decreased in <str<strong>on</strong>g>the</str<strong>on</strong>g> early spring<br />
<strong>and</strong> summer due to increase <str<strong>on</strong>g>of</str<strong>on</strong>g> temperature <strong>and</strong> early<br />
melting <str<strong>on</strong>g>of</str<strong>on</strong>g> snow reserves.<br />
Table 11. Results <str<strong>on</strong>g>of</str<strong>on</strong>g> <strong>Mann</strong>-<strong>Kendall</strong> <strong>test</strong> for m<strong>on</strong>thly <strong>and</strong> annual mean discharge.<br />
Stati<strong>on</strong> Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Ann.<br />
Tazek<strong>and</strong> L L L L N N N N N N N N N<br />
Polanian N N N N N N N N N N N N N<br />
Dashb<strong>and</strong> L L L N N N L L L L L L L<br />
Kuter L L L L N N N N N N N N N<br />
Beytas N N N N N N N N N N N N N<br />
Peyghale L N L N N N N N N N N N N<br />
Dizag L N L L L N N N N L L L N<br />
Mirabad L L L N N N N N N N N N N<br />
U: Ascending trend N: Without trend L: Descending trend.<br />
Table 12. Training <strong>and</strong> calibrati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> precipitati<strong>on</strong>-run<str<strong>on</strong>g>of</str<strong>on</strong>g>f model.<br />
Stati<strong>on</strong> Training Validati<strong>on</strong> Test<br />
Tazek<strong>and</strong> 8 / 88<br />
8 / 03<br />
8 / 04<br />
Polanian 8 / 83<br />
8 / 03<br />
8 / 04<br />
Dashb<strong>and</strong> 8 / 88<br />
8 / 08<br />
8 / 33<br />
Beytas 8 / 83<br />
8 / 08<br />
8 / 08<br />
Peyghale 8 / 88<br />
8 / 03<br />
8 / 08<br />
Mirabad 8 / 88<br />
8 / 01<br />
8 / 38<br />
Dizag 8 / 88<br />
8 / 014<br />
8 / 38<br />
Kuter 8 / 82<br />
8 / 38<br />
8 / 38<br />
120 | Khaneshan et al
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