Seasonal Prediction of Discharge Entering in to Uremia Lake by Using Climatic Large Scale Signals

The main objective of this study is to evaluate the effects of climatic signals on the discharge rate of the two nominated stations and fluctuation of Uremia lake water، during the time period of 22-years (1986-2007). To do this، data of the two nominated stations، monthly data of Southern Fluctuation Index (SOI)، North Atlantic fluctuation (NAO) and ENSO index in NINO1+2، NINO3، NINO4 and NINO3. 4 were used. Large-scale climatic data signals were obtained from NCEP data center. Data about average monthly discharge rate of Dashband and Sarighmish stations was prepared from the data center of the Ministry of Energy. Firstly، for primary study of the data and the correlation between them in order to provide the best model to predict discharge rate، time steps of 0، 3 and 6 months were considered. In examining the discharge rate in various time intervals of the under study stations، it was obtained that the correlation in the delayed time intervals of six-months is more than the simultaneous and three months delay. After explaining the relation and its type، the forecasting model was designed using artificial neural network and the results of the model were evaluated and analyzed. Given the significant correlation in time intervals، it was realized that large-scale climatic indices، from the view point of common atmospheric circulation and large atmospheric systems in the study area have a significant impact on temperature، rainfall، discharge rate and fluctuation of Uremia lake water. Study the output models of artificial neural network indicates that the most effective signals of the discharge rate is NINO3. 4، NINO3، NINO1+ 2and least effective signals are NAO، SOI. According to the findings، it can be concluded that a significant relationship exists between discharge rate and climatic signals.