This study analyzed the annual streamflow of Karkheh River in Karkheh river basin in the west of Iran for flood forecasting using stochastic models. For this purpose, we collected annual stremflow (peak and maximum discharge) during the period from 1958 to 2015 in Jelogir Majin hydrometric station (upstream of Karkheh dam reservoir). A time series model (stochastic model or ARIMA) has three stages consists of: model identification, parameter estimation and diagnostic check. Model identification was done by visual inspection on the Autocorrelation and Partial Autocorrelation Function. Three types of ARIMA(p,d,q) models (0,1,1), (1,1,1) and (4,1,1) suggested for the studied series. The suggested model parameters were computed using the Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods. In model verification, the chosen criterion for model parsimony was the Akaike Information Criteria (AIC) and the diagnostic checks include independence of residuals. The best ARIMA model for this series was (4,1,1), with their AIC values of 88.9 and 77.8 for annual peak and maximum streamflow respectively. Forecast series up to a lead time of ten years future (2006 to 2015) were generated using the accepted ARIMA models. Model accuracy was checked by comparing the predicted and observation series by coefficient of determination (R2). Results show that the ARIMA model was adequate for the flood analysis in Karkheh River and the forecast of the series in short time at future.

Stochastic models (time series models) have been proposed as one technique to generate scenarios of future climate change. Precipitation, temperature and evaporation are among the main indicators in climate study. The goal of this study is the simulation and modeling of climatic parameters such as annual precipitation, temperature and evaporation using stochastic methods (time series analysis). The 40-year data of precipitation and 37-year data of temperature and evaporation at Jelogir Majin station (upstream of Karkheh dam reservoir) in western of Iran has been used in this study and based on ARIMA model, The auto-correlation and partial auto-correlation methods, assessment of parameters and types of model, the suitable models to forecast annual precipitation, temperature and evaporation were obtained. After model validation and evaluation, the Predicting was made for the ten future years (2006 to 2015). In view of the Predicting made, the precipitation amounts will be decreased than recent years. As regards the mean of annual temperature and evaporation, the findings of the Predicting show an increase in temperature and evaporation.

Forecasting the inflow of rivers to reservoirs of dams has high importance and complexity. Design and optimal operation of the dams is essential. Mathematical and analytical methods use for understanding estimating and prediction of inflow to reservoirs in the future. Various methods including stochastic models can be used as a management tool to predict future values of these systems. In this study stochastic models (ARIMA) are applied to records of mean annual flow Karkheh river entrance to Karkheh dam in the west of Iran. For this purpose we collected annual flow during the period from 1958/1959 to 2005/2006 in Jelogir Majin hydrometric station. The available data consists of 48 years of mean Annual discharge. Three types of ARIMA (p, d, q) models (0, 1, 1), (1, 1, 1) and (4, 1, 1) suggested, and the selected model is the one which give minimum Akaike Information Criterion (AIC). The Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods are used to estimate the model parameters. It is found that the model which corresponds to the minimum AIC is the (4, 1, 1) model in CLS estimation method. Port Manteau Lack of fit test and Residual Autocorrelation Function (RACF) test are applied as diagnostic checking. Forecasting of annual inflow for the period from 2006 to 2015 are compared with observed inflow for the same period and since agreement is very good adequacy of the selected model is confirmed.

Time series analysis and prediction has become a major tool in different applications in meteorological and hydrological phenomena, such as rainfall, temperature, evaporation, flood, drought etc. Among the most effective approaches for analyzing time series data is the auto regressive integrated moving average (ARIMA) model introduced by Box and Jenkins. In this study we used Box-Jenkins methodology to build non-seasonal ARIMA model for annual rainfall data of Karkheh dam reservoir in Iran for Jelogir Majin and Pole Zal stations (upstream of Karkheh dam reservoir) for the period 1966-2015. In this paper, ARIMA 8.1.1 and 9.1.1 models were found adequate for annual rainfall at Jelogir Majin and Pole Zal stations, respectively, and these models were used to predict the annual rainfall for the coming ten years to help decision makers to establish priorities in terms of water demand management. The statistical analysis system (SAS) and statistical package for the social science (SPSS) softwares were used to determine the best model to use for these series.

Abstract:The optimal exploitation of water from a dam reservoir requires a comprehensive knowledge of future availability of water resources. In this case the amount of water that will be available in the future is important. Also, we need to examine the flows at the dam from a shortterm perspective. This is necessary to avoid overflowing and to minimize damage. In order to facilitate forecasting of the water resources, many different techniques have been developed through the years. In this paper, using annual mean flow data (since 1958-2005) obtained from Jelogir Majin hydrometric station at Karkheh River (upstream of Karkheh dam), an Auto Regressive Integrated Moving Average (ARIMA) model, for prediction of annual mean inflow to Karkheh dam reservoir was accomplished. On the basic of comparison the results of the model with measured data, the performance of ARIMA (4,1,1) model by Conditional Least Square (CLS) estimation parameter method is acceptable. The SAS and SPSS softwares were used to implement of the models.