Abstract
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.
Highlights
The application of statistical hydrology in earlier days was restricted to surface water problems, especially for analyzing the hydrologic extremes such as floods and droughts
Time series analysis is used for building mathematical models to generate synthetic hydrologic records, to forecast hydrologic events, to detect trends and shifts in hydrologic records, and to fill in missing data and extend records
Various methods including stochastic models can be used as a management tool to predict future values of these systems
Summary
The application of statistical hydrology in earlier days was restricted to surface water problems, especially for analyzing the hydrologic extremes such as floods and droughts. During past three decades, the statistical domain of hydrology with the advent of fast computing technology has broadened to encompass the problems of both surface water and groundwater systems With such a broad domain, statistics has emerged as a powerful tool for analyzing hydrologic time series. Over the last few decades, predicting future values of hydrologic systems have come to the focus of the researchers consideration for planning and management of water resources. For this purpose, various methods including stochastic models can be used as a management tool to predict future values of these systems
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