Modelling rainfall in Karkheh dam reservoir of Iran using time series analysis (stochastic ARIMA models).

Abstract

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.