Application of linear stochastic models to monthly flow data of Kelkit Stream

This paper presents a methodology on modeling of historical data for monthly flows from Kelkit Stream. The watershed is located on the north Anatolia and the stream is formed by joining together of small streams. For the modeling purpose, linear stochastic models known as either Box–Jenkins or ARIMA (autoregressive-moving average) were used to simulate monthly data. Diagnostic checks were done for all the models selected from the autocorrelation function (ACF) and partial autocorrelation function (PACF). The models that have the minimum Schwarz Bayesian Criterion (SBC) among the selected models fulfilled all the diagnostic checks were assumed as the best model for monthly data. For five years, the predicted data using the best models is compared to the observed data. The basic statistical properties of the observed and predicted data were compared. The results show that generated data preserve the basic statistical properties of the original series.