AUTOREGRESSIVE MODELLING OF MONTHLY RAINFALL IN SAKARYA BASIN

Abstract

In this study, periodic autoregressive models were established to predict future behaviour of monthly rainfall data of Sakarya Basin which is one of the big and important basin in Turkey. Mathematical equations of the Periodic Autoregressive Models (PAR) were also determined. Optimum models were selected based on Akaike Information Criterion (AIC). Although the parameters are calculated according to "maximum probability method" in AIC, "moments method" was used in this study; the comparison of the results of both mentioned parameter estimation methods was thought to be considered in another study's scope. The Port Manteau lack of fit test for the selected models have indicated that residuals are white noise. By using the selected models for the stations, 50 set of synthetic series which have the same length with the historical series for the monthly average rainfalls have been generated, and statistical characteristics (mean, standard deviation, autocorrelation structure) of these synthetic series have been compared with statistical characteristics of historical series. By determining the stochastic models of monthly average rainfall of 25 stations, 4 different PAR models were obtained, namely as PAR(0), PAR(1), PAR(2) and PAR(3)