Premium
Stochastic modelling of mean monthly flows of Karasu River, in Turkey
Author(s) -
Şengül Selim,
Can İbrahim
Publication year - 2011
Publication title -
water and environment journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 37
eISSN - 1747-6593
pISSN - 1747-6585
DOI - 10.1111/j.1747-6593.2009.00186.x
Subject(s) - akaike information criterion , autoregressive model , autocorrelation , white noise , stochastic modelling , series (stratigraphy) , autoregressive–moving average model , statistics , standard deviation , statistical model , environmental science , meteorology , mathematics , econometrics , geography , geology , paleontology
Stochastic modelling of streamflows is vital for planning water resource systems. In this study, a stochastic model of the mean monthly streamflows at 2154 Aşağιkağdariç Gauging Station on Karasu River was constructed. Studies were carried out using data from the water yearbooks published by chk later onEIE. The modelling procedure for streamflows with constant coefficient autoregressive moving average (ARMA) models was given in detail and indicated models were constructed. Analysis with streamflows at 2154 Aşağιkağdariç Gauging Station showed that the autoregressive (AR) (1) model is the most appropriate model among the competing models. While selecting the most efficient model the Akaike information criterion (AIC) was used. The Port–Manteau test showed that residuals are white noise series. Using the AR(1) model, 100 synthetic series were generated and the time series generated were found to have the same statistical parameters (monthly mean, monthly standard deviation and autocorrelation) as historic time series within 95% confidence intervals.