Abstract

Reservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for forecasting monthly Koga River flow with Gnu Regression, Econometrics and Time-series Library (GRETL) software. The stationarity of historical river flow sequence was checked by Augmented Dickey-Fuller (ADF) unit root analysis. Then, seasonality was removed from the river flow time series by seasonal differencing. Using seasonally differenced correlogram characteristics various SARIMA models were identified and evaluated, their parameters were optimized and diagnostic checks of forecasts were performed using residual correlograms and Ljung-Box tests. Finally, based on minimum Akaike Information criteria, SARIMA (1, 0, 1) (3, 1, 3)12 model was selected for Koga River flow forecasting. The stationarity test of the forecasted values of this model has proved the similarity of forecast values and patterns with those of the historical ones. Thus, irrigation managers could use this model and forecast information for optimal irrigation planning and development of reservoir operation strategies in order to protect farmers and downstream environment from water shortages. Moreover, the use of stationarity test of forecast flow patterns is useful and applicable in selecting best forecast model during forecasting of any river flows.

Highlights

  • For reservoir based irrigation schemes for which cropping pattern varies year to year due to many factors, adaptive reservoir operation is vital in order to balance reservoir water supply and crop water demands

  • The fundamental theory in modeling time series is that the future is a manifestation of the precedent and any statistical relation that could be originated in the precedent statistics can be utilized to the future (Vahdat et al, 2011)

  • Augmented Dickey- Fuller (ADF) unit root test statistic value -4.73 is smaller than test critical value -0.27 for α = 5%

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Summary

Introduction

For reservoir based irrigation schemes for which cropping pattern varies year to year due to many factors, adaptive reservoir operation is vital in order to balance reservoir water supply and crop water demands. The management is adaptive when the judgments are based on both present and forecasted inflows (Labadie et al, 1981). Reservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows (Karamouz et al, 2003). Forecasting is a planning instrument which aids decision makers to anticipate the upcoming improbability based on the characteristics of historical and current observations (Box et al, 2008). A time series modeling is one of the many techniques used for forecasting.

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