Real Time Forecasting of Daily Inflow to Karun-III Reservoir using Combination of Auto-Regressive Technique and Precipitation Forecasts

Document Type : Original Article

Authors

1 Assistant Professor, Shahrekord University and Chairman of Water Resources Research Center

2 Senior Engineer, PurAb Consultant Company.

Abstract

Real time forecasting of river flow is an essential tool in optimum operation of water recourses systems espacially storage dams. Because of high complexity of hydro climatology events, real time inflow forecasting is a difficult task. Many researches have accordingly been carried out and various models have been developed for inflow real time forecasting.
Many storage dams, mainly hydropower dams, were constructed or are under construction in the Great Karun basin in south western Iran. The optimization of hydropower generation in this system is therefore of great importance. In this regard the inflow real time forecasting would also be an important item. In this study the Karun-III reservoir inflow real time forecasting was conducted using combination of autoregressive technique and the precipitation forecasts information available for a period of 22, November, 2004 to 20, March, 2005. In this research, various stochastic series data are generated based on the 5-day observed discharges. A series with the most agreement with the daily precipitation forecasts in the following days, is then selected to forecast the daily inflow in the next 5 days. Results showed that relative errors of the proposed method for rainy and non-rainy days was about %31.8 and %12.7, respectively. The average error for all the 109 days forecasted data was also reported as %21 which is quite below the average relative errors obtained in simple autoregressive technique which was about %30.

Keywords

References

USCE (US Army corps of Engineers), (1971), "HEC-4 Monthly stream flow simulation", the Hydrologic Engineering Center CPD-4, 85p.
Kisi, O., (2005), "DailyRiver Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models", Turkish J. Eng. Env. Sci., 29, pp.9-20.
Kitanidis, P.K. and Bras, R.L., (1980), "Real-Time Forecasting With a Conceptual Hydrological Model", Water Resour. Res., 16., pp.740-748.
Hsu, K., Gupta, H.V. and Sorooshian,S., (1995), "Artifcial Neural Network Modeling of the Rainfall-Runoff Process", Water. Resour. Res. J., 31., pp.2517-2530.
Yapo, P., Gupta, V.K. and Sorooshian, S., (1996), "Calibration of Conceptual Rainfall-Runoff Models: Sensitivity to Calibration Data", J.Hydrol.,181., pp.23-48.
Ochoa-Rivera, J.C., R. García-Bartual and J. Andreu, (2002), Multivariate synthetic stream flow generation using a hybrid model based on artificial neural networks, J. Hydrology and Earth System Sciences , 6(4), pp. 641–654 
YU, P.Sh. and  Yih Tseng, T., (1996), A model to forecast flow with uncertainty analysis, Hydrological Sciences -Journal, 4l(i), pp.327-344.
http://weather.yahoo.com/regional/IRXX.htm
 
Iran-Water Resources Research
Volume 4, Issue 2
June 2008
Pages 14-22

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History

  • Receive Date: 21 November 2006
  • Accept Date: 29 September 2008

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