Resumen
With the rapid economic growth in China, a large number of hydropower projects have been planned and constructed. The sediment deposition of the reservoirs is one of the most important disputes during the construction and operation, because there are many heavy sediment-laden rivers. The analysis and prediction of the runoff and sediment into a reservoir is of great significance for reservoir operation. With knowledge of the incoming runoff and sediment characteristics, the regulator can adjust the reservoir discharge to guarantee the water supply, and flush more sediment at appropriate times. In this study, the long-term characteristics of runoff and sediment, including trend, jump point, and change cycle, are analyzed using various statistical approaches, such as accumulated anomaly analysis, the Fisher ordered clustering method, and Maximum Entropy Spectral Analysis (MESA). Based on the characteristics, a prediction model is established using the Auto-Regressive Moving Average (ARIMA) method. The whole analysis and prediction system is applied to The Three Gorges Project (TGP), one of the biggest hydropower-complex projects in the world. Taking hydrologic series from 1955 to 2010 as the research objectives, the results show that both the runoff and the sediment are decreasing, and the reduction rate of sediment is much higher. Runoff and sediment into the TGP display cyclic variations over time, with a cycle of about a decade, but catastrophe points for runoff and sediment appear in 1991 and 2001, respectively. Prediction models are thus built based on monthly average hydrologic series from 2003 to 2010. ARIMA (1, 1, 1) × (1, 1, 1)12 and ARIMA (0, 1, 1) × (0, 1, 1)12 are selected for the runoff and sediment predictions, respectively, and the parameters of the models are also calibrated. The analysis of autocorrelation coefficients and partial autocorrelation coefficients of the residuals indicates that the models built in this study are feasible for representing and predicting the runoff and sediment inflow into the TGP with a high accuracy.