To understand the runoff-sediment discharge relationship in the source region of the Yellow River, this study examined the annual runoff and sediment discharge data obtained from the Tangnaihai hydrometric station. The data were decomposed into multiple time scales through Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN). Furthermore, double cumulative curves were plotted and the cointegration theory was employed to analyze the microscopic and macroscopic multi-temporal correlations between the runoff and the sediment discharge and their detailed evolution. Multi-temporal component composite models were then constructed considering structural breaks. The simulation results were compared with the actual values to examine the accuracy of the models. The results suggested that the runoff and the sediment discharge variations in the source region of the Yellow River showed reasonable consistency as a whole. However, their relationship at different time scales varied slightly. The runoff-sediment discharge double cumulative curves in the multi-temporal components exhibited high goodness of fit. The curves of the intrinsic mode function 1 and 2 (IMF1 and IMF2) components provided a more satisfactory goodness of fit, whereas distinct breakpoints were present in those of IMF3 and IMF4. The variations in the runoff-sediment discharge relationship of the raw data series resulted from the different time scales. The medium- and long-term runoff-sediment discharge relationships were insignificant, which affected the raw data series. With the help of the variable structure cointegration composite model, the smallest average relative error for the simulated annual runoff (7.82%) was obtained. This composite model could more accurately reflect the long-term equilibrium and short-term fluctuating relationships between the runoff and the sediment discharge in the source region of the Yellow River.
In order to reveal the multi-time scale of rainfall, runoff and sediment in the source area of the Yellow River and improve the accuracy of annual runoff forecast, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) method is introduced to decompose the measured rainfall, runoff and sediment data series of the Tangnahai hydrological station in the source area of the Yellow River of China. With the co-integration theory, two new error correction models(ECM) for the forecast of annual runoff in the source area of the Yellow River are constructed. The results show that rainfall, runoff and sediment in the source area of the Yellow River have multi-time scales and the component sequences have co-integration relationships. For two new ECM models, the CEEMDAN component ECM model has better forecast accuracy than the original sequence one. The relative error of all forecasted values is less than 15% except 2009, and the accuracy has reached level A.