Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River

Abstract

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 (ECMs) for the forecast of annual runoff in the source area of the Yellow River are constructed. The application of these two methods solves the problem of pseudo-regression caused by nonlinearity and non-stationary of hydrological time series. 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 ECMs, the CEEMDAN component ECM 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. HIGHLIGHTS The research on the multi-time scale change law of hydrological variables reveals the multi periodic change law of hydrological variables and provides a scientific basis for the rational development of water resources.; The non-stationary and nonlinear processing of hydrological variables can avoid spurious regression and make the result more accurate.; Study on the co-integration relationship of rainfall, runoff and sediment.; Study on multi-time scale dynamic relationship among rainfall, runoff and sediment.; Multi-time scale prediction of river runoff provides a technical reference for the effective protection and scientific operation of water resources.;

Keywords

• ceemdan
• co-integration
• multi-time scale
• runoff forecast
• source area of the yellow river