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.

为了揭示黄河源区降雨，径流和泥沙的多次尺度，提高年径流量预报的准确性，引入了基于自适应噪声的完全集成经验模式分解法（CEEMDAN）。黄河源区唐纳海水文站实测降雨，径流和泥沙数据系列。利用协整理论，建立了两种新的黄河源区径流预报误差校正模型（ECM）。这两种方法的应用解决了水文时间序列的非线性和非平稳性造成的拟回归问题。结果表明，降雨 黄河源区的径流和泥沙具有多时标，各组分序列具有协整关系。对于两个新的ECM，CEEMDAN组件ECM的预测准确性要高于原始序列之一。除2009年外，所有预测值的相对误差均小于15％，准确度已达到A级。