Urban waterlogging becomes a challenge with the higher urbanization. This paper aims to assess waterlogging risk in the high-tech district of Zhengzhou city in China by coupling land surface hazard-pregnant (LSHP) features and underground pipe network operation (UPNO) characteristics. The LSHP risk is assessed according to the regional surface features firstly, and then with the UPNO characteristics by the Storm Water Management Model (SWMM), the conduit, junction and inundation risk indices are proposed to evaluate UPNO risk. Based on the LSHP risk and UPNO risk, the integrated waterlogging risks of different land use types are evaluated with rainstorm in different return periods (such as 1, 3, 5 and 10 years) and the “7.20 rainstorm event”. The results show that the LSHP risk is not matched with the UPNO risk of each sub-region. From downtown to suburb, the LSHP risk increases first and then decreases, while the UPNO risk decreases. Combing with the LSHP and UPNO risk indices, the urban waterlogging risk is fully revealed, and it indicates that the study area can effectively resist rainstorm with return periods of 1 and 3 years. But for rainstorm with return period of more than 3 year, the rainwater will occur in the whole area. With the situation of the “7.20 rainstorm event”, the anti-waterlogging engineering will be almost lost their functions. Generally, the waterlogging risk is higher in the northwest and southeast of the downtown and it becomes smaller in the suburbs.

To understand the runoff-sediment discharge relationship , 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.

This study investigated the influence of data extension on the decomposition and prediction accuracy of runoff data series. To this end, an original data series was constructed using annual runoff data from a hydrological station in China (Tang Naihai) for the period 1956–2013, and radial basis function neural network (RBFNN) extension was applied to the original data series. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was then applied to both data series, and their decomposition and prediction results were compared. The decomposition results indicate that the end effect significantly lowers the accuracy of low–middle frequency components. Nevertheless, the end effect could be effectively suppressed and decomposition error could be reduced by applying RBFNN extension. At the end points, the extension data series could more accurately reflect the real fluctuation characteristics of components and subsequent variation trends. Regarding component prediction, the prediction results followed the variation trend of the components themselves, with a rather large gap in the prediction results of low-frequency components between the two groups of data series. The final prediction results obtained from the reconstruction of the component prediction results suggest that the extension sequence has a clearly superior prediction accuracy than the original data series. Hence, when using the CEEMDAN method to process non-stationary hydrological data, multi-time-scale information of the data series can be obtained through reasonable extension after decomposition of the original data series. The acquired information provides evidence for the analysis and prediction of the evolution law of hydrological elements.

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.