No abstract

The Xiaolangdi Reservoir is the second largest water conservancy project in China and the last comprehensive water conservancy hub on the mainstream of the Yellow River, playing a vital role in the middle and lower reaches of the Yellow River. To study the effects of the construction of the Xiaolangdi Reservoir (1997–2001) on the runoff and sediment transport in the middle and lower reaches of the Yellow River, runoff and sediment transport data from 1963 to 2021 were based on the hydrological stations of Huayuankou, Gaocun, and Lijin. The unevenness coefficient, cumulative distance level method, Mann-Kendall test method, and wavelet transform method were used to analyze the runoff and sediment transport in the middle and lower reaches of the Yellow River at different time scales. The results of the study reveal that the completion of the Xiaolangdi Reservoir in the interannual range has little impact on the runoff in the middle and lower reaches of the Yellow River and a significant impact on sediment transport. The interannual runoff volumes of Huayuankou station, Gaocun station, and Lijin station were reduced by 20.1%, 20.39%, and 32.87%, respectively. In addition, the sediment transport volumes decreased by 90.03%, 85.34%, and 83.88%, respectively. It has a great influence on the monthly distribution of annual runoff. The annual runoff distribution is more uniform, increasing the runoff in the dry season, reducing the runoff in the wet season, and bringing forward the peak flow. The runoff and Sediment transport have obvious periodicity. After the operation of the Xiaolangdi Reservoir, the main cycle of runoff increases and the second main cycle disappears. The main cycle of Sediment transport did not change obviously, but the closer it was to the estuary, the less obvious the cycle was. The research results can provide a reference for ecological protection and high-quality development in the middle and lower reaches of the Yellow River.

As one of the important hydrological elements of rivers, flow is of great significance to the development and utilization of water resources and the ecological environment. Based on the excellent nonlinear processing capability of CEEMDAN and the advantages of BILSTM in time-series data modeling, a coupled CEEMDAN-BILSTM model is constructed for flow prediction, and the I-month flows from 1951–2016 are used to predict the i-month flows from 2017–2021. The results show that the CEEMDAN-BILSTM coupled model predicts the trend more closely with the actual data variation, and the minimum relative error is 0.56 and maximum 9.48, which are maintained within 10%, and the deterministic coefficients are all greater than 0.9, so the prediction accuracy is high. The flow in month I of 5 years was picked up by monthly predictions for 66 consecutive years, which provides a new way of thinking about the prediction of river flow.

No abstract

Scientific precipitation predicting is of great value and guidance to regional water resources development and utilisation, agricultural production and drought and flood control. Precipitation is a non-linear, non-smooth time series with significant stochasticity and uncertainty. In this paper, a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with Long Short-Term Memory (LSTM) model is developed for predicting annual precipitation in Zhengzhou City. Which is compared with a single Long Short-Term Memory model, an EEMD (Ensemble Empirical Mode Decomposition)-LSTM model, a CEEMD (Complementary Ensemble Empirical Mode Decomposition)-LSTM model, CEEMDAN-ARIMA (Auto Regressive Integrated Moving Average) and CEEMDAN-RNN (Recurrent Neural Network). The results show that the mean absolute error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) of the coupled CEEMDAN-LSTM model are 2.69%, 17.37 mm and 0.9863, respectively. The prediction accuracy is significantly higher than that of the other five models, indicating that the proposed model has high prediction accuracy and can be used for annual precipitation forecasting in Zhengzhou City.