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The Xiaolangdi Reservoir has entered the later sediment-retaining period, and new sediment transport phenomena and channel re-establishing behaviors are appearing. A physical model test was used to forecast the scouring and silting trends of the lower Yellow River. Based on water and sediment data from the lower Yellow River during the period from 1960 to 2012, and using a statistical method, this paper analyzed the sediment transport in sediment-laden flows with different discharges and sediment concentrations in the lower Yellow River. The results show that rational water-sediment regulation is necessary to avoid silting in the later sediment-retaining period. The combination of 3 000 m 3 /s < Q < 4 000 m 3 /s and 20 kg/m 3 < S < 60 kg/m 3 (where Q is the discharge and S is the sediment concentration) at the Huayuankou section is considered an optimal combination for equilibrium sediment transport in the lower Yellow River over a long period of time.

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With global climate change and extreme weather events, water scarcity and environmental degradation are becoming increasingly prominent. Ecological replenishment is one important measure to enhance water quality in reservoirs. To address the severe pollution and ecological degradation problems of the Luhun Reservoir, this paper uses MIKE21 software to construct a coupled hydrodynamic-water quality model. Design six recharge scenarios to compare and explore the enhancement of reservoir water quality for these scenarios. Evaluate the water quality status of these options using the single-factor index method and the combined pollution index method. The results show that due to the ecological replenishment, the pollutant concentration leads the phenomenon of gradient diffusion to the center of the reservoir, and the average improvement rate of water quality is up to 40.22%. Schemes 2 and 5 have the lowest integrated pollution index. The most significant improvement in water quality was achieved at the same total recharge conditions, using 0.5 times the flow rate and two times the recharge time of the actual recharge project. The study results provide theoretical and technical support for the future management of the reservoir water environment.

Accurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.