The disturbance in river ecosystems caused by reservoirs and dams has become a critical topic, attracting increasing attention. However, the extent to which reservoir and dam construction and operation impact downstream river ecosystem health and ecosystem service functions is not fully understood. This research examines the Xiaolangdi Reservoir and the Lower Yellow River (LYR) ecosystem in China as a case study. We analyzed the complex material and energy flows in the LYR ecosystem using emergy theory and developed a set of emergy-based indicators for the quantitative assessment of river ecosystem health and services under reservoir operation interference. The results indicate that the total natural capital and environmental endowments of the LYR ecosystem have remained relatively stable after the operation of the Xiaolangdi Reservoir, with an increase in renewable emergy input. The ecosystem’s vigor decreased slightly, while the biomass emergy diversity index remained stable. However, the total emergy inputs increased significantly, with external feedback inputs becoming the most important emergy source for the LYR ecosystem. The resilience of the LYR ecosystem improved, with a significant increase in emergy density and a decrease in the emergy sustainability index. These findings suggest that although the river ecosystem continues to provide supporting services to human society, the extent of these services has diminished compared to pre-perturbation levels. In this research, a methodology for analyzing the impact of key reservoir operations on the ecosystem health and services of a large river is proposed to provide support for large river sustainable development studies.

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.

To investigate the influence mechanism of Yellow River silt powder on the hydration process, microstructure, and strength development of shotcrete, and to promote the resource utilization of Yellow River sediment, this study systematically investigated the effects of different silt powder replacement levels (0%, 10%, 30%, and 50%) on a cement–accelerator system. A combination of setting time tests, isothermal calorimetry, and mechanical strength measurements was employed, together with microstructural characterization techniques including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential thermal analysis (DTA), and scanning electron microscopy (SEM). The results indicate that the silt powder content exerts a two-stage effect on the setting behavior of shotcrete. At low replacement levels (0–30%), both initial and final setting times are significantly prolonged, whereas at higher replacement levels (>30%), the setting time is anomalously shortened, approaching that of the reference mixture. The incorporation of silt powder delays the onset of the pre-induction period, prolongs the induction stage, and reduces the cumulative heat release, with the reduction exhibiting a staged trend characterized by gradual, pronounced, and then moderate changes as the replacement level increases. With increasing silt powder content, both compressive strength and splitting tensile strength decrease continuously. At a 50% replacement level, the 28-day compressive strength loss reaches 48.35%, while the splitting tensile strength loss reaches 43.30%, with more pronounced deterioration observed at early ages. The tensile-to-compressive strength ratio increases with silt powder content at early ages, while converging to similar values among all mixtures at later ages. Microstructural analysis indicates that silt powder primarily affects hydration through physical dilution and ion adsorption. At low dosages, nucleation effects slightly promote early hydration, whereas at high dosages, the hydration of calcium silicate phases is inhibited, resulting in reduced C–S–H gel formation and increased porosity. Additionally, AFt morphology transitions from dense prismatic crystals to loosely distributed needle-like structures. This study provides a systematic understanding of the role of silt powder in shotcrete and offers theoretical guidance for mix design optimization and the sustainable utilization of Yellow River sediment.

River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristic influencing factors and the occurrence and absence of RBTS were analysed, and prediction models based on machine learning algorithms were constructed. The results showed that under the existing data conditions, the maximum sediment concentration Sm, average sediment concentration Sp, flood growth rate ν, and shape coefficient δ were the four key indices to more easily distinguish whether RBTS will occur. The support vector machine algorithm model had the best performance results and exhibited higher accuracy and precision in predicting its occurrence compared with other models under given water and sediment conditions. The method proposed in this study provides a new method for accurately predicting RBTS in the Yellow River.

为揭示调水调沙对小浪底库区水环境的影响, 本研究通过典型断面水环境指标系统监测, 分析了调水调沙前、中、后 3 个时期库区水体的水质及营养物质的时空响应特征. 结果表明: 调水调沙中近坝断面的叶绿素（Chl）显著富集, 库中后段呈现总磷（TP）热区, 水位下降和出库流量增加会显著增加库区浊度和硝态氮（NO3-N）、TP浓度, 降低Chl含量. 调水调沙影响了部分水环境因子之间的相关性, 其中总有机碳（TOC）与浊度之间及NO3-N与pH、DO、TP之间的相关关系发生显著改变. 综合营养状态指数（TLI）显示调水调沙增加水库富营养化水平, 调水调沙中为中营养状态. 研究成果初步揭示了高强度人工调度影响下水库水环境的响应特征，为小浪底库区生态调度方案优化提供关键科学证据.

The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was developed by coupling the Muskingum method with multiple machine-learning algorithms (Ridge, LASSO, RF, and LSTM) to predict and correct Muskingum residuals. Global Muskingum parameters were identified using the L-BFGS-B algorithm to represent basin-scale routing characteristics. For rolling forecast, a multidimensional feature space was constructed by integrating routing gradients and hydraulic interaction terms. The results indicated that all hybrid models outperformed the traditional Muskingum method across lead times. The Ridge-based hybrid model achieved the best performance at short lead times, with the Nash–Sutcliffe efficiency (NSE) at a 4-h lead time increasing from 0.56 for the physical baseline to 0.977. For longer lead times (12–24 h), the LASSO-based hybrid model demonstrated higher robustness, which was attributed to L1-regularization-based feature selection. Overall, the proposed framework alleviates systematic underestimation during high-flow periods and provides a predictive scheme for arid-region rivers that preserves physical interpretability while improving forecasting accuracy.