With the rapid development of China’s social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

In the real-time flood control operation of multi-reservoir systems, it is of great significance to establish a dynamic operating system with high efficiency based on the spatiotemporal variation of flood control situations. This paper proposes a self-adaptive modeling framework for real-time flood control operation of multi-reservoirs based on the cyber–physical system (CPS) theory. Firstly, the random flood samples considering the randomness of both space and magnitude are generated, and then the multi-reservoir real-time flood control hybrid operation (MRFCHO) model is established based on the dynamic identification of effective reservoirs. Then, the CPS theory is introduced to put forward the multi-reservoir real-time flood control hybrid operation cyber–physical system (MRFCHOCPS), which integrates real-time monitoring, control center, database, computation module, and communication network. Finally, the proposed framework is demonstrated in terms of accuracy, efficiency, and adaptability in real-time flood control operations. A case study of the multi-reservoir system upstream of the Lutaizi point in the Huaihe River basin in China reveals that (1) the equivalent qualified rate of the MRFCHO model is 84.9% for random flood samples; (2) the efficiency of solving the MRFCHO model is much higher than the efficiency of solving the MRFCJO model under the premise of ensuring the flood control effect, so it provides a reliable method for the real-time operation of basin-wide floods; (3) the MRFCHOCPS has good adaptability in real-time dynamic modeling and operation of large-scale multi-reservoir systems.

Graphical abstract showing extraction of the dominant main-channel centerline from satellite imagery and its use to quantify spatiotemporal river migration. Main-channel migration reflects the stability and adjustment of braided rivers, yet its intra-annual spatiotemporal variation remains poorly quantified in the braided reach of the Lower Yellow River (LYR). Using Sentinel-2 imagery from 2018 to 2022, this study developed a method based on the Euclidean Distance Transform to extract the main-channel centerline and quantify its migration width in a multi-channel river. Results confirmed that main-channel migration was highly episodic and mainly concentrated during the flood season, with additional adjustment in some reaches after the flood period. Spatially, the most active migration was concentrated from Mayugou to Guanzhuangyu and from Heishi to Youfangzhai, where the channel was relatively wide and multi-threaded. Finally, an empirical model was established to predict reach-scale migration width from mean discharge, incoming sediment coefficient, and interval duration. Calibration with 2018–2021 data and validation with 2022 data yielded relative root mean square errors of 23% and 36%, respectively. This study provides a remote-sensing-based framework for quantifying short-term planform adjustment of braided rivers and improves a basis for river regulation and channel-stability assessment in the LYR.