Water and flow reductions in the channels downstream of water storage and hydropower projects have significant impacts on aquatic ecosystems. Understanding and analyzing the ecosystem status is of great significance to facilitate the protection of riverine ecosystems. A database was established based on the 2000–2017 environmental impact assessment (EIA) reports on water storage and hydropower projects in China, and corresponding analysis software was built based on an ArcGIS spatial analysis platform. The projects in China are mainly found in the Yangtze and Pearl River basins and in south-western China. The hydropower projects have a larger influence than the water storage projects on the flow of downstream rivers sections, and most of the hydropower projects, especially the water diversion projects, cause flow reductions in the downstream rivers. An ecological flow management mechanism in China implemented in 2006 provided a promising method to alleviate river flow reductions. However, there is still only one kind of ecological flow calculation method and few ecological flow regulation measures in use. Based on the advantages and problems of the existing ecological flow management system, this paper proposes a management scheme based on a regional-engineering calculation method. The results can facilitate decision making in ecological flow management.

The construction and operation of water storage and hydropower projects affects the structure of water ecosystems of downstream rivers, and the establishment of ecological flow in rivers below the water storage and hydropower projects has significant impacts on maintaining the stability of river ecosystems. A database was established based on 2000–2017 environmental impact assessment (EIA) reports on water storage and hydropower projects in China and ecological flow (e-flow) methods, and the three widely used e-flow methods for water storage and hydropower projects in China were identified on the database. Furthermore, an ecological flow satisfaction degree model was used to evaluate the methods using long series of historical hydrological data from the hydrological stations in the Yellow River basin, the Yangtze River basin, and the Liao River basin. The results showed that hydrological methods are the type most often used for water storage and hydropower projects in China, including the Tennant method, the minimum monthly average flow with 90% guarantee rate method (Mm9M method), and the measured historical minimum daily average flow rate method (MDM method). However, the ecological flow methods selected are not significantly different among different basins, indicating that the selection of ecological flow methods is rather arbitrary, and adaptability analysis is not available. The results of the ecological flow satisfaction model showed that the Tennant method is not suitable for large river basins. The results of this study can provide technical support for establishment and management decisions surrounding ecological flow.

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

To study the vertical water temperature structure and thermodynamic characteristics of tropical lake-like reservoirs, a water temperature model was developed by a vertical one-dimensional numerical model for Songtao Reservoir in Hainan Province, China. The model was verified by the measured water temperature data, and sensitivity analysis of key model parameters was carried out. The results show that water temperature simulated by the model in Songtao Reservoir agreed with the observations quite well, and the model is feasible for water temperature simulations in large reservoirs in tropical zones. The sensitivity of vertical water temperature structure to different model parameters varied. For example, the extinction coefficient greatly affected surface water temperature, which is important for the formation and development of the surface water temperature hybrid layer. The vertical mixing coefficient significantly influenced the inflection point position and thickness of the thermocline. The vertical water temperature structure in Songtao Reservoir was stratified. Reservoir surface water temperature varied from 19.4 °C to 33.8 °C throughout a year. The hypolimnion mainly appeared in elevation below 150 m, where the water temperature is basically maintained at 19 °C throughout the year. This study also found that the surface water temperature of Songtao Reservoir in the tropical zone was higher than the air temperature throughout a year, with an annual average of 3.5 °C higher than that of air temperature. The preliminary analysis found out that the higher surface water temperature may be caused by the strong air temperature and solar radiation in tropical zones, in addition to the enhanced capacity of heat absorption and heat storage due to the slow water flow in the reservoir.

The parameters governing a water temperature model play a pivotal role in determining the uncertainties associated with the model’s outcome. In this study, a two-dimensional (2D) hydrodynamic and water temperature coupling model is constructed, focusing on the Nuozhadu Reservoir situated along the Lancang River. Employing a single-factor analysis approach, the sensitivity of the thermal balance parameters and hydrodynamic parameters in the model is assessed. This study overcomes the shortcomings of previous sensitivity analyses of hydrodynamic parameters in reservoir water temperature models. The findings reveal that the attenuation parameters of light and Beer’s law parameter exhibit minimal sensitivity to the vertical temperature structure. Conversely, radiation parameter A and radiation parameter B exert tenfold disparate influences on the surface and bottom temperatures of the reservoir. Among the hydrodynamic parameters considered, the horizontal viscosity factor shows no sensitivity to the vertical temperature structure, whereas the vertical viscosity factor serves as a crucial determinant, directly influencing the intensity of vertical temperature stratification. An increased vertical viscosity factor promotes heat exchange between the upper and lower water layers, thereby reducing the vertical temperature gradient and weakening stratification. Conversely, diminishing this factor intensifies stratification. Thus, when conducting water temperature simulations in high dams and large reservoirs, careful attention should be given to calibrating vertical viscosity factor.

The Priority-Flood algorithm, widely recognized for its computational efficiency in hydrological analysis, serves as the fundamental method for depression identification in DEMs, and the efficiency of the Priority-Flood algorithm hinges largely on the core component—priority queue implementation. Existing studies have focused predominantly on reducing the amount of data processed by queues, with few systematic reports on concrete queue implementations and corresponding performance analyses. In this study, six priority queues in the Priority-Flood algorithm are compared: a mini-heap (Heap), an AVL tree, a red-black tree (RBTree), a pairing heap (PairingHeap), a skip list (SkipList), and the Hash Heap (HHeap) structure proposed herein. Using multiscale DEM datasets as benchmarks, the results show that HHeap consistently outperforms the other structures across all scales, with particular advantages in ultralarge queues and in scenarios with high data duplication, rendering it the most effective choice for priority queues. The pairing heap approach typically ranks second in terms of overall runtime, whereas the AVL tree exhibits stable performance across scales; min-heap shows pronounced weaknesses under large-scale data conditions. This study provides empirical evidence to guide efficient priority queue selection and implementation and offers a viable technical pathway for ultralarge-scale terrain analysis. Future work will explore integrating HHeap with learning-based sorting and parallelization to further enhance processing performance and robustness in massive DEM contexts.