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.

Rapid economic and social development has created significant ecological and resource problems in the Beijing-Tianjin-Hebei (BTH) region, making it necessary to identify ways of implementing sustainable regional development. The interactions between water, energy, food, and ecology are characterized by a high degree of relevance and complexity. In studying the relationships between the four systems in depth and choosing representative indicators for each system, a comprehensive development model of the water-energy-food-ecology (WEFE) system in the BTH region has been established. The coupling coordination degree model was used to analyze the coupling synergy relationship between the WEFE systems in the BTH region from 2001 to 2020. The primary contributing elements determining the development of linked synergy in the WEFE system were investigated using a gray correlation model. According to the findings, Beijing’s total coupling coordination development level shows a gradual upward trend and is in excellent coordination; Hebei has progressed the most, experiencing a significant change from little coordination to good coordination; and Tianjin has had the least improvement, only improving from basic to good coordination. The exploitation of water resources and ecological protection of the environment are the aspects that have the greatest impact on the WEFE system. Additionally, the linked and synergistic growth of the WEFE system in the BTH region is significantly influenced by economic, social, and technological advancements in the industrial and agricultural sectors. The coupling coordination development of regional WEFE systems, which takes into consideration the synergistic optimization of many subsystems, is provided by this study as a scientific foundation.

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.

Low-quality input data (such as sparse rainfall gauges, low spatial resolution soil type and land use maps) have limited the application of physically-based distributed hydrological models in operational practices in many data-sparse regions. It is necessary to quantify the uncertainty in the deterministic forecast results of distributed models. In this paper, the TOPographic Kinematic Approximation and Integration (TOPKAPI) distributed model was used for deterministic forecasts with low-quality input data, and then the Hydrologic Uncertainty Processor (HUP) was used to provide the probabilistic forecast results for operational practices. Results showed that the deterministic forecasts by TOPKAPI performed poorly in some flood seasons, such as the years 1997, 2001 and 2008, despite which the overall accuracy of the whole study period 1996–2008 could be acceptable and generally reproduced the hydrological behaviors of the catchment (Lushi basin, China). The HUP model can not only provide probabilistic forecasts (e.g., 90% predictive uncertainty bounds), but also provides deterministic forecasts in terms of 50% percentiles. The 50% percentiles obviously improved the forecast accuracy of selected flood events at the leading time of one hour. Besides, the HUP performance decayed with the leading time increasing (6, 12 h). This work revealed that deterministic model outputs had large uncertainties in flood forecasts, and the HUP model may provide an alternative for operational flood forecasting practices in those areas with low-quality data.