This study proposes a hedging rule model which is composed of a two-period reservior operation model considering the damage depth and hedging rule parameter optimization model. The former solves hedging rules based on a given poriod’s water supply weighting factor and carryover storage target, while the latter optimization model is used to optimize the weighting factor and carryover storage target based on the hedging rules. The coupling model gives the optimal poriod’s water supply weighting factor and carryover storage target to guide release. The conclusions achieved from this study as follows: (1) the water supply weighting factor and carryover storage target have a direct impact on the three elements of the hedging rule; (2) parameters can guide reservoirs to supply water reasonably after optimization of the simulation and optimization model; and (3) in order to verify the utility of the hedging rule, the Heiquan reservoir is used as a case study and particle swarm optimization algorithm with a simulation model is adopted for optimizing the parameter. The results show that the proposed hedging rule can improve the operation performances of the water supply reservoir.

Many dams have been constructed or are planned all around China. These dams significantly change the hydrological regime and sand concentration downstream, and subsequently affect the river habitat and riverbed substances. Therefore, a good understanding of the river habitat is urgently required to undertake efficient measures for fish diversity conservation. A multi-objective assessment method based on the Physical Habitat Simulation system (PHABSIM) was utilized to calculate the ecological river flow demand using maximum weighted usable area (WUA) and minimum river discharge as the main objectives. The study employed this method to assess ecological water flow demand in the National Nature Reserve for Rare and Endemic Fish in the Upper Reaches of the Yangtze River in China. Multiple factors such as the degree of endangerment, fish value (ecological value, economic value and scientific research value), data acquisition difficulty, and species representativeness were taken into consideration during selection of indicator fish for coupled habitat analysis. Requirements for both growth and breeding during the study period were considered. Ten species of fishes were chosen as indicator fishes, including floating egg and sinking egg fishes. Additionally, we applied the principle of “minimization of habitat demand and maximization of ecological demand” to include the needs of all indicator fishes. Further, this method comprehensively considered requirements for ecological flow and economic development. The results highlighted that an optimal ecological river flow demand of 2395 m³/s was needed to satisfy the needs for habitat protection and 1890 m³/s was required to meet the needs of social and economic development. The methods used in this study and results obtained, provide a valuable reference for water resources planning and ecosystem protection in other rivers and lakes.

Motivated by the emerging need for long-term and sustainable reservoir management, this paper developed a multicriteria decision-making (MCDM) model of reservoir operation with consideration of balanced applicability in both the past and the future. The proposed method consists of four modules: (1) the multiobjective optimization model of reservoir operation is built based on historical streamflow and then solved by the evolutionary multiobjective direct policy search (EMOPDS) with the nondominated sorting genetic algorithm-II (NSGA-II); (2) an evaluation criteria matrix is made considering the benefits and risks of the same objectives under future climate change; (3) objective interrelationships are qualitatively identified by visual analytics and quantitatively described by the structural equation model (SEM); and (4) an objective-based space coordinate system (SCS) is established to calculate the evaluation criteria weights based on SEM results, from which the optimum Pareto solution is determined by a decision-making function. The Three Gorges Reservoir (TGR) was selected as a case study, which simultaneously provides hydropower generation (pow), ecology (eco), and water storage (stor). Results indicate that eco negatively interacts with both pow and stor; however, pow is positively correlated with stor. Moreover, the design of the SCS-MCDM model can significantly improve benefits in both pow and stor and slightly sacrifice benefit in eco, compared with fuzzy optimum selection (FOS)-1 and FOS-2. Generally, the SCS-MCDM model outperforms FOS-1 and FOS-2 in terms of benefits for past and future and robustness in operation process under climate change. Therefore, the SCS-MCDM model facilitates decision makers to make a sustainable and balanced decision for reservoir management under uncertain climate change.

Exploring the trade-off between cost and system reliability of water distribution systems (WDSs) has been focused for two decades. Due to the intensive computation associated with the reliability analysis, it is popular in the research community to replace this procedure by using surrogate indicators. However, the discussion on the correlation among different types of such indicators is generally lacking, which implies that a deeper understanding of this aspect is needed. This paper proposes a novel methodology of investigating the relationships among many commonly used surrogate indicators for measuring the mechanical reliability of WDSs. In particular, the optimal design of WDSs is formulated as a many-objective optimization problem, using cost and each surrogate indicator as an individual goal. Two benchmark design problems of different scales and complexities are considered for verifying the proposed method. The well-known multi-objective evolutionary algorithm (MOEA), namely Borg that is suitable for coping with problems involving many objectives, is used to obtain the best approximation to the Pareto-optimal fronts for both cases. Afterward, the one-pipe burst testing is conducted to quantify the correlation between mechanical reliability and surrogate indicators. Results suggest that investigating the correlation of surrogate indicators from the perspective of many-objective optimization provides a direct and efficient way of distinguishing better indicators from worse ones. Resilience-based surrogate indicators and the Redundancy indicator that only depends on nodal pressures are highly related to the mechanical reliability of WDSs. In contrast, entropy-based indicators exhibit poor performance in reflecting the mechanical reliability. These insights contribute to the selection of more appropriate surrogate indicators for the optimal design of WDSs for researchers and practitioners.