水库生态调度优化模拟研究进展与展望OA

Reservoirs served as essential infrastructure for water resource utilization and regulation,providing significant benefits in flood control,hydropower generation,water supply,and navigation.However,the construction and operation of large-scale reservoirs significantly altered the natural hydrological regimes of rivers.These changes were observed as the flattening of flow processes,attenuation of flood peaks,stratification of water temperature,alteration of sediment transport,and variability in water mixing characteristics.These changes ultimately resulted in habitat degradation,obstruction of fish migration,biodiversity loss,and a decline in ecological functions.They also affected the geochemical behavior of nutrients and interfered with ecological processes.Against this background,the operation paradigm of reservoirs shifted from a focus on maximizing engineering benefits toward the emerging concept of ecological operation,which aimed to reconcile human demands with ecological requirements. A comprehensive review of recent research on simulating and optimizing reservoir ecology was conducted.The evolution of ecological-constraint-based and ecological-objective-based models was systematically traced.The former treated ecological flow as a constraint within traditional optimization frameworks,whereas the latter directly integrated ecological objectives into multi-objective models.A variety of solution strategies,including evolutionary algorithms,swarm intelligence algorithms,hybrid approaches,and classical optimization algorithms such as dynamic programming and its variations,were compared.Each algorithm's theoretical mechanism,computational efficiency,adaptability to complex systems,and limitations were carefully examined.Furthermore,attention was given to the generation and selection of Pareto-optimal solutions in multi-objective problems,with emphasis on the challenges of screening ecologically favorable solutions when confronted with economic objectives such as power generation and water supply. The review results indicated that ecological-constraint-based models maintained basic ecological flows while preserving economic benefits,but their contribution to ecosystem restoration was limited.Ecological-objective-based models improved responsiveness to ecological needs and allowed for a more holistic view of ecosystem processes,but quantifying ecological objectives presented significant methodological challenges.The"curse of dimensionality"in large-scale reservoir systems limited the theoretical rigor and global optimality that classical optimization techniques offered under simplified conditions.Strong global search capabilities and robustness in producing a variety of non-dominated solutions were demonstrated by evolutionary algorithms,especially genetic algorithms and their enhanced versions.Swarm intelligence algorithms,such as particle swarm optimization,achieved rapid convergence with fewer parameters but were prone to premature convergence.Hybrid algorithms,which combine mathematical programming,heuristic search,and local refinement,have shown promising results in large-scale and long-horizon scheduling problems.Overall,existing methods had achieved progress in balancing ecological and economic objectives,but challenges persisted in the accurate quantification of ecological demands,the scientific selection of non-dominated solutions,and the treatment of uncertainty from climate variability,hydrological forecasting errors,and socio-economic dynamics. Future research on reservoir ecological operation should address several unresolved issues.First,robust methods for the multidimensional quantification of ecological objectives are needed that go beyond simple indicators to capture complex ecological processes,such as spawning pulses,habitat dynamics,and water quality thresholds.Second,more systematic frameworks are necessary to screen Pareto-optimal solutions by evaluating negative externalities and ensuring that ecological objectives are not marginalized in decision-making.To address these shortcomings,a decision-making approach based on negative externality evaluation and multidimensional game theory was proposed,enabling the transition from a set of non-dominated solutions to the identification of an ecologically optimal solution.Finally,the application of large-scale models and digital twin technologies was envisaged to revolutionize ecological operation.While digital twins enable real-time interaction,scenario simulation,and adaptive optimization,large-scale models enable the coupling of physical mechanisms with data-driven insights.These technologies,combined with advances in remote sensing and sensor networks,are expected to create intelligent ecological operation systems capable of dynamic adjustment and risk prevention.Interdisciplinary collaboration among engineers,ecologists,policymakers,and stakeholders would be essential to ensure that future reservoir operation frameworks are technically sound,ecologically sustainable,and socially acceptable.Thus,this study provided theoretical guidance as well as practical opportunities for moving reservoir ecological operation toward a paradigm of efficiency,ecological integrity,and sustainability.