The multi-objective optimal operation and the joint scheduling of giant-scale reservoir systems are of great significance for water resource management; the interactions and mechanisms between the objectives are the key points. Taking the reservoir system composed of 30 reservoirs in the upper reaches of the Yangtze River as the research object, this paper constructs a multi-objective optimal operation model integrating four objectives of power generation, ecology, water supply, and shipping under the constraints of flood control to analyze the inside interaction mechanisms among the objectives. The results are as follows. (1) Compared with single power generation optimization, multi-objective optimization improves the benefits of the system. The total power generation is reduced by only 4.09% at most, but the water supply, ecology, and shipping targets are increased by 98.52%, 35.09%, and 100% at most under different inflow conditions, respectively. (2) The competition between power generation and the other targets is the most obvious; the relationship between water supply and ecology depends on the magnitude of flow required by the control section for both targets, and the restriction effect of the shipping target is limited. (3) Joint operation has greatly increased the overall benefits. Compared with the separate operation of each basin, the benefits of power generation, water supply, ecology, and shipping increased by 5.50%, 45.99%, 98.49%, and 100.00% respectively in the equilibrium scheme. This study provides a widely used method to analyze the multi-objective relationship mechanism, and can be used to guide the actual scheduling rules.2 of 23 requirements for the water head and flow of hydropower stations between power generation and other benefit objectives, there exists mutual influence and interdependence among these benefit objectives [5]. Therefore, how to deal with the impact between these objectives and maximize the benefit of limited water resources is the focus and difficult point of current research. Many scholars have focused on the optimal operation of reservoirs and reservoir groups in specific areas.Many algorithms can be used to solve reservoir optimal operation and water management problems with the development of computing ability; such algorithms can be classified as classic or evolutionary methods [6]. However, the classic methods always perform poorly in solving complex problems, which makes the evolutionary methods develop rapidly [7]. Therefore, evolutionary methods are frequently used in water management problems, such as particle swarm optimization (PSO) [8-10], genetic algorithm (GA) [11][12][13], and so on [14][15][16]. With the increase in the pursuit of multi-objective benefits by decision makers, the multi-objective optimization algorithms (MOEAs) have received more attention and been improved a lot, such as gravity search algorithm (GSA) [7], strength Pareto evolutionary algorithm (SPEA) [17], non-dominated sorting genetic algorithm-III (NSGA-III) [18], and so on. With the de...
<p>Forest status in natural catchment is substantially important for hydrology and water quality, but it has been increasingly altered by human activities and climatic factors. Due to recent rapid changes in forest cover, there is an urgent need for hydrological water quality models which can adapt to these changing environmental conditions. The objective of this study was to analyse the impact of rapid continuous forest decline on nitrogen losses in a temperate mountain range catchment using a dynamic setting of the HYPE (HYdrological Predictions for the Environment) model. The modified model was applied to the Gro&#223;e Ohe catchment, Germany, which has experienced severe forest dieback (caused by bark beetle infestations) and its recovery over the last three decades. The model was validated by using also additional 25 years data from an internal gauge station (Forellenbach) and two soil measurement sites. Three scenarios, namely, no forest change, deforestation with subsequent regeneration, and deforestation without regeneration, were compared to identify key factors influencing catchment discharge and nitrogen export due to deforestation and regeneration. Results showed that the model performed well at the Gro&#223;e Ohe catchment scale, with Nash-Sutcliffe Efficiency values of 0.77 and 0.57 for discharge and IN concentration, respectively, and percentage BIAS values of -11.6% and 0.5%, respectively, during the validation period. Similar good performances were also observed at other scales. The simulation results proved that the improved model was able to (1) well capture the timing of peak flows and the seasonal dynamics of inorganic nitrogen (IN) concentration, and more importantly, (2) reflect the first increasing and then decreasing trend of discharge and IN concentration, in accordance with the deforestation and forest regeneration, respectively. By comparing scenarios, after experienced forest dieback without regeneration, the discharge and IN concentration exports were 24.9% and 160%, respectively, greater than those of scenario without forest change. However, the discharge and IN concentration exports were only 3.63% and 39.6% greater, respectively, with the help of continuous regeneration, indicating that forest regeneration is important for restoring hydrological and water quality status in the catchment. Compared to non-change scenario, the deforestation scenario exhibited decreased annual plant uptake of 34.7%, and strong increase in annual denitrification and N mineralization suggesting that the increased nitrogen export was likely induced by the reduction in vegetation uptake and the increased availability of soil nitrogen from tree residues. Overall, the adapted mechanistic modelling under the changing catchment forest conditions can strongly support forest management in terms of water quality.</p>
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