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Purpose: The release of endogenous phosphorus from sediments leads to the eutrophication of water bodies. Bacteria in sediments play a vital role in phosphorus cycling. A high hydrostatic pressure can alter the microecology and affect bacterial functions at the water–sediment interface. The aim of this study was to analyze the expression patterns of functional genes in bacteria associated with phosphorus transformation at the water–sediment interface of a deep reservoir under different hydrostatic pressures. Furthermore, the effects of hydrostatic pressure on phosphorus cycling pathways mediated by bacteria were explored. Materials and methods: A device was designed to simulate the microecological environment in sediments from a reservoir in Xi'an, China, at atmospheric pressure, 0.2 MPa, 0.5 MPa, and 0.8 MPa. Phosphorus transformation genes, phoD, ppk, and pqqC, were quantified using real-time PCR. The bacterial community composition and diversity were analyzed using high-throughput sequencing of the ppk gene. Results and discussion: The total phosphorus contents in the sediments were 724.72, 646.97, 630.92, and 682.88 mg kg–1 at the four respective hydrostatic pressures, respectively. Of this, inorganic phosphorus accounted for 68.8–79.0%, and Fe/Al-P accounted for 47.4–57.2%. The expression of phoD facilitated the transformation of organic to inorganic phosphorus and of Ca-P to Fe/Al-P, whereas the expression of ppk and pqqC regulated organic phosphorus hydrolysis by affecting phosphatase activity. The abundance and diversity of ppk-harboring bacteria were highest at 0.2 MPa, while the dominant genera did not change at the four pressures. The five most abundant genera were Pseudomonas, Bordetella, Cupriavidus, Achromobacter, and Rhizobium. Conclusions: High hydrostatic pressure facilitates the transformation of Fe/Al-P to Ca-P in, and phosphorus release from, reservoir sediments by regulating phoD, ppk, and pqqC expression in bacteria. Changing the hydrostatic pressure can alter the bacterial community structure at the genus level, but not the dominant populations harboring the ppk gene. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new method to derive multi-purpose reservoir operation chart with the objectives of maximizing power generation and ecological conservation and minimizing ecological damage under future climate change. This double-parameter ecological operation chart (DEOC) was obtained by incorporating limited ecological curves into original conventional operation chart (COC) and then establishing and solving the operation optimization model considering both ecological profits and hydropower generation. Unlike COC where reservoirs are operated based on the current water level, reservoir operation under DEOC is based on both current reservoir water level and inflow. A case study was conducted in Jasajiang (JS) and Madushan (MDS) cascade reservoirs on the Yuan River in southwestern China. The monthly runoff data for the period 2021–2050 under RCP4.5 and RCP8.5 was simulated using the Soil and Water Assessment Tool (SWAT) driven by the General Circulation Models (GCMs), and the optimal ecological flow range calculated by PHABSIM for a given river section was used to determine the limited ecological curve. The operation optimization model considering both ecological profits and hydropower generation was established and solved to obtain DEOC. Compared with COC, DEOC results in a significant increase in power generation (>9%) and a reduction in ecological damage frequency without affecting the overall ecological conservation rate, especially at an ecological target of 70% and 80%, which can improve the adaptation of reservoir operation to climate change. [ABSTRACT FROM AUTHOR]

In order to mitigate the adverse impacts of climate change, adaptive operating rules (AOR) are generally derived using an ensemble of General Circulation Models (GCMs). Up to date, most of related literatures only focus on one fold of the following issues concerning the derivation of AOR using the GCMs ensemble, including: (1) consideration of different scenario weighing methods, or (2) analysis of different positions to locate scenario weights. And less concern is given to the latter compared with the former. However, few studies identify the relationship between (1) and (2) in the derivation of AOR based on the GCMs ensemble. In this study, we attempt to investigate where to use Equal and REA scenario weights in the derivation of reservoir operating rules under climate change. Equal weights (EW) and unequal weights based on the reliability ensemble average (REA) method are used in two positions: (I) the optimization objective of the reservoir operation model, which is to maximize the weighted average hydropower generation for all future scenarios; and (II) the incorporation of GCMs ensemble climate projections into the weighted climate conditions, and then it is input into the reservoir operation model with the objective of maximizing annual hydropower generation. Four AORs, including EW-AOR(I), REA-AOR(I), EW-AOR(II) and REA-AOR(II), are derived, and their optimized parameters are obtained by the simulation-based optimization (SBO) method with the Complex algorithm. The case study in the Jinxi Reservoir in China indicates that REA-AOR(I) outperforms the other three operation schemes, and EW-AOR(II) performs better than REA-AOR(II). Therefore, equal weights are preferably used to incorporate climate conditions, while unequal weights based on REA method can improve the performance of the reservoir operation model. Generally, REA-AOR(I) and EW-AOR(II) are suggested for adaptive reservoir management under climate change. [ABSTRACT FROM AUTHOR]

Highlights • The trade-offs for flood risk among tributaries was considered for reservoir operating rules. • WNSGA II was applied to the joint operation of large-scale mixed reservoir system. • PLO-WNSGA II outperform conventional operating rules (COR) and PLO-GA. • PLO-WNSGA II is more resilient under inflow uncertainty. Abstract The optimization of the mixed reservoir system, consisting of parallel and cascade reservoirs, is a complex issue due to its nonlinear storage curves, complex hydraulic connection and coordination. This paper aims to derive optimal operating rules for the flood control of large-scale mixed reservoir using a new algorithm known as weighted non-dominated sorting genetic algorithm II (WNSGA II), which could efficiently locate the Pareto front in the non-dominated region due to directed searching with the weighted crowding distance. A case study was performed in the Xijiang river basin in China, where there are eight flood control reservoirs and five flood control sections in four tributaries and the mainstream. Three scenarios, including conventional operating rules (COR), single-objective piecewise linear operating rules derived by genetic algorithm (PLO-GA), and multi-objective piecewise linear operating rules derived by WNSGA II (PLO-WNSGA II), are compared, and the resilience of three reservoir operating rules under inflow uncertainty is investigated as well. The results indicate that: (1) WNSGA II can locate non-dominated solutions more efficiently and provide better Pareto front than non-dominated sorting genetic algorithm II (NSGA II) due to the weighted crowding distance; (2) PLO-WNSGA II outperforms COR and PLO-GA for the control of basin-wide floods; and (3) PLO-WNSGA II is more resilient under inflow uncertainty. Therefore, PLO-WNSGA II is effective and efficient for the control of basin-wide floods in a mixed reservoir system. [ABSTRACT FROM AUTHOR]

This study proposes and tests a new approach to detect and analyze changes in flood regime using a distributed hydrological model (EasyDHM), using the Second Songhua River basin, China, as a case study. Model calibration and parameter sensitivity were used to represent flood regimes in a 60-year series (1954-2013), with three different flood regime periods identified. The changes in flood regime were estimated by model parameters, flood result residuals and the overall process in the hydrological model in the three periods. The results show that human activities significantly impacted flood regimes, with significant flood regime change largely attributed to increases in water storage in multiple small reservoirs. Flood volume was reduced significantly between the periods in all three watersheds. The parameters also changed in variety between the periods. The study highlights the importance of incorporating data on small-reservoir constructions in flood control systems. [ABSTRACT FROM AUTHOR]

Joint operation of multiple reservoir system in inter-basin water transfer-supply project is a complex problem because of the complicated structure and cooperated operation policy. The combination of high-dimensional, multi-peak and multiple constraints makes it incredibly difficult to obtain the optimal rule curves for multi-reservoir operation. In view of this, we constructed a joint optimization operation model, considering both water supply and transfer, and proposed the concept of 'shape constraints'. To obtain the solution of this high-dimensional optimization model, a novel progressive optimum seeking method, namely Progressive Reservoir Algorithm-Particle Swarm Optimization (PRA-PSO), is presented based on the nature of progressive optimization algorithm (POA) and standard particle swarm optimization (PSO). The water transfer project in northeast China, consisting of three routes eight reservoirs, is selected as a case study. The results show that (1) PRA-PSO is yielding much more promising results when compared with other optimization techniques; (2) shape constraints would narrow the scope of feasible solution area but increase the convergence of algorithm; (3) because of the strong interaction between water transfer and water supply action, the progressive setting of PRA-PSO should be in accordance with the order of reservoir water transfer. The case study indicates the novel optimization method could effectively increase the chance of jumping out of local optimal points, thereby searching for better solutions. [ABSTRACT FROM AUTHOR]

Since agriculture development would be affected by climate change, the reservoir operation for agricultural irrigation should be adjusted. However, there are to date few literatures addressing how to design adaptive operating rules for an irrigation reservoir. This study aims to analyze the adaption of fixed operating rules and to derive adaptive operating rules under climate change. The deterministic optimization model is established with the solving method of two-dimensional dynamic programming (TDDP), and its optimal trajectory is supplied to derive reservoir operating rules at time intervals of crop growth periods. Then, two alternative operating rules, including fixed operating rules based on historical data and adaptive operating rules based on climate change data, are extracted using the fitting method with the multiple linear regression model. The alteration of reservoir inflow under climate change is calculated by the Budyko formula. A case study of the China’s Dongwushi Reservoir shows that: (1) fixed operating rules are unable to adapt climate change in the future scenario. Thus, adaptive operating rules should be established, (2) adaptive operating rules can reduce profits loss resulting from climate change, and improve field soil water storages, and (3) precipitation reduction by 7%/40a is the major cause for agricultural profits loss, whereas, the decrement of agricultural profits is less than that of precipitation, which indicates agricultural crops have the resilience to resist the adverse influence from precipitation decrease. These findings are helpful for adaptive operation of irrigation reservoirs under climate change. [ABSTRACT FROM AUTHOR]

Water quality deterioration of reservoir served as municipal water source has been a potential threat to public health. Crowned as one of the largest reservoirs and water resource, Miyun reservoir has been paid attention to water quality protection. For the purpose of effective reservoir protection and management, comprehensive understanding of water quality spatiotemporal pattern is demanded. In this study, three dimensional (3-D) dynamic Environmental Fluid Dynamics Code (EFDC) model was used to simulate hydrodynamic and algal processes for Miyun reservoir. Main challenge was to demonstrate the water quality pattern and implying impacts on variations. Model structure and functionality were briefly described. Field data and estimated loads were used as nutrient forces. Calibration and summary statistics were presented. Results showed that spatiotemporal pattern of water temperature, dissolved oxygen, chlorophyll-a, total phosphorous, nitrate and ammonia were consistent with in situ measurements. Several important impacts on the water quality variations in Miyun reservoir were discussed. It indicated that the existence of the patterns were highly related to the geomorphogical and hydrological characteristics as well as nutrient inputs. Temporal tendency was also linked to the seasonal climate. It proved that EFDC model was feasible for hydro-environmental simulation in Miyun reservoir, providing managers an efficient tool for strong supports on water quality management and water source protection. [ABSTRACT FROM AUTHOR]

Diminishing marginal utility is an important characteristic of water resources systems. With the assumption of diminishing marginal utility (i.e., concavity) of reservoir utility functions, this paper derives a monotonic relationship between reservoir storage and optimal release decision under both deterministic and stochastic conditions, and proposes an algorithm to improve the computational efficiency of both deterministic dynamic programming (DP) and stochastic dynamic programming (SDP) for reservoir operation with concave objective functions. The results from a real-world case study show that the improved DP and SDP exhibit higher computational efficiency than conventional DP and SDP. The computation complexity of the improved DP and SDP is (order of , the number of state discretization) compared to with conventional DP and SDP. [ABSTRACT FROM AUTHOR]

Abstract: Based on the physically characterized distributed hydrological modeling scheme – WEP-L – a more generalized and expandable method – AutoWEP – has been developed that is equipped with updated modules for pre-processing and automatic parameter identification. Sub-basin scale classifications of land use and soil are undertaken by incorporating remote sensing data and geographic information system techniques. In the process of developing the AutoWEP modeling scheme, a new concept of parameter partitioning is proposed and an automatic delineation of parameter partitions is achieved through programming. The sensitivity analysis algorithm, LH-OAT, and the parameter optimization algorithm, SCE-UA, are embedded in the model. Its application to the upstream watershed of the Miyun Reservoir shows that AutoWEP features time-savings, improved efficiency and suitable generalizations, that result in a long series of acceptable simulations. [Copyright &y& Elsevier]

Hydropower is a source of clean, low-carbon renewable energy. However, because of the evaporation of water from reservoir surfaces, a large amount of water is lost, which may result in water scarcity. As for multi-purpose reservoirs, there is no accepted method to allocate water losses among their various functions. Ignoring the multiple requirements of the different ecological service functions of the reservoir will inevitably lead to overestimation of the hydropower blue water footprint (WF). In this study, taking Miyun Reservoir Hydropower Station during 1985–2013 as an example, we showed that distributing water loss between multiple uses by calculating an allocation coefficient greatly reduces the amount of water allocated to electricity production. For spatial analysis, we collected published data on 68 hydropower stations and found that the linear correlation coefficient of WF and spatial variation is 0.94, whereas for four multi-purpose reservoirs calculated using allocation coefficients is 0.99. Further, a temporal analysis indicated that the time step of evaporation calculation may affect the calculation of the blue WF. We also confirmed previous studies showing that the research area we analyzed has experienced severe water scarcity over the years. The results of this study can provide an effective tool for comprehensive evaluation of multi-purpose hydropower stations, and calculation of the environmental costs of hydropower development as clean energy. [ABSTRACT FROM AUTHOR]

A groundwater model is needed to describe the complex groundwater confluence process of the groundwater system in karst areas. This is because surface water flows through dolines, grikes, and by other means and is directly exchanged with the groundwater. In this study, using the Xin'anjiang model, the conversion of surface water into groundwater and the influence of multiple series-parallel underground reservoirs on groundwater confluence through the generalization of dolines in karst areas were simulated. The water cycle process in the Sancha River Basin was simulated with measured data using multiobjective particle swarm optimization. Then, model parameters were validated with measured runoff data and compared with simulation results obtained using the traditional Xin'anjiang model based on its optimal parameters. The results showed that the determination coefficients of all hydrological stations over the study period were >0.76, and the Nash efficiency coefficient was >0.76, which were better than those for the improved Xin'anjiang model. Next, the simulation accuracy of the flood period in the karst area was analyzed. The model achieved a high fitting rate for the main flood peaks in a year, and the passing rate for the worst hydrological stations was 53%. Finally, the influence of karst development on the runoff was examined. The results indicate that different karst development stages and the heterogeneity of the karst in the basin have different effects on runoff. [ABSTRACT FROM AUTHOR]

Highlights • A novel method is proposed to derive FAD operating rules. • FAD operating rules could adapt for flood variability. • AGDP model could solve the curse of dimensionality for large-scale reservoir system. • FAD considers the trade-offs between flood loss of tributaries and mainstream. Abstract The potential of aggregation-decomposition (AGDP) for flood control of mixed reservoir system remains unclear due to the flood variability and complex hydraulic connections. In this study, we develop flood classification-aggregation-decomposition (FAD) operating rules to adapt for flood variability and to account for the trade-offs between flood loss of the tributaries and that of the mainstream. Three steps are involved in the proposed FAD operating rules: (1) flood classification by projection pursuit (PP) coupled with genetic algorithm (GA); (2) definition of FAD operating rules using the AGDP model coupled with piecewise linear function based on flood classification; and (3) optimization of FAD operating rules by parameterization-simulation–optimization (PSO). A case study is performed with a large-scale mixed reservoir system in the Xijiang river basin (China) in three scenarios: conventional operation for the status quo year (CO-SQY), conventional operation for the perspective year 2030 (CO-2030) and FAD operation for the perspective year 2030 (FAD-2030). The results clearly show that flood classification enables reservoir operating rules to better adapt for flood variability, and the FAD operating rules in the scenario of FAD-2030 outperform the conventional operating rules in the scenarios of CO-SQY and CO-2030, as it considers flood variability and the trade-offs between flood loss of the tributaries and that of the mainstream. [ABSTRACT FROM AUTHOR]

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA. [ABSTRACT FROM AUTHOR]

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately. [ABSTRACT FROM AUTHOR]

The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier. [ABSTRACT FROM AUTHOR]

Summary Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China’s the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions. [ABSTRACT FROM AUTHOR]

• Activation and allocation of flood control capacity for the cascade reservoir. • The 'reverse order impoundment method of cascade reservoir' mode was proposed. • The improved artificial bee colony algorithm was adopted to solve the optimal model. • The flood control standard downstream was improved greatly. • The peak inflow to the Three Gorges Reservoir was reduced. Flood control in the Yangtze River Basin has always been a thorny issue in Chinese history. Along with the development of water science and technology, a large number of reservoirs have been constructed in the Yangtze River Basin, especially the progress of the reservoir operation theory, the joint flood control simulation and decision-making of reservoir groups has been applied extensively, which proved an effective way to reduce the flood risks and losses in the middle and lower reaches of the Yangtze River Basin. However, an increase in the constraints of the reservoir operation (or a single reservoir becoming a multiple-reservoir system) results in an exponential increase in the computational dimensions of the joint flood control simulation, often referred to as the 'curse of dimensionality'. Therefore, based on the aggregation-decomposition (AGDP) method, this paper proposes a cascaded reservoir group integrated operation mode, namely, the 'reverse order impoundment method of cascade reservoir' (ROIMCR) mode, and adopts the improved artificial bee colony (IABC) algorithm to solve the optimization problem of the ROIMCR mode (IABC-ROIMCR) and establish an optimal operation model for cascade reservoir joint flood control. The results show that: 1. With the 'conventional operation' (CO) mode, the ROIMCR mode has more advantages, the average peak flow reduction rate with 32.89% is higher than that of the CO mode 2.91%. 2. In solving the flood optimization operation problem of the cascade reservoir system, the application of the IABC algorithm can effectively reduce the dimensions in the mathematical mode, and has evident effects. The average peak flow reduction rate in the IABC-ROIMCR mode is 44.87%, and 14.89% higher than those of the CO mode. 3. With the IABC-ROIMCR mode, the flood crest standard of cascade reservoirs can be reduced from a 100-year return period to less than a 5-year period, and the flood crest standard of the Lizhuang Hydrological Station from more than a 50-year return period to less than 5-year period. The flood crest standard of the Zhutuo Hydrological Station can be reduced from a 100-year return period to approximately a 5-year period, thereby greatly improving the flood control standard in the upper and middle reaches of the Yangtze River Basin. In addition, the peak inflow to the Three Gorges Reservoir is reduced, and as a result, the flood control pressure in the lower reaches of the Yangtze River Basin is alleviated to a certain extent. [ABSTRACT FROM AUTHOR]

There are increasing calls to incorporate ecological requirements into hydropower operation, however it remains a challenge to balance average ecological benefit over a long period of time and ecological damage under wet/dry conditions. This study proposes a new hydropower-ecological operation chart of cascade hydropower stations in which limited ecological curves were introduced and all guiding curves were optimized, aiming to improve the long-term average fish habitat quality and to mitigate the negative impacts caused by drought/flood events on fish habitat without reducing power generation compared to the conventional reservoir operation. The JiaSA(JSA)-MaduShan (MS) cascaded reservoirs in Yuan Basin were chosen as the research area. The physical habitat simulation model (PHABSIM) is used to quantify the hydrological requirements of the target fish species at different life stages. At an ecological need fitness target of 70%, under the guidance of hydropower-ecological operation chart, without reducing the power generation, the average ecological need fitness at the downstream of JSA and MS hydropower stations is increased by 0.09% and 1.06%, the frequency of ecological damage is reduced by 6.12% and 25.00%, and the ecological damage degree during wet/dry periods is reduced by 1.96% and 0.52%, respectively. However, when ecological need fitness target is higher than 80%, the frequency and magnitude of ecological damage are significantly increased. Therefore, the proposed operation chart has the potential to increase the multi-objective benefits of hydropower system and can be easily applied to other cascaded reservoirs in different basins. [ABSTRACT FROM AUTHOR]

• Propose a sensitivity metric that considers fluctuation induced by varying input boundary conditions. • Derive the logical connection between parameter sensitivity and EnKF ensemble evolution. • Correction of terrain and roughness error in hydrodynamic model by ISS-SRC-assisted EnKF. • Achieve accurate simulation of real-world events using coarse terrain data. Evaluating the risk of flows related to reservoir releases requires a comprehensive grasp of the associated hydrodynamic process. Due to the obstacle posed by bathymetry in mountainous areas, the accuracy of the hydrodynamic simulation is hindered by the absence of reliable terrain data. To evaluate both the overall parameter sensitivities (e.g., sensitivities to cross-section geometry and roughness) within the feasible domain and the varying responses induced by changes in the input forcing (e.g., inflow series), we introduce a generalized global sensitivity indicator, namely, the input space sampled standardized regression coefficients (ISS-SRC). Through mathematical derivation, we found that the parameter whose ISS-SRC index is close to zero will not be significantly updated by the EnKF. Thus, ISS-SRC can be employed to assist the EnKF by predicting the correction performance and reducing the dimensionality of identifiable factors. The results of synthetic experiments confirm the connection between the evolution of the parameter ensemble and the dynamic sensitivity indicators. The ISS-SRC-assisted EnKF is employed to modify the physical and geometrical parameters of riverbed sections in the hydrodynamic model of a real-world reservoir release case and the simulation accuracy is greatly enhanced. This success demonstrates the potential of obtaining reliable flood simulation results with only coarse-resolution topographic information. [ABSTRACT FROM AUTHOR]

• The hydrologic impact of existing and alternative reservoir network layouts was investigated. • Few large reservoirs regulate the streamflow more significantly than many small ones with similar inflow. • Locating reservoirs somewhere with similar inflow is unlikely to change the streamflow condition. • Assigning reservoirs higher inflow without changing their location can increase their functionality. Reservoirs have been continuously built in the Poyang Lake Basin (PLB) for the local economy. With pending removal and reconstruction of aging reservoirs and planning of new reservoirs in this area, it is advisable to evaluate the likely impact of the existing and alternative reservoir network layouts on the hydrologic regime for more effective water resources planning and management. In this study, reservoirs of multiple sizes in the PLB were integrated into a coupled land surface-hydrologic model (CLHMS) fully coupled with a reservoir scheme. The extended model was then validated and applied to investigate the impact of existing reservoirs. Seven hypothetical scenarios were additionally set up by removing all the reservoirs, replacing smaller reservoirs with few larger ones, replacing larger reservoirs with many smaller ones, moving all reservoirs downstream or upstream, increasing or decreasing the mean inflow of reservoirs, respectively. In all, eight simulations were conducted and analyzed using the Indictors of Hydrologic Alterations (IHA) for the period of 1981–1999. Results indicate that the existing reservoirs in the PLB are able to effectively displace the excessive streamflow in the wet season to the dry season and mitigate the hydrological extremes. By increasing the mean inflow of reservoirs, the effectiveness of reservoirs in downstream flow regulation can be further enhanced. Similarly, replacing many small reservoirs with a few large ones with a same total capacity can lead to more resiliency against the downstream flow variability and extremes, only if the total mean inflow of the new large reservoirs is not considerably lower than that of the small ones. Relocating reservoirs to a different place with similar mean inflow, however, has a relatively minor and probably case-dependent impact on the hydrologic regime. On the other hand, the ecological analysis on the IHA parameters indicates that the layout preferable for the effective flow regulation may not necessarily be ecologically preferable. In general, this study comprehensively evaluates the hydrological impact of reservoir network layouts, and can provide implications for stakeholders to reach a trade-off between reservoir capacity distribution, locations and mean inflow not only in the PLB but also in different regions worldwide. [ABSTRACT FROM AUTHOR]