Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098-0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Adaptive reservoir operation considering water quantity and quality objectives: Application of parallel cellular automata and sub-seasonal streamflow forecasts.

This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives.

Key hydrodynamic principles for controlling algal blooms using emergency reservoir operation strategies.

Reservoir operation strategies with low cost and high efficiency have been proposed to control algal blooms. However, the key hydrodynamic principle for performing reservoir operation strategies is still unknown, posing an obstacle to practical applications. To address this challenge, we proposed short-term emergency reservoir operation strategies (EROSs), established a three-dimensional (3D) eutrophication model of the Zipingpu Reservoir, and designed six 14-day reservoir operation cases to explore the mechanism of EROSs in controlling algal blooms. Large outflows with rapid water exchange should be adopted early in EROSs to control algal blooms in the reservoir. Small variations in the surface water temperature or the mixed layer depth/euphotic layer depth (Zmix/Zeu) ratio were found for different EROSs, indicating that these variations might not have been responsible for the differences in the algal blooms in the reservoir. The EROSs induced high surface flow velocity (Vs) and depth-averaged velocity (Vd) values in the reservoir, thereby controlling algal blooms by inhibiting algal growth and disrupting algal accumulation in the upper water layers. The flow of Vs against the direction of the water intake was detected during the execution of the EROSs, suggesting that increasing Vs might enhance water retention in the reservoir. Increasing Vd not only promoted water exchange to disrupt algal accumulation but also enhanced Vs to inhibit algal growth. Moreover, Vd demonstrated a strong linear relationship with the inhibition ratio of algal blooms. These results demonstrate that Vd is the key hydrodynamic indicator for performing EROSs and that accelerating Vd to exceed 0.039 m s-1 in the near-dam region can control algal blooms. Overall, in this study, we develop a novel EROS and elucidate corresponding principles for the use of EROSs to control algal blooms in reservoirs.

Streamflow Prediction in Highly Regulated, Transboundary Watersheds Using Multi-Basin Modeling and Remote Sensing Imagery.

Despite the potential of remote sensing for monitoring reservoir operation, few studies have investigated the extent to which reservoir releases can be inferred across different spatial and temporal scales. Through evaluating 21 reservoirs in the highly regulated Greater Mekong region, remote sensing imagery was found to be useful in estimating daily storage volumes for within-year and over-year reservoirs (correlation coefficients [CC] ≥ 0.9, normalized root mean squared error [NRMSE] ≤ 31%), but not for run-of-river reservoirs (CC < 0.4, 40% ≤ NRMSE ≤ 270%). Given a large gap in the number of reservoirs between global and local databases, the proposed framework can improve representation of existing reservoirs in the global reservoir database and thus human impacts in hydrological models. Adopting an Integrated Reservoir Operation Scheme within a multi-basin model was found to overcome the limitations of remote sensing and improve streamflow prediction at ungauged cascade reservoir systems where previous modeling approaches were unsuccessful. As a result, daily regulated streamflow was predicted competently across all types of reservoirs (median values of CC = 0.65, NRMSE = 8%, and Kling-Gupta efficiency [KGE] = 0.55) and downstream hydrological stations (median values of CC = 0.94, NRMSE = 8%, and KGE = 0.81). The findings are valuable for helping to understand the impacts of reservoirs and dams on streamflow and for developing more useful adaptation measures to extreme events in data sparse river basins.

MASR: A novel monitoring method coupled with interpretation platform for near-term management in thermal stratified reservoirs.

Good water quality is critical to public health and aquatic ecological security of global reservoirs. In stratified reservoirs, increasing near-term management demands foster extremely high monitoring and forecasting needs. In this study, a management assistant for stratified reservoirs (MASR) was developed, including a wave-driven monitoring platform and interpretation platform for multiple reservoir water quality variables. The wave-driven platform can adapt to the characteristics of water level changes and transmit the monitoring data through a mobile network to the reservoir manager, which are processed by the interpretation platform in real time for near-term management. After several months of application, MASR monitored 1237 groups of valid profile water quality data with an acceptable error, which showed a strong capacity for capturing the water quality dynamics in a stratified reservoir. The effective identification of thermal stratification structures and anoxic zones can help managers to design withdrawal schemes for reservoirs. Moreover, the prediction of algae state based on the back propagation (BP) neural network provided the basis for making operation plans to proactively control algae blooms. Our study provides an economical and convenient solution for stratified reservoirs to address near-term management issues.

Water-energy-ecosystem nexus modeling using multi-objective, non-linear programming in a regulated river: Exploring tradeoffs among environmental flows, cascaded small hydropower, and inter-basin water diversion projects.

Small hydropower (SHP) possesses significant economic, technical, and environmental advantages, and accounts for a large proportion of hydropower development in China. However, the concentrated, cascaded, and diversion-type development of SHP has resulted in long-distance dewatering of river sections, and inter-basin water transfers have led to severe exploitation of water resources and damage to river ecosystems. In this paper, the Datong River Basin, a secondary sub-basin of the Yellow River Basin in China, was selected as the illustrative case, which includes 22 hydropower projects (HPPs) and three inter-basin water diversion projects (WDPs). A nexus system model was established that used weighted multi-objective programming to consider three main objectives: the water resources utilization (local water withdrawal and inter-basin water transfer), energy production (by cascaded HPPs), and riverine environmental conservation. The Tennant method was used to estimate the environmental flows (e-flows) at the cross-sections immediately downstream of the dam/sluice gate and immediately downstream of the hydropower plant of diversion-type HPPs. The decreased percentage of regulated flow in comparison with runoff and the guaranteed rate of e-flow at the control cross-section were introduced to assess the degree of environmental impact to the river. Using a historical series of runoff data during 1956-2016 as the model input (i.e., implicit stochastic method), the Multi-start solver of nonlinear programming of LINGO software was used to conduct optimizations and analyses for multiple scenarios (with/without e-flow, with consideration of various levels of e-flow, and with/without water resources utilization). The sectoral linkages relating to the water-energy-ecosystem (WEE) nexus were quantitatively identified. The possible influences of different boundary conditions (i.e., initial/final reservoir storage, inter-basin water diversion capacity, and climate change) on the WEE nexus were further explored. The present study aims to provide an exemplar for the optimal operation and scientific management of a complicated water resources system in a regulated river with cascaded SHP and inter-basin WDPs.

Integrated scheduling-assessing system for drought mitigation in the river-connected lake.

Lakes are important inland surface water resources and have great influence on the ecological environment as well as the surrounding residential life. However, global lake water resources showed a depleting tendency over the past decades because of the climate change and human activities. To mitigate the drought of lakes linked to a regulated main river, this study proposes an integrated scheduling-assessing system (ISAS) based on the machine learning methodology for a large river-lake system controlled by upstream reservoirs. Closely calibrated to observational data, the ISAS was applied to the middle Yangtze River to mitigate the Poyang Lake drought. The results show that the drought situation in the downstream lake could be improved through the reservoir optimal operation. For the Poyang Lake case, the lowest lake level is not obviously improved, while the starting data of the drought could be delayed by 12, 11, and 17 days, comparing to the conventional scheme in typical dry, normal, and wet years, respectively. Moreover, the duration of the drought could be 20, 19, and 21 days less. It is illustrated that accelerating the reservoir filling speed and decelerating the emptying speed is beneficial to alleviate the drought situation of downstream river-connected lakes.

An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change.

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.5) emission scenario are adopted and utilized to predict the climate variables over a 30-year planning period. To solve this problem, an improved version of our recently proposed fuzzy multi-objective particle swarm optimization (f-MOPSO) algorithm, named f-MOPSO-II, is proposed. The f-MOPSO takes a novel approach to handle multi-objective nature of the optimization problems. In this approach, the common concept of "diversity" is replaced with "extremity," to choose the better guides of the search agents in the algorithm. The f-MOPSO-II is based on the f-MOPSO. However, it is aimed at simultaneously mitigating the f-MOPSO computational complexity and enhancing the quality of the final results presented by this algorithm. The results obtained by the f-MOPSO-II were then compared with those yielded by the popular non-dominated sorting genetic algorithm-II (NSGA-II). As the results suggest, the f-MOPSO-II is capable of simultaneously meeting the water demands and holding the reservoir storage sustainable, much better than the NSGA-II.

Reservoir water quality simulation with data mining models.

Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE, NSE, RMSE, and R2 goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R2 = 0.97, NSE = 0.92) and the lowest values (MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.

Climate-environment-water: integrated and non-integrated approaches to reservoir operation.

Integrated water planning and management face multiple challenges, among which are the competing interests of several water-using sectors and changing climatic trends. This paper presents integrated and non-integrated climate-environment-water approaches for reservoir operation, illustrated with Karkhe reservoir, Iran. Reservoir operation objectives are meeting municipal, environmental, and agricultural water demands. Results show the integrated approach, which relies on multi-objective optimization of municipal, environmental, and agricultural water supply, improves the municipal, environmental, and agricultural objectives by 70, 32, and 65% compared with the objectives' values achieved with the non-integrated approach, which implements a standard operating policy.

Optimal reservoir operation using multi-objective evolutionary algorithms for potential estuarine eutrophication control.

Increased nutrient loads and changed nutrient ratios in estuarine waters have enhanced the occurrence of eutrophication and harmful algae blooms. Most of these consequences are caused by the new proliferation of toxin-producing non-siliceous algae. In this study, we propose a multi-objective reservoir operation model based on 10-day time scale for estuarine eutrophication control to reduce the potential non-siliceous algae outbreak. This model takes the hydropower generation and social economy water requirement in reservoir into consideration, minimizing the ICEP (indicator of estuarine eutrophication potential) as an ecological objective. Three modern multi-objective evolutionary algorithms (MOEAs) are applied to solve the proposed reservoir operation model. The Three Gorges Reservoir and its operation effects on the Yangtze Estuary were chosen as a case study. The performances of these three algorithms were evaluated through a diagnostic assessment framework of modern MOEAs' abilities. The results showed that the multi-objective evolutionary algorithm based on decomposition with differential evolution operator (MOEA/D-DE) achieved the best performance for the operation model. It indicates that single implementation of hydrological management cannot make effective control of potential estuarine eutrophication, while combined in-estuary TP concentration control and reservoir optimal operation is a more realistic, crucial and effective strategy for controlling eutrophication potential of non-siliceous algae proliferation. Under optimized operation with controlled TP concentration and estuarine water withdrawal of 1470 m3/s, ecological satiety rate for estuarine drinking water source increased to 77.78%, 88.89% and 83.33% for wet, normal and dry years, the corresponding values in practical operation were only 72.22%, 58.33% and 55.56%, respectively. The results suggest that these operations will not negatively affect the economic and social interests. Therefore, the proposed integrated management approaches can provide guidance for water managers to reach a stable trophic control of estuarine waters.

Real-time reservoir operation using data mining techniques.

The optimal operation of hydropower reservoirs is essential for the planning and efficient management of water resources and the production of hydroelectric energy. Various techniques such as the genetic algorithm (GA), artificial neural networks (ANN), support vector machine (SVM), and dynamic programming (DP) have been employed to calculate reservoir operation rules. This paper implements the data mining techniques SVM and ANN to calculate the optimal release rule of hydropower reservoirs under "forecasting" and "non-forecasting" scenarios. The employment of data mining techniques accounting for data uncertainty to calculate optimal hydropower reservoir operation is novel in the field of water resource systems analysis. The optimal operation of the Karoon 3 reservoir, Iran, serves as a test of the proposed methodology. The upstream streamflow, storage records, and several lagged variables are model inputs. Data obtained from solving the reservoir optimization problem with nonlinear programming (NLP) are applied to train (calibrate) the SVM, and ANN, SVM, and ANN are executed in the "non-forecasting" scenario based on all inputs along with their time-lagged variables. In contrast, current parameters are removed from the set of inputs in the "forecasting" scenario. The results of the SVM model are compared with those of the ANN model with the correlation coefficient (R), the mean error (ME), and the root mean square error (RMSE). This paper's results indicate performance of the SVM is better than that of the ANN model by 1.5%, 400%, and 10% with respect to the R, ME, and RMSE diagnostic statistics, respectively. In addition, SVM and ANN overcome data uncertainty ("forecasting" scenario) to produce optimal reservoir operation.

Reservoir operation using a robust evolutionary optimization algorithm.

In this research, a significant improvement in reservoir operation was achieved using a state-of-the-art evolutionary algorithm named Borg MOEA. A real-world multipurpose dam was used to test the algorithm's performance, and the target of the reservoir operation policy was to fulfil downstream water demands in drought condition while maintaining a sustainable quantity of water in the reservoir for the next year. The reservoir's performance was improved by increasing the maximum reservoir storage by 14.83 million m3. Furthermore, sustainable water storage in the reservoir was achieved for the next year, for the simulated low flow condition considered, while the total annual imbalance between the monthly reservoir releases and water demands was reduced by 64.7%. The algorithm converged quickly and reliably, and consistently good results were obtained. The methodology and results will be useful to decision makers and water managers for setting the policy to manage the reservoir efficiently and sustainably.

Air-water CO2 and CH4 fluxes along a river-reservoir continuum: Case study in the Pengxi River, a tributary of the Yangtze River in the Three Gorges Reservoir, China.

Water surface greenhouse gas (GHG) emissions in freshwater reservoirs are closely related to limnological processes in the water column. Affected by both reservoir operation and seasonal changes, variations in the hydro-morphological conditions in the river-reservoir continuum will create distinctive patterns in water surface GHG emissions. A one-year field survey was carried out in the Pengxi River-reservoir continuum, a part of the Three Gorges Reservoir (TGR) immediately after the TGR reached its maximum water level. The annual average water surface CO2 and CH4 emissions at the riverine background sampling sites were 6.23 ± 0.93 and 0.025 ± 0.006 mmol h-1 m-2, respectively. The CO2 emissions were higher than those in the downstream reservoirs. The development of phytoplankton controlled the downstream decrease in water surface CO2 emissions. The presence of thermal stratification in the permanent backwater area supported extensive phytoplankton blooms, resulting in a carbon sink during several months of the year. The CH4 emissions were mainly impacted by water temperature and dissolved organic carbon. The greatest water surface CH4 emission was detected in the fluctuating backwater area, likely due to a shallower water column and abundant organic matter. The Pengxi River backwater area did not show significant increase in water surface GHG emissions reported in tropical reservoirs. In evaluating the net GHG emissions by the impoundment of TGR, the net change in the carbon budget and the contribution of nitrogen and phosphorus should be taken into consideration in this eutrophic river-reservoir continuum.

Application of PSO algorithm in short-term optimization of reservoir operation.

The optimization of the operation of existing water systems such as dams is very important for water resource planning and management especially in arid and semi-arid lands. Due to budget and operational water resource limitations and environmental problems, the operation optimization is gradually replaced by new systems. The operation optimization of water systems is a complex, nonlinear, multi-constraint, and multidimensional problem that needs robust techniques. In this article, the practical swarm optimization (PSO) was adopted for solving the operation problem of multipurpose Mahabad reservoir dam in the northwest of Iran. The desired result or target function is to minimize the difference between downstream monthly demand and release. The method was applied with considering the reduction probabilities of inflow for the four scenarios of normal and drought conditions. The results showed that in most of the scenarios for normal and drought conditions, released water obtained by the PSO model was equal to downstream demand and also, the reservoir volume was reducing for the probabilities of inflow. The PSO model revealed a good performance to minimize the reservoir water loss, and this operation policy can be an appropriate policy in the drought condition for the reservoir.

Succession of phytoplankton assemblages in response to large-scale reservoir operation: a case study in a tributary of the Three Gorges Reservoir, China.

The Three Gorges Dam (TGD) has greatly altered ecological and environmental conditions within the reservoir region, but it is not known how these changes affect phytoplankton structure and dynamics. Here, a bimonthly monitoring program was implemented from 2007 to 2009 to study the impact of damming on phytoplankton assemblages in the backwater area of the Pengxi River (PBA). By application of the phytoplankton functional group (C strategists, competitive species; S strategists, stress-tolerant species; R strategists, rapid propagation species), seasonal changes in phytoplankton relative to environmental variations were evaluated using ordination analysis. Seasonal patterns of phytoplankton dynamics were detected during this study, with CS/S strategists causing algal blooms from mid-spring to early summer, CS/CR strategists often observed during flood season, and CS strategists dominant during mid-autumn. CR/R groups dominated during winter and caused algal blooms in February. Our results indicated that phytoplankton assemblages were directly related to reservoir operation effects. Generally, the TGD had a low water level during flood season, resulting in a relatively short hydraulic retention time and intensive variability, which supported the cooccurrence of CS and CR species. During the winter drought season, water storage in the TGD increased the water level and the hydraulic retention time in the PBA, enabling R/CR strategists to overcome the sedimentation effect and to out-compete S/CS species in winter. As expected, these diversity patterns were significantly correlated with the hydraulic retention time and nutrient limitation pattern in the PBA. This study provides strategic insight for evaluating the impacts of reservoir operations on phytoplankton adaptation.

Selective withdrawal optimization in river-reservoir systems; trade-offs between maximum allowable receiving waste load and water quality criteria enhancement.

In this paper, a new systematic approach is designed to maximize the demand coverage and receiving waste load by river-reservoir systems while enhancing water quality criteria. The approach intends to control the reservoir eutrophication while developing a trade-off between the maximum receiving load and shortage on demand coverage. To simulate the system, a hybrid process-based and data-driven model is tailored. Initially, the two-dimensional hydrodynamics and water quality simulation model (CE-QUAL-W2) is linked with an effective single and/or multiple optimization algorithms (PSO) to evaluate the proposed scenarios. To increase the computational efficiencies, the simulation model is substituted with a surrogate model (ANN) in an adaptive-dynamically refined routine. The proposed method is illustrated by a case study in Iran, namely, Karkheh River Reservoir, for 180-monthly periods. The results showed the applicability of the methodology especially to solve high-dimensional multi-period complex water resource optimization problems. Also, the results demonstrated that eutrophication could be reduced under the optimal inflow phosphate control and reservoir operation, regulating the total phosphorous concentration in the reservoir.

Grid-wide subdaily hydrologic alteration under massive wind power penetration in Chile.

Hydropeaking operations can severely degrade ecosystems. As variable renewable sources (e.g. wind power) are integrated into a power grid, fluctuations in the generation-demand balance are expected to increase. In this context, compensating technologies, notably hydropower reservoir plants, could operate in a stronger peaking scheme. This issue calls for an integrated modeling of the entire power system, including not only hydropower reservoirs, but also all other plants. A novel methodology to study the link between the short-term variability of renewable energies and the subdaily hydrologic alteration, due to hydropower reservoir operations is presented. Grid operations under selected wind power portfolios are simulated using a short-term hydro-thermal coordination tool. The resulting turbined flows by relevant reservoir plants are then compared in terms of the Richard-Baker flashiness index to both the baseline and the natural flow regime. Those are then analyzed in order to: i) detect if there is a significant change in the degree of subdaily hydrologic alteration (SDHA) due to a larger wind penetration, and ii) identify which rivers are most affected. The proposed scheme is applied to Chile's Central Interconnect System (SIC) for scenarios up to 15% of wind energy penetration. Results show a major degree of SDHA under the baseline as compared to the natural regime. As wind power increases, so does the SDHA in two important rivers. This suggests a need for further ecological studies in those rivers, along with an analysis of operational constraints to limit the SDHA.

Multi-purpose reservoir operation oncomitant with estimating hydropower potential using multifarious hydrological models.

The research aims at determining the optimal release rule to increase the capacity of Rib reservoir. The reservoir inflow using HBV-light hydrological model embracing optimal reservoir operation through HEC-ResSim model were used to prepare an optimum operational plan. The potential of the river for hydropower generation prioritise the demand at a specified level regarding storage capacity (m3), level of reservoir (m), and the relation between inflow and outflow of the reservoir. From the model performance features, the coefficient of correlation (R2) and Nash Sutcliffe Efficiency (NSE) were determined to be, respectively, 0.77 and 0.73 for calibration and 0.72 and 0.70 for validation. The Sobol approach was used for detailed sensitivity analysis of DROP model parameters based on the performance of C2M on outflows and volumes. The results suggest that the threshold coefficient characterizing the demand-controlled release level is the most significant parameter. According to the simulation's findings, the reservoir's average regulated release is calculated to be 22.86 m3/s, and its average monthly hydropower output is 6.73 MW. Average annual hydropower energy was estimated as 58.955 GW h/year and mean annual inflow of reservoir volume of water to be 223.54 Mm3. This volume of water is adequate to accommodate total annual irrigation demand, environmental obligation, and other respective requirements in the downstream. The demand for hydropower and irrigation and supply from reservoir capacity can be counterbalanced from the simulated result without any hindrance.

Methane dynamics altered by reservoir operations in a typical tributary of the Three Gorges Reservoir.

Substantial nutrient inputs from reservoir impoundment typically increase sedimentation rate and primary production. This can greatly enhance methane (CH4) production, making reservoirs potentially significant sources of atmospheric CH4. Consequently, elucidating CH4 emissions from reservoirs is crucial for assessing their role in the global methane budget. Reservoir operations can also influence hydrodynamic and biogeochemical processes, potentially leading to pronounced spatiotemporal heterogeneity, especially in reservoirs with complex tributaries, such as the Three Gorges Reservoir (TGR). Although several studies have investigated the spatial and temporal variations in CH4 emissions in the TGR and its tributaries, considerable uncertainties remain regarding the impact of reservoir operations on CH4 dynamics. These uncertainties primarily arise from the limited spatial and temporal resolutions of previous measurements and the complex underlying mechanisms of CH4 dynamics in reservoirs. In this study, we employed a fast-response automated gas equilibrator to measure the spatial distribution and seasonal variations of dissolved CH4 concentrations in XXB, a representative area significantly impacted by TGR operations and known for severe algal blooms. Additionally, we measured CH4 production rates in sediments and diffusive CH4 flux in the surface water. Our multiple campaigns suggest substantial spatial and temporal variability in CH4 concentrations across XXB. Specifically, dissolved CH4 concentrations were generally higher upstream than downstream and exhibited a vertical stratification, with greater concentrations in bottom water compared to surface water. The peak dissolved CH4 concentration was observed in May during the drained period. Our results suggest that the interplay between aquatic organic matter, which promotes CH4 production, and the dilution process caused by intrusion flows from the mainstream primarily drives this spatiotemporal variability. Importantly, our study indicates the feasibility of using strategic reservoir operations to regulate these factors and mitigate CH4 emissions. This eco-environmental approach could also be a pivotal management strategy to reduce greenhouse gas emissions from other reservoirs.