Reservoir Operation Strategies Research Articles

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Effect of total dissolved gas supersaturation on the passage behavior of silver carp (Hypophthalmichthys molitrix) and ya-fish (Schizothorax prenanti) through an experimental vertical slot fishway

Total dissolved gas (TDG) supersaturation caused by flood discharge water poses a threat to vital activities such as migration, foraging, and evasion in fish species upstream of the Yangtze River, which may impair the ability of fish to pass through fishways during the migration period, causing poor utilization of fishways. Previous studies have shown that TDG supersaturation reduces the critical and burst swimming abilities of fish, suggesting potential adverse effects on swimming performance. However, studies focusing on the impact of TDG on fish swimming behavior in experimental vertical-slot fishways remain scarce. Therefore, in this study, silver carp (Hypophthalmichthys molitrix) and ya-fish (Schizothorax prenanti) were used as the study species, and comparative passage experiments were carried out in an experimental vertical slot fishway to systematically analyze the effects of TDG supersaturation on their passage behavior. The passage success of the silver carp was 57%, 39%, 26%, and 27% at TDG levels of 100%, 110%, 120%, and 130%, respectively. Passage success of ya-fish was 73%, 37%, 31%, and 35% at TDG concentrations of 100%, 110%, 120%, and 130%, respectively. The passage time for both species increased significantly with increasing TDG levels. Furthermore, the passage routes of silver carp changed significantly compared to the control group, whereas the passage routes of ya-fish changed insignificantly. High levels of TDG supersaturation (≥120%) also contributed to a higher mortality rate of ya-fish passing through the vertical slot fishway. The research results provide valuable data on the influence of TDG supersaturation on fish movement behavior responses in experimental vertical slot fishways, offering a reference for the design of fishways and the formulation of reservoir operation schemes.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs

Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for managing water resources and establishing early-warning systems. However, this can be a formidable challenge due to numerous uncertainties, particularly in cascade reservoir systems. To seamlessly quantify aleatoric (data-caused) and epistemic (model network–caused) uncertainties simultaneously in a single framework, we estimated the posterior distribution of parameters in a Bayesian neural network while measuring prediction variance to reflect the noise in data. By randomly discarding certain units within the network, the estimated posterior distribution can be combined with a new loss function. This sophisticated deep learning framework for a long short-term memory network has also included advanced supporting approaches, such as input variable selection, data transformation, and hyperparameter optimization. The model trained in this study was applied to two cascade reservoir systems with four reservoirs in Vietnam, providing uncertainty estimates from two distinct sources. Comparing three global reservoir operation schemes, we found that the proposed model achieved superior performance in all cases, and uncertainty in the forecasts was due primarily to data noise, rather than uncertainty in the model itself. Preprocessing data using a wavelet transform can reduce noise that the model cannot classify on its own, resulting in improved performance, particular with longer lead times. The satisfactory performance of the prediction results confirms that the framework can effectively assess the uncertainty of hydrologic predictions using deep learning.

Keeping Water in Climate-Changed Headwaters Longer

Climate-change projections for California confidently describe a future with warmer temperatures, more evaporative demand, less snow, more rain, earlier and flashier runoff and streamflow, and drier summer conditions. The future of annual precipitation is much less certain, but a fairly unanimous projection of drier, more drought-prone conditions punctuated by occasional stronger-than-historical storms is almost as common among projections as is the warming itself. Rather than focusing on the less certain annual precipitation changes, we recommend more focus on keeping water in the headwaters longer. Doing so will involve reducing winter flood flows from headwater catchments, reducing the summer aridification (and wildfire risks) there, salvaging some groundwater recharge that would likely otherwise be lost, and overall, perpetuating headwater (and downstream) hydrologies under more historical and natural conditions. Among the available near-term adaptation strategies for keeping water in the headwaters longer, we discuss several examples here: (1) an increased emphasis on soils and percolation management as a priority and co-benefit in forest-health restoration activities; (2) beaver-population restoration or proliferation of beaver-inspired infrastructures; and (3) upstream-focused, forecast-informed reservoir-operation (FIRO) strategies.

An integrated reservoir operation framework for enhanced water resources planning

Climate change induced spatiotemporal variation in global water availability modifies the proposed design criteria of water infrastructure structures like dams and reservoirs. Although reservoir operation is treated as a potential adaptation option, obsolescence of existing operation rules in the climate change scenarios could cause devastating situation through faulty water management practices. Presently onboard simulation–optimization based reservoir operation schemes fail to capture the uncertainty involved in the climate change scenario. Hence, there is a need to identify the limiting application scenario of the existing reservoir operation rule, and subsequently, revise the operation framework to address the future supply–demand uncertainty adequately. This research develops an integrated Soil and Water Assessment Tool (SWAT) (hydrologic), HEC-ResSim (hydraulic), and genetic algorithm (GA) (optimization) based adaptive reservoir operation framework, which is competent enough in accounting the future supply–demand uncertainty. Incorporation of the newly proposed environmental flow assessment approach in the reservoir operation would assist the decision makers in guiding the reservoir release for maintaining the water quality and sustenance of the downstream aquatic species. Certainly, corresponding to the existing operation rules under both the baseline and future climate change scenarios of RCP 4.5 and 8.5, the developed SWAT-HEC-ResSim-GA based reservoir operation scheme could improve the performance of the Kangsabati reservoir with the time and volume reliability estimates of 0.631 and 0.736, respectively. Conclusively, the developed approach in this study could be the best feasible alternative for hydrologic characterization in complex reservoir catchment-command regions with the option for enhanced reservoir planning in global catchment-command regions.

Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.

Fish entrainment model for decision support in ecosystem management: A case study from China's Maling Reservoir

Fish entrainment from reservoirs to downstream rivers by various water release structures may result in serious consequences for reservoir ecosystems. This study proposed an integrated modeling approach for determining fish entrainment risk zones and suitable reservoir regulations. The modeling approach combines hydrodynamic and fish risk assessment modeling with using fuzzy rules set defined by expert knowledge in fish ecology. We applied this modeling approach in the Maling Reservoir in China, where fish entrainment in the reservoir has been observed in the turbine, spillway, and irrigation system. Percocypris pingi (Tchang, 1930) and Onychostoma simum (Sauvage & Dabry de Thiersant, 1874), representing the main and protected fish species, were selected as target fish species. Five reservoir operation schemes were investigated under normal and designed water levels. The modeling results suggest that the fish entrainment risk for protected fish species (Onychostoma simum) is higher than that for the main fish species (Percocypris pingi). The results indicate a ranking of the fish entrainment risk from high to low in the following scenarios: with all spillways in operations, with one spillway in operation, with intakes in operation, with an irrigation system in operation, and with intakes and the irrigation system in combined operations. In addition, under normal and designed water levels, dead fish entrainment zone areas and percentages were extremely low compared to the entire forebay of the reservoir. These results indicate that the operation of the Maling Reservoir does not affect the ecological status of fish. These findings are generally applicable to any fish species in reservoirs that balance reservoir operations and ecological protection of fish, and could assist reservoir managers in making informed decisions on how to mitigate and compensate for fish entrainment.

A two-dimensional double layer-averaged model of hyperconcentrated turbidity currents with non-Newtonian rheology

Hyperconcentrated turbidity currents typically display non-Newtonian characteristics that influence sediment transport and morphological evolution in alluvial rivers. However, hydro-sediment-morphological processes involving hyperconcentrated turbidity currents are poorly understood, with little known about the effect of the non-Newtonian rheology. The current paper extends a recent two-dimensional double layer-averaged model to incorporate non-Newtonian constitutive relations. The extended model is benchmarked against experimental and numerical data for cases including subaerial mud flow, subaqueous debris flow, and reservoir turbidity currents. The computational results agree well with observations for the subaerial mud flow and independent numerical simulations of subaqueous debris flow. Differences between the non-Newtonian and Newtonian model results become more pronounced in terms of propagation distance and sediment transport rate as sediment concentration increases. The model is then applied to turbidity currents in the Guxian Reservoir planned for middle Yellow River, China, which connects to a tributary featuring hyperconcentrated sediment-laden flow. The non-Newtonian model predicts slower propagation of turbidity currents and more significant bed aggradation at the confluence between the tributary Wuding River and the Yellow River in the reservoir than its Newtonian counterpart. This difference in model performance could be of considerable importance when optimizing reservoir operation schemes.

Identifying Reservoir-Induced Hydrological Alterations in the Upper Yangtze River Basin through Statistical and Modeling Approaches

Elucidating the impact of reservoir operation on hydrological signatures is crucial for the effective management of large rivers under the changing climate. This study first revised the reservoir operation scheme in the Soil and Water Assessment Tool (SWAT) model to improve its description of actual operation laws of reservoirs in the upper Yangtze River basin (UYRB). Then, we identified the reservoir-induced hydrological alteration through a hydrological index method driven by observed and simulated daily streamflow from 1960 to 2017. The results revealed the superiority of the revised reservoir algorithm in the SWAT model in simulating streamflow and floods at Cuntan and Yichang stations with the Nash-Sutcliffe efficiency (NSE) coefficient and the Kling-Gupta efficiency (KGE) coefficient improved from 0.01 to 0.08 and 0.01 to 0.05, respectively. Relative to the baseline period (1960–2002), the hydrological signatures in the impact period (2003–2017) changed substantially after 2003. Reservoirs induced a remarkable increase of 27.76% and 55.97% in streamflow from January to March, accompanied by a notable decrease of 6.95% and 20.92% in streamflow from September to October after 2003 at Cuntan and Yichang stations, respectively. Meanwhile, the annual streamflow range contracted, and the flow became more stable with a reduced variation in daily streamflow, extremely low flow spell duration, and extremely high flow spell duration. Consequently, our results improved the quantitative understanding of reservoir-induced alteration and informed the management and planning of reservoir construction in the UYRB under climate change.

Reliability of Emergency Water Supply for a Reservoir and Enhancement through Floating Photovoltaics in a Long-Distance Water Diversion Project

An emergency water supply is occasionally necessary for a reservoir; its reliability should be investigated. The operation schemes of reservoirs mainly consider the long-term normal water supply; the emergency water supply has been less studied. Floating photovoltaics (FPVs) can be set on the open canals of water diversion projects (WDPs) to decrease evaporation loss and save water for the emergency water supply for connected reservoirs, the effects of which must be quantified. Thus, a framework of reliability for the emergency water supply of a reservoir (REWS) is proposed in this study, including stochastic simulation of reservoir operation schemes, a set of objectives for an emergency water supply, and quantification of the REWS. The Danjiangkou reservoir (DR) is considered as a case study; it is the water source of the Han River and middle route of the South-to-North Water Diversion Project (MRSNWDP). The effects of FPV coverage above the MRSNWDP on water evaporation and the REWS have been reported. The results show that the REWSs are 37.6%, 45.5%, and 56.5%, respectively, for the average, 75% probability, and 95% probability water diversion schemes of the MRSNWDP. After setting the FPV system, the annual evaporation of the MRSNWDP was reduced to 1.15×107 m3 (−74.7%), increasing the REWS to 49.6% (+12.0%), 53.4% (+7.9%), and 61.3% (+4.8%) for the average, 75% probability, and 95% probability water diversion schemes of the MRSNWDP, respectively. The REWS framework is useful for reservoir operation and water resource planning. FPV coverage is suggested for WDPs with open canals.

Multi-objective optimal reservoir operation considering algal bloom control in reservoirs

Reservoir operation strategies (ROSs) are considered an efficient and low-cost method to control algal blooms. However, reservoir operations must consider regular objectives, including flood prevention and power generation. To address this multi-objective optimization problem, we coupled the non-dominated sorting genetic algorithm-II (NSGA-II) model and the General Lake Model-Aquatic EcoDynamics library (GLM-AED) model to optimize reservoir operations. Taking the Zipingpu Reservoir as a case study, we found the peak of outflow discharge (POD) could be reduced from 1059.5 to 861.4 m3 s−1 (19%), the total power generation (TPG) could be increased from 6.6 × 108 to 7.1 × 108 kW h (8%), and the peak of chlorophyll a concentration (PCC) could be decreased from 42.7 to 27.2 μg L−1 (36%) compared with the original reservoir operation in the early flood period. The obtained Pareto frontier revealed the tradeoffs between algal bloom control, flood prevention, and power generation. Reservoir operation schemes that achieved low PCC were typically associated with large POD and moderate TPG. In particular, under fixed start and end water levels, maintaining a higher average water level during May and June could result in larger outflows, effectively inhibiting algal accumulation and bloom development, thereby leading to a lower PCC. Slight variations in average water age were found among the minimum PCC scheme, maximum TPG scheme, and minimum POD scheme, indicating that water exchange varied little and has not been responsible for the differences in PCC. Collectively, enhancing outflow was determined to play a vital role in reducing PCC, particularly when operating under constrained rules. These findings contribute new insights into optimal reservoir operations considering algal bloom control and emphasize the importance of enhancing outflow as a governing mechanism. Furthermore, the coupled model offers a transferable technical framework for reservoir managers to mitigate eutrophication through ROSs.

Application of watershed-scale habitat modeling and decision-support tools for reservoir reoperations in coastal northern California

Water managers must often balance the needs of both aquatic habitat and human water supply. However, they frequently only have the tools to manage water delivery alone. Existing modeling tools for habitat have gaps in providing detailed biological estimates at a watershed scale and in simulating water supply operations and habitat suitability simultaneously. A new modeling platform and calculation framework, Aquatic Habitat Assessment, was applied in a case study to quantify habitat suitability and fish passage at a watershed scale for local species of Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (Oncorhynchus mykiss). Aquatic Habitat Assessment was coupled with a suite of tools, including HEC-RAS used for hydraulics, WEAP for water allocations, and Tableau for visualization. The tools ensemble was used to simulate the operations of a water utility system near San Francisco Bay in California to evaluate the effects of reservoir reoperations on both human water supply and aquatic habitat. The suite of tools was successful in bringing a range of conflicting parties to coalesce around a common solution for reservoir operations. Two sets of alternative reservoir operation schemes were developed, which largely involve higher winter and lower summer releases, aligning more closely with natural Mediterranean patterns and functional flows connected to the biological needs of local aquatic species. Results quantify tradeoffs across reservoir operation schemes, with modeled fish passage habitat suitability increased under the reservoir reoperations, but human water supply delivery decreased. The modeled spawning, incubation, and rearing habitat suitability increased during the winter release period under reoperations, but decreased during the summer release period.

National‐Scale Detection of Reservoir Impacts Through Hydrological Signatures

AbstractReservoirs play a vital role in the supply and management of water resources and their operation can significantly alter downstream flow. Despite this, reservoirs are frequently excluded or poorly represented in large‐scale hydrological models, which can be partly attributed to a lack of open‐access data describing reservoir operations, inflow and storage. To help inform the development of reservoir operation schemes, we collate a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large‐scales from downstream flow records only. By removing the need for pre‐and‐post‐reservoir flow timeseries (a requirement of many pre‐existing techniques), these signatures facilitate the assessment of flow alteration across a much wider range of catchments. To demonstrate their application, we calculate the signatures across Great Britain in 111 benchmark (i.e., near‐natural) catchments and 186 reservoir catchments (where at least one upstream reservoir is present). We find that abstractions from water resource reservoirs induce deficits in the water balance, and that pre‐defined flow releases (e.g., the compensation flow) reduce variability in the downstream flow duration curve and in intra‐annual low flows. By comparing signatures in benchmark and reservoir catchments, we define thresholds above which the influence of reservoirs can be distinguished from natural variability and identify 40 catchments significantly impacted by the presence of reservoirs. The signatures also provide insights into local reservoir operations, which can inform the development of tailored reservoir operation schemes, and identify locations where current modeling practices (which lack reservoir representation) will be insufficient.

Multi-objective operation-decision-making-risk propagation analysis for cascade reservoirs affected by future streamflow process variations

Significant climate variations have decreased the stability of water resource systems, leading to multiple uncertainties in streamflow response, reservoir operation optimization, decision-making, and adaptive adjustments for water resource scheduling. Understanding the impact of climate change on reginal streamflow is necessary and crucial to identifying reservoir operation strategies and decision-making responses. In this study, we created an integrated systematic “uncertain streamflow responses”– “reservoir operation”– “optimization”– “decision-making risk analysis” chain. Three bias-corrected and downscaled general circulation models (GCMs) were used to analyze the inter-model uncertainties under three representative concentration pathways (RCPs). The streamflow responses and uncertainty in the future were determined using a distributed hydrological model and the fuzzy extension principle under predefined scenarios and uncertainty levels. Then, a stochastic simulation model and modified stochastic multi-criteria decision-making model were applied to identify the effects of climate change projections and streamflow responses on reservoir multi-objective operation and decision-making. Moreover, risk quantification indices were used to determine the uncertainty propagation and potential risks accumulated in the chain. We applied this framework to cascade reservoirs in the Qing River Basin. The results indicate that the mean annual streamflow projected using selected GCMs will increase, enhancing the hydropower response and weakening the ecological benefit response. The Pareto non-dominated solutions optimized based on the streamflow projections obtained using the GCMs (under the same RCP) and hydrological model are more distinct than those based on different RCPs and the same GCM. Moreover, a high emission scenario may increase the uncertainty of the streamflow projections and reservoir operation responses, which is consistent with the finding that the decision-making process becomes more variable and sensitive with increasing streamflow uncertainty. Finally, we identified the preferred solutions for reservoir operation under different uncertainties, the respective expected values, and the 95% confidence interval bands to enhance the adaptability of future reservoir operation.

Evaluation of reservoir operation schemes: from rule curve to many-objective optimisation

Quantitative comparisons of different reservoir operation schemes can provide valuable suggestions for decision making and help to promote new planning tools under changing environments. The full spectrum of reservoir operation schemes (rule-curve-based setup (RBS), single-objective optimisation setup (SOS), bi-objective optimisation setup (BOS) and many-objective optimisation setup (MOS)) was considered in this work. A large reservoir in the southwest of China (Qinshitan reservoir) was used as a case study. The four reservoir operation schemes were established using similar formulations and were evaluated quantitatively under typical hydrological years. For the considered optimisation problem, progressive improvement on the solution was obtained from RBS to SOS, then BOS and finally MOS. Among the models, the MOS provided a comprehensive perspective on reservoir operation and achieved the best performance in terms of offering the most options for decision making. The MOS also discovered a step-wise operational scheme that had not been captured by the RBS, the SOS or the BOS.

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm.

Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes

AbstractAssessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in‐situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model‐simulated reservoir inflow with conceptual reservoir operation schemes within a land surface‐hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography.

Assessing the Future Water and Energy Security of a Regulated River Basin with a Coupled Land Surface and Hydrologic Model

To address the water-related issues faced by humans, the planning and construction of dams, water diversion projects, and other water infrastructures have been continuously adopted by decision makers worldwide. This is especially the case for the Yalong River Basin (YRB) in China, which is expected to be one of the most regulated rivers due to reservoir construction and the planned South-to-North Water Diversion project. To understand the potential impact of these water infrastructures on the water resources and hydropower production of the basin and downstream areas, we employ a land surface–hydrologic model with explicit representations of dam operation and water diversions in order to quantify the impact of reservoir operation and water diversion on the future water and energy security of the YRB. In particular, a conceptual reservoir operation scheme and a hydropower-optimized reservoir operation scheme are employed to predict the future release, storage and hydropower generation of the YRB, respectively. Results indicate that reservoirs can have noticeable, cumulative effects in enhancing the water security by reducing the wet season streamflow by 19% and increasing the dry season streamflow by 66%. The water diversion can result in an overall decrease in the streamflow, while the downstream reservoirs are expected to fully mitigate the decline in the dry season streamflow. The hydropower production is likely to decrease by 16% and 10% with conventional and optimized operation schemes, respectively, which suggests that the adaptation of operation rules alone cannot reverse the decline in the electricity production. Our findings can provide implications for sustainable water resource management.

A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions

Reservoirs have been built worldwide to address the water-related issues. To fully understand their potential impacts on the hydrologic regime, explicitly parameterizing reservoir operation in hydrologic models is often required. In this study, two data-driven reservoir operation schemes based on extreme gradient boosting (XGBoost) and artificial neural network (ANN) are respectively developed to predict the reservoir release and storage in hydrologic models for reservoirs with historic in-situ inflow, storage, release data. Then, a hybrid modelling framework is proposed by coupling a high-resolution (3 km) hydrologic model with (1) the developed data-driven reservoir operation schemes and (2) a calibration-free conceptual reservoir operation scheme designed for data-scarce reservoirs. This allows quantitative assessment of the cumulative impacts of dam operation on the hydrologic regime under different reservoir data availability. The framework is applied to the Upper Yangtze River Basin (UYRB) in China that is one of the most regulated river basins across the country due to extensive reservoir construction. Results indicate that both data-driven reservoir operation schemes can well reconstruct the reservoir releases and storage in the UYRB (daily NSE of ∼ 0.9), and the XGBoost performs slightly better than ANN. By coupling reservoir operation, the model shows a remarkably improved performance in reconstructing the daily streamflow of the basin. The major reservoirs in the UYRB can redistribute excessive water in the wet season to the dry season and attenuate the high and low flows, leading to enhanced water security along the river. Our approach provides a practical framework for reservoir impact assessment and predictions.